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1.
JAMA Psychiatry ; 2022 May 11.
Article in English | MEDLINE | ID: covidwho-1838122

ABSTRACT

Importance: Individuals surviving severe COVID-19 may be at increased risk of neuropsychiatric sequelae. Robust assessment of these risks may help improve clinical understanding of the post-COVID syndrome, aid clinical care during the ongoing pandemic, and inform postpandemic planning. Objective: To quantify the risks of new-onset neuropsychiatric conditions and new neuropsychiatric medication prescriptions after discharge from a COVID-19-related hospitalization, and to compare these with risks after discharge from hospitalization for other severe acute respiratory infections (SARI) during the COVID-19 pandemic. Design, Setting, and Participants: In this cohort study, adults (≥18 years of age) were identified from QResearch primary care and linked electronic health record databases, including national SARS-CoV-2 testing, hospital episode statistics, intensive care admissions data, and mortality registers in England, from January 24, 2020, to July 7, 2021. Exposures: COVID-19-related or SARI-related hospital admission (including intensive care admission). Main Outcomes and Measures: New-onset diagnoses of neuropsychiatric conditions (anxiety, dementia, psychosis, depression, bipolar disorder) or first prescription for relevant medications (antidepressants, hypnotics/anxiolytics, antipsychotics) during 12 months of follow-up from hospital discharge. Maximally adjusted hazard ratios (HR) with 95% CIs were estimated using flexible parametric survival models. Results: In this cohort study of data from 8.38 million adults (4.18 million women, 4.20 million men; mean [SD] age 49.18 [18.45] years); 16 679 (0.02%) survived a hospital admission for SARI, and 32 525 (0.03%) survived a hospital admission for COVID-19. Compared with the remaining population, survivors of SARI and COVID-19 hospitalization had higher risks of subsequent neuropsychiatric diagnoses. For example, the HR for anxiety in survivors of SARI was 1.86 (95% CI, 1.56-2.21) and for survivors of COVID-19 infection was 2.36 (95% CI, 2.03-2.74); the HR for dementia for survivors of SARI was 2.55 (95% CI, 2.17-3.00) and for survivors of COVID-19 infection was 2.63 (95% CI, 2.21-3.14). Similar findings were observed for all medications analyzed; for example, the HR for first prescriptions of antidepressants in survivors of SARI was 2.55 (95% CI, 2.24-2.90) and for survivors of COVID-19 infection was 3.24 (95% CI, 2.91-3.61). There were no significant differences observed when directly comparing the COVID-19 group with the SARI group apart from a lower risk of antipsychotic prescriptions in the former (HR, 0.80; 95% CI, 0.69-0.92). Conclusions and Relevance: In this cohort study, the neuropsychiatric sequelae of severe COVID-19 infection were found to be similar to those for other SARI. This finding may inform postdischarge support for people surviving SARI.

2.
Arch Dis Child ; 2022 Mar 22.
Article in English | MEDLINE | ID: covidwho-1759310

ABSTRACT

OBJECTIVE: To investigate childhood, teenage and young adult cancer diagnostic pathways during the first wave of the COVID-19 pandemic in England. DESIGN: Population-based cohort study. SETTING AND PARTICIPANTS: QResearch, a nationally representative primary care database, linked to hospital admission, mortality and cancer registry data, was used to identify childhood, teenage and young adult cancers (0-24 years) diagnosed between 1 January 2017 and 15 August 2020. MAIN OUTCOMES: Main outcomes of interest were: (1) number of incident cancer diagnoses per month, (2) diagnostic, treatment time intervals and (3) cancer-related intensive care admissions. RESULTS: 2607 childhood, teenage and young adult cancers were diagnosed from 1 January 2017 to 15 August 2020; 380 were diagnosed during the pandemic period. Overall, 17% (95% CI -28.0% to -4.0%) reduction in the incidence rate ratio of cancers was observed during the pandemic. Specific decreases were seen for central nervous system tumour (-38% (95% CI -52% to -21%)) and lymphoma (-28% (95% CI -45% to -5%)) diagnoses. Additionally, childhood cancers diagnosed during the pandemic were significantly more likely to have intensive care admissions (adjusted OR 2.2 (95% CI 1.33 to 3.47)). Median time-to-diagnosis did not significantly differ across periods (+4.5 days (95% CI -20.5 to +29.5)), while median time-to-treatment was shorter during the pandemic (-0.7 days (95% CI -1.1 to -0.3)). CONCLUSIONS: Collectively, our findings of a significant reduction in cancer diagnoses and increase in intensive care admissions provide initial insight into the changes that occurred to childhood, teenage and young adult cancer diagnostic pathways during the first wave of the pandemic.

3.
Int J Popul Data Sci ; 5(4): 1697, 2020.
Article in English | MEDLINE | ID: covidwho-1754159

ABSTRACT

Introduction: COVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society. Objectives: To validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK. Methods: We conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24th January-30th April 2020 and 1st May-28th July 2020) to assess algorithm performance. Results: 1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell's C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes. Conclusions: The QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.


