Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023

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A strong correlation has been found between COVID-19 treatment outcomes and noncommunicable diseases such as cancers. However, there is limited information on the outcomes of cancer patients who were hospitalised for COVID-19. Methods We conducted an analysis on data collected in a large prospective cohort study set-up by the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC). All patients with laboratory-confirmed or clinically-diagnosed SARS-CoV-2 infection were included. Cancer was defined as having a current solid organ or haematological malignancy. The following outcomes were assessed; 30-day in-hospital mortality, intensive care unit (ICU) admission, length of hospitalization and receipt of higher-level care. Results Of the 560,547 hospitalised individuals who were analysed, 27,243 (4.9%) had cancer. Overall, cancer patients were older and had more comorbidities than non-cancer patients. Patients with cancer had higher 30-day in-hospital mortality than non-cancer patients (29.1.3% vs 18.0%) and longer hospital stays (median of 12 days vs 8 days). However, patients with cancer were admitted less often to intensive care units than non-cancer patients (12.6% vs 17.1%) and received less invasive mechanical ventilation than non-cancer patients (4.5% vs 7.6%). The hazard ratio of dying from cancer, adjusted for age, sex and country income level was 1.18 (95%CI: 1.15-1.2). Conclusions This study’s findings underscore the heightened vulnerability of hospitalized COVID-19 patients with cancer, revealing a higher mortality rate, longer hospital stays, and an unstructured pattern of care that reflects the complexity of managing severely ill patients during a public health crisis like the COVID-19 pandemic. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-673/v1", "name": "Association of cancer and outcomes of patients hospitalized for COVID-19..." } } ] } Home Browse Association of cancer and outcomes of patients hospitalized for COVID-19... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] Abdulai Tejan Jalloh https://orcid.org/0009-0003-1804-0916 1 , Laura Merson https://orcid.org/0000-0002-4168-1960 2 , Divya Nair https://orcid.org/0000-0001-5497-2858 3 , [...] Shermarke Hassan 4,5 , Ibrahim Franklyn Kamara 6 , Innocent Nuwagira 6 , Sia Morenike Tengbe https://orcid.org/0000-0001-7287-4426 1 , Yusuf Sheku Tejan https://orcid.org/0000-0001-6113-7420 1 , Mustapha Kabba 1 , Sulaiman Lakoh 1,7 , Donald S Grant 1,7 , Robert J Samuels 1 , Rugiatu Z Kamara 8 , Robert F Terry https://orcid.org/0000-0003-3849-7705 9 Abdulai Tejan Jalloh https://orcid.org/0009-0003-1804-0916 1 , Laura Merson https://orcid.org/0000-0002-4168-1960 2 , [...] Divya Nair https://orcid.org/0000-0001-5497-2858 3 , Shermarke Hassan 4,5 , Ibrahim Franklyn Kamara 6 , Innocent Nuwagira 6 , Sia Morenike Tengbe https://orcid.org/0000-0001-7287-4426 1 , Yusuf Sheku Tejan https://orcid.org/0000-0001-6113-7420 1 , Mustapha Kabba 1 , Sulaiman Lakoh 1,7 , Donald S Grant 1,7 , Robert J Samuels 1 , Rugiatu Z Kamara 8 , Robert F Terry https://orcid.org/0000-0003-3849-7705 9 PUBLISHED 21 Jun 2024 Author details Author details 1 Ministry of Health, Government of Sierra Leone, Freetown, Sierra Leone 2 ISARIC, Pandemic Science Institute, University of Oxford, Oxford, England, UK 3 International Union Against TB and Lung Disease, Paris, France 4 Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, England, UK 5 Infectious Diseases Data Observatory, University of Oxford, Oxford, England, UK 6 World Health Organization, Freetown, Sierra Leone 7 College of Medicine and Allied Health Sciences, University of Sierra Leone, Freetown, Western Area, Sierra Leone 8 United States Centers for Disease Control and Prevention County Office, Freetown, Sierra Leone 9 TDR, the Special Programme for Research and Training in Tropical Diseases, World Health Organization, Geneva, Switzerland Abdulai Tejan Jalloh Roles: Conceptualization, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Laura Merson Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Divya Nair Roles: Conceptualization, Formal Analysis, Methodology, Visualization, Writing – Review & Editing Shermarke Hassan Roles: Conceptualization, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Ibrahim Franklyn Kamara Roles: Conceptualization, Methodology, Writing – Review & Editing Innocent Nuwagira Roles: Conceptualization, Methodology, Writing – Review & Editing Sia Morenike Tengbe Roles: Conceptualization, Methodology, Writing – Review & Editing Yusuf Sheku Tejan Roles: Conceptualization, Methodology, Writing – Review & Editing Mustapha Kabba Roles: Conceptualization, Methodology, Writing – Review & Editing Sulaiman Lakoh Roles: Conceptualization, Methodology, Writing – Review & Editing Donald S Grant Roles: Conceptualization, Methodology, Writing – Review & Editing Robert J Samuels Roles: Conceptualization, Methodology, Writing – Review & Editing Rugiatu Z Kamara Roles: Conceptualization, Methodology, Writing – Review & Editing Robert F Terry Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the TDR gateway. This article is included in the TDR: Ebola and Emerging Infections in West and Central Africa collection. This article is included in the Coronavirus (COVID-19) collection. Abstract Background The coronavirus disease 2019 (COVID-19) has caused substantial morbidity and mortality on a global scale. A strong correlation has been found between COVID-19 treatment outcomes and noncommunicable diseases such as cancers. However, there is limited information on the outcomes of cancer patients who were hospitalised for COVID-19. Methods We conducted an analysis on data collected in a large prospective cohort study set-up by the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC). All patients with laboratory-confirmed or clinically-diagnosed SARS-CoV-2 infection were included. Cancer was defined as having a current solid organ or haematological malignancy. The following outcomes were assessed; 30-day in-hospital mortality, intensive care unit (ICU) admission, length of hospitalization and receipt of higher-level care. Results Of the 560,547 hospitalised individuals who were analysed, 27,243 (4.9%) had cancer. Overall, cancer patients were older and had more comorbidities than non-cancer patients. Patients with cancer had higher 30-day in-hospital mortality than non-cancer patients (29.1.3% vs 18.0%) and longer hospital stays (median of 12 days vs 8 days). However, patients with cancer were admitted less often to intensive care units than non-cancer patients (12.6% vs 17.1%) and received less invasive mechanical ventilation than non-cancer patients (4.5% vs 7.6%). The hazard ratio of dying from cancer, adjusted for age, sex and country income level was 1.18 (95%CI: 1.15-1.2). Conclusions This study’s findings underscore the heightened vulnerability of hospitalized COVID-19 patients with cancer, revealing a higher mortality rate, longer hospital stays, and an unstructured pattern of care that reflects the complexity of managing severely ill patients during a public health crisis like the COVID-19 pandemic. READ ALL READ LESS Keywords COVID-19, cancer, comorbidities, mortality, hazard ratio, risk factor, ISARIC, SORT IT Corresponding Author(s) Abdulai Tejan Jalloh ( [email protected] ) Close Corresponding author: Abdulai Tejan Jalloh Competing interests: No competing interests were disclosed. Grant information: This SORT IT Programme was funded by the Special Programme for Research and Training in Tropical Diseases (TDR), Geneva, Switzerland (Grant Number HQTDR 2422924-4.1-72863. The APC was also funded by TDR. TDR is able to conduct its work thanks to the commitment and support from a variety of funders. A full list of TDR donors is available at: https://tdr.who.int/about-us/our-donors The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2024 World Health Organisation. This is an open access article distributed under the terms of the Creative Commons Attribution IGO License , which permits copying, adaptation and distribution in any medium or format for any purpose, provided the original work is properly cited, a link is provided to the license, and any changes made are indicated. Any such copying, adaptation and distribution must not in any way suggest that World Health Organisation endorses you or your use. How to cite: Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) First published: 21 Jun 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) Latest published: 21 Nov 2025, 13 :673 ( https://doi.org/10.12688/f1000research.150761.3 )  There is a newer version of this article available. Suppress this message for one day. Introduction Early in the COVID-19 pandemic, data were collected to identify risk factors for poor outcomes that could inform a risk-based approach to health policy and patient management. Risk factors including age, sex, and several comorbidities were reported to be associated with an increased risk of death. 1 , 2 The most common comorbidities identified in hospitalised patients during the first wave of the COVID-19 pandemic were chronic cardiac or cardiovascular diseases, diabetes mellitus, hypertension, non-asthmatic chronic pulmonary disease, obesity, and chronic kidney disease. 1 , 3 – 6 Understanding which individuals are likely to have a poor prognosis could help inform vaccine prioritisation, shielding policies, or allocation of health care resources in future infectious disease outbreaks and pandemics. Several studies have reported COVID-19 patients with cancer to be at higher risk of adverse outcomes compared with COVID-19 patients without cancer. 7 , 8 In a study from China, COVID-19 patients with cancer had higher observed increased rates of death, intensive care unit (ICU) admission, and need for invasive mechanical ventilation. 9 A study of COVID-19 patients in the United States of America reported that cancer patients were at higher risk of death and hospitalisation but were not found to have significantly different rates of ICU admission or ventilator use compared to non-cancer patients. 10 Data from the United States Centre for Disease Control showed that in 2020 and 2021 respectively, 2.0% and 2.4% of people who died of cancer had COVID-19 listed as the underlying cause of death. 11 There is a dearth of evidence on the outcomes of patients with cancer in middle- and low-income countries. The studies referenced above and other national studies have shown that patients with cancer have worse outcomes than those without cancer when hospitalised due to COVID-19. 12 , 13 However, to our knowledge, no study has been conducted to evaluate the association between cancer and hospital outcomes among hospitalised COVID-19 patients using an international data set. This study, seeking to build on the collection of existing evidence, uses secondary COVID-19 patient data collected in 54 countries via the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). 14 We investigated the association of cancer as a comorbidity with 30-day in-hospital mortality, ICU admission, length of hospitalization and receipt of higher-level care in COVID-19 patients with and without cancer. Methods Study design and setting This was a prospective cohort study that utilised secondary data from the COVID-19 clinical database hosted by the Infectious Diseases Data Observatory (IDDO). The database contains individual patient data from more than 800,000 hospitalised patients in more than 1,200 institutions from 60 countries across 6 continents. The data were collected using the ISARIC-WHO case report form as a part of the ISARIC-WHO Clinical Characterisation Protocol. 15 , 16 Study population We included hospitalised patients of any age with clinically or laboratory-diagnosed SARS-CoV-2 infection. Patients were enrolled between 30 th January 2020 and 10 th January 2023. Patients with unknown cancer status were excluded. Patients were followed from the time of hospital admission to discharge or death. Study variables We compared the differences in demographic characteristics, comorbidities, treatment with intensive interventions, length of hospitalisation, death (defined as 30-day in-hospital mortality), and hospital outcomes to characterise hospitalised COVID-19 patients with and without cancer. Severe disease was defined as treatment with higher-level care, including one or more of the following events: admission to an ICU, treatment with invasive mechanical ventilation (IMV), non-invasive ventilation (NIV), high-flow nasal cannula (HFNC), inotropes and/or vasopressors. Length of hospital stay was censored at 100 days. The presence of cancer was self-reported by patients and recorded as a binary variable classified as malignant neoplasm in the ISARIC-WHO case report form. Cancer was defined as having a current solid organ or haematological malignancy. Malignancies that had been declared ‘cured’ ≥5 years with no evidence of ongoing disease, non-melanoma skin cancer and benign growths or dysplasia were not included in this definition. Data collection and validation We used international prospectively collected observational data on demographics, clinical features and outcomes of patients hospitalized with COVID-19 with or without cancer (coded as ‘malignant neoplasm’). Data were collected using the ISARIC-WHO Clinical Characterisation Protocol and contributed to a central repository at the University of Oxford, England. Participating sites used the ISARIC-WHO case report form to enter data onto a Research Electronic Data Capture ( REDCap , version 8.11.11, Vanderbilt University, Nashville, TN) database or used local databases before uploading to the central data repository. 15 Open Data Kit is a suitable open access alternative. Centrally collated data were wrangled and mapped to the structure and controlled terminologies of the Study Data Tabulation Model (version 1.7, Clinical Data Interchange Standards Consortium, Austin, TX) using Trifacta ® software version 9.7.1. OpenRefine is a suitable open access alternative to using Trifacta ® . The data collection, aggregation, curation, and harmonisation process has been previously described. 16 Though more than 50% of the data were collected from low- and middle-income countries, most data on patients with cancer were collected from patients in higher income countries. Analysis and statistical method Continuous variables such as age and length of hospital stay were summarised as means with standard deviations or medians with interquartile ranges depending upon the distribution of data. Categorical variables (sex, presence of cancer, hospital exit outcomes, etc.) were summarised as frequencies and percentages. Categorical variables such as death and treatment with intensive interventions between patients with cancer and those without cancer were compared using the chi-square test. Continuous variables such as length of hospital stay were compared between the two groups using the unpaired t-test or Mann Whitney U test depending on the distribution of data. A Kaplan-Meier curve was plotted to show the cumulative incidence of mortality during hospitalization. To assess the independent effect of cancer on mortality in hospitalized COVID-19 patients, a survival analysis model was fitted to the data. The model was adjusted for the following confounders: age, sex, and country income-level. Unadjusted and adjusted hazard ratios with 95% confidence intervals were reported as measures of association. Denominators on individual analyses differ due to availability of data on different variables across the dataset. A P-value of <0.05 was considered statistically significant. Information on country income level was obtained from the World Bank ( https://datacatalog.worldbank.org/search/dataset/0038543 ). All analyses were performed using R version 4.2.2 (IDE PBC, Boston, MA, USA), an open access software. (R: The R Project for Statistical Computing) Ethics considerations Execution of the ISARIC-WHO Clinical Characterisation Protocol was approved by the WHO Ethics Review Committee (RPC571 and RPC572, 25 April 2013) and by local or national ethics committees for participating sites. Approvals include the South Central—Oxford C Research Ethics Committee for England (Ref. 13/SC/0149), the Scotland A Research Ethics Committee (Ref. 20/SS/0028) for Scotland, and the Human Research Ethics Committee (Medical) at the University of the Witwatersrand in South Africa as part of a national surveillance programme (M160667), which collectively represent most of the data. Written patient consent for data to be collected and used in research was obtained or waived according to local norms determined by the responsible Ethics Committee. The data were collected using the ISARIC-WHO case COVID-19 report form, locally-tailored versions of the form, or independently designed forms. Arrangements surrounding the pooling, storage, curation and sharing of these data are covered by the IDDO Governance processes. 17 All data were deidentified and ensured of low risk for identification of individuals by a statistical disclosure process prior to sharing. Data were shared under a Data Access Agreement following approval from the IDDO Data Access Committee. 