Establishing the true death toll of a pandemic like COVID-19 is difficult, as laboratory testing is generally too limited to directly count the number of deaths that can be attributed to a particular pathogen. To overcome this, researchers analyse excess mortality – that is, they compare the observed number of deaths with the expected level based on trends in prior years. These techniques have been used for over 100 years to estimate the burden of pandemic influenza and became a popular way to estimate deaths due to the COVID-19 pandemic.

Excess mortality can also reveal the impact of COVID-19 on sub-populations with chronic conditions. For example, previous studies showed that deaths with diabetes, heart disease and Alzheimer’s disease listed as the primary cause of death increased during waves of COVID-19. Cancer deaths did not show such a pattern, however, despite some epidemiological studies identifying cancer as a risk factor for COVID-19 mortality.

To understand why this may be the case, Hansen et al. reviewed death certificates from different states in the United States during the first year of the pandemic. Their analyses of multiple-cause death records (listing cancer anywhere on the death certificate, not just as the primary cause of death) showed that death certificate coding practices during the pandemic did not explain the absence of excess cancer mortality. While a low level of excess mortality was detectable for cancers with longer life expectancy (breast cancer, for example), no elevation was observed for cancers with lower life expectancy, such as pancreatic cancer. The analyses demonstrate that the lack of excess mortality for especially deadly cancers can be explained through competing risks – in other words, the high risk of dying from the cancer itself vastly outweighs the additional risk posed by COVID-19.

These findings shed light on how competing mortality risks might mask the true impact of COVID-19 on cancer mortality and explain the apparent discrepancy between cohort studies and excess mortality studies. To fully comprehend the impact of COVID-19 on patients living with cancers, future research should look at the possibility of longer-term increases in cancer mortality due to late diagnosis during pandemic lockdowns, and an elevated risk of severe illness.

The dominant risk factors for COVID-19 mortality have consistently been shown to be advanced age, male gender, and certain chronic diseases such as diabetes, obesity, and heart disease (Chavez-MacGregor et al., 2022; Rüthrich et al., 2021; Williamson et al., 2020). Cancer has also been identified as a high-risk condition based on case-control and cohort studies, although these studies have provided conflicting results. In a large cohort study of ~500,000 COVID-19 inpatients, only cancer patients under recent treatment were at increased risk of COVID-19-related deaths (OR = 1.7) relative to non-cancer patients (Chavez-MacGregor et al., 2022). Conversely, a smaller European study of 3000 COVID-19 inpatients found that cancer was not a risk factor (Rüthrich et al., 2021), as did an international, multicenter study of 4000 confirmed COVID-19 inpatients (Raad et al., 2023). More recently a meta-analysis of 35 studies from Europe, North America, and Asia found a twofold increased risk of COVID-19 mortality among cancer patients (Di Felice et al., 2022). Similarly, a large analysis from the UK found that the risk of COVID-19 mortality for cancer patients had declined over the course of the pandemic but remained 2.5 times higher than for non-cancer patients into 2022 (Starkey et al., 2023). Taken together, such observational studies provide a mixed picture of cancer as a COVID-19 mortality risk factor, with several studies reporting that controlling for important factors such as age is a challenge. Furthermore, cancer is often considered as a single disease category despite the diversity of conditions and patients represented.

Further evidence for the relationship between cancer and COVID-19 comes from population-level analysis of vital statistics. A recent US study showed no elevation in underlying cancer deaths concomitant with COVID-19 waves, in stark contrast to the sharp rise in mortality from other chronic diseases (Lee et al., 2023a). In several other countries, including Sweden, Italy, Latvia, Brazil, England, and Wales, underlying cancer mortality was found to be stable or decreasing during the first year of the pandemic (Alicandro et al., 2023; Fernandes et al., 2021; Gobiņa et al., 2022; Grande et al., 2022; Kontopantelis et al., 2022; Lundberg et al., 2023). Further, an excess mortality study of 240,000 cancer patients in Belgium found a 33% rise in mortality in April 2020, but concluded that this was no different from the rise observed in the general population (Silversmit et al., 2021). The apparent lack of association between cancer mortality and COVID-19 on a population-level raises the question of the true relationship between cancer and COVID-19.