Subject(s)
COVID-19 , Adult , Aged , Aged, 80 and over , Algorithms , Cohort Studies , Female , Humans , Male , Middle Aged , Retrospective Studies , Wales/epidemiology , Young Adult
4.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329510

ABSTRACT

Objective To assess the risk of death involving COVID-19 following infection from Omicron (B.1.1.539/BA.1) relative to Delta (B.1.617.2). Design Retrospective cohort study. Setting England, UK, 1 December 2021 to 25 January 2022. Participants 1,035,163 people aged 18-100 years who tested positive for SARS-CoV-2 in the national surveillance programme, and had an infection identified as either Omicron- or Delta compatible. Main outcome measures Death involving COVID-19 as identified from death certification records. The exposure of interest was the SARS-CoV-2 variant identified from NHS Test and Trace PCR positive tests taken in the community (pillar 2) and analysed by Lighthouse laboratories. Cause-specific Cox proportional hazard regression models were adjusted for sex, age, vaccination status, previous infection, calendar time, ethnicity, Index of Multiple Deprivation rank, household deprivation, university degree, keyworker status, country of birth, main language, region, disability, and comorbidities. Additionally, we tested for interactions between variant and sex, age, vaccination status and comorbidities. Results The risk of death involving COVID-19 was 67% lower for Omicron compared to Delta and the reduction in the risk of death involving COVID-19 for Omicron compared to Delta was more pronounced in males than in females and in people under 70 years old than in people aged 70 years or over. Regardless of age, reduction of the risk of death from Omicron relative to Delta more was more pronounced in people who had received a booster than in those having received only two doses. Conclusions Our results support early work showing the relative reduction in severity of Omicron compared to Delta in terms of hospitalisation and extends this research to assess COVID-19 mortality. Our work also highlights the importance of the vaccination booster campaign, where the reduction in risk of death involving COVID-19 is most pronounced in individuals who had received a booster. What is already known on this topic The Omicron variant, which refers to the whole lineage (BA.1, BA.2, BA.3) had already been shown to be more transmissible than the Delta variant, but there is emerging evidence suggests that the risk of hospitalisation and risk of death within 28 days after a SARS-COV-2 test is lower. However, with a highly transmissible infection and high levels of population testing, definition of death within 28 days is more likely to be susceptible to misclassification bias due to asymptomatic or co-incidental infection. There is no study so far comparing the risk of COVID-19 death as identified from death certification records, with the cause of death assessed by the physician who attended the patient in the last illness. What this study adds Using data from a large cohort of COVID-19 infections that occurred in December 2021, we examined the difference in the risk COVID-19 death, as identified from death certification records, between the Delta and Omicron BA.1 variant. Our study shows that risk of death involving COVID-19 was reduced by 67% following infection with the Omicron BA.1 variant relative to the Delta variant after adjusting for a wide range of potential confounders, including vaccination status and comorbidities. Importantly, we found that the relative risk of COVID-19 mortality following Omicron versus Delta infection varied by age and sex, with lower relative risk in younger individuals and for males than females. The reduction in risk of death involving COVID-19 was also most pronounced in individuals who had received a booster.

5.
Int J Epidemiol ; 2022 Feb 18.
Article in English | MEDLINE | ID: covidwho-1706511

ABSTRACT

BACKGROUND: Smoking is a risk factor for most respiratory infections, but it may protect against SARS-CoV-2 infection. The objective was to assess whether smoking and e-cigarette use were associated with severe COVID-19. METHODS: This cohort ran from 24 January 2020 until 30 April 2020 at the height of the first wave of the SARS-CoV-2 epidemic in England. It comprised 7 869 534 people representative of the population of England with smoking status, demographic factors and diseases recorded by general practitioners in the medical records, which were linked to hospital and death data. The outcomes were COVID-19-associated hospitalization, intensive care unit (ICU) admission and death. The associations between smoking and the outcomes were assessed with Cox proportional hazards models, with sequential adjustment for confounding variables and indirect causal factors (body mass index and smoking-related disease). RESULTS: Compared with never smokers, people currently smoking were at lower risk of COVID-19 hospitalization, adjusted hazard ratios (HRs) were 0.64 (95% confidence intervals 0.60 to 0.69) for <10 cigarettes/day, 0.49 (0.41 to 0.59) for 10-19 cigarettes/day, and 0.61 (0.49 to 0.74) for ≥20 cigarettes/day. For ICU admission, the corresponding HRs were 0.31 (0.24 to 0.40), 0.15 (0.06 to 0.36), and 0.35 (0.17 to 0.74) and death were: 0.79 (0.70 to 0.89), 0.66 (0.48 to 0.90), and 0.77 (0.54 to 1.09) respectively. Former smokers were at higher risk of severe COVID-19: HRs: 1.07 (1.03 to 1.11) for hospitalization, 1.17 (1.04 to 1.31) for ICU admission, and 1.17 (1.10 to 1.24) for death. All-cause mortality was higher for current smoking than never smoking, HR 1.42 (1.36 to 1.48). Among e-cigarette users, the adjusted HR for e-cigarette use and hospitalization with COVID-19 was 1.06 (0.88 to 1.28), for ICU admission was 1.04 (0.57 to 1.89, and for death was 1.12 (0.81 to 1.55). CONCLUSIONS: Current smoking was associated with a reduced risk of severe COVID-19 but the association with e-cigarette use was unclear. All-cause mortality remained higher despite this possible reduction in death from COVID-19 during an epidemic of SARS-CoV-2. Findings support investigating possible protective mechanisms of smoking for SARS-CoV-2 infection, including the ongoing trials of nicotine to treat COVID-19.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-322472

