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  1. Article ; Online: Deriving "definitive" results in observational COVID-19 research: A problematic endeavor.

    Clift, Ash K

    Journal of medical virology

    2020  Volume 93, Issue 2, Page(s) 681–682

    MeSH term(s) COVID-19/complications ; COVID-19/etiology ; COVID-19/physiopathology ; Humans ; Meta-Analysis as Topic ; Observational Studies as Topic/standards ; Research/standards ; Smoking
    Keywords covid19
    Language English
    Publishing date 2020-09-29
    Publishing country United States
    Document type Letter
    ZDB-ID 752392-0
    ISSN 1096-9071 ; 0146-6615
    ISSN (online) 1096-9071
    ISSN 0146-6615
    DOI 10.1002/jmv.26481
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Deriving “definitive” results in observational COVID‐19 research

    Clift, Ash K.

    Journal of Medical Virology ; ISSN 0146-6615 1096-9071

    A problematic endeavor

    2020  

    Keywords Virology ; Infectious Diseases ; covid19
    Language English
    Publisher Wiley
    Publishing country us
    Document type Article ; Online
    DOI 10.1002/jmv.26481
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Preexisting Neuropsychiatric Conditions and Associated Risk of Severe COVID-19 Infection and Other Acute Respiratory Infections.

    Ranger, Tom Alan / Clift, Ash Kieran / Patone, Martina / Coupland, Carol A C / Hatch, Robert / Thomas, Karen / Watkinson, Peter / Hippisley-Cox, Julia

    JAMA psychiatry

    2022  Volume 80, Issue 1, Page(s) 57–65

    Abstract: Importance: Evidence indicates that preexisting neuropsychiatric conditions confer increased risks of severe outcomes from COVID-19 infection. It is unclear how this increased risk compares with risks associated with other severe acute respiratory ... ...

    Abstract Importance: Evidence indicates that preexisting neuropsychiatric conditions confer increased risks of severe outcomes from COVID-19 infection. It is unclear how this increased risk compares with risks associated with other severe acute respiratory infections (SARIs).
    Objective: To determine whether preexisting diagnosis of and/or treatment for a neuropsychiatric condition is associated with severe outcomes from COVID-19 infection and other SARIs and whether any observed association is similar between the 2 outcomes.
    Design, setting, and participants: Prepandemic (2015-2020) and contemporary (2020-2021) longitudinal cohorts were derived from the QResearch database of English primary care records. Adjusted hazard ratios (HRs) with 99% CIs were estimated in April 2022 using flexible parametric survival models clustered by primary care clinic. This study included a population-based sample, including all adults in the database who had been registered with a primary care clinic for at least 1 year. Analysis of routinely collected primary care electronic medical records was performed.
    Exposures: Diagnosis of and/or medication for anxiety, mood, or psychotic disorders and diagnosis of dementia, depression, schizophrenia, or bipolar disorder.
    Main outcomes and measures: COVID-19-related mortality, or hospital or intensive care unit admission; SARI-related mortality, or hospital or intensive care unit admission.
    Results: The prepandemic cohort comprised 11 134 789 adults (223 569 SARI cases [2.0%]) with a median (IQR) age of 42 (29-58) years, of which 5 644 525 (50.7%) were female. The contemporary cohort comprised 8 388 956 adults (58 203 severe COVID-19 cases [0.7%]) with a median (IQR) age of 48 (34-63) years, of which 4 207 192 were male (50.2%). Diagnosis and/or treatment for neuropsychiatric conditions other than dementia was associated with an increased likelihood of a severe outcome from SARI (anxiety diagnosis: HR, 1.16; 99% CI, 1.13-1.18; psychotic disorder diagnosis and treatment: HR, 2.56; 99% CI, 2.40-2.72) and COVID-19 (anxiety diagnosis: HR, 1.16; 99% CI, 1.12-1.20; psychotic disorder treatment: HR, 2.37; 99% CI, 2.20-2.55). The effect estimate for severe outcome with dementia was higher for those with COVID-19 than SARI (HR, 2.85; 99% CI, 2.71-3.00 vs HR, 2.13; 99% CI, 2.07-2.19).
    Conclusions and relevance: In this longitudinal cohort study, UK patients with preexisting neuropsychiatric conditions and treatments were associated with similarly increased risks of severe outcome from COVID-19 infection and SARIs, except for dementia.
    MeSH term(s) Adult ; Humans ; Male ; Female ; Middle Aged ; COVID-19/epidemiology ; Longitudinal Studies ; Psychotic Disorders/diagnosis ; Psychotic Disorders/epidemiology ; Cohort Studies ; Dementia
    Language English
    Publishing date 2022-11-09
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2701203-7
    ISSN 2168-6238 ; 2168-622X
    ISSN (online) 2168-6238
    ISSN 2168-622X
    DOI 10.1001/jamapsychiatry.2022.3614
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Ethnic disparities in COVID-19 outcomes: a multinational cohort study of 20 million individuals from England and Canada.