The relationship between these two diseases could occur via multiple biological mechanisms. First, immunosuppression in cancer patients could increase susceptibility to SARS-CoV-2 infection and/or risk of severe clinical outcome upon infection. Conversely, immunosuppression could act as a protective factor in the face of a severe respiratory infection that kills by over-stimulating the immune system – the immune incompetence rescue hypothesis (Reichert et al., 2004). This hypothesis was put forward to explain the observed absence in excess cancer mortality during the 1968 influenza pandemic, a departure from elevated mortality seen for other high-risk conditions such as heart disease and diabetes (Reichert et al., 2004). A further mechanism that could affect the observed relationship between cancer deaths and COVID-19 is changing guidelines for establishing the primary cause of death. Coding guidelines evolved throughout the pandemic as testing for SARS-CoV-2 infection became more widespread, which presumably affected vital statistics studies.

To further elucidate the relationship between cancer mortality and COVID-19 on a population level, we analyzed US vital statistics in detail to understand the potential role of death certificate coding changes during the pandemic and explored putative differences in mortality patterns between different types of cancer. We considered death certificates listing cancer as the underlying cause (UC) of death or anywhere on the death certificate (multiple cause [MC]). Assuming there is a high propensity to attribute a primary code of COVID-19 during the pandemic in any patient with COVID-19, deaths among individuals with both cancer and COVID-19 near the time of death would be coded as UC COVID-19. However, cancer should still be captured in the MC data, and thus, analysis of MC death data should control for any changes in death certificate coding practices during the pandemic (Fedeli et al., 2024). The US provides a particularly useful case study as the timing of COVID-19 waves varied considerably between states, so that elevations in cancer deaths, should they exist, should also be heterogeneous. For comparison, we also assessed population-level excess mortality patterns for other chronic conditions such as diabetes, ischemic heart disease (IHD), kidney disease, and Alzheimer’s, for which the association with COVID-19 is less debated.

Cancer is generally thought of as a risk factor for severe COVID-19 outcomes, yet observational studies have produced conflicting evidence. With recent availability of more detailed US vital statistics data, we used statistical time series approaches to generate excess mortality estimates for MC death data, different types of cancer, and several geographical locations during 2020. We accounted for potential changes in coding practices during the pandemic, for instance capturing a COVID-19 patient with cancer whose death may have been coded as an underlying COVID-19 death and not a cancer death. Based on MC death data, we estimated 13,600 national COVID-19-related excess cancer deaths, which aligns well with reporting on death certificate data, where 13,400 deaths are ascribed to COVID-19 in cancer patients (Appendix 1—figure 9; Fedeli et al., 2024). Yet these deaths only represent a 3% elevation over the expected baseline cancer mortality. Percent mortality elevation was measurably higher for less deadly cancers (breast and colorectal) than cancers with a poor 5-year survival (lung and pancreatic). Consistent with other studies (Chavez-MacGregor et al., 2022; Han et al., 2022b; Rüthrich et al., 2021; Williamson et al., 2020), we found that the largest mortality increase for specific cancer types was seen in hematological cancers with a 7% elevation over baseline in national data. Across the board, the largest elevations in cancer mortality were observed in the states most impacted by the first year of the COVID-19 pandemic (e.g. NY), lending support to the specificity of our excess mortality approach.

In contrast to cancer, we observed substantial COVID-19-related excess mortality for diabetes and Alzheimer’s, temporally and geographically consistent with the three-wave ‘signature’ pattern observed in reported COVID-19 cases and deaths across the US. To investigate whether demographic differences in underlying patient populations (age distribution, population size, and baseline risk of death due to chronic condition) could explain differences in excess mortality during the pandemic, we ran a simple demographic model for each condition – first assuming the condition in and of itself was not a risk factor for COVID-19-related mortality (null hypothesis). Doing so we found that the rise in cancer deaths during COVID-19 was expected to remain low compared to these other chronic conditions, largely driven by the higher risk of death from cancer itself compared to diabetes and Alzheimer’s. These demographic projections illustrate the importance of competing risks (Figure 6), where the risk of cancer death predominates over the risk of COVID-19 death in 2020. This is exacerbated in cancers with high mortality rates. For instance, even if pancreatic cancer had in fact doubled the risk of dying of COVID-19 (IFR = 4.2% vs 2.1%), we would only expect a rise in excess mortality around 0.4% during the pandemic (Table 3), while the 2019 baseline risk of death for pancreatic cancer itself is over 60% (Figure 6). On the other hand, for conditions with a lower baseline level mortality, such as diabetes, we expect substantial COVID-19-driven elevations in mortality.