ABSTRACT

Background: As COVID-19 vaccination programs are being rolled out globally, we studied the ethnic, deprivation, household size and comorbidity ‘patterning’ of existing vaccination programs in populations at high-risk for COVID-19, to inform risk-stratified vaccination strategies and mitigate health inequalities. Methods: A population-level cohort study of UK adults aged 65 years or older, using a large primary care database. We used multivariable logistic regression to assess uptake of influenza, pneumococcal and shingles vaccination across ethnic groups, deprivation quintile, household size, and comorbidities, computing odds ratios (OR) adjusted for age, sex, demographics, body mass index and smoking. Offers and refusals of each vaccination type were analysed in those not receiving them. Findings: The cohort comprised 2,054,463 patients from 1,318 general practices. 1,452,014 (70.7%) patients received influenza vaccine, 1,391,228 (67.7%) received pneumococcal vaccine, and 690,783 (53.4%) received shingles vaccine. Compared to Whites, influenza vaccination uptake was lower in Pakistani (adjusted odds ratio (OR) 0.82;99% confidence interval: 0.74-0.90), Black Caribbean (OR 0.46;0.43-0.48), Black African (OR 0.63;0.58-0.68), Chinese (OR 0.70;0.64-0.76) and ‘Other ethnic group’ (OR 0.65;0.63-0.69). The Black Caribbean group had higher vaccination refusal than the White group for influenza vaccination (OR 1.17;1.05-1.30). Vaccination uptake was lower among the more deprived and those living in household sizes above 3 or more persons, with some significant interactions between ethnicity and comorbidities. Uptake of all three vaccines was higher in those with asthma, COPD, type 2 diabetes, hypertension and learning disability, whilst lower in those with dementia. Interpretation: Whilst uptake and refusal of influenza, shingles and pneumococcal vaccination are patterned by ethnicity, deprivation, household size and comorbidities, vaccination offer is mostly patterned by comorbidities. This information can inform national policies to ensure equitable implementation of COVID-19 vaccination programs to avoid exacerbating health inequalities.Funding Statement: This project was funded by the Medical Research Council (Grant Ref: MR/V027778/1).Declaration of Interests: PST reports previous consultation with AstraZeneca and Duke-NUS outside the submitted work. KK is a Member of the Scientific Advisory Group for Emergencies (SAGE), Member of Independent SAGE, Director of the University of Leicester Centre for Black Minority Health and Trustee of the south Asian Health Foundation. JHC is a member of several SAGE committees and chair of the risk stratification subgroup of the NERVTAG. She is unpaid director of QResearch and founder and former medical director of ClinRisk Ltd (outside the submitted work). MP, AKC, HDM, DS, TAR, FZ, BRS, SJG, CC, CG have no interests to declare.

7.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-321605

ABSTRACT

Background: The QCovid algorithm is a risk prediction tool for COVID-19 hospitalisation and mortality that can be used to stratify patients by risk into vulnerability groups . We carried out an external validation of the QCovid algorithm in Scotland.Methods: We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription polymerase chain reaction (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisation and deaths in our dataset for two time periods: 1 March, 2020 to 30 April, 2020, and 1 May, 2020 to 30 June, 2020.Findings: Our dataset comprised 5,384,819 individuals, representing 99% of the estimated population (5,463,300) resident in Scotland in 2020. The algorithm showed excellent calibration in both time periods with close correspondence between observed and predicted risks. Harrell ’s C for deaths in males and females in the first period was 0.946 (95% CI: 0.941 - 0.951) and 0.925 (95% CI: 0.919 - 0.931) respectively. Harrell’s C for hospitalisations in males and females in the first period was 0.809 (95% CI: 0.801 - 0.817) and 0.816 (95% CI: 0.808 - 0.823) respectively.Interpretation: The QCovid algorithm shows high levels of external validity in predicting the risk of COVID- 19 hospitalisation and death in the population of Scotland.Funding: Medical Research Council, National Institute for Health Research Health Technology Assessment Programme, funded through the UK Research and Innovation Industrial Strategy Challenge Fund Health Data Research UK.Declaration of Interests: Dr. Hippisley-Cox reports grants from MRC, grants from Wellcome Trrust, grants from NIHR, during the conduct of the study;other from ClinRisk Ltd, outside the submitted work. Dr. Sheikh reports grants from NIHR, grants from MRC, grants from HRR UK, during the conduct of the study. All other authors report no conflict of interest.Ethics Approval Statement: Ethical permission for this study was granted from South East Scotland Research Ethics Committee 02 [12/SS/0201]. The Public Benefit and Privacy Panel Committee of Public Health Scotland, approved the linkage and analysis of the de-identified datasets for this project [1920-0279].