    Zaccardi, Francesco / Tan, Pui San / Shah, Baiju R / Everett, Karl / Clift, Ash Kieran / Patone, Martina / Saatci, Defne / Coupland, Carol / Griffin, Simon J / Khunti, Kamlesh / Dambha-Miller, Hajira / Hippisley-Cox, Julia

    BMC public health

    2023  Volume 23, Issue 1, Page(s) 399

    Abstract: Background: Heterogeneous studies have demonstrated ethnic inequalities in the risk of SARS-CoV-2 infection and adverse COVID-19 outcomes. This study evaluates the association between ethnicity and COVID-19 outcomes in two large population-based cohorts ...

    Abstract Background: Heterogeneous studies have demonstrated ethnic inequalities in the risk of SARS-CoV-2 infection and adverse COVID-19 outcomes. This study evaluates the association between ethnicity and COVID-19 outcomes in two large population-based cohorts from England and Canada and investigates potential explanatory factors for ethnic patterning of severe outcomes.
    Methods: We identified adults aged 18 to 99 years in the QResearch primary care (England) and Ontario (Canada) healthcare administrative population-based datasets (start of follow-up: 24th and 25th Jan 2020 in England and Canada, respectively; end of follow-up: 31st Oct and 30th Sept 2020, respectively). We harmonised the definitions and the design of two cohorts to investigate associations between ethnicity and COVID-19-related death, hospitalisation, and intensive care (ICU) admission, adjusted for confounders, and combined the estimates obtained from survival analyses. We calculated the 'percentage of excess risk mediated' by these risk factors in the QResearch cohort.
    Results: There were 9.83 million adults in the QResearch cohort (11,597 deaths; 21,917 hospitalisations; 2932 ICU admissions) and 10.27 million adults in the Ontario cohort (951 deaths; 5132 hospitalisations; 1191 ICU admissions). Compared to the general population, pooled random-effects estimates showed that South Asian ethnicity was associated with an increased risk of COVID-19 death (hazard ratio: 1.63, 95% CI: 1.09-2.44), hospitalisation (1.53; 1.32-1.76), and ICU admission (1.67; 1.23-2.28). Associations with ethnic groups were consistent across levels of deprivation. In QResearch, sociodemographic, lifestyle, and clinical factors accounted for 42.9% (South Asian) and 39.4% (Black) of the excess risk of COVID-19 death.
    Conclusion: International population-level analyses demonstrate clear ethnic inequalities in COVID-19 risks. Policymakers should be cognisant of the increased risks in some ethnic populations and design equitable health policy as the pandemic continues.
    MeSH term(s) Adult ; Humans ; Cohort Studies ; COVID-19 ; SARS-CoV-2 ; Ontario/epidemiology ; England/epidemiology
    Language English
    Publishing date 2023-02-27
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2041338-5
    ISSN 1471-2458 ; 1471-2458
    ISSN (online) 1471-2458
    ISSN 1471-2458
    DOI 10.1186/s12889-023-15223-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Ethnicity and risks of severe COVID-19 outcomes associated with glucose-lowering medications: A cohort study.

    Zaccardi, Francesco / Tan, Pui San / Coupland, Carol / Shah, Baiju R / Clift, Ash Kieran / Saatci, Defne / Patone, Martina / Griffin, Simon J / Dambha-Miller, Hajira / Khunti, Kamlesh / Hippisley-Cox, Julia

    Diabetes, obesity & metabolism

    2022  Volume 25, Issue 2, Page(s) 611–617

    MeSH term(s) Humans ; Cohort Studies ; COVID-19/complications ; Glucose ; Ethnicity ; Diabetes Mellitus, Type 2/complications ; Diabetes Mellitus, Type 2/drug therapy
    Chemical Substances Glucose (IY9XDZ35W2)
    Language English
    Publishing date 2022-09-29
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1454944-x
    ISSN 1463-1326 ; 1462-8902
    ISSN (online) 1463-1326
    ISSN 1462-8902
    DOI 10.1111/dom.14872
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

    Clift, Ash K / Coupland, Carol A C / Keogh, Ruth H / Diaz-Ordaz, Karla / Williamson, Elizabeth / Harrison, Ewen M / Hayward, Andrew / Hemingway, Harry / Horby, Peter / Mehta, Nisha / Benger, Jonathan / Khunti, Kamlesh / Spiegelhalter, David / Sheikh, Aziz / Valabhji, Jonathan / Lyons, Ronan A / Robson, John / Semple, Malcolm G / Kee, Frank /
    Johnson, Peter / Jebb, Susan / Williams, Tony / Hippisley-Cox, Julia

    BMJ (Clinical research ed.)