Figure 6

Download asset Open asset

Our analysis revealed interesting differences between types of cancers. Both nationally and at the state level, the observed excess mortality for non-hematological cancers was consistent with a one- to twofold increase in COVID-19 mortality risk in patients with these types of cancer. Importantly, our analysis ignores any behavioral effects associated with the pandemic. It is conceivable that cancer patients may have shielded themselves from COVID-19 more than the average person in 2020. Our projections assume an average risk of infection for a typical individual over 65 years as there is no serologic data on infection attack rates for specific clinical population subgroups (of any age). If shielding from exposure to SARS-CoV-2 was high among cancer patients, our projections of cancer excess mortality during the pandemic would be inflated. In other words, if shielding was particularly pronounced, cancer may conceivably be a higher risk factor than shown here. Retrospective serologic analysis of banked sera from the first year of the pandemic, broken down by underlying comorbidities, may shed light on whether infection risk may have varied by chronic condition.

State-level mortality patterns can potentially provide complementary insights on the question of shielding. Because NY state experienced the earliest and most intense COVID-19 wave of the US, with over 20% of the population infected in spring 2020 (Stadlbauer et al., 2021), and because social distancing did not come into effect until March 2020, shielding would have had a more limited impact there than in other states. Thus, a biological relationship between cancer and COVID-19 would have been most dramatic in NY in spring 2020. Indeed, cancer excess mortality was exacerbated in NY, including an 9–16% increase in colorectal and breast cancer mortality, consistent with a twofold increase in COVID-19 mortality risk from these cancers, and a 10% increase in hematological cancers, consistent with a fivefold increase in COVID-19 mortality risk. In NY, the absence of excess mortality in lethal cancers, such as pancreatic and lung cancers (1% over baseline) are, as discussed above, still consistent with what would be expected under a high competing risk situation.

We used diabetes and Alzheimer’s as positive controls for a known biological association between COVID-19 and chronic conditions. Diabetes stood out in our analyses with the highest absolute and relative increases in excess mortality during the pandemic. The magnitude of the mortality increases, both nationally and at the state level, were close to what would be expected if diabetes increased COVID-19 mortality by fivefold. Many studies have shown that diabetes increases the risk of COVID-19 mortality, with an effect size around 2 (Williamson et al., 2020; Huang et al., 2020; Kastora et al., 2022). Impaired immune function and chronic inflammation have been identified as mechanisms driving poor outcomes for diabetes patients (Figueroa-Pizano et al., 2021). The discrepancy between the observed excess and our expectations may come down to uncertainty in the SARS-CoV-2 infection rates assumed in our demographic model. The population living with diabetes is slightly younger than that of the other conditions (mean age, 58–60 years), while we used serologic infection rates reported for individuals over 65 years in our main analysis. The SARS-CoV-2 attack rate among those 50–64 years was 10.1% at the end of 2020, compared to 6.3% in individuals over 65 (Centers for Disease Control and Prevention, 2023). A sensitivity analysis using this higher attack rate in our demographic model lends more support to the hypothesis that diabetes increases COVID-19 mortality by twofold, rather than fivefold as found in our main analysis.