8.
Lancet Infect Dis ; 21(11): 1518-1528, 2021 11.
Article in English | MEDLINE | ID: covidwho-1636381

ABSTRACT

BACKGROUND: A more transmissible variant of SARS-CoV-2, the variant of concern 202012/01 or lineage B.1.1.7, has emerged in the UK. We aimed to estimate the risk of critical care admission, mortality in patients who are critically ill, and overall mortality associated with lineage B.1.1.7 compared with non-B.1.1.7. We also compared clinical outcomes between these two groups. METHODS: For this observational cohort study, we linked large primary care (QResearch), national critical care (Intensive Care National Audit & Research Centre Case Mix Programme), and national COVID-19 testing (Public Health England) databases. We used SARS-CoV-2 positive samples with S-gene molecular diagnostic assay failure (SGTF) as a proxy for the presence of lineage B.1.1.7. We extracted two cohorts from the data: the primary care cohort, comprising patients in primary care with a positive community COVID-19 test reported between Nov 1, 2020, and Jan 26, 2021, and known SGTF status; and the critical care cohort, comprising patients admitted for critical care with a positive community COVID-19 test reported between Nov 1, 2020, and Jan 27, 2021, and known SGTF status. We explored the associations between SARS-CoV-2 infection with and without lineage B.1.1.7 and admission to a critical care unit (CCU), 28-day mortality, and 28-day mortality following CCU admission. We used Royston-Parmar models adjusted for age, sex, geographical region, other sociodemographic factors (deprivation index, ethnicity, household housing category, and smoking status for the primary care cohort; and ethnicity, body-mass index, deprivation index, and dependency before admission to acute hospital for the CCU cohort), and comorbidities (asthma, chronic obstructive pulmonary disease, type 1 and 2 diabetes, and hypertension for the primary care cohort; and cardiovascular disease, respiratory disease, metastatic disease, and immunocompromised conditions for the CCU cohort). We reported information on types and duration of organ support for the B.1.1.7 and non-B.1.1.7 groups. FINDINGS: The primary care cohort included 198 420 patients with SARS-CoV-2 infection. Of these, 117 926 (59·4%) had lineage B.1.1.7, 836 (0·4%) were admitted to CCU, and 899 (0·4%) died within 28 days. The critical care cohort included 4272 patients admitted to CCU. Of these, 2685 (62·8%) had lineage B.1.1.7 and 662 (15·5%) died at the end of critical care. In the primary care cohort, we estimated adjusted hazard ratios (HRs) of 2·15 (95% CI 1·75-2·65) for CCU admission and 1·65 (1·36-2·01) for 28-day mortality for patients with lineage B.1.1.7 compared with the non-B.1.1.7 group. The adjusted HR for mortality in critical care, estimated with the critical care cohort, was 0·91 (0·76-1·09) for patients with lineage B.1.1.7 compared with those with non-B.1.1.7 infection. INTERPRETATION: Patients with lineage B.1.1.7 were at increased risk of CCU admission and 28-day mortality compared with patients with non-B.1.1.7 SARS-CoV-2. For patients receiving critical care, mortality appeared to be independent of virus strain. Our findings emphasise the importance of measures to control exposure to and infection with COVID-19. FUNDING: Wellcome Trust, National Institute for Health Research Oxford Biomedical Research Centre, and the Medical Sciences Division of the University of Oxford.


Subject(s)
COVID-19/mortality , Critical Care/statistics & numerical data , Intensive Care Units/statistics & numerical data , SARS-CoV-2/isolation & purification , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/therapy , COVID-19/virology , COVID-19 Nucleic Acid Testing/statistics & numerical data , England/epidemiology , Female , Hospital Mortality , Humans , Male , Middle Aged , Risk Assessment/statistics & numerical data , Risk Factors , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Severity of Illness Index , Young Adult
9.
Nat Med ; 28(2): 410-422, 2022 02.
Article in English | MEDLINE | ID: covidwho-1575259

ABSTRACT

Although myocarditis and pericarditis were not observed as adverse events in coronavirus disease 2019 (COVID-19) vaccine trials, there have been numerous reports of suspected cases following vaccination in the general population. We undertook a self-controlled case series study of people aged 16 or older vaccinated for COVID-19 in England between 1 December 2020 and 24 August 2021 to investigate hospital admission or death from myocarditis, pericarditis and cardiac arrhythmias in the 1-28 days following adenovirus (ChAdOx1, n = 20,615,911) or messenger RNA-based (BNT162b2, n = 16,993,389; mRNA-1273, n = 1,006,191) vaccines or a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive test (n = 3,028,867). We found increased risks of myocarditis associated with the first dose of ChAdOx1 and BNT162b2 vaccines and the first and second doses of the mRNA-1273 vaccine over the 1-28 days postvaccination period, and after a SARS-CoV-2 positive test. We estimated an extra two (95% confidence interval (CI) 0, 3), one (95% CI 0, 2) and six (95% CI 2, 8) myocarditis events per 1 million people vaccinated with ChAdOx1, BNT162b2 and mRNA-1273, respectively, in the 28 days following a first dose and an extra ten (95% CI 7, 11) myocarditis events per 1 million vaccinated in the 28 days after a second dose of mRNA-1273. This compares with an extra 40 (95% CI 38, 41) myocarditis events per 1 million patients in the 28 days following a SARS-CoV-2 positive test. We also observed increased risks of pericarditis and cardiac arrhythmias following a positive SARS-CoV-2 test. Similar associations were not observed with any of the COVID-19 vaccines, apart from an increased risk of arrhythmia following a second dose of mRNA-1273. Subgroup analyses by age showed the increased risk of myocarditis associated with the two mRNA vaccines was present only in those younger than 40.