18 Execution of this secondary analysis was approved by the Union Ethics Advisory Group of the International Union against Tuberculosis and Lung Disease, Paris, France (EAG number 18/23, dated 8 th September 2023). Results Among 841,640 individual records in the dataset, 560,547 (66.6%) met the criteria for analysis. Of those that did not, 73,327 (8.7%) did not have clinical or laboratory confirmation of SARS-CoV-2 infection; a further 3,879 (0.5%) were not admitted to hospitals between January 30 th 2020 and January 10 th 2023; and 203,887 (24.2%) did not have information on cancer status available. Demographics and comorbidities Of the 560,547 individuals analysed, 27,243 (4.9%) had cancer. Furthermore, 219,922 (39.2%) individuals that met the criteria for analysis were hospitalised in high-income countries. There were differences in age, sex, country income level, and other comorbidities in the group of patients with cancer versus those without cancer. Those with cancer were older (84.4% versus 46.3% aged ≥60 years), were more likely to be male (58.1% versus 49.1%) and were more likely to come from a high-income country (90.6% versus 36.6%). Of the 10 comorbidities most common in the whole population, all except obesity were more prevalent in the group of patients with cancer ( Table 1 ). Table 1. Demographic characteristics and comorbidities of COVID-19 patients with and without cancer hospitalised between 2020-2023 and enrolled to the ISARIC-WHO Clinical Characterisation Protocol. Cancer (N=27243) Non-cancer (N=533304) Age in years 0-4 66 (0.2%) 9074 (1.7%) 5-14 153 (0.6%) 10811 (2.0%) 15-29 217 (0.8%) 39848 (7.5%) 30-44 824 (3.0%) 90335 (16.9%) 45-59 2981 (10.9%) 136110 (25.5%) 60 and above 23002 (84.4%) 247126 (46.3%) Gender Male 15812 (58.1%) 261479 (49.1%) Female 11395 (41.9%) 271435 (50.9%) Countries, by income High income 24692 (90.6%) 195230 (36.6%) Upper middle income 2497 (9.2%) 330196 (61.9%) Lower middle income 29 (0.1%) 5684 (1.1%) Low income 25 (0.1%) 2190 (0.4%) Hypertension Yes 12037 (50.8%) 185555 (36.6%) No 11681 (49.2%) 321205 (63.4%) Chronic cardiac disease Yes 9018 (34.7%) 61224 (11.5%) No 16965 (65.3%) 469010 (88.5%) Smoking Yes 7674 (52.3%) 58420 (31.4%) No 7005 (47.7%) 127878 (68.6%) Diabetes Yes 7362 (28.0%) 124843 (23.9%) No 18901 (72.0%) 397550 (76.1%) Chronic pulmonary disease Yes 5168 (19.9%) 42312 (8.0%) No 20812 (80.1%) 488232 (92.0%) Chronic rheumatological disorder Yes 3727 (15.8%) 23328 (11.1%) No 19867 (84.2%) 186487 (88.9%) Chronic neurological disorder Yes 3179 (13.2%) 22432 (10.4%) No 20831 (86.8%) 193715 (89.6%) Dementia Yes 2954 (12.6%) 21656 (10.4%) No 20569 (87.4%) 187540 (89.6%) Asthma Yes 2654 (10.3%) 46029 (8.7%) No 23144 (89.7%) 484495 (91.3%) Obesity Yes 2411 (11.1%) 40334 (14.7%) No 19390 (88.9%) 234157 (85.3%) Mortality, severity, and length of hospitalization Patients with cancer had higher 30-day in-hospital mortality (29.1% vs 18.0%) and longer duration of hospitalization (median of 12 days (IQR 6.0-22.0) vs 8 days (IQR 4.0-14.0)) compared with those without cancer ( Table 2 and Figures 1 and 2 ). Table 2. Mortality, hospital admission and high-level care in COVID-19 patients with and without cancer hospitalised between 2020-2023 and enrolled to the ISARIC-WHO Clinical Characterisation Protocol. All patients Cancer (N=27243) Non-cancer (N=533304) 30-day in-hospital mortality Yes 7940 (29.1%) 95896 (18.0%) No 19303 (70.9%) 437408 (82.0%) Median duration of hospitalization (IQR) in days 12.0 (6.00, 22.0) 8.00 (4.00, 14.0) In the subset of patients who had data available on higher-level care Cancer (N=23994) Non-cancer (N=271842) Receipt of higher-level care Yes 6929 (28.9%) 81111 (29.8%) No 17065 (71.1%) 190731 (70.2%) Treated with high-flow nasal cannulas Yes 4201 (17.5%) 43680 (16.1%) No 19793 (82.5%) 228162 (83.9%) Admitted to ICU Yes 3023 (12.6%) 46372 (17.1%) No 20971 (87.4%) 225470 (82.9%) Treated with non-invasive ventilation Yes 2781 (11.6%) 31871 (11.7%) No 21213 (88.4%) 239971 (88.3%) Treated with invasive ventilation Yes 1070 (4.5%) 20545 (7.6%) No 22924 (95.5%) 251297 (92.4%) Treated with inotropes and/or vasopressors Yes 828 (3.5%) 12213 (4.5%) No 23166 (96.5%) 259629 (95.5%) Treated with extracorporeal membrane oxygenation Yes 30 (0.1%) 1232 (0.5%) No 23964 (99.9%) 270610 (99.5%) Figure 1. Boxplot showing length of hospitalisation among COVID-19 patients with and without cancer hospitalised between 2020-2023 and enrolled to the ISARIC-WHO Clinical Characterisation Protocol. Figure 2. Kaplan-Meier plot of COVID-19 patients with and without cancer hospitalised between 2020-2023 and enrolled to the ISARIC-WHO Clinical Characterisation Protocol. However, patients with cancer were reported to have received higher-level care slightly less often than those without cancer (28.9% vs 29.8%) including lower rates of ICU admission (12.6% vs 17.1%) and invasive mechanical ventilation (4.5% vs 7.6%). There were similar levels of treatment with high-flow nasal cannulas (17.5% vs 16.1%), extracorporeal membrane oxygenation (0.1% and 0.5%), non-invasive ventilation (11.6% vs 11.7%), and treatment with inotropes or vasopressors (3.5% vs 4.5%) across both groups ( Table 2 ). The effect of cancer and other comorbidities on 30-day in-hospital mortality among COVID-19 patients is reported in Table 3 and Figure 3 . Hospitalised COVID-19 patients with cancer had a higher risk of 30-day in-hospital mortality compared to those without cancer. The hazard ratio of dying from cancer, adjusted for age, sex and country income level was 1.18 (1.15-1.2). Table 3. Factors influencing 30-day in-hospital mortality among COVID-19 patients with cancer hospitalised between 2020-2023 and enrolled to the ISARIC-WHO Clinical Characterisation Protocol. Total (N=560547) Deaths (N=103836) Unadjusted hazard ratio (95% CI) Adjusted hazard ratio * (95% CI) Age 60 years and above 270128 76514 2.01 (1.98-2.04) 2.43 (2.39-2.46) 0-59 years 290247 27309 ref ref Diabetes mellitus Yes 132205 34293 1.4 (1.38-1.42) 1.32 (1.31-1.34) No 416451 67133 ref ref Chronic pulmonary disease Yes 47480 13571 1.31 (1.28-1.33) 1.30 (1.28-1.33) No 509044 89157 ref ref Gender Male 277291 56727 1.11 (1.1-1.12) 1.19 (1.18-1.21) Female 282830 47020 ref ref Cancer Yes 27243 7940 1.16 (1.13-1.18) 1.18 (1.15-1.2) No 533304 95896 ref ref Chronic cardiac disease Yes 70242 20692 1.2 (1.19-1.22) 1.15 (1.13-1.17) No 485975 81965 ref ref Obesity Yes 42745 8327 0.97 (0.95-0.99) 1.15 (1.13-1.18) No 253547 48963 ref ref Hypertension Yes 197592 48449 1.37 (1.35-1.38) 1.13 (1.12-1.15) No 332886 47568 ref ref Dementia Yes 24610 8212 1.51 (1.48-1.55) 1.08 (1.05-1.1) No 208109 35207 ref ref Smoking Yes 66094 14328 1.04 (1.02-1.06) 1.06 (1.04-1.08) No 134883 22727 ref ref Asthma Yes 48683 8790 0.93 (0.91-0.95) 1.04 (1.02-1.07) No 507639 93839 ref ref Chronic neurological disorder Yes 25611 6384 1.13 (1.1-1.16) 1.02 (0.99-1.04) No 214546 38379 ref ref Chronic rheumatological disorder Yes 27055 6398 1.13 (1.1-1.16) 0.96 (0.94-0.99) No 206354 37244 ref ref * Adjustment made for age, sex and country income level. Figure 3. Effect of comorbidities (including cancer) on 30-day in-hospital mortality, after adjustment for age, sex and country income level. Adjusted hazard ratios were higher for age and gender compared with those for cancer. Adjusted for sex and country income level, individuals aged ≥ 60 years had the highest hazard ratio 2.43 (2.39-2.46). Adjusted for age and country income level, male sex had a hazard ratio of 1.19 (1.18-1.21). Among all comorbidities, only diabetes mellitus (HR: 1.32, 95%CI: 1.31-1.34) and chronic pulmonary disease (HR: 1.30, 95%CI: 1.28-1.33) were more strongly associated with an increased risk of death compared with cancer, after adjusting for age, sex and country income level. Discussion Our study findings underscore the heightened vulnerability of cancer patients hospitalized with COVID-19, revealing a higher mortality rate, longer hospital stays, and a nuanced pattern of care that reflects the complexity of managing severely ill patients during a public health crisis. These outcomes align with the existing literature on the association of cancer with COVID-19 prognosis and treatment approaches during the pandemic. 13 , 19 In keeping with our findings, other studies conducted in high-income countries have also documented that the proportion of COVID-19 patients with cancer and other comorbidities is higher in the elderly (>60 years) as compared to the general population. 13 , 20 , 21 A meta-analysis of 4 studies (4691 non-cancer patients, 154 cancer patients) that looked at mortality in cancer patients versus non-cancer patients reported a pooled odds ratio of death of 3.91 (95%CI: 2.70-5.67). 12 This is higher than reported in our study. This could be explained by the lack of adjustment for potential confounders in the meta-analysis. It is also unclear whether or not the patients in these studies were primarily admitted for COVID-19, for cancer, or for other reasons. When considering other significant risk factors for mortality, we observed that cancer ranked prominently. Cancer demonstrated a stronger association with mortality compared to all other comorbidities, except for diabetes mellitus and chronic pulmonary disease. Despite the higher mortality risk, cancer patients in our study were slightly less likely to receive higher-level care compared to patients without cancer (28.9% vs 29.8%). Specifically, cancer patients were less frequently admitted to the ICU (12.6% vs. 17.1%) and had invasive mechanical ventilation less often (4.5% vs. 7.6%). These findings diverge from the expectation that higher-risk patients would necessitate more aggressive treatment. Though these event rates align with other studies of cancer patients, few comparators with non-cancer patients hospitalised for COVID-19 are in the literature. Marta et al. (2020) reported ICU admission rates of 39.1% in cancer patients with COVID-19 and use of invasive mechanical ventilation in 84.4 %. 22 Elgohary et al.’s (2021) systematic review and meta- analysis of cancer patients with COVID -19 reported an ICU admission rate of 14.5% (95% CI: 8.5-20.4) and a mechanical ventilation rate of 11.7% (95% CI: 5.5-18). 12 When comparing cancer patients with non-cancer patients, Abuhelwa et.al (2022) found cancer patients hospitalized for COVID-19 had similar rates of invasive mechanical ventilation compared to those without cancer (10.14% vs 9.36.%). 13 We found differences in mean hospital stay between patients with cancer and those without cancer. The longer hospital stay might be related to cancer patients having several other comorbidities and the management of the side effects of cancer treatment. However, we cannot explain why they stayed longer in hospital but received less high-level care compared to COVID-19 patients without cancer. Abuhelwa’s 2022 nation-wide study reported no difference in hospital stays between these patient groups (8.07 vs 7.46 days). The difference between these findings and ours may reflect differences in admission policy or availability of hospital beds. The lower mortality rates in Abuhelwa’s study as compared to our findings may indicate less severe disease, and therefore a population requiring less in-hospital care. Strengths and limitations One key strength of our study was the use of a large sample size, orders of magnitude larger than most previous studies. Therefore, our estimates should be more generalisable and should have a higher power to demonstrate significant associations than previously published studies. We adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting study findings. This analysis has several limitations. This study encompassed all patients with current cancer, but we couldn’t distinguish between various cancer types as detailed information on each patients’ cancer diagnosis, staging and treatment modalities were not available. However, other studies have highlighted lung cancer and haematological cancers as being those most closely linked to mortality in COVID-19 patients. 13 , 23 , 24 Differences in reporting of the type and details of cancer diagnosis across the available literature make it challenging to make comparisons. Studies that analysed data through the use of electronic health records may have included patients in remission. Though enrolment to the ISARIC-WHO Clinical Characterisation Protocol focused on individuals admitted for COVID-19 illness, some individuals may have been admitted due to cancer related illness. In order to enable future research to disaggregate cancer patients by the following characteristics i.e. type of cancer, staging of cancer and treatment modalities, - a more comprehensive case report form is needed. Having these data available would enable a more personalized risk assessment and a better understanding of how management strategies relate to patient outcomes. Additionally, the availability of vaccination data will allow the examination of the impact of COVID-19 vaccination status on outcomes in cancer patients. The majority of data on patients with cancer (90.6%) were collected from patients in high income countries. so no inferences could be drawn from patient outcomes linked with World Bank income classifications. Conclusions Our study found that patients with cancer were older with more comorbidities. They had an increased risk of mortality with longer duration of hospital stay as compared to non-cancer patients but received less high-level care including ICU admission and invasive mechanical ventilation. This highlights the importance of collecting data in emerging infections to identify at-risk groups, facilitating appropriate resource allocation and informing policy decisions aimed at resource allocation during health emergencies. The availability and collection of data on our platforms were predominantly from high-income countries. To prepare for a future pandemic, data availability and coverage must be more universal. More must also be done to support data collection and the capacity to analyse those data within low- and middle-income countries. Data availability Underlying data The data that underpin this analysis are available via a governed data access mechanism following review of a data access committee. Data can be requested via the IDDO COVID-19 Data Sharing Platform ( http://www.iddo.org/covid-19 ). The Data Access Application, Terms of Access and details of the Data Access Committee are available on the website. Briefly, the requirements for access are a request from a qualified researcher working with a legal entity who have a health and/or research remit; a scientifically valid reason for data access which adheres to appropriate ethical principles. The full terms are at: https://www.iddo.org/document/covid-19-data-access-guidelines . These data are a part of https://doi.org/10.48688/cpwp-ft84 Acknowledgements This research was conducted through the Structured Operational Research and Training Initiative (SORT IT), a global partnership led by TDR, the Special Programme for Research and Training in Tropical Diseases hosted at the World Health Organization. The specific SORT IT program that led to this publication is a SORT IT partnership with the WHO Emergency Medical Teams (Geneva), WHO-AFRO (Brazzaville), WHO Country Offices and Ministries of health of Guinea, Liberia, Sierra Leone, and the Democratic Republic of the Congo, the Infectious Diseases Data Repository (IDDO); The International Union Against Tuberculosis and Lung Diseases, Paris, France and South East Asia offices, Delhi, India; The Tuberculosis Research and Prevention Center Non-Governmental Organization, Yerevan, Armenia; I-Tech, Lilongwe, Malawi; Medwise solutions, Nairobi, Kenya; All India Institute of Medical Sciences, Hyderabad, India; and the National Training and Research Centre in Rural Health, Maferinyah, Guinea. The views expressed in this article are those of the authors and do not necessarily reflect those of their affiliated institutions. References 1. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 21 Jun 2024 ADD YOUR COMMENT Comment Author details Author details 1 Ministry of Health, Government of Sierra Leone, Freetown, Sierra Leone 2 ISARIC, Pandemic Science Institute, University of Oxford, Oxford, England, UK 3 International Union Against TB and Lung Disease, Paris, France 4 Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, England, UK 5 Infectious Diseases Data Observatory, University of Oxford, Oxford, England, UK 6 World Health Organization, Freetown, Sierra Leone 7 College of Medicine and Allied Health Sciences, University of Sierra Leone, Freetown, Western Area, Sierra Leone 8 United States Centers for Disease Control and Prevention County Office, Freetown, Sierra Leone 9 TDR, the Special Programme for Research and Training in Tropical Diseases, World Health Organization, Geneva, Switzerland Abdulai Tejan Jalloh Roles: Conceptualization, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Laura Merson Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Divya Nair Roles: Conceptualization, Formal Analysis, Methodology, Visualization, Writing – Review & Editing Shermarke Hassan Roles: Conceptualization, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Ibrahim Franklyn Kamara Roles: Conceptualization, Methodology, Writing – Review & Editing Innocent Nuwagira Roles: Conceptualization, Methodology, Writing – Review & Editing Sia Morenike Tengbe Roles: Conceptualization, Methodology, Writing – Review & Editing Yusuf Sheku Tejan Roles: Conceptualization, Methodology, Writing – Review & Editing Mustapha Kabba Roles: Conceptualization, Methodology, Writing – Review & Editing Sulaiman Lakoh Roles: Conceptualization, Methodology, Writing – Review & Editing Donald S Grant Roles: Conceptualization, Methodology, Writing – Review & Editing Robert J Samuels Roles: Conceptualization, Methodology, Writing – Review & Editing Rugiatu Z Kamara Roles: Conceptualization, Methodology, Writing – Review & Editing Robert F Terry Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This SORT IT Programme was funded by the Special Programme for Research and Training in Tropical Diseases (TDR), Geneva, Switzerland (Grant Number HQTDR 2422924-4.1-72863. The APC was also funded by TDR. TDR is able to conduct its work thanks to the commitment and support from a variety of funders. A full list of TDR donors is available at: https://tdr.who.int/about-us/our-donors The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (3) version 3 Revised Published: 21 Nov 2025, 13:673 https://doi.org/10.12688/f1000research.150761.3 version 2 Revised Published: 07 Apr 2025, 13:673 https://doi.org/10.12688/f1000research.150761.2 version 1 Published: 21 Jun 2024, 13:673 https://doi.org/10.12688/f1000research.150761.1 Copyright © 2024 World Health Organisation. This is an open access article distributed under the terms of the Creative Commons Attribution IGO License , which permits copying, adaptation and distribution in any medium or format for any purpose, provided the original work is properly cited, a link is provided to the license, and any changes made are indicated. Any such copying, adaptation and distribution must not in any way suggest that World Health Organisation endorses you or your use. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 21 Jun 2024 Views 0 Cite How to cite this report: Fowler T. Reviewer Report For: Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.5256/f1000research.165357.r295443 ) The direct URL for this report is: https://f1000research.com/articles/13-673/v1#referee-response-295443 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Sep 2024 Tom Fowler , UK Health Security Agency, William Harvey Research Institute and the Barts Cancer Institute, Queen Mary University of London, London, UK Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.165357.r295443 This paper examining an important area of whether outcomes in hospitalized Covid 19 cancer patients compared to other hospitalized patients for Covid 19 have worse outcomes. It uses data from International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) and a ... Continue reading READ ALL This paper examining an important area of whether outcomes in hospitalized Covid 19 cancer patients compared to other hospitalized patients for Covid 19 have worse outcomes. It uses data from International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) and a substantial strength is the international nature of this cohort and its overall size. The paper identifies demographic differences between those hospitalised for Covid 19 with Cancer and those without along with different outcomes in key measures (e.g. 30-day in hospital mortality). There are however a number of major and minor issues that need to be addressed. In part areas of analysis may be restricted due to the availability of data - in which case this should be acknowledged and addressed in the discussion. 1. It is unclear to me the extent to which individuals were hospitalised for a specific reason (e.g. due to their cancer ) and identified as having Covid 19 or were hospitalised for respiratory symptoms and identified as having another diagnosis such as cancer. This is particularly important as there are conclusions about different care between these groups and the demographic characteristics of those with cancer are substantially different from others admitted and this may be driven by the reason for admission. Additionally, the implications for the difference in demographic characteristics are not fully explored in the discussion 2. The major results are the comparison of the outcomes of those with cancer versus those without cancer. The authors point out there are other individuals with conditions with increased risk in their comparison group. As such by combining all other groups the approach is likely to underestimate the increase in risk of those with cancer. This is possibly the output in figure 3, however this is not explicitly stated and the reporting of adjusted results for other variables (such as age and sex) appear to be without adjusting for this. In other results this is not addressed, e.g. Data in Table 2 seems to be a direct comparison between those with and without cancer only. 3. The analysis does not include an examination of change over time of risk associated with cancer. It also does not look at the impact of vaccination on risk (which we become more prevalent over time). There is literature on changing risk over the course of the pandemic of individuals with cancer - e.g. Ref 1 and the impact of vaccination and vaccine response Ref 2 As such I would expect consideration of vaccination status to be part of the methods/ analysis. 4. It is unclear on the rationale of covariates included in the analysis and why others have been omitted. For example, a key strength is the international nature of this dataset, which allows exploration of differences between countries. It is stated in the paper the authors are unaware of any other analysis looking at outcomes using and international data set. There is between countries differences in provision of health care, even of those with similar income level. However the rationale for why country income level was chosen and its implications is not discussed or why a more granular variable reflecting country could not be used. Rather it is stated that due to the predominance of cases from high income countries they were unable draw inferences, suggesting that using world bank income classification is not an appropriate variable to use. The authors need to reconsider what is used to assess international differences and include this in the analysis Other examples, including those mentioned above, vaccine status, other clinical comorbidities, likely variant (or time as a proxy), severity at admission (or length of hospitalization) etc, 6. The presentation of results needs review to ensure it is clear. For example the Figure 2 Kaplan Meier plot references inclusion of people between 2020-2023 - this is the only reference to a time period, the x axis is labeled as time and is in days. I assume this is the risk from the date of admission, however that is not explicitly stated - as such the presentation is not clear. Note, If it is the number of days hospitalized, rather than the days since admission, part of the downward trend may be driven by those no longer hospitalized being removed from the analysis as time continues, leaving only those hospitalized for longer being included in survival risk at later timepoints . Without clear description in is challenging to determine if this is a relevant point. Other examples include Figure 3 - while the graphical representation is helpful - it is not possible to ascertain the exact hazard ratio and confidence intervals are omitted 7. It is stated the presence of cancer was self reported, It may be that more detail of this process would clarify potential biases, however given findings that the cancer group were, for example, admitted less to ICUs it is conceivable that those presenting with more severe symptoms at the time of recruitment were unable to self report if they had cancer. Greater clarity on either the process of collecting relevant clinical details or discussion of potential implications of data collection approaches are needed. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Starkey T, Ionescu MC, Tilby M, Little M, et al.: A population-scale temporal case-control evaluation of COVID-19 disease phenotype and related outcome rates in patients with cancer in England (UKCCP). Sci Rep . 2023; 13 (1): 11327 PubMed Abstract | Publisher Full Text 2. Lee LYW, Tilby M, Starkey T, Ionescu MC, et al.: Association of SARS-CoV-2 Spike Protein Antibody Vaccine Response With Infection Severity in Patients With Cancer: A National COVID Cancer Cross-sectional Evaluation. JAMA Oncol . 2023; 9 (2): 188-196 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Public Heath, Genomics, Pandemics I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Fowler T. Reviewer Report For: Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.5256/f1000research.165357.r295443 ) The direct URL for this report is: https://f1000research.com/articles/13-673/v1#referee-response-295443 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 09 Jul 2025 Abdulai Tejan Jalloh , Ministry of Health, Government of Sierra Leone, Freetown, Sierra Leone 09 Jul 2025 Author Response Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved]. F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) REBUTTAL ... Continue reading Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved]. F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) REBUTTAL Firstly the authors would like to thank the reviewer for the time spent providing comments on this paper and the provision of two extremely helpful references. We note there is a significant delay between the receipt of the review and this response. This is partly due to the first two authors being deployed to Rwanda as part of international efforts to support its response to the Marburg outbreak in 2024. We provide our replies below and have edited the paper in line with the recommendations where we can. We have also explained where we can’t make changes largely due to the constraints of using secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). [1] Our hope is that this revised version addresses the concerns within the limitations of the data and improves the clarity of the findings as well as providing an improved version that may attract a second positive review to enable this paper to be fully published and indexed. PR1. It is unclear to me the extent to which individuals were hospitalised for a specific reason (e.g. due to their cancer ) and identified as having Covid 19 or were hospitalised for respiratory symptoms and identified as having another diagnosis such as cancer. This is particularly important as there are conclusions about different care between these groups and the demographic characteristics of those with cancer are substantially different from others admitted and this may be driven by the reason for admission. Additionally, the implications for the difference in demographic characteristics are not fully explored in the discussion. Response Our study uses secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). This is a strength of our study as it allows analysis of a large, international dataset. However, we also acknowledge the limitations in that, although the target population for the protocol is patients admitted due to COVID-19 illness, the data recorded did not include the primary reason for presentation at the hospital and subsequent admission as a validation of this population. In the Conclusion we acknowledge the lack of certainty of the reason for presenting to hospital and we have edited the paper to highlight this in the Strengths and Limitations section. However, we feel our Results are still relevant, and in line with a number of similar, published studies, as they report the treatment subsequent to admission of patients with confirmed COVID-19 with respect to the presence or absence of cancer. The results of this analysis can help to inform resource planning and non communicable disease management like cancer in future outbreaks of respiratory disease. We do agree that it is unknown if a treatment regime might have differed due to demographics or characteristics at admission and have added this to the Limitations and Discussion of Results. PR2. The major results are the comparison of the outcomes of those with cancer versus those without cancer. The authors point out there are other individuals with conditions with increased risk in their comparison group. As such by combining all other groups the approach is likely to underestimate the increase in risk of those with cancer. This is possibly the output in figure 3, however this is not explicitly stated and the reporting of adjusted results for other variables (such as age and sex) appear to be without adjusting for this. In other results this is not addressed, e.g. Data in Table 2 seems to be a direct comparison between those with and without cancer only. Response Thank you for pointing this out. The adjusted hazards ratio for cancer was obtained from a multivariable model that included age, sex, and country income level with no explicit adjustment made for further co-morbidities. We agree data presented in table 2 are crude that are not adjusted for any patient characteristics, however we sought to represent the data in relation to our study question. Following your comments we have undertaken two further sensitivity analyses and present the findings below and in the revised paper. First by adjusting for the comorbidity status in addition to age, sex, and income status in the multivariable model to estimate the hazard ratio of cancer on mortality (sensitivity analysis table1). Second, a multivariable model with all the predictors listed in Table 2 included in the analysis (sensitivity analysis table 2 below). The findings from these two sensitivity analyses indicates that the qualitative association between any of the predictors and outcomes remain relatively stable with some or minor differences in the estimated hazards ratio, apart from chronic neurological disorder. However, it has to be cautioned that such a multivariable model with all the predictors included is subject to large missingness (as indicated in sensitivity analysis 2 table) that makes the analysis susceptible to bias due to missing data. These results from the two sensitivity analyses are presented as Table 4 and Table 5 in the revised manuscript. Sensitivity analysis table 1: Hazards ratio of mortality among those with cancer, adjusted for comorbidities Adjustment set Hazards ratio [95% CI] Results presented in main manuscript Not adjusted for any variables (from Table 3) 1.16 (1.13-1.18) Adjusted for age, sex, and income levels (from Table 3) 1.18 (1.15-1.20) Sensitivity analyses Adjustment set: age, sex, income level, hypertension, diabetes, COPD, obesity, chronic cardiac diseases, dementia, asthma, neurological disorder, rheumatological disorder 1.18 (1.14-1.21) Sensitivity analysis table 2: Multivariable model with all the predictors listed in Table 2 included in the analysis (n=102,184 patients, 16,105 events, and 458,363 missing observations excluded from the multivariable analysis). Unadjusted hazards ratio (from Table 3) Adjusted hazards ratio (95% CI) Cancer (reference: no) 1.16 (1.13-1.18) 1.20 (1.16-1.26) 60 years and above (reference: 0-59y) 2.01 (1.98-2.04) 2.63 (2.50-2.77) Diabetes mellitus (ref: no) 1.4 (1.38-1.42) 1.20 (1.17-1.24) Chronic pulmonary disease (ref: no) 1.31 (1.28-1.33) 1.33 (1.28-1.38) Male (ref: female) 1.11 (1.1-1.12) 1.24 (1.20-1.28) Chronic cardiac disease (ref: no) 1.2 (1.19-1.22) 1.26 (1.21-1.30) Obesity (ref: no) 0.97 (0.95-0.99) 1.10 (1.06-1.15) Hypertension (ref: no) 1.37 (1.35-1.38) 1.10 (1.01-1.14) Dementia (ref: no) 1.51 (1.48-1.55) 1.16 (1.10-1.22) Smoking (ref: no) 1.04 (1.02-1.06) 1.04 (1.00-1.08) Asthma (ref: no) 0.93 (0.91-0.95) 1.03 (0.98-1.08) Chronic neurological disorder (ref: no) 1.13 (1.1-1.16) 0.95 (0.91-0.99) Chronic rheumatological disorder (ref: no) 1.13 (1.1-1.16) 0.96 (0.93-1.00) PR3. The analysis does not include an examination of change over time of risk associated with cancer. It also does not look at the impact of vaccination on risk (which we become more prevalent over time). There is literature on changing risk over the course of the pandemic of individuals with cancer - e.g. Ref 1 and the impact of vaccination and vaccine response Ref 2 . Response Our study includes patients from January 30, 2020, to January 10, 2023. During this period, COVID-19 underwent significant changes in genomics, treatment, and epidemiology, with vaccines introduced at varying times across countries. However, our dataset lacks genotyping and reliable vaccination information, which are crucial for analyzing temporal changes accurately. Without data on these key factors, especially vaccination status, we cannot provide a robust analysis of changes over time. The impact of evolving vaccination rates on outcomes is likely substantial but impossible to calculate with our current data. We acknowledge this limitation more explicitly in this revision and our findings have already informed improvements to ISARIC's case report forms for future outbreaks to address these data gaps. PR4. It is unclear on the rationale