tetano
Editor, Senior Moderator
Cureus
. 2021 Aug 5;13(8):e16899.
doi: 10.7759/cureus.16899. eCollection 2021 Aug.
Fatality Rate and Survival Time of Laboratory-Confirmed COVID-19 for Patients in England During the First Wave of SARS-CoV-2 Infection: A Modelling Study
Christopher R Hillyar[SUP] 1 [/SUP], Anjan Nibber[SUP] 1 [/SUP], Christine E Jones[SUP] 2 [/SUP], Mark G Jones[SUP] 2 [/SUP]
Affiliations
Abstract
Background Fatality rate estimates for coronavirus disease 2019 (COVID-19) have varied widely. A major confounding factor in fatality rate estimates is the survival time (time from diagnosis to death). Predictive models that incorporate the survival time benefit from greater accuracy due to the elimination of sampling bias. This study outlines a survival time-based predictive model that estimates a precise fatality rate for patients with laboratory-confirmed COVID-19. This model was utilised to predict deaths for COVID-19 patients who died during the first wave of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in England. Methodology This study included Public Health England (PHE) data for cumulative laboratory-confirmed COVID-19 cases (n = 143,463) and deaths (n = 30,028) that were reported by PHE between 30 January and 14 May 2020 in England, that is, from the first COVID-19 case in England and the most recently available data at the time of conducting this study. Fatality rate and survival time were estimated by linear regression analysis. This enabled the predicted cumulative COVID-19 deaths to be calculated up to 21 May 2020. Time periods with significantly different rates in daily deaths were identified using Joinpoint trend analysis. Results A fatality rate of 21.9% (95% confidence interval = 21.8% to 22.0%) with a survival time of seven days was determined for patients in England with laboratory-confirmed COVID-19 during the first wave of SARS-CoV-2 infection. Based on these estimates, predicted trends for cumulative and daily laboratory-confirmed COVID-19 deaths were generated with >99% and >96% accuracy with reported data, respectively. This model predicted that the number of cumulative laboratory-confirmed COVID-19 deaths in England was likely to be 31,420 by 21 May 2020. Joinpoint trend analysis identified significant differences in predicted daily laboratory-confirmed COVID-19 deaths during the following periods: 10.5 (6 to 17 March), 111.0 (17 to 27 March), 446.8 (27 March to 4 April), 817.0 (4 to 23 April), 536.3 (23 April to 7 May), and 266.7 (7 to 21 May) daily deaths (P < 0.001). Conclusions During the first wave of SARS-CoV-2 infection in England, the fatality rate of laboratory-confirmed COVID-19 was 21.9%. The survival time of these patients was seven days. The predictive model presented in this study can be adapted for estimating COVID-19 deaths in different geographical regions. As such, this study has utility for clinicians, scientists, and policymakers responding to new waves of SARS-CoV-2 infection because the methodology can be applied to more recent time periods as new data for COVID-19 cases and deaths become available.
Keywords: covid-19; fatality rate; joinpoint; modelling; mortality rate; sars-cov-2; survival time.
. 2021 Aug 5;13(8):e16899.
doi: 10.7759/cureus.16899. eCollection 2021 Aug.
Fatality Rate and Survival Time of Laboratory-Confirmed COVID-19 for Patients in England During the First Wave of SARS-CoV-2 Infection: A Modelling Study
Christopher R Hillyar[SUP] 1 [/SUP], Anjan Nibber[SUP] 1 [/SUP], Christine E Jones[SUP] 2 [/SUP], Mark G Jones[SUP] 2 [/SUP]
Affiliations
- PMID: 34513472
- PMCID: PMC8412062
- DOI: 10.7759/cureus.16899
Abstract
Background Fatality rate estimates for coronavirus disease 2019 (COVID-19) have varied widely. A major confounding factor in fatality rate estimates is the survival time (time from diagnosis to death). Predictive models that incorporate the survival time benefit from greater accuracy due to the elimination of sampling bias. This study outlines a survival time-based predictive model that estimates a precise fatality rate for patients with laboratory-confirmed COVID-19. This model was utilised to predict deaths for COVID-19 patients who died during the first wave of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in England. Methodology This study included Public Health England (PHE) data for cumulative laboratory-confirmed COVID-19 cases (n = 143,463) and deaths (n = 30,028) that were reported by PHE between 30 January and 14 May 2020 in England, that is, from the first COVID-19 case in England and the most recently available data at the time of conducting this study. Fatality rate and survival time were estimated by linear regression analysis. This enabled the predicted cumulative COVID-19 deaths to be calculated up to 21 May 2020. Time periods with significantly different rates in daily deaths were identified using Joinpoint trend analysis. Results A fatality rate of 21.9% (95% confidence interval = 21.8% to 22.0%) with a survival time of seven days was determined for patients in England with laboratory-confirmed COVID-19 during the first wave of SARS-CoV-2 infection. Based on these estimates, predicted trends for cumulative and daily laboratory-confirmed COVID-19 deaths were generated with >99% and >96% accuracy with reported data, respectively. This model predicted that the number of cumulative laboratory-confirmed COVID-19 deaths in England was likely to be 31,420 by 21 May 2020. Joinpoint trend analysis identified significant differences in predicted daily laboratory-confirmed COVID-19 deaths during the following periods: 10.5 (6 to 17 March), 111.0 (17 to 27 March), 446.8 (27 March to 4 April), 817.0 (4 to 23 April), 536.3 (23 April to 7 May), and 266.7 (7 to 21 May) daily deaths (P < 0.001). Conclusions During the first wave of SARS-CoV-2 infection in England, the fatality rate of laboratory-confirmed COVID-19 was 21.9%. The survival time of these patients was seven days. The predictive model presented in this study can be adapted for estimating COVID-19 deaths in different geographical regions. As such, this study has utility for clinicians, scientists, and policymakers responding to new waves of SARS-CoV-2 infection because the methodology can be applied to more recent time periods as new data for COVID-19 cases and deaths become available.
Keywords: covid-19; fatality rate; joinpoint; modelling; mortality rate; sars-cov-2; survival time.