Subject(s)
/adverse effects , Arrhythmias, Cardiac/epidemiology , /adverse effects , Myocarditis/epidemiology , Pericarditis/epidemiology , /immunology , Adolescent , Adult , COVID-19/pathology , COVID-19/prevention & control , England/epidemiology , Female , Humans , Length of Stay , Male , SARS-CoV-2/immunology , Vaccination/adverse effects , Young Adult
11.
Thorax ; 77(5): 497-504, 2022 May.
Article in English | MEDLINE | ID: covidwho-1526530

ABSTRACT

BACKGROUND: The QCovid algorithm is a risk prediction tool that can be used to stratify individuals by risk of COVID-19 hospitalisation and mortality. Version 1 of the algorithm was trained using data covering 10.5 million patients in England in the period 24 January 2020 to 30 April 2020. We carried out an external validation of version 1 of the QCovid algorithm in Scotland. METHODS: We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisations and deaths in our dataset for two time periods matching the original study: 1 March 2020 to 30 April 2020, and 1 May 2020 to 30 June 2020. RESULTS: Our dataset comprised 5 384 819 individuals, representing 99% of the estimated population (5 463 300) resident in Scotland in 2020. The algorithm showed good calibration in the first period, but systematic overestimation of risk in the second period, prior to temporal recalibration. Harrell's C for deaths in females and males in the first period was 0.95 (95% CI 0.94 to 0.95) and 0.93 (95% CI 0.92 to 0.93), respectively. Harrell's C for hospitalisations in females and males in the first period was 0.81 (95% CI 0.80 to 0.82) and 0.82 (95% CI 0.81 to 0.82), respectively. CONCLUSIONS: Version 1 of the QCovid algorithm showed high levels of discrimination in predicting the risk of COVID-19 hospitalisations and deaths in adults resident in Scotland for the original two time periods studied, but is likely to need ongoing recalibration prospectively.


Subject(s)
COVID-19 , Adult , Algorithms , Calibration , Cohort Studies , Female , Hospitalization , Humans , Male , Scotland/epidemiology
12.
Nat Med ; 27(12): 2144-2153, 2021 12.
Article in English | MEDLINE | ID: covidwho-1483142

ABSTRACT

Emerging reports of rare neurological complications associated with COVID-19 infection and vaccinations are leading to regulatory, clinical and public health concerns. We undertook a self-controlled case series study to investigate hospital admissions from neurological complications in the 28 days after a first dose of ChAdOx1nCoV-19 (n = 20,417,752) or BNT162b2 (n = 12,134,782), and after a SARS-CoV-2-positive test (n = 2,005,280). There was an increased risk of Guillain-Barré syndrome (incidence rate ratio (IRR), 2.90; 95% confidence interval (CI): 2.15-3.92 at 15-21 days after vaccination) and Bell's palsy (IRR, 1.29; 95% CI: 1.08-1.56 at 15-21 days) with ChAdOx1nCoV-19. There was an increased risk of hemorrhagic stroke (IRR, 1.38; 95% CI: 1.12-1.71 at 15-21 days) with BNT162b2. An independent Scottish cohort provided further support for the association between ChAdOx1nCoV and Guillain-Barré syndrome (IRR, 2.32; 95% CI: 1.08-5.02 at 1-28 days). There was a substantially higher risk of all neurological outcomes in the 28 days after a positive SARS-CoV-2 test including Guillain-Barré syndrome (IRR, 5.25; 95% CI: 3.00-9.18). Overall, we estimated 38 excess cases of Guillain-Barré syndrome per 10 million people receiving ChAdOx1nCoV-19 and 145 excess cases per 10 million people after a positive SARS-CoV-2 test. In summary, although we find an increased risk of neurological complications in those who received COVID-19 vaccines, the risk of these complications is greater following a positive SARS-CoV-2 test.


Subject(s)
/adverse effects , Bell Palsy/epidemiology , COVID-19/pathology , Guillain-Barre Syndrome/epidemiology , Hemorrhagic Stroke/epidemiology , Adolescent , Adult , Aged , Aged, 80 and over , Bell Palsy/virology , COVID-19/diagnosis , COVID-19/immunology , England/epidemiology , Female , Guillain-Barre Syndrome/virology , Hemorrhagic Stroke/virology , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Nervous System Diseases/epidemiology , Nervous System Diseases/virology , SARS-CoV-2/immunology , Scotland/epidemiology , Young Adult
13.
Thorax ; 77(1): 65-73, 2022 01.
Article in English | MEDLINE | ID: covidwho-1440837

ABSTRACT

BACKGROUND: Conflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity. METHODS: We undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase). RESULTS: There were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1-9/day: OR 2.14, 95% CI 0.87 to 5.24; 10-19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72). INTERPRETATION: Congruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.


Subject(s)
COVID-19 , Biological Specimen Banks , COVID-19 Testing , England , Humans , SARS-CoV-2 , Smoking/adverse effects
14.
BMJ ; 374: n2244, 2021 09 17.
Article in English | MEDLINE | ID: covidwho-1430185

ABSTRACT

OBJECTIVES: To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination. DESIGN: Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries. SETTINGS: Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021. MAIN OUTCOME MEASURES: Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices. RESULTS: Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down's syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson's disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%. CONCLUSION: This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/mortality , Hospitalization/statistics & numerical data , Vaccination/statistics & numerical data , Adult , Aged , Aged, 80 and over , COVID-19/immunology , COVID-19 Vaccines/immunology , Comorbidity , Databases, Factual , Female , Humans , Male , Middle Aged , Prospective Studies , Risk Assessment , SARS-CoV-2 , United Kingdom/epidemiology
15.
Lancet Respir Med ; 9(8): 909-923, 2021 08.
Article in English | MEDLINE | ID: covidwho-1411740