of covariates included in the analysis and why others have been omitted. For example, a key strength is the international nature of this dataset, which allows exploration of differences between countries. It is stated in the paper the authors are unaware of any other analysis looking at outcomes using and international data set. There is between countries differences in provision of health care, even of those with similar income level. However, the rationale for why country income level was chosen and its implications is not discussed or why a more granular variable reflecting country could not be used. Rather it is stated that due to the predominance of cases from high income countries they were unable draw inferences, suggesting that using world bank income classification is not an appropriate variable to use. The authors need to reconsider what is used to assess international differences and include this in the analysis. Other examples, including those mentioned above, vaccine status, other clinical comorbidities, likely variant (or time as a proxy), severity at admission (or length of hospitalization) etc, Response Despite its faults, our original analysis plan included an analysis of differences in patient outcomes between countries with different World Bank classifications as a proxy for the quality of healthcare systems between these two economic realities. Other analyses of this database have used this approach due to the sensitivities and inaccuracies of labelling a select collection of hospitals in participating countries as country-level results. We have added this explanation to the methodology. While we agree that there are huge differences between health care institutions within these national income brackets, we feel that presenting a descriptive analysis is useful to identify a signal of difference that should be explored with a targeted study or datasets that include the details needed. The impact of comorbidities has been explored in our analysis. Unfortunately, we did not have data on vaccination status. (There was no PR5 in the peer review report) PR6. The presentation of results needs review to ensure it is clear. For example, the Figure 2 Kaplan Meier plot references inclusion of people between 2020-2023 - this is the only reference to a time period, the x axis is labelled as time and is in days. I assume this is the risk from the date of admission, however that is not explicitly stated - as such the presentation is not clear. Note, If it is the number of days hospitalized, rather than the days since admission, part of the downward trend may be driven by those no longer hospitalized being removed from the analysis as time continues, leaving only those hospitalized for longer being included in survival risk at later timepoints . Without clear description in is challenging to determine if this is a relevant point. Other examples include Figure 3 - while the graphical representation is helpful - it is not possible to ascertain the exact hazard ratio and confidence intervals are omitted Response The study population statement clarifies that patients were enrolled between 30 th January 2020 and 10 th January 2023 and were followed for up to 30 days from admission. We agree the labelling and legends of the tables and graphs should be improved to provide more context as standalone figures and tables and have edited the paper accordingly. We have additionally removed Figure 3 as these results are presented with 95% CI in Table 3, and further in Tables 4 and 5. PR7. It is stated the presence of cancer was self-reported, It may be that more detail of this process would clarify potential biases, however given findings that the cancer group were, for example, admitted less to ICUs it is conceivable that those presenting with more severe symptoms at the time of recruitment were unable to self-report if they had cancer. Greater clarity on either the process of collecting relevant clinical details or discussion of potential implications of data collection approaches are needed. Response Cancer status was obtained from patients who were able to report or family members for many who could not. Those who could not report and did not have family were excluded from the analysis, and hence the potential for bias to arise due to lack of patients’ ability of self-report is likely mitigated in this analysis. However, we appreciate that this approach can lead to bias if those with missing self-reported cancer status have different characteristics than those who were included in the analysis. Appropriate analysis requires investigation of the mechanism that led to the missingness of the self-reported cancer status and this was beyond the scope of the current work. We have clarified this in the Methods section to make this more explicit and reduce the concern for bias. NB we note reference 14 to IDDO is incomplete and the link to the CRF as ref 14 in the Introduction is incorrect it should link to ref 15. We have corrected this in our revision. Thank you. [1] https://isaric.org/research/covid-19-clinical-research-resources/covid-19-crf/ Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved]. F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) REBUTTAL Firstly the authors would like to thank the reviewer for the time spent providing comments on this paper and the provision of two extremely helpful references. We note there is a significant delay between the receipt of the review and this response. This is partly due to the first two authors being deployed to Rwanda as part of international efforts to support its response to the Marburg outbreak in 2024. We provide our replies below and have edited the paper in line with the recommendations where we can. We have also explained where we can’t make changes largely due to the constraints of using secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). [1] Our hope is that this revised version addresses the concerns within the limitations of the data and improves the clarity of the findings as well as providing an improved version that may attract a second positive review to enable this paper to be fully published and indexed. PR1. It is unclear to me the extent to which individuals were hospitalised for a specific reason (e.g. due to their cancer ) and identified as having Covid 19 or were hospitalised for respiratory symptoms and identified as having another diagnosis such as cancer. This is particularly important as there are conclusions about different care between these groups and the demographic characteristics of those with cancer are substantially different from others admitted and this may be driven by the reason for admission. Additionally, the implications for the difference in demographic characteristics are not fully explored in the discussion. Response Our study uses secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). This is a strength of our study as it allows analysis of a large, international dataset. However, we also acknowledge the limitations in that, although the target population for the protocol is patients admitted due to COVID-19 illness, the data recorded did not include the primary reason for presentation at the hospital and subsequent admission as a validation of this population. In the Conclusion we acknowledge the lack of certainty of the reason for presenting to hospital and we have edited the paper to highlight this in the Strengths and Limitations section. However, we feel our Results are still relevant, and in line with a number of similar, published studies, as they report the treatment subsequent to admission of patients with confirmed COVID-19 with respect to the presence or absence of cancer. The results of this analysis can help to inform resource planning and non communicable disease management like cancer in future outbreaks of respiratory disease. We do agree that it is unknown if a treatment regime might have differed due to demographics or characteristics at admission and have added this to the Limitations and Discussion of Results. PR2. The major results are the comparison of the outcomes of those with cancer versus those without cancer. The authors point out there are other individuals with conditions with increased risk in their comparison group. As such by combining all other groups the approach is likely to underestimate the increase in risk of those with cancer. This is possibly the output in figure 3, however this is not explicitly stated and the reporting of adjusted results for other variables (such as age and sex) appear to be without adjusting for this. In other results this is not addressed, e.g. Data in Table 2 seems to be a direct comparison between those with and without cancer only. Response Thank you for pointing this out. The adjusted hazards ratio for cancer was obtained from a multivariable model that included age, sex, and country income level with no explicit adjustment made for further co-morbidities. We agree data presented in table 2 are crude that are not adjusted for any patient characteristics, however we sought to represent the data in relation to our study question. Following your comments we have undertaken two further sensitivity analyses and present the findings below and in the revised paper. First by adjusting for the comorbidity status in addition to age, sex, and income status in the multivariable model to estimate the hazard ratio of cancer on mortality (sensitivity analysis table1). Second, a multivariable model with all the predictors listed in Table 2 included in the analysis (sensitivity analysis table 2 below). The findings from these two sensitivity analyses indicates that the qualitative association between any of the predictors and outcomes remain relatively stable with some or minor differences in the estimated hazards ratio, apart from chronic neurological disorder. However, it has to be cautioned that such a multivariable model with all the predictors included is subject to large missingness (as indicated in sensitivity analysis 2 table) that makes the analysis susceptible to bias due to missing data. These results from the two sensitivity analyses are presented as Table 4 and Table 5 in the revised manuscript. Sensitivity analysis table 1: Hazards ratio of mortality among those with cancer, adjusted for comorbidities Adjustment set Hazards ratio [95% CI] Results presented in main manuscript Not adjusted for any variables (from Table 3) 1.16 (1.13-1.18) Adjusted for age, sex, and income levels (from Table 3) 1.18 (1.15-1.20) Sensitivity analyses Adjustment set: age, sex, income level, hypertension, diabetes, COPD, obesity, chronic cardiac diseases, dementia, asthma, neurological disorder, rheumatological disorder 1.18 (1.14-1.21) Sensitivity analysis table 2: Multivariable model with all the predictors listed in Table 2 included in the analysis (n=102,184 patients, 16,105 events, and 458,363 missing observations excluded from the multivariable analysis). Unadjusted hazards ratio (from Table 3) Adjusted hazards ratio (95% CI) Cancer (reference: no) 1.16 (1.13-1.18) 1.20 (1.16-1.26) 60 years and above (reference: 0-59y) 2.01 (1.98-2.04) 2.63 (2.50-2.77) Diabetes mellitus (ref: no) 1.4 (1.38-1.42) 1.20 (1.17-1.24) Chronic pulmonary disease (ref: no) 1.31 (1.28-1.33) 1.33 (1.28-1.38) Male (ref: female) 1.11 (1.1-1.12) 1.24 (1.20-1.28) Chronic cardiac disease (ref: no) 1.2 (1.19-1.22) 1.26 (1.21-1.30) Obesity (ref: no) 0.97 (0.95-0.99) 1.10 (1.06-1.15) Hypertension (ref: no) 1.37 (1.35-1.38) 1.10 (1.01-1.14) Dementia (ref: no) 1.51 (1.48-1.55) 1.16 (1.10-1.22) Smoking (ref: no) 1.04 (1.02-1.06) 1.04 (1.00-1.08) Asthma (ref: no) 0.93 (0.91-0.95) 1.03 (0.98-1.08) Chronic neurological disorder (ref: no) 1.13 (1.1-1.16) 0.95 (0.91-0.99) Chronic rheumatological disorder (ref: no) 1.13 (1.1-1.16) 0.96 (0.93-1.00) PR3. The analysis does not include an examination of change over time of risk associated with cancer. It also does not look at the impact of vaccination on risk (which we become more prevalent over time). There is literature on changing risk over the course of the pandemic of individuals with cancer - e.g. Ref 1 and the impact of vaccination and vaccine response Ref 2 . Response Our study includes patients from January 30, 2020, to January 10, 2023. During this period, COVID-19 underwent significant changes in genomics, treatment, and epidemiology, with vaccines introduced at varying times across countries. However, our dataset lacks genotyping and reliable vaccination information, which are crucial for analyzing temporal changes accurately. Without data on these key factors, especially vaccination status, we cannot provide a robust analysis of changes over time. The impact of evolving vaccination rates on outcomes is likely substantial but impossible to calculate with our current data. We acknowledge this limitation more explicitly in this revision and our findings have already informed improvements to ISARIC's case report forms for future outbreaks to address these data gaps. PR4. It is unclear on the rationale of covariates included in the analysis and why others have been omitted. For example, a key strength is the international nature of this dataset, which allows exploration of differences between countries. It is stated in the paper the authors are unaware of any other analysis looking at outcomes using and international data set. There is between countries differences in provision of health care, even of those with similar income level. However, the rationale for why country income level was chosen and its implications is not discussed or why a more granular variable reflecting country could not be used. Rather it is stated that due to the predominance of cases from high income countries they were unable draw inferences, suggesting that using world bank income classification is not an appropriate variable to use. The authors need to reconsider what is used to assess international differences and include this in the analysis. Other examples, including those mentioned above, vaccine status, other clinical comorbidities, likely variant (or time as a proxy), severity at admission (or length of hospitalization) etc, Response Despite its faults, our original analysis plan included an analysis of differences in patient outcomes between countries with different World Bank classifications as a proxy for the quality of healthcare systems between these two economic realities. Other analyses of this database have used this approach due to the sensitivities and inaccuracies of labelling a select collection of hospitals in participating countries as country-level results. We have added this explanation to the methodology. While we agree that there are huge differences between health care institutions within these national income brackets, we feel that presenting a descriptive analysis is useful to identify a signal of difference that should be explored with a targeted study or datasets that include the details needed. The impact of comorbidities has been explored in our analysis. Unfortunately, we did not have data on vaccination status. (There was no PR5 in the peer review report) PR6. The presentation of results needs review to ensure it is clear. For example, the Figure 2 Kaplan Meier plot references inclusion of people between 2020-2023 - this is the only reference to a time period, the x axis is labelled as time and is in days. I assume this is the risk from the date of admission, however that is not explicitly stated - as such the presentation is not clear. Note, If it is the number of days hospitalized, rather than the days since admission, part of the downward trend may be driven by those no longer hospitalized being removed from the analysis as time continues, leaving only those hospitalized for longer being included in survival risk at later timepoints . Without clear description in is challenging to determine if this is a relevant point. Other examples include Figure 3 - while the graphical representation is helpful - it is not possible to ascertain the exact hazard ratio and confidence intervals are omitted Response The study population statement clarifies that patients were enrolled between 30 th January 2020 and 10 th January 2023 and were followed for up to 30 days from admission. We agree the labelling and legends of the tables and graphs should be improved to provide more context as standalone figures and tables and have edited the paper accordingly. We have additionally removed Figure 3 as these results are presented with 95% CI in Table 3, and further in Tables 4 and 5. PR7. It is stated the presence of cancer was self-reported, It may be that more detail of this process would clarify potential biases, however given findings that the cancer group were, for example, admitted less to ICUs it is conceivable that those presenting with more severe symptoms at the time of recruitment were unable to self-report if they had cancer. Greater clarity on either the process of collecting relevant clinical details or discussion of potential implications of data collection approaches are needed. Response Cancer status was obtained from patients who were able to report or family members for many who could not. Those who could not report and did not have family were excluded from the analysis, and hence the