    2020  Volume 371, Page(s) m3731

    Abstract: Objective: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.: Design: Population based cohort study.: Setting and participants: QResearch ... ...

    Abstract Objective: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.
    Design: Population based cohort study.
    Setting and participants: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020.
    Main outcome measures: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period.
    Results: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R
    Conclusion: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
    MeSH term(s) Adult ; Aged, 80 and over ; Algorithms ; Betacoronavirus/isolation & purification ; COVID-19 ; Clinical Decision Rules ; Cohort Studies ; Coronavirus Infections/mortality ; Coronavirus Infections/therapy ; Databases, Factual/statistics & numerical data ; England/epidemiology ; Female ; Hospitalization/statistics & numerical data ; Humans ; Male ; Mortality ; Pandemics ; Pneumonia, Viral/mortality ; Pneumonia, Viral/therapy ; Prognosis ; Reproducibility of Results ; Risk Assessment/methods ; Risk Assessment/standards ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-10-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj.m3731
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Association Between Race and COVID-19 Outcomes Among 2.6 Million Children in England.

    Saatci, Defne / Ranger, Tom A / Garriga, Cesar / Clift, Ash Kieran / Zaccardi, Francesco / Tan, Pui San / Patone, Martina / Coupland, Carol / Harnden, Anthony / Griffin, Simon J / Khunti, Kamlesh / Dambha-Miller, Hajira / Hippisley-Cox, Julia

    JAMA pediatrics

    2021  Volume 175, Issue 9, Page(s) 928–938

    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 ... ...

    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.
    MeSH term(s) Adolescent ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19 Testing/statistics & numerical data ; Child ; Child Health ; Child Welfare/statistics & numerical data ; Child, Preschool ; Cohort Studies ; England ; Ethnicity/statistics & numerical data ; Female ; Humans ; Infant ; Infant, Newborn ; Male ; Risk Factors ; SARS-CoV-2/isolation & purification ; Socioeconomic Factors
    Language English
    Publishing date 2021-06-04
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2701223-2
    ISSN 2168-6211 ; 2168-6203
    ISSN (online) 2168-6211
    ISSN 2168-6203
    DOI 10.1001/jamapediatrics.2021.1685
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

    Clift, Ash K / Coupland, Carol A C / Keogh, Ruth H / Diaz-Ordaz, Karla / Williamson, Elizabeth / Harrison, Ewen M / Hayward, Andrew / Hemingway, Harry / Horby, Peter / Mehta, Nisha / Benger, Jonathan / Khunti, Kamlesh / Spiegelhalter, David / Sheikh, Aziz / Valabhji, Jonathan / Lyons, Ronan A / Robson, John / Semple, Malcolm G / Kee, Frank /
    Johnson, Peter / Jebb, Susan / Williams, Tony / Hippisley-Cox, Julia

    BMJ

    Abstract: OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, ... ...

    Abstract OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. MAIN OUTCOME MEASURES: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. RESULTS: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. CONCLUSION: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #883340
    Database COVID19

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  9. Book ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults

    Clift, Ash K / Coupland, Carol A C / Keogh, Ruth H / Diaz-Ordaz, Karla / Williamson, Elizabeth / Harrison, Ewen M / Hayward, Andrew / Hemingway, Harry / Horby, Peter / Mehta, Nisha / Benger, Jonathan / Khunti, Kamlesh / Spiegelhalter, David / Sheikh, Aziz / Valabhji, Jonathan / Lyons, Ronan A / Robson, John / Semple, Malcolm G / Kee, Frank /
    Johnson, Peter / Jebb, Susan / Williams, Tony / Hippisley-Cox, Julia

    national derivation and validation cohort study

    2020  

    Abstract: ... Objective ... To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. ... ... Design ... Population based cohort study. ... ... < ... ...

    Abstract <sec><st>Objective</st> To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. </sec> <sec><st>Design</st> Population based cohort study. </sec> <sec><st>Setting and participants</st> QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. </sec> <sec><st>Main outcome measures</st> The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. </sec> <sec><st>Results</st> 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. </sec> <sec><st>Conclusion</st> The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves. </sec>
    Keywords RESEARCH ; covid19
    Language English
    Publishing date 2020-10-20 16:06:04.0
    Publisher BMJ Publishing Group Ltd
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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