ABSTRACT

BACKGROUND: Previous studies suggested that the prevalence of chronic respiratory disease in patients hospitalised with COVID-19 was lower than its prevalence in the general population. The aim of this study was to assess whether chronic lung disease or use of inhaled corticosteroids (ICS) affects the risk of contracting severe COVID-19. METHODS: In this population cohort study, records from 1205 general practices in England that contribute to the QResearch database were linked to Public Health England's database of SARS-CoV-2 testing and English hospital admissions, intensive care unit (ICU) admissions, and deaths for COVID-19. All patients aged 20 years and older who were registered with one of the 1205 general practices on Jan 24, 2020, were included in this study. With Cox regression, we examined the risks of COVID-19-related hospitalisation, admission to ICU, and death in relation to respiratory disease and use of ICS, adjusting for demographic and socioeconomic status and comorbidities associated with severe COVID-19. FINDINGS: Between Jan 24 and April 30, 2020, 8 256 161 people were included in the cohort and observed, of whom 14 479 (0·2%) were admitted to hospital with COVID-19, 1542 (<0·1%) were admitted to ICU, and 5956 (0·1%) died. People with some respiratory diseases were at an increased risk of hospitalisation (chronic obstructive pulmonary disease [COPD] hazard ratio [HR] 1·54 [95% CI 1·45-1·63], asthma 1·18 [1·13-1·24], severe asthma 1·29 [1·22-1·37; people on three or more current asthma medications], bronchiectasis 1·34 [1·20-1·50], sarcoidosis 1·36 [1·10-1·68], extrinsic allergic alveolitis 1·35 [0·82-2·21], idiopathic pulmonary fibrosis 1·59 [1·30-1·95], other interstitial lung disease 1·66 [1·30-2·12], and lung cancer 2·24 [1·89-2·65]) and death (COPD 1·54 [1·42-1·67], asthma 0·99 [0·91-1·07], severe asthma 1·08 [0·98-1·19], bronchiectasis 1·12 [0·94-1·33], sarcoidosis 1·41 [0·99-1·99), extrinsic allergic alveolitis 1·56 [0·78-3·13], idiopathic pulmonary fibrosis 1·47 [1·12-1·92], other interstitial lung disease 2·05 [1·49-2·81], and lung cancer 1·77 [1·37-2·29]) due to COVID-19 compared with those without these diseases. Admission to ICU was rare, but the HR for people with asthma was 1·08 (0·93-1·25) and severe asthma was 1·30 (1·08-1·58). In a post-hoc analysis, relative risks of severe COVID-19 in people with respiratory disease were similar before and after shielding was introduced on March 23, 2020. In another post-hoc analysis, people with two or more prescriptions for ICS in the 150 days before study start were at a slightly higher risk of severe COVID-19 compared with all other individuals (ie, no or one ICS prescription): HR 1·13 (1·03-1·23) for hospitalisation, 1·63 (1·18-2·24) for ICU admission, and 1·15 (1·01-1·31) for death. INTERPRETATION: The risk of severe COVID-19 in people with asthma is relatively small. People with COPD and interstitial lung disease appear to have a modestly increased risk of severe disease, but their risk of death from COVID-19 at the height of the epidemic was mostly far lower than the ordinary risk of death from any cause. Use of inhaled steroids might be associated with a modestly increased risk of severe COVID-19. FUNDING: National Institute for Health Research Oxford Biomedical Research Centre and the Wellcome Trust.


Subject(s)
Adrenal Cortex Hormones , COVID-19 , Pulmonary Disease, Chronic Obstructive , Administration, Inhalation , Adrenal Cortex Hormones/administration & dosage , Adrenal Cortex Hormones/adverse effects , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/physiopathology , COVID-19 Testing , Comorbidity , England/epidemiology , Female , Hospitalization/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Mortality , Pulmonary Disease, Chronic Obstructive/diagnosis , Pulmonary Disease, Chronic Obstructive/drug therapy , Pulmonary Disease, Chronic Obstructive/epidemiology , Risk Assessment , SARS-CoV-2/isolation & purification , Social Class
16.
BMJ ; 374: n1931, 2021 08 26.
Article in English | MEDLINE | ID: covidwho-1376469