potential for bias to arise due to lack of patients’ ability of self-report is likely mitigated in this analysis. However, we appreciate that this approach can lead to bias if those with missing self-reported cancer status have different characteristics than those who were included in the analysis. Appropriate analysis requires investigation of the mechanism that led to the missingness of the self-reported cancer status and this was beyond the scope of the current work. We have clarified this in the Methods section to make this more explicit and reduce the concern for bias. NB we note reference 14 to IDDO is incomplete and the link to the CRF as ref 14 in the Introduction is incorrect it should link to ref 15. We have corrected this in our revision. Thank you. [1] https://isaric.org/research/covid-19-clinical-research-resources/covid-19-crf/ Competing Interests: No competing interest to disclose Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 09 Jul 2025 Abdulai Tejan Jalloh , Ministry of Health, Government of Sierra Leone, Freetown, Sierra Leone 09 Jul 2025 Author Response Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved]. F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) REBUTTAL ... Continue reading Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved]. F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) REBUTTAL Firstly the authors would like to thank the reviewer for the time spent providing comments on this paper and the provision of two extremely helpful references. We note there is a significant delay between the receipt of the review and this response. This is partly due to the first two authors being deployed to Rwanda as part of international efforts to support its response to the Marburg outbreak in 2024. We provide our replies below and have edited the paper in line with the recommendations where we can. We have also explained where we can’t make changes largely due to the constraints of using secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). [1] Our hope is that this revised version addresses the concerns within the limitations of the data and improves the clarity of the findings as well as providing an improved version that may attract a second positive review to enable this paper to be fully published and indexed. PR1. It is unclear to me the extent to which individuals were hospitalised for a specific reason (e.g. due to their cancer ) and identified as having Covid 19 or were hospitalised for respiratory symptoms and identified as having another diagnosis such as cancer. This is particularly important as there are conclusions about different care between these groups and the demographic characteristics of those with cancer are substantially different from others admitted and this may be driven by the reason for admission. Additionally, the implications for the difference in demographic characteristics are not fully explored in the discussion. Response Our study uses secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). This is a strength of our study as it allows analysis of a large, international dataset. However, we also acknowledge the limitations in that, although the target population for the protocol is patients admitted due to COVID-19 illness, the data recorded did not include the primary reason for presentation at the hospital and subsequent admission as a validation of this population. In the Conclusion we acknowledge the lack of certainty of the reason for presenting to hospital and we have edited the paper to highlight this in the Strengths and Limitations section. However, we feel our Results are still relevant, and in line with a number of similar, published studies, as they report the treatment subsequent to admission of patients with confirmed COVID-19 with respect to the presence or absence of cancer. The results of this analysis can help to inform resource planning and non communicable disease management like cancer in future outbreaks of respiratory disease. We do agree that it is unknown if a treatment regime might have differed due to demographics or characteristics at admission and have added this to the Limitations and Discussion of Results. PR2. The major results are the comparison of the outcomes of those with cancer versus those without cancer. The authors point out there are other individuals with conditions with increased risk in their comparison group. As such by combining all other groups the approach is likely to underestimate the increase in risk of those with cancer. This is possibly the output in figure 3, however this is not explicitly stated and the reporting of adjusted results for other variables (such as age and sex) appear to be without adjusting for this. In other results this is not addressed, e.g. Data in Table 2 seems to be a direct comparison between those with and without cancer only. Response Thank you for pointing this out. The adjusted hazards ratio for cancer was obtained from a multivariable model that included age, sex, and country income level with no explicit adjustment made for further co-morbidities. We agree data presented in table 2 are crude that are not adjusted for any patient characteristics, however we sought to represent the data in relation to our study question. Following your comments we have undertaken two further sensitivity analyses and present the findings below and in the revised paper. First by adjusting for the comorbidity status in addition to age, sex, and income status in the multivariable model to estimate the hazard ratio of cancer on mortality (sensitivity analysis table1). Second, a multivariable model with all the predictors listed in Table 2 included in the analysis (sensitivity analysis table 2 below). The findings from these two sensitivity analyses indicates that the qualitative association between any of the predictors and outcomes remain relatively stable with some or minor differences in the estimated hazards ratio, apart from chronic neurological disorder. However, it has to be cautioned that such a multivariable model with all the predictors included is subject to large missingness (as indicated in sensitivity analysis 2 table) that makes the analysis susceptible to bias due to missing data. These results from the two sensitivity analyses are presented as Table 4 and Table 5 in the revised manuscript. Sensitivity analysis table 1: Hazards ratio of mortality among those with cancer, adjusted for comorbidities Adjustment set Hazards ratio [95% CI] Results presented in main manuscript Not adjusted for any variables (from Table 3) 1.16 (1.13-1.18) Adjusted for age, sex, and income levels (from Table 3) 1.18 (1.15-1.20) Sensitivity analyses Adjustment set: age, sex, income level, hypertension, diabetes, COPD, obesity, chronic cardiac diseases, dementia, asthma, neurological disorder, rheumatological disorder 1.18 (1.14-1.21) Sensitivity analysis table 2: Multivariable model with all the predictors listed in Table 2 included in the analysis (n=102,184 patients, 16,105 events, and 458,363 missing observations excluded from the multivariable analysis). Unadjusted hazards ratio (from Table 3) Adjusted hazards ratio (95% CI) Cancer (reference: no) 1.16 (1.13-1.18) 1.20 (1.16-1.26) 60 years and above (reference: 0-59y) 2.01 (1.98-2.04) 2.63 (2.50-2.77) Diabetes mellitus (ref: no) 1.4 (1.38-1.42) 1.20 (1.17-1.24) Chronic pulmonary disease (ref: no) 1.31 (1.28-1.33) 1.33 (1.28-1.38) Male (ref: female) 1.11 (1.1-1.12) 1.24 (1.20-1.28) Chronic cardiac disease (ref: no) 1.2 (1.19-1.22) 1.26 (1.21-1.30) Obesity (ref: no) 0.97 (0.95-0.99) 1.10 (1.06-1.15) Hypertension (ref: no) 1.37 (1.35-1.38) 1.10 (1.01-1.14) Dementia (ref: no) 1.51 (1.48-1.55) 1.16 (1.10-1.22) Smoking (ref: no) 1.04 (1.02-1.06) 1.04 (1.00-1.08) Asthma (ref: no) 0.93 (0.91-0.95) 1.03 (0.98-1.08) Chronic neurological disorder (ref: no) 1.13 (1.1-1.16) 0.95 (0.91-0.99) Chronic rheumatological disorder (ref: no) 1.13 (1.1-1.16) 0.96 (0.93-1.00) PR3. The analysis does not include an examination of change over time of risk associated with cancer. It also does not look at the impact of vaccination on risk (which we become more prevalent over time). There is literature on changing risk over the course of the pandemic of individuals with cancer - e.g. Ref 1 and the impact of vaccination and vaccine response Ref 2 . Response Our study includes patients from January 30, 2020, to January 10, 2023. During this period, COVID-19 underwent significant changes in genomics, treatment, and epidemiology, with vaccines introduced at varying times across countries. However, our dataset lacks genotyping and reliable vaccination information, which are crucial for analyzing temporal changes accurately. Without data on these key factors, especially vaccination status, we cannot provide a robust analysis of changes over time. The impact of evolving vaccination rates on outcomes is likely substantial but impossible to calculate with our current data. We acknowledge this limitation more explicitly in this revision and our findings have already informed improvements to ISARIC's case report forms for future outbreaks to address these data gaps. PR4. It is unclear on the rationale of covariates included in the analysis and why others have been omitted. For example, a key strength is the international nature of this dataset, which allows exploration of differences between countries. It is stated in the paper the authors are unaware of any other analysis looking at outcomes using and international data set. There is between countries differences in provision of health care, even of those with similar income level. However, the rationale for why country income level was chosen and its implications is not discussed or why a more granular variable reflecting country could not be used. Rather it is stated that due to the predominance of cases from high income countries they were unable draw inferences, suggesting that using world bank income classification is not an appropriate variable to use. The authors need to reconsider what is used to assess international differences and include this in the analysis. Other examples, including those mentioned above, vaccine status, other clinical comorbidities, likely variant (or time as a proxy), severity at admission (or length of hospitalization) etc, Response Despite its faults, our original analysis plan included an analysis of differences in patient outcomes between countries with different World Bank classifications as a proxy for the quality of healthcare systems between these two economic realities. Other analyses of this database have used this approach due to the sensitivities and inaccuracies of labelling a select collection of hospitals in participating countries as country-level results. We have added this explanation to the methodology. While we agree that there are huge differences between health care institutions within these national income brackets, we feel that presenting a descriptive analysis is useful to identify a signal of difference that should be explored with a targeted study or datasets that include the details needed. The impact of comorbidities has been explored in our analysis. Unfortunately, we did not have data on vaccination status. (There was no PR5 in the peer review report) PR6. The presentation of results needs review to ensure it is clear. For example, the Figure 2 Kaplan Meier plot references inclusion of people between 2020-2023 - this is the only reference to a time period, the x axis is labelled as time and is in days. I assume this is the risk from the date of admission, however that is not explicitly stated - as such the presentation is not clear. Note, If it is the number of days hospitalized, rather than the days since admission, part of the downward trend may be driven by those no longer hospitalized being removed from the analysis as time continues, leaving only those hospitalized for longer being included in survival risk at later timepoints . Without clear description in is challenging to determine if this is a relevant point. Other examples include Figure 3 - while the graphical representation is helpful - it is not possible to ascertain the exact hazard ratio and confidence intervals are omitted Response The study population statement clarifies that patients were enrolled between 30 th January 2020 and 10 th January 2023 and were followed for up to 30 days from admission. We agree the labelling and legends of the tables and graphs should be improved to provide more context as standalone figures and tables and have edited the paper accordingly. We have additionally removed Figure 3 as these results are presented with 95% CI in Table 3, and further in Tables 4 and 5. PR7. It is stated the presence of cancer was self-reported, It may be that more detail of this process would clarify potential biases, however given findings that the cancer group were, for example, admitted less to ICUs it is conceivable that those presenting with more severe symptoms at the time of recruitment were unable to self-report if they had cancer. Greater clarity on either the process of collecting relevant clinical details or discussion of potential implications of data collection approaches are needed. Response Cancer status was obtained from patients who were able to report or family members for many who could not. Those who could not report and did not have family were excluded from the analysis, and hence the potential for bias to arise due to lack of patients’ ability of self-report is likely mitigated in this analysis. However, we appreciate that this approach can lead to bias if those with missing self-reported cancer status have different characteristics than those who were included in the analysis. Appropriate analysis requires investigation of the mechanism that led to the missingness of the self-reported cancer status and this was beyond the scope of the current work. We have clarified this in the Methods section to make this more explicit and reduce the concern for bias. NB we note reference 14 to IDDO is incomplete and the link to the CRF as ref 14 in the Introduction is incorrect it should link to ref 15. We have corrected this in our revision. Thank you. [1] https://isaric.org/research/covid-19-clinical-research-resources/covid-19-crf/ Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved]. F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) REBUTTAL Firstly the authors would like to thank the reviewer for the time spent providing comments on this paper and the provision of two extremely helpful references. We note there is a significant delay between the receipt of the review and this response. This is partly due to the first two authors being deployed to Rwanda as part of international efforts to support its response to the Marburg outbreak in 2024. We provide our replies below and have edited the paper in line with the recommendations where we can. We have also explained where we can’t make changes largely due to the constraints of using secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). [1] Our hope is that this revised version addresses the concerns within the limitations of the data and improves the clarity of the findings as well as providing an improved version that may attract a second positive review to enable this paper to be fully published and indexed. PR1. It is unclear to me the extent to which individuals were hospitalised for a specific reason (e.g. due to their cancer ) and identified as having Covid 19 or were hospitalised for respiratory symptoms and identified as having another diagnosis such as cancer. This is particularly important as there are conclusions about different care between these groups and the demographic characteristics of those with cancer are substantially different from others admitted and this may be driven by the reason for admission. Additionally, the implications for the difference in demographic characteristics are not fully explored in the discussion. Response Our study uses secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). This is a strength of our study as it allows analysis of a large, international dataset. However, we also acknowledge the limitations in that, although the target population for the protocol is patients admitted due to COVID-19 illness, the data recorded did not include the primary reason for presentation at the hospital and subsequent admission as a validation of this population. In the Conclusion we acknowledge the lack of certainty of the reason for presenting to hospital and we have edited the paper to highlight this in the Strengths and Limitations section. However, we feel our Results are still relevant, and in line with a number of similar, published studies, as they report the treatment subsequent to admission of patients with confirmed COVID-19 with respect to the presence or absence of cancer. The results of this analysis can help to inform resource planning and non communicable disease management like cancer in future outbreaks of respiratory disease. We do agree that it is unknown if a treatment regime might have differed due to demographics or characteristics at admission and have added this to the Limitations and Discussion of Results. PR2. The major results are the comparison of the outcomes of those with cancer