Our second positive control, Alzheimer’s, revealed surprising results. Although we observed significant excess mortality in MC Alzheimer’s data, it was still less than expected under the null hypothesis that Alzheimer’s was not a risk factor for COVID-19 mortality. This is unexpected in light of several observational studies that have shown Alzheimer’s to be a risk factor (Tahira et al., 2021; Wang et al., 2021; Zhang et al., 2021). As with cancer and diabetes, there is uncertainty in the SARS-CoV-2 infection rates used in the demographic model, due to the potential effect of shielding and the age-specific SARS-CoV-2 infection risk of the Alzheimer’s population. We estimated that the average age of the population living with Alzheimer’s disease was 80–81 years, and the infection rates for the general population over 65 years may not accurately reflect exposure in this subpopulation. Decreasing the attack rates by 20–30% (down to 4.4–5.0%) puts the observed estimates in the range of the expectations under the null hypothesis. Overall, given uncertainty in SARS-CoV-2 attack rates and the age and size of the population-at-risk for all studied conditions, our demographic model projections are not an exact tool to titrate excess mortality nor the RR associated with each condition. Our model merely serves as an illustration of the role of demography and competing risks.

Most vital statistics studies of the COVID-19 pandemic have relied on UC-specific deaths, which are prone to changes in coding practices. Our initial hypothesis going into this work was that coding changes associated with a better recognition of the impact of SARS-CoV-2 led to an underestimation of excess mortality from cancer, affecting our perception of the relationship between cancer and COVID-19. We certainly found an effect of coding changes, where for instance a drop in excess mortality in underlying cancer deaths turned into an increase in MC (any-listed) cancer deaths, particularly in the first COVID-19 pandemic wave. A similar observation was made by Fedeli et al., 2024. The impact of coding changes was also seen in mortality from other chronic conditions but was particularly important for cancer. Yet both the absolute and relative excess mortality elevation remained modest for cancer, even after adjustment for coding changes, highlighting the importance of additional mechanisms such as competing mortality risks between COVID-19 and cancer.

An interesting hypothesis was put forward 20 years ago proposing that immunosuppression from cancer may explain the lack of excess cancer mortality in the 1968 influenza pandemic – the immune incompetence rescue hypothesis (Reichert et al., 2004). This hypothesis contends that it is a detrimental immune response that leads to influenza death. A similar hypothesis was put forward to explain the extreme mortality in young healthy adults in the 1918 pandemic (Short et al., 2018). However, observational studies have found that patients with hematological cancers have twice the risk of dying compared to patients without cancer, likely due to the immunosuppression associated with their malignancy and treatment (Han et al., 2022a; Starkey et al., 2023; Williamson et al., 2020). Under the immune incompetence rescue hypothesis, hematological cancers would be expected to have the lowest excess mortality of all types of cancers. Our excess mortality analysis reveals instead that hematological cancers were the most impacted by the pandemic, relative to other types of cancer, with observed mortality patterns consistent with a fivefold increase in risk of COVID-19 death in patients with hematological cancers. Overall, we do not find any support for the immune competence rescue hypothesis.

Our study is subject to limitations. First, we did not study the potential long-term consequences of the pandemic on cancer care, which may have resulted in avoidance of the healthcare system for diagnosis or treatment. We did not see any delayed pandemic effect on mortality from pancreatic cancer, which may have manifested in 2020 given the very low survival rate of this cancer (Lemanska et al., 2023), but we cannot rule out longer-term effects on breast or colorectal cancers that would not be seen until 2021 or later (Doan et al., 2023; Han et al., 2023; Haribhai et al., 2023; Lee et al., 2023b; Nascimento de Lima et al., 2023; Nickson et al., 2023; Nonboe et al., 2023; Tope et al., 2023). Interestingly, in the US, all-cause underlying cancer mortality rates do not appear to rise between 2020 and 2023 (Appendix 1—figure 10), but data prior to the pandemic show a rise in cancer incidence, largely driven by increasing cancer rates in younger adults (Han et al., 2023; Siegel et al., 2024). Additional years of data will be important to evaluate the long-term impacts of the COVID-19 pandemic and these changing demographics on cancer mortality rates. Additional years of data will also be important for assessing the impact of vaccination on the relationship between cancer and COVID-19; there is evidence that vaccines may be less immunogenic in patients with cancer compared to those without (Seneviratne et al., 2022). Another limitation of our study is the reliance on mortality as an outcome, and not the risk of COVID-19-related hospitalization and morbidity, and Long COVID in cancer patients. A small US study reported that 60% of cancer patients suffered Long COVID symptoms (Dagher et al., 2023). Future analyses using hospitalization data and electronic medical records may provide additional insights on how different cancer stages or other comorbidities may contribute to increased risk of severe COVID-19 outcomes. Lastly, a few methodological limitations are worth raising. Though it was important to assess excess mortality in state-level data because of asynchrony in pandemic waves, confidence intervals in state-level estimates were large, particularly for specific types of cancers, affecting significance levels. Additional methodological limitations relate to our demographic model, especially as regards assumptions about SARS-CoV-2 infection rates in populations of different ages and with different chronic conditions. Importantly, our conclusions regarding the importance of competing risks are robust to these assumptions. Lastly, our study is a time-trend analysis and – like cohort and case-control studies – correlation does not necessarily imply causation. However, the intensity and brevity of COVID-19 pandemic waves in space and time lends support to our analyses.