ABSTRACT

OBJECTIVE: To assess the association between covid-19 vaccines and risk of thrombocytopenia and thromboembolic events in England among adults. DESIGN: Self-controlled case series study using national data on covid-19 vaccination and hospital admissions. SETTING: Patient level data were obtained for approximately 30 million people vaccinated in England between 1 December 2020 and 24 April 2021. Electronic health records were linked with death data from the Office for National Statistics, SARS-CoV-2 positive test data, and hospital admission data from the United Kingdom's health service (NHS). PARTICIPANTS: 29 121 633 people were vaccinated with first doses (19 608 008 with Oxford-AstraZeneca (ChAdOx1 nCoV-19) and 9 513 625 with Pfizer-BioNTech (BNT162b2 mRNA)) and 1 758 095 people had a positive SARS-CoV-2 test. People aged ≥16 years who had first doses of the ChAdOx1 nCoV-19 or BNT162b2 mRNA vaccines and any outcome of interest were included in the study. MAIN OUTCOME MEASURES: The primary outcomes were hospital admission or death associated with thrombocytopenia, venous thromboembolism, and arterial thromboembolism within 28 days of three exposures: first dose of the ChAdOx1 nCoV-19 vaccine; first dose of the BNT162b2 mRNA vaccine; and a SARS-CoV-2 positive test. Secondary outcomes were subsets of the primary outcomes: cerebral venous sinus thrombosis (CVST), ischaemic stroke, myocardial infarction, and other rare arterial thrombotic events. RESULTS: The study found increased risk of thrombocytopenia after ChAdOx1 nCoV-19 vaccination (incidence rate ratio 1.33, 95% confidence interval 1.19 to 1.47 at 8-14 days) and after a positive SARS-CoV-2 test (5.27, 4.34 to 6.40 at 8-14 days); increased risk of venous thromboembolism after ChAdOx1 nCoV-19 vaccination (1.10, 1.02 to 1.18 at 8-14 days) and after SARS-CoV-2 infection (13.86, 12.76 to 15.05 at 8-14 days); and increased risk of arterial thromboembolism after BNT162b2 mRNA vaccination (1.06, 1.01 to 1.10 at 15-21 days) and after SARS-CoV-2 infection (2.02, 1.82 to 2.24 at 15-21 days). Secondary analyses found increased risk of CVST after ChAdOx1 nCoV-19 vaccination (4.01, 2.08 to 7.71 at 8-14 days), after BNT162b2 mRNA vaccination (3.58, 1.39 to 9.27 at 15-21 days), and after a positive SARS-CoV-2 test; increased risk of ischaemic stroke after BNT162b2 mRNA vaccination (1.12, 1.04 to 1.20 at 15-21 days) and after a positive SARS-CoV-2 test; and increased risk of other rare arterial thrombotic events after ChAdOx1 nCoV-19 vaccination (1.21, 1.02 to 1.43 at 8-14 days) and after a positive SARS-CoV-2 test. CONCLUSION: Increased risks of haematological and vascular events that led to hospital admission or death were observed for short time intervals after first doses of the ChAdOx1 nCoV-19 and BNT162b2 mRNA vaccines. The risks of most of these events were substantially higher and more prolonged after SARS-CoV-2 infection than after vaccination in the same population.


Subject(s)
COVID-19 Vaccines/adverse effects , COVID-19/prevention & control , Thrombocytopenia/epidemiology , Thromboembolism/epidemiology , Vaccination/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Vaccines/administration & dosage , England/epidemiology , Female , Humans , Male , Middle Aged , Pandemics , Risk Assessment , SARS-CoV-2 , Young Adult
18.
JAMA Pediatr ; 175(9): 928-938, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1274655

ABSTRACT

Importance: Although children mainly experience mild COVID-19 disease, hospitalization rates are increasing, with limited understanding of underlying factors. There is an established association between race and severe COVID-19 outcomes in adults in England; however, whether a similar association exists in children is unclear. Objective: To investigate the association between race and childhood COVID-19 testing and hospital outcomes. Design, Setting, Participants: In this cohort study, children (0-18 years of age) from participating family practices in England were identified in the QResearch database between January 24 and November 30, 2020. The QResearch database has individually linked patients with national SARS-CoV-2 testing, hospital admission, and mortality data. Exposures: The main characteristic of interest is self-reported race. Other exposures were age, sex, deprivation level, geographic region, household size, and comorbidities (asthma; diabetes; and cardiac, neurologic, and hematologic conditions). Main Outcomes and Measures: The primary outcome was hospital admission with confirmed COVID-19. Secondary outcomes were SARS-CoV-2-positive test result and any hospital attendance with confirmed COVID-19 and intensive care admission. Results: Of 2 576 353 children (mean [SD] age, 9.23 [5.24] years; 48.8% female), 410 726 (15.9%) were tested for SARS-CoV-2 and 26 322 (6.4%) tested positive. A total of 1853 children (0.07%) with confirmed COVID-19 attended hospital, 343 (0.01%) were admitted to the hospital, and 73 (0.002%) required intensive care. Testing varied across race. White children had the highest proportion of SARS-CoV-2 tests (223 701/1 311 041 [17.1%]), whereas Asian children (33 213/243 545 [13.6%]), Black children (7727/93 620 [8.3%]), and children of mixed or other races (18 971/147 529 [12.9%]) had lower proportions. Compared with White children, Asian children were more likely to have COVID-19 hospital admissions (adjusted odds ratio [OR], 1.62; 95% CI, 1.12-2.36), whereas Black children (adjusted OR, 1.44; 95% CI, 0.90-2.31) and children of mixed or other races (adjusted OR, 1.40; 95% CI, 0.93-2.10) had comparable hospital admissions. Asian children were more likely to be admitted to intensive care (adjusted OR, 2.11; 95% CI, 1.07-4.14), and Black children (adjusted OR, 2.31; 95% CI, 1.08-4.94) and children of mixed or other races (adjusted OR, 2.14; 95% CI, 1.25-3.65) had longer hospital admissions (≥36 hours). Conclusions and Relevance: In this large population-based study exploring the association between race and childhood COVID-19 testing and hospital outcomes, several race-specific disparities were observed in severe COVID-19 outcomes. However, ascertainment bias and residual confounding in this cohort study should be considered before drawing any further conclusions. Overall, findings of this study have important public health implications internationally.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/diagnosis , Child Welfare/statistics & numerical data , /statistics & numerical data , Adolescent , COVID-19/epidemiology , Child , Child Health , Child, Preschool , Cohort Studies , England , Female , Humans , Infant , Infant, Newborn , Male , Risk Factors , SARS-CoV-2/isolation & purification , Socioeconomic Factors
19.
Lancet Digit Health ; 3(7): e425-e433, 2021 07.
Article in English | MEDLINE | ID: covidwho-1246269