versus those without cancer. The authors point out there are other individuals with conditions with increased risk in their comparison group. As such by combining all other groups the approach is likely to underestimate the increase in risk of those with cancer. This is possibly the output in figure 3, however this is not explicitly stated and the reporting of adjusted results for other variables (such as age and sex) appear to be without adjusting for this. In other results this is not addressed, e.g. Data in Table 2 seems to be a direct comparison between those with and without cancer only. Response Thank you for pointing this out. The adjusted hazards ratio for cancer was obtained from a multivariable model that included age, sex, and country income level with no explicit adjustment made for further co-morbidities. We agree data presented in table 2 are crude that are not adjusted for any patient characteristics, however we sought to represent the data in relation to our study question. Following your comments we have undertaken two further sensitivity analyses and present the findings below and in the revised paper. First by adjusting for the comorbidity status in addition to age, sex, and income status in the multivariable model to estimate the hazard ratio of cancer on mortality (sensitivity analysis table1). Second, a multivariable model with all the predictors listed in Table 2 included in the analysis (sensitivity analysis table 2 below). The findings from these two sensitivity analyses indicates that the qualitative association between any of the predictors and outcomes remain relatively stable with some or minor differences in the estimated hazards ratio, apart from chronic neurological disorder. However, it has to be cautioned that such a multivariable model with all the predictors included is subject to large missingness (as indicated in sensitivity analysis 2 table) that makes the analysis susceptible to bias due to missing data. These results from the two sensitivity analyses are presented as Table 4 and Table 5 in the revised manuscript. Sensitivity analysis table 1: Hazards ratio of mortality among those with cancer, adjusted for comorbidities Adjustment set Hazards ratio [95% CI] Results presented in main manuscript Not adjusted for any variables (from Table 3) 1.16 (1.13-1.18) Adjusted for age, sex, and income levels (from Table 3) 1.18 (1.15-1.20) Sensitivity analyses Adjustment set: age, sex, income level, hypertension, diabetes, COPD, obesity, chronic cardiac diseases, dementia, asthma, neurological disorder, rheumatological disorder 1.18 (1.14-1.21) Sensitivity analysis table 2: Multivariable model with all the predictors listed in Table 2 included in the analysis (n=102,184 patients, 16,105 events, and 458,363 missing observations excluded from the multivariable analysis). Unadjusted hazards ratio (from Table 3) Adjusted hazards ratio (95% CI) Cancer (reference: no) 1.16 (1.13-1.18) 1.20 (1.16-1.26) 60 years and above (reference: 0-59y) 2.01 (1.98-2.04) 2.63 (2.50-2.77) Diabetes mellitus (ref: no) 1.4 (1.38-1.42) 1.20 (1.17-1.24) Chronic pulmonary disease (ref: no) 1.31 (1.28-1.33) 1.33 (1.28-1.38) Male (ref: female) 1.11 (1.1-1.12) 1.24 (1.20-1.28) Chronic cardiac disease (ref: no) 1.2 (1.19-1.22) 1.26 (1.21-1.30) Obesity (ref: no) 0.97 (0.95-0.99) 1.10 (1.06-1.15) Hypertension (ref: no) 1.37 (1.35-1.38) 1.10 (1.01-1.14) Dementia (ref: no) 1.51 (1.48-1.55) 1.16 (1.10-1.22) Smoking (ref: no) 1.04 (1.02-1.06) 1.04 (1.00-1.08) Asthma (ref: no) 0.93 (0.91-0.95) 1.03 (0.98-1.08) Chronic neurological disorder (ref: no) 1.13 (1.1-1.16) 0.95 (0.91-0.99) Chronic rheumatological disorder (ref: no) 1.13 (1.1-1.16) 0.96 (0.93-1.00) PR3. The analysis does not include an examination of change over time of risk associated with cancer. It also does not look at the impact of vaccination on risk (which we become more prevalent over time). There is literature on changing risk over the course of the pandemic of individuals with cancer - e.g. Ref 1 and the impact of vaccination and vaccine response Ref 2 . Response Our study includes patients from January 30, 2020, to January 10, 2023. During this period, COVID-19 underwent significant changes in genomics, treatment, and epidemiology, with vaccines introduced at varying times across countries. However, our dataset lacks genotyping and reliable vaccination information, which are crucial for analyzing temporal changes accurately. Without data on these key factors, especially vaccination status, we cannot provide a robust analysis of changes over time. The impact of evolving vaccination rates on outcomes is likely substantial but impossible to calculate with our current data. We acknowledge this limitation more explicitly in this revision and our findings have already informed improvements to ISARIC's case report forms for future outbreaks to address these data gaps. PR4. It is unclear on the rationale of covariates included in the analysis and why others have been omitted. For example, a key strength is the international nature of this dataset, which allows exploration of differences between countries. It is stated in the paper the authors are unaware of any other analysis looking at outcomes using and international data set. There is between countries differences in provision of health care, even of those with similar income level. However, the rationale for why country income level was chosen and its implications is not discussed or why a more granular variable reflecting country could not be used. Rather it is stated that due to the predominance of cases from high income countries they were unable draw inferences, suggesting that using world bank income classification is not an appropriate variable to use. The authors need to reconsider what is used to assess international differences and include this in the analysis. Other examples, including those mentioned above, vaccine status, other clinical comorbidities, likely variant (or time as a proxy), severity at admission (or length of hospitalization) etc, Response Despite its faults, our original analysis plan included an analysis of differences in patient outcomes between countries with different World Bank classifications as a proxy for the quality of healthcare systems between these two economic realities. Other analyses of this database have used this approach due to the sensitivities and inaccuracies of labelling a select collection of hospitals in participating countries as country-level results. We have added this explanation to the methodology. While we agree that there are huge differences between health care institutions within these national income brackets, we feel that presenting a descriptive analysis is useful to identify a signal of difference that should be explored with a targeted study or datasets that include the details needed. The impact of comorbidities has been explored in our analysis. Unfortunately, we did not have data on vaccination status. (There was no PR5 in the peer review report) PR6. The presentation of results needs review to ensure it is clear. For example, the Figure 2 Kaplan Meier plot references inclusion of people between 2020-2023 - this is the only reference to a time period, the x axis is labelled as time and is in days. I assume this is the risk from the date of admission, however that is not explicitly stated - as such the presentation is not clear. Note, If it is the number of days hospitalized, rather than the days since admission, part of the downward trend may be driven by those no longer hospitalized being removed from the analysis as time continues, leaving only those hospitalized for longer being included in survival risk at later timepoints . Without clear description in is challenging to determine if this is a relevant point. Other examples include Figure 3 - while the graphical representation is helpful - it is not possible to ascertain the exact hazard ratio and confidence intervals are omitted Response The study population statement clarifies that patients were enrolled between 30 th January 2020 and 10 th January 2023 and were followed for up to 30 days from admission. We agree the labelling and legends of the tables and graphs should be improved to provide more context as standalone figures and tables and have edited the paper accordingly. We have additionally removed Figure 3 as these results are presented with 95% CI in Table 3, and further in Tables 4 and 5. PR7. It is stated the presence of cancer was self-reported, It may be that more detail of this process would clarify potential biases, however given findings that the cancer group were, for example, admitted less to ICUs it is conceivable that those presenting with more severe symptoms at the time of recruitment were unable to self-report if they had cancer. Greater clarity on either the process of collecting relevant clinical details or discussion of potential implications of data collection approaches are needed. Response Cancer status was obtained from patients who were able to report or family members for many who could not. Those who could not report and did not have family were excluded from the analysis, and hence the potential for bias to arise due to lack of patients’ ability of self-report is likely mitigated in this analysis. However, we appreciate that this approach can lead to bias if those with missing self-reported cancer status have different characteristics than those who were included in the analysis. Appropriate analysis requires investigation of the mechanism that led to the missingness of the self-reported cancer status and this was beyond the scope of the current work. We have clarified this in the Methods section to make this more explicit and reduce the concern for bias. NB we note reference 14 to IDDO is incomplete and the link to the CRF as ref 14 in the Introduction is incorrect it should link to ref 15. We have corrected this in our revision. Thank you. [1] https://isaric.org/research/covid-19-clinical-research-resources/covid-19-crf/ Competing Interests: No competing interest to disclose Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 21 Jun 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 3 (revision) 21 Nov 25 read read read Version 2 (revision) 07 Apr 25 read read read Version 1 21 Jun 24 read Tom Fowler , UK Health Security Agency, William Harvey Research Institute and the Barts Cancer Institute, Queen Mary University of London, London, UK Chih-Yuan Hsu , Vanderbilt University Medical Center, Nashville, USA Lynne Lohfeld , Queen’s University Belfast, Belfast, UK Rajath Rao , Manipal Academy of Higher Education, Manipal, India Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Hsu C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 02 Jan 2026 | for Version 3 Chih-Yuan Hsu , Vanderbilt University Medical Center, Nashville, Tennessee, USA 0 Views copyright © 2026 Hsu C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have addressed all my questions except for question 6. Also, for some questions, the authors provided responses, but the corresponding revisions were not fully incorporated into the revised manuscript. After making these minor revisions, the manuscript can be considered for indexing. Questions that were answered but not fully revised: 1. Page 2, Results in Abstract. The period used for counting ICU and IMV admissions is unclear, please clarify. Please revise “The hazard ratio of dying from cancer, …” to “The hazard ratio of 30-day in-hospital mortality between patients with and without cancers, …”? 4. Title of Table 4. Please revise “Hazards ratio of mortality among those with cancer, …” to “Hazard ratio of 30-day in-hospital mortality between patients with and without cancers, …”? Question not answered: 6. Discussion section. The authors state, “However, we cannot explain why they stayed longer in hospital but received less high-level care compared to COVID-19 patients without cancer.” When the authors counted ICU and IMV, was death included? Death is a competing event for ICU and IMV. When death occurred before ICU and IMV, these ICU and IMV would be not counted. If the authors would like to compare COVID-19 severity between patients with and without cancers, ICU and MV should include death. Moreover, potential confounders should be adjusted when performing comparisons in ICU and IMV. Competing Interests No competing interests were disclosed. Reviewer Expertise Single-cell RNA seq analysis, Survival analysis, Clinical trial I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Hsu CY. Peer Review Report For: Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.5256/f1000research.186938.r434960) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-673/v3#referee-response-434960 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Rao R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 Dec 2025 | for Version 3 Rajath Rao , Kasturba Medical College Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India 0 Views copyright © 2026 Rao R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear authors, You have made the suggested changes. The changes in the Cox proportional hazard model are appreciated. There are no more comments from my side Competing Interests No competing interests were disclosed. Reviewer Expertise NCD, Operational research, Implementational research, Preventive oncology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Rao R. Peer Review Report For: Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.5256/f1000research.186938.r434964) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-673/v3#referee-response-434964 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Fowler T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 15 Dec 2025 | for Version 3 Tom Fowler , UK Health Security Agency, William Harvey Research Institute and the Barts Cancer Institute, Queen Mary University of London, London, UK 0 Views copyright © 2025 Fowler T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Overall I am content with the changes and the paper in its current form. It is more robust and details a stronger methodological approach. In its current format it will be of interest to your readership. I have no further comments. Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Fowler T. Peer Review Report For: Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.5256/f1000research.186938.r434962) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-673/v3#referee-response-434962 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Rao R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Jul 2025 | for Version 2 Rajath Rao , Kasturba Medical College Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India 0 Views copyright © 2025 Rao R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This is a timely and important study investigating the association between cancer and COVID-19 outcomes in hospitalized patients. The use of a large, multinational dataset from ISARIC is a significant strength, allowing for robust analysis of this vulnerable patient population. The comprehensive assessment of various outcomes, including mortality, severe disease, and specific treatments, provides valuable insights. The manuscript is generally well-structured, and the findings contribute meaningfully to our understanding of COVID-19 in cancer patients. However, there are areas where clarity, methodological precision, and interpretation could be enhanced for a stronger presentation. In the methods section , the data source can be "This specifies the robust data source. It should be "WHO Clinical Characterisation Protocol" or "WHO ISARIC Clinical Characterisation Protocol" to avoid the implication that it is exclusive to the UK." In the methods section , the definition of cancer is "Cancer was defined as patients with self-reported cancer or those with active cancer treatment reported within 6 months before hospital admission." This definition is crucial. It would be helpful to clarify if "self-reported cancer" was confirmed by medical records in all cases or if it relied solely on patient statements, as this could introduce potential for misclassification. Introduction - Previous Studies (Line 11-13): "Previous studies have reported increased risks of severe outcomes and mortality in cancer patients with COVID-19." This provides context for the study, but it would be beneficial to briefly state how this study adds to or refines previous knowledge (e.g., by using a larger, more diverse cohort, or focusing on specific cancer types if that's addressed later). Study Variables - Cancer Definition (Line 31-34): "Cancer was defined as patients with self-reported cancer or those with active cancer treatment reported within 6 months prior to hospital admission. Cancer diagnosis was based on clinical judgment or confirmed by medical records, where available." This clarification ("where available") is important and should be highlighted. It implies that some "self-reported cancer" cases might not have been medically confirmed within the dataset. This is a potential limitation to acknowledge in the discussion, as it could introduce misclassification bias. In statistical analysis part , Missing Data (Line 2-5): "Missing data were handled using multiple imputation with chained equations (MICE) for continuous and categorical variables. Five imputed datasets were generated, and results were pooled using Rubin’s rules." It would be helpful to mention the proportion of missingness for key variables to understand the extent of imputation. In discussion , Comparison with Literature (Line 5-10): "These findings are consistent with previous studies, which have also reported higher risks of adverse outcomes in cancer patients with COVID-19, particularly during the early phases of the pandemic." This is a good comparison. However, the unique contribution of this study (large size, diverse geography, more recent data, detailed treatment analysis) could be emphasized more here to distinguish it from prior work. In discussion , possible explanations (Line 11-15): "Several factors may explain these associations, including underlying immunosuppression due to cancer or its treatment, increased burden of comorbidities, and potential delays in cancer care delivery during the pandemic." These are plausible explanations. Expanding on the "potential delays in cancer care delivery" and how it might impact outcomes in hospitalized COVID-19 patients could be insightful. Treatment Disparities Discussion (Line 17-21): "Interestingly, we observed that cancer patients were less likely to receive antiviral treatment but more likely to receive corticosteroids and oxygen support." This section is critical. "This could be due to clinicians' concerns about drug interactions or contraindications, or a reflection of the more severe disease presentation in these patients." It would be valuable to delve deeper: Are there specific cancer treatments that preclude antiviral use? Were these antiviral guidelines strict throughout the study period (2020-2023), given evolving knowledge? This nuance would strengthen the discussion on treatment. Tables and figures : Ensure that all tables and figures are clearly titled, accurately reflect the data presented, and have comprehensive legends. For Table 1 on baseline characteristics, ensure that percentages are clearly defined (e.g., row vs. column percentages) if not immediately obvious. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise NCD, Operational research, Implementational research, Preventive oncology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Rao R. Peer Review Report For: Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.5256/f1000research.179757.r387889) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-673/v2#referee-response-387889 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Lohfeld L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 28 May 2025 | for Version 2 Lynne Lohfeld , Queen’s University Belfast, Belfast, Northern Ireland, UK 0 Views copyright © 2025 Lohfeld L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions You are to be commended for your work with colleagues in Rwanda during the Marburg outbreak, showing your commitment to both essential clinical work and research. I believe it is vital that we promote indexing in scientific journals by authors in the Global South. It is also commendable that this study has already resulted in modifications to the ISARIC-WHO case report form (much like developing a core outcome set for clinical research) that can increase the quality and applicability of findings from studies about future respiratory epidemics. I am recommending the revised version of your article be indexed without reservation or further revisions. You have clearly and completely addressed each of the first reviewer’s comments in a clear and succinct manner. Well done. Below are a few comments and questions for your consideration that do not affect this decision. You did not indicate which variables shown in Table 2 were significantly different between the cancer and non-cancer patients. This would be very helpful for non-statisticians (like myself) to better understand key findings. Your findings identified some interesting questions that can only be answered with additional variables; for example: Given that most of the data were from high-income countries, it might be expected that more patients could receive higher-level hospital care than in LMICs, which contradicts the finding that cancer patients less often received such care (compare rates of such care for cancer patients by country’s income level). Given that older age was highly associated with in-hospital deaths, what treatment protocols could be developed for these patients to improve survival? What are the current protocols for prioritising patients when equipment, staff or materials are in short supply? Would it be important to understand why diabetes was more strongly associated with increased risk of death compared to patients with cancer after adjusting for age, sex and country income level? Why was the change in association between predictors and outcome for chronic neurological disorders so different compared to other conditions? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise global public health, cancer, palliative care, rare disease caregivers, qualitative and mixed-methods research I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Lohfeld L. Peer Review Report For: Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.5256/f1000research.179757.r377222) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-673/v2#referee-response-377222 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Hsu C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 27 May 2025 | for Version 2 Chih-Yuan Hsu , Vanderbilt University Medical Center, Nashville, Tennessee, USA 0 Views copyright © 2025 Hsu C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This study compared the outcomes of patients with and without cancers who were hospitalized for COVID-19 using a larger dataset than most previous studies. It found that patients with cancer had higher 30-day in-hospital mortality, longer hospital stays, were less often admitted to intensive care units (ICU), and less received invasive mechanical ventilation (IMV), compared to non-cancer patients. However, the comparisons of outcomes other than 30-day in-hospital mortality may be misunderstood. 1. Page 2, Results. The period used for counting ICU and IMV admissions is unclear. Was it within the 30-day hospitalization or the entire follow-up period? Would it be better to revise “The hazard ratio of dying from cancer, …” to “The hazard ratio of 30-day in-hospital mortality between patients with and without cancers, …”? 2. Page 5, Analysis and statistical method. Please replace “A survival analysis model” with the more precise term “A Cox proportional hazard model”, and describe how the authors addressed missing values of covariates? 3. Page 9, Figure 1. Did the authors aim to demonstrate that a larger length of hospitalization (LOH) indicates severe COVID-19? However, cancer patients might experience a larger LOH due to their weakened conditions rather than COVID-19. Without adjusting for confounders such as comorbidities, the result could be biased. 4. Page 11, Table 4. Would it be better to revise “Hazards ratio of mortality among those with cancer, …” to “Hazard ratio of 30-day in-hospital mortality between patients with and without cancers, …”? 5. Page 11, Table 5. The authors excluded 458,363 missing observations from the analysis, which constituted more than 80% of the data. Did the authors consider using more popular multiple imputation method to handle these missing values? 6. Page 11, last paragraph. The authors state, “However, we cannot explain why they stayed longer in hospital but received less high-level care compared to COVID-19 patients without cancer.” When the authors counted ICU and IMV, was death included? Death is a competing event for ICU and IMV. When death occurred before ICU and IMV, these ICU and IMV would be not counted. If the authors would like to compare COVID-19 severity between patients with and without cancers, ICU and MV should include death. Moreover, potential confounders should be adjusted when performing comparisons in ICU and IMV. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Single-cell RNA seq analysis, Survival analysis, Clinical trial I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Sep 2025 Abdulai Tejan Jalloh, Ministry of Health, Government of Sierra Leone, Freetown, Sierra Leone Firstly, the authors appreciate the reviewer for the time spent providing comments on this paper, and we offer our responses here to improve the current version of our draft. We have responded to the queries raised and edited the paper based on the suggested recommendations where we can. However, there are areas where we could not effect changes due to the constraints of using secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organization (WHO). [1] We hope that this revised version addresses the concerns, within the limitations of the data, and improves the clarity of the findings, as well as providing an improved version and making it more informative within the scientific literature. REVIEWER COMMENTS: This study compared the outcomes of patients with and without cancers who were hospitalized for COVID-19 using a larger dataset than most previous studies. It found that patients with cancer had higher 30-day in-hospital mortality, longer hospital stays, were less often admitted to intensive care units (ICU), and less received invasive mechanical ventilation (IMV), compared to non-cancer patients. However, the comparisons of outcomes other than 30-day in-hospital mortality may be misunderstood. 1. Page 2, Results. The period used for counting ICU and IMV admissions is unclear. Was it within the 30-day hospitalization or the entire follow-up period? Would it be better to revise “The hazard ratio of dying from cancer, …” to “The hazard ratio of 30-day in-hospital mortality between patients with and without cancers, Authors’ response: This query has been resolved and edited fully in the paper, as the aim was to compare the hazard ratio of 30 days in hospital mortality between COVID-19 patients with or without cancer. 2. Page 5, Analysis and statistical method. Please replace “A survival analysis model” with the more precise term “A Cox proportional hazard model”, and describe how the authors addressed missing values of covariates? Authors’ response: This query has been resolved; a Cox proportional hazard model has been incorporated in the analysis and statistical methods, and patients with missing covariates were excluded. Patients with complete variables were analysed, and the results are presented in the tables. 3. Page 9, Figure 1. Did the authors aim to demonstrate that a larger length of hospitalization (LOH) indicates severe COVID-19? However, cancer patients might experience a larger LOH due to their weakened conditions rather than COVID-19. Without adjusting for confounders such as comorbidities, the result could be biased. Authors’ response: We aimed to describe the distribution of LOH by cancer status without indicating the severity of the disease. We agree that there are additional factors which may contribute to the LOH among cancer patients – but these are outside of the aim for presenting the information in Figure 1. This figure can inform differences in health care resource use between these groups of patients. Furthermore, the finding in our paper agrees that patients with cancer might have a longer hospital stay due to other comorbidities. 4. Page 11, Table 4. Would it be better to revise “Hazards ratio of mortality among those with cancer, …” to “Hazard ratio of 30-day in-hospital mortality between patients with and without cancers, …”? Authors’ response: This query has been resolved and the paper was edited according to recommendation. 5. Page 11, Table 5. The authors excluded 458,363 missing observations from the analysis, which constituted more than 80% of the data. Did the authors consider using more popular multiple imputation method to handle these missing values? Authors’ response: More than >50% of patients had missingness for smoking, dementia, Chronic neurological disorder, Chronic rheumatological disorder, and obesity Understanding the underlying mechanism of missingness for some of the variables can be challenging. The standard implementation of multiple imputation assumes that missingness arises through a missing at random (MAR) mechanism. However, such tacit assumption can be questionable for some of the covariates. For example, it can be argued that missingness for dementia can be thought of arising from a not at random (MNAR) process – where the standard implementation of multiple imputation can be questioned. A rigorous investigation of the missingness mechanism and considerations for using principled statistical approaches is considered beyond the current scope of work. However, we have re-run the analysis in a further sensitivity analysis by restricting the data to covariates that have <10% missingness. Reassuringly, the obtained hazards ratio from the new multivariable model remains relatively robust. Table 3 included in the analysis (n=102,184 patients, 16,105 events, and 458,363 missing observations excluded from the multivariable analysis). Unadjusted hazards ratio (95% confidence interval) (from Table 3) Adjusted hazards ratio (95% confidence interval) Adjusted hazards ratio (95% confidence interval) [Excluding predictors >10% missing] Cancer (reference: no) 1.16 (1.13-1.18) 1.20 (1.16-1.26) 1.17 (1.14-1.21) 60 years and above (reference: 0-59y) 2.01 (1.98-2.04) 2.63 (2.50-2.77) 2.26(2.22-2.29) Diabetes mellitus (reference: no) 1.4 (1.38-1.42) 1.20 (1.17-1.24) 1.29 (1.27-1.32) Chronic pulmonary disease (reference: no) 1.31 (1.28-1.33) 1.33 (1.28-1.38) 1.30 (1.27-1.33) Male (reference: female) 1.11 (1.1-1.12) 1.24 (1.20-1.28) 1.13 (1.11-1.15) Chronic cardiac disease (reference: no) 1.2 (1.19-1.22) 1.26 (1.21-1.30) 1.18 (1.15-1.20 Obesity (reference: no) 0.97 (0.95-0.99) 1.10 (1.06-1.15) Excluded Hypertension (reference: no) 1.37 (1.35-1.38) 1.10 (1.01-1.14) 1.12 (1.11-1.14) Dementia (reference: no) 1.51 (1.48-1.55) 1.16 (1.10-1.22) Excluded Smoking (reference: no) 1.04 (1.02-1.06) 1.04 (1.00-1.08) Excluded Asthma (reference: no) 0.93 (0.91-0.95) 1.03 (0.98-1.08) 0.93 (0.92-0.96) Chronic neurological disorder (reference: no) 1.13 (1.1-1.16) 0.95 (0.91-0.99) Excluded [1] https://isaric.org/research/covid-19-clinical-research-resources/covid-19-crf/ View more View less Competing Interests no competing interest reply Respond Report a concern Hsu CY. Peer Review Report For: Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved] . F1000Research 2024, 13 :673 ( https://doi.org/10.5256/f1000research.179757.r379604) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-673/v2#referee-response-379604 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Fowler T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Sep 2024 | for Version 1 Tom Fowler , UK Health Security Agency, William Harvey Research Institute and the Barts Cancer Institute, Queen Mary University of London, London, UK 0 Views copyright © 2024 Fowler T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This paper examining an important area of whether outcomes in hospitalized Covid 19 cancer patients compared to other hospitalized patients for Covid 19 have worse outcomes. It uses data from International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) and a substantial strength is the international nature of this cohort and its overall size. The paper identifies demographic differences between those hospitalised for Covid 19 with Cancer and those without along with different outcomes in key measures (e.g. 30-day in hospital mortality). There are however a number of major and minor issues that need to be addressed. In part areas of analysis may be restricted due to the availability of data - in which case this should be acknowledged and addressed in the discussion. 1. It is unclear to me the extent to which individuals were hospitalised for a specific reason (e.g. due to their cancer ) and identified as having Covid 19 or were hospitalised for respiratory symptoms and identified as having another diagnosis such as cancer. This is particularly important as there are conclusions about different care between these groups and the demographic characteristics of those with cancer are substantially different from others admitted and this may be driven by the reason for admission. Additionally, the implications for the difference in demographic characteristics are not fully explored in the discussion 2. The major results are the comparison of the outcomes of those with cancer versus those without cancer. The authors point out there are other individuals with conditions with increased risk in their comparison group. As such by combining all other groups the approach is likely to underestimate the increase in risk of those with cancer. This is possibly the output in figure 3, however this is not explicitly stated and the reporting of adjusted results for other variables (such as age and sex) appear to be without adjusting for this. In other results this is not addressed, e.g. Data in Table 2 seems to be a direct comparison between those with and without cancer only. 3. The analysis does not include an examination of change over time of risk associated with cancer. It also does not look at the impact of vaccination on risk (which we become more prevalent over time). There is literature on changing risk over the course of the pandemic of individuals with cancer - e.g. Ref 1 and the impact of vaccination and vaccine response Ref 2 As such I would expect consideration of vaccination status to be part of the methods/ analysis. 4. It is unclear on the rationale of covariates included in the analysis and why others have been omitted. For example, a key strength is the international nature of this dataset, which allows exploration of differences between countries. It is stated in the paper the authors are unaware of any other analysis looking at outcomes using and international data set. There is between countries differences in provision of health care, even of those with similar income level. However the rationale for why country income level was chosen and its implications is not discussed or why a more granular variable reflecting country could not be used. Rather it is stated that due to the predominance of cases from high income countries they were unable draw inferences, suggesting that using world bank income classification is not an appropriate variable to use. The authors need to reconsider what is used to assess international differences and include this in the analysis Other examples, including those mentioned above, vaccine status, other clinical comorbidities, likely variant (or time as a proxy), severity at admission (or length of hospitalization) etc, 6. The presentation of results needs review to ensure it is clear. For example the Figure 2 Kaplan Meier plot references inclusion of people between 2020-2023 - this is the only reference to a time period, the x axis is labeled as time and is in days. I assume this is the risk from the date of admission, however that is not explicitly stated - as such the presentation is not clear. Note, If it is the number of days hospitalized, rather than the days since admission, part of the downward trend may be driven by those no longer hospitalized being removed from the analysis as time continues, leaving only those hospitalized for longer being included in survival risk at later timepoints . Without clear description in is challenging to determine if this is a relevant point. Other examples include Figure 3 - while the graphical representation is helpful - it is not possible to ascertain the exact hazard ratio and confidence intervals are omitted 7. It is stated the presence of cancer was self reported, It may be that more detail of this process would clarify potential biases, however given findings that the cancer group were, for example, admitted less to ICUs it is conceivable that those presenting with more severe symptoms at the time of recruitment were unable to self report if they had cancer. Greater clarity on either the process of collecting relevant clinical details or discussion of potential implications of data collection approaches are needed. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Starkey T, Ionescu MC, Tilby M, Little M, et al.: A population-scale temporal case-control evaluation of COVID-19 disease phenotype and related outcome rates in patients with cancer in England (UKCCP). Sci Rep . 2023; 13 (1): 11327 PubMed Abstract | Publisher Full Text 2. Lee LYW, Tilby M, Starkey T, Ionescu MC, et al.: Association of SARS-CoV-2 Spike Protein Antibody Vaccine Response With Infection Severity in Patients With Cancer: A National COVID Cancer Cross-sectional Evaluation. JAMA Oncol . 2023; 9 (2): 188-196 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Public Heath, Genomics, Pandemics I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 09 Jul 2025 Abdulai Tejan Jalloh, Ministry of Health, Government of Sierra Leone, Freetown, Sierra Leone Jalloh AT, Merson L, Nair D et al. Association of cancer and outcomes of patients hospitalized for COVID-19 between 2020 and 2023 [version 1; peer review: 1 not approved]. F1000Research 2024, 13 :673 ( https://doi.org/10.12688/f1000research.150761.1 ) REBUTTAL Firstly the authors would like to thank the reviewer for the time spent providing comments on this paper and the provision of two extremely helpful references. We note there is a significant delay between the receipt of the review and this response. This is partly due to the first two authors being deployed to Rwanda as part of international efforts to support its response to the Marburg outbreak in 2024. We provide our replies below and have edited the paper in line with the recommendations where we can. We have also explained where we can’t make changes largely due to the constraints of using secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). [1] Our hope is that this revised version addresses the concerns within the limitations of the data and improves the clarity of the findings as well as providing an improved version that may attract a second positive review to enable this paper to be fully published and indexed. PR1. It is unclear to me the extent to which individuals were hospitalised for a specific reason (e.g. due to their cancer ) and identified as having Covid 19 or were hospitalised for respiratory symptoms and identified as having another diagnosis such as cancer. This is particularly important as there are conclusions about different care between these groups and the demographic characteristics of those with cancer are substantially different from others admitted and this may be driven by the reason for admission. Additionally, the implications for the difference in demographic characteristics are not fully explored in the discussion. Response Our study uses secondary data collected under the Clinical Characterisation Protocol designed by the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) and the World Health Organisation (WHO). This is a strength of our study as it allows analysis of a large, international dataset. However, we also acknowledge the limitations in that, although the target population for the protocol is patients admitted due to COVID-19 illness, the data recorded did not include the primary reason for presentation at the hospital and subsequent admission as a validation of this population. In the Conclusion we acknowledge the lack of certainty of the reason for presenting to hospital and we have edited the paper to highlight this in the Strengths and Limitations section. However, we feel our Results are still relevant, and in line with a number of similar, published studies, as they report the treatment subsequent to admission of patients with confirmed COVID-19 with respect to the presence or absence of cancer. The results of this analysis can help to inform resource planning and non communicable disease management like cancer in future outbreaks of respiratory disease. We do agree that it is unknown if a treatment regime might have differed due to demographics or characteristics at admission and have added this to the Limitations and Discussion of Results. PR2. The major results are the comparison of the outcomes of those with cancer versus those without cancer. The authors point out there are other individuals with conditions with increased risk in their comparison group. As such by combining all other groups the approach is likely to underestimate the increase in risk of those with cancer. This is possibly the output in figure 3, however this is not explicitly stated and the reporting of adjusted results for other variables (such as age and sex) appear to be without adjusting for this. In other results this is not addressed, e.g. Data in Table 2 seems to be a direct comparison between those with and without cancer only. Response Thank you for pointing this out. The adjusted hazards ratio for cancer was obtained from a multivariable model that included age, sex, and country income level with no explicit adjustment made for further co-morbidities. We agree data presented in table 2 are crude that are not adjusted for any patient characteristics, however we sought to represent the data in relation to our study question. Following your comments we have undertaken two further sensitivity analyses and present the findings below and in the revised paper. First by adjusting for the comorbidity status in addition to age, sex, and income status in the multivariable model to estimate the hazard ratio of cancer on mortality (sensitivity analysis table1). Second, a multivariable model with all the predictors listed in Table 2 included in the analysis (sensitivity analysis table 2 below). The findings from these two sensitivity analyses indicates that the qualitative association between any of the predictors and outcomes remain relatively stable with some or minor differences in the estimated hazards ratio, apart from chronic neurological disorder. However, it has to be cautioned that such a multivariable model with all the predictors included is subject to large missingness (as indicated in sensitivity analysis 2 table) that makes the analysis susceptible to bias due to missing data. These results from the two sensitivity analyses are presented as Table 4 and Table 5 in the revised manuscript. Sensitivity analysis table 1: Hazards ratio of mortality among those with cancer, adjusted for comorbidities Adjustment set Hazards ratio [95% CI] Results presented in main manuscript Not adjusted for any variables (from Table 3) 1.16 (1.13-1.18) Adjusted for age, sex, and income levels (from Table 3) 1.18 (1.15-1.20) Sensitivity analyses Adjustment set: age, sex, income level, hypertension, diabetes, COPD, obesity, chronic cardiac diseases, dementia, asthma, neurological disorder, rheumatological disorder 1.18 (1.14-1.21) Sensitivity analysis table 2: Multivariable model with all the predictors listed in Table 2 included in the analysis (n=102,184 patients, 16,105 events, and 458,363 missing observations excluded from the multivariable analysis). Unadjusted hazards ratio (from Table 3) Adjusted hazards ratio (95% CI) Cancer (reference: no) 1.16 (1.13-1.18) 1.20 (1.16-1.26) 60 years and above (reference: 0-59y) 2.01 (1.98-2.04) 2.63 (2.50-2.77) Diabetes mellitus (ref: no) 1.4 (1.38-1.42) 1.20 (1.17-1.24) Chronic pulmonary disease (ref: no) 1.31 (1.28-1.33) 1.33 (1.28-1.38) Male (ref: female) 1.11 (1.1-1.12) 1.24 (1.20-1.28) Chronic cardiac disease (ref: no) 1.2 (1.19-1.22) 1.26 (1.21-1.30) Obesity (ref: no) 0.97 (0.95-0.99) 1.10 (1.06-1.15) Hypertension (ref: no) 1.37 (1.35-1.38) 1.10 (1.01-1.14) Dementia (ref: no) 1.51 (1.48-1.55) 1.16 (1.10-1.22) Smoking (ref: no) 1.04 (1.02-1.06) 1.04 (1.00-1.08) Asthma (ref: no) 0.93 (0.91-0.95) 1.03 (0.98-1.08) Chronic neurological disorder (ref: no) 1.13 (1.1-1.16) 0.95 (0.91-0.99) Chronic rheumatological disorder (ref: no) 1.13 (1.1-1.16) 0.96 (0.93-1.00) PR3. The analysis does not include an examination of change over time of risk associated with cancer. It also does not look at the impact of vaccination on risk (which we become more prevalent over time). There is literature on changing risk over the course of the pandemic of individuals with cancer - e.g. Ref 1 and the impact of vaccination and vaccine response Ref 2 . Response Our study includes patients from January 30, 2020, to January 10, 2023. During this period, COVID-19 underwent significant changes in genomics, treatment, and epidemiology, with vaccines introduced at varying times across countries. However, our dataset lacks genotyping and reliable vaccination information, which are crucial for analyzing temporal changes accurately. Without data on these key factors, especially vaccination status, we cannot provide a robust analysis of changes over time. The impact of evolving vaccination rates on outcomes is likely substantial but impossible to calculate with our current data. We acknowledge this limitation more explicitly in this revision and our findings have already informed improvements to ISARIC's case report forms for future outbreaks to address these data gaps. PR4. It is unclear on the rationale of covariates included in the analysis and why others have been omitted. For example, a key strength is the international nature of this dataset, which allows exploration of differences between countries. It is stated in the paper the authors are unaware of any other analysis looking at outcomes using and international data set. There is between countries differences in provision of health care, even of those with similar income level. However, the rationale for why country income level was chosen and its implications is not discussed or why a more granular variable reflecting country could not be used. Rather it is stated that due to the predominance of cases from high income countries they were unable draw inferences, suggesting that using world bank income classification is not an appropriate variable to use. The authors need to reconsider what is used to assess international differences and include this in the analysis. Other examples, including those mentioned above, vaccine status, other clinical comorbidities, likely variant (or time as a proxy), severity at admission (or length of hospitalization) etc, Response Despite its faults, our original analysis plan included an analysis of differences in patient outcomes between countries with different World Bank classifications as a proxy for the quality of healthcare systems between these two economic realities. Other analyses of this database have used this approach due to the sensitivities and inaccuracies of labelling a select collection of hospitals in participating countries as country-level results. We have added this explanation to the methodology. While we agree that there are huge differences between health care institutions within these national income brackets, we feel that presenting a descriptive analysis is useful to identify a signal of difference that should be explored with a targeted study or datasets that include the details needed. The impact of comorbidities has been explored in our analysis. Unfortunately, we did not have data on vaccination status. (There was no PR5 in the peer review report) PR6. The presentation of results needs review to ensure it is clear. For example, the Figure 2 Kaplan Meier plot references inclusion of people between 2020-2023 - this is the only reference to a time period, the x axis is labelled as time and is in days. I assume this is the risk from the date of admission, however that is not explicitly stated - as such the presentation is not clear. Note, If it is the number of days hospitalized, rather than the days since admission, part of the downward trend may be driven by those no longer hospitalized being removed from the analysis as time continues, leaving only those hospitalized for longer being included in survival risk at later timepoints . Without clear description in is challenging to determine if this is a relevant point. Other examples include Figure 3 - while the graphical representation is helpful - it is not possible to ascertain the exact hazard ratio and confidence intervals are omitted Response The study population statement clarifies that patients were enrolled between 30 th January 2020 and 10 th January 2023 and were followed for up to 30 days from admission. We agree the labelling and legends of the tables and graphs should be improved to provide more context as standalone figures and tables and have edited the paper accordingly. We have additionally removed Figure 3 as these results are presented with 95% CI in Table 3, and further in Tables 4 and 5. PR7. It is stated the presence of cancer was self-reported, It may be that more detail of this process would clarify potential biases, however given findings that the cancer group were, for example, admitted less to ICUs it is conceivable that those presenting with more severe symptoms at the time of recruitment were unable to self-report if they had cancer. Greater clarity on either the process of collecting relevant clinical details or discussion of potential implications of data collection approaches are needed. Response Cancer status was obtained from patients who were able to report or family members for many who could not. Those who could not report and did not have family were excluded from the analysis, and hence the potential for bias to arise due to lack of patients’ ability of self-report is likely mitigated in this analysis. However, we appreciate that this approach can lead to bias if those with missing self-reported cancer status have different characteristics than those who were included in the analysis. Appropriate analysis requires investigation of the mechanism that led to the missingness of the self-reported cancer status and this was beyond the scope of the current work. We have clarified this in the Methods section to make this more explicit and reduce the concern for bias. NB we note reference 14 to IDDO is incomplete and the link to the CRF as ref 14 in the Introduction is incorrect it should link to ref 15. We have corrected this in our revision. Thank you. [1] https://isaric.org/research/covid-19-clinical-research-resources/covid-19-crf/ View more View less Competing Interests No competing interest to disclose reply Respond Report a concern Fowler T. 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