ABSTRACT

BACKGROUND: Public policy measures and clinical risk assessments relevant to COVID-19 need to be aided by risk prediction models that are rigorously developed and validated. We aimed to externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England. METHODS: We did a population-based cohort study using the UK Office for National Statistics Public Health Linked Data Asset, a cohort of individuals aged 19-100 years, based on the 2011 census and linked to Hospital Episode Statistics, the General Practice Extraction Service data for pandemic planning and research, and radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two periods were used: (1) Jan 24 to April 30, 2020, and (2) May 1 to July 28, 2020. We assessed the performance of the QCovid algorithms using measures of discrimination and calibration. Using predicted 90-day risk of COVID-19 death, we calculated r2 values, Brier scores, and measures of discrimination and calibration with corresponding 95% CIs over the two time periods. FINDINGS: We included 34 897 648 adults aged 19-100 years resident in England. 26 985 (0·08%) COVID-19 deaths occurred during the first period and 13 177 (0·04%) during the second. The algorithms had good discrimination and calibration in both periods. In the first period, they explained 77·1% (95% CI 76·9-77·4) of the variation in time to death in men and 76·3% (76·0-76·6) in women. The D statistic was 3·761 (3·732-3·789) for men and 3·671 (3·640-3·702) for women and Harrell's C was 0·935 (0·933-0·937) for men and 0·945 (0·943-0·947) for women. Similar results were obtained for the second time period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first period was 65·94% for men and 71·67% for women. INTERPRETATION: The QCovid population-based risk algorithm performed well, showing high levels of discrimination for COVID-19 deaths in men and women for both time periods. QCovid has the potential to be dynamically updated as the pandemic evolves and, therefore, has potential use in guiding national policy. FUNDING: UK National Institute for Health Research.


Subject(s)
Algorithms , COVID-19/mortality , Risk Assessment/statistics & numerical data , Adult , Aged , Aged, 80 and over , Cohort Studies , Databases, Factual , England/epidemiology , Female , Humans , Male , Middle Aged , Pandemics , SARS-CoV-2 , Young Adult
20.
Lancet Diabetes Endocrinol ; 9(6): 350-359, 2021 06.
Article in English | MEDLINE | ID: covidwho-1208494

ABSTRACT

BACKGROUND: Obesity is a major risk factor for adverse outcomes after infection with SARS-CoV-2. We aimed to examine this association, including interactions with demographic and behavioural characteristics, type 2 diabetes, and other health conditions. METHODS: In this prospective, community-based, cohort study, we used de-identified patient-level data from the QResearch database of general practices in England, UK. We extracted data for patients aged 20 years and older who were registered at a practice eligible for inclusion in the QResearch database between Jan 24, 2020 (date of the first recorded infection in the UK) and April 30, 2020, and with available data on BMI. Data extracted included demographic, clinical, clinical values linked with Public Health England's database of positive SARS-CoV-2 test results, and death certificates from the Office of National Statistics. Outcomes, as a proxy measure of severe COVID-19, were admission to hospital, admission to an intensive care unit (ICU), and death due to COVID-19. We used Cox proportional hazard models to estimate the risk of severe COVID-19, sequentially adjusting for demographic characteristics, behavioural factors, and comorbidities. FINDINGS: Among 6 910 695 eligible individuals (mean BMI 26·78 kg/m2 [SD 5·59]), 13 503 (0·20%) were admitted to hospital, 1601 (0·02%) to an ICU, and 5479 (0·08%) died after a positive test for SARS-CoV-2. We found J-shaped associations between BMI and admission to hospital due to COVID-19 (adjusted hazard ratio [HR] per kg/m2 from the nadir at BMI of 23 kg/m2 of 1·05 [95% CI 1·05-1·05]) and death (1·04 [1·04-1·05]), and a linear association across the whole BMI range with ICU admission (1·10 [1·09-1·10]). We found a significant interaction between BMI and age and ethnicity, with higher HR per kg/m2 above BMI 23 kg/m2 for younger people (adjusted HR per kg/m2 above BMI 23 kg/m2 for hospital admission 1·09 [95% CI 1·08-1·10] in 20-39 years age group vs 80-100 years group 1·01 [1·00-1·02]) and Black people than White people (1·07 [1·06-1·08] vs 1·04 [1·04-1·05]). The risk of admission to hospital and ICU due to COVID-19 associated with unit increase in BMI was slightly lower in people with type 2 diabetes, hypertension, and cardiovascular disease than in those without these morbidities. INTERPRETATION: At a BMI of more than 23 kg/m2, we found a linear increase in risk of severe COVID-19 leading to admission to hospital and death, and a linear increase in admission to an ICU across the whole BMI range, which is not attributable to excess risks of related diseases. The relative risk due to increasing BMI is particularly notable people younger than 40 years and of Black ethnicity. FUNDING: NIHR Oxford Biomedical Research Centre.


Subject(s)
Body Mass Index , COVID-19/epidemiology , Diabetes Mellitus, Type 2/epidemiology , Independent Living/trends , Severity of Illness Index , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , Cohort Studies , Databases, Factual , Diabetes Mellitus, Type 2/diagnosis , England/epidemiology , Female , Follow-Up Studies , Hospitalization/trends , Humans , Male , Middle Aged , Prospective Studies , Risk Factors , Young Adult
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