Individual-level mortality data with exact date of death were obtained from the National Center for Healthcare Statistics (NCHS). Individual-level data with exact date of death are not publicly available due to privacy concerns, but descriptive characteristics have been summarized in Table 1 and Appendix 1—table 1. Researchers wishing to access these data must submit an application to NCHS following the instructions provided here: https://www.cdc.gov/nchs/nvss/nvss-restricted-data.htm. Individual-level data without the exact data of death are publicly available and can be downloaded from here: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. All analyses shown in this paper can be replicated based on the aggregated data and code posted at the following public GitHub repository: https://github.com/chelsea-hansen/Disentangling-the-relationship-between-cancer-mortality-and-COVID-19 (copy archived at Hansen, 2024). Additional weekly, aggregated mortality data for trends in cancer, diabetes, and Alzheimer’s mortality post-2020 are publicly available through CDC Wonder and have been included in the GitHub repository. Data used for the demographic model were gathered from publicly available sources. These data, along with the code for the model, have also been posted to the GitHub repository. Weekly, state-level data on recorded COVID-19 cases and deaths are publicly available. Data were downloaded from here: https://data.cdc.gov/Case-Surveillance/Weekly-United-States-COVID-19-Cases-and-Deaths-by-/pwn4-m3yp and have also been posted as a .csv file to the GitHub repository.

1. 1. Alicandro G
   2. La Vecchia C
   3. Islam N
   4. Pizzato M

   (2023) A comprehensive analysis of all-cause and cause-specific excess deaths in 30 countries during 2020

   European Journal of Epidemiology 38:1153–1164.

   https://doi.org/10.1007/s10654-023-01044-x

   • PubMed
   • Google Scholar
2. Website

   1. CDC, Division of Population Health

   (2022) Chronic disease indicators. Diabetes among adults

   Accessed May 6, 2024.

   https://cdi.cdc.gov/?location=US&category=DIA&indicators=DIA01
3. Data

   1. Centers for Disease Control and Prevention

   (authors) (2022) Weekly united states COVID-19 cases and deaths by state - ARCHIVED

   Public Domain U.S. Government.

   https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html
4. Website

   1. Centers for Disease Control and Prevention

   (2023) COVID-19 serology surveillance

   Accessed May 6, 2024.

   https://www.cdc.gov/coronavirus/2019-ncov/covid-data/serology.html
5. 1. Chavez-MacGregor M
   2. Lei X
   3. Zhao H
   4. Scheet P
   5. Giordano SH

   (2022) Evaluation of COVID-19 mortality and adverse outcomes in US patients with or without cancer

   JAMA Oncology 8:69–78.

   https://doi.org/10.1001/jamaoncol.2021.5148

   • PubMed
   • Google Scholar
6. 1. COVID-19 Forecasting Team

   (2022) Variation in the COVID-19 infection-fatality ratio by age, time, and geography during the pre-vaccine era: a systematic analysis

   Lancet 399:1469–1488.

   https://doi.org/10.1016/S0140-6736(21)02867-1

   • PubMed
   • Google Scholar
7. 1. Dagher H
   2. Chaftari AM
   3. Subbiah IM
   4. Malek AE
   5. Jiang Y
   6. Lamie P
   7. Granwehr B
   8. John T
   9. Yepez E
   10. Borjan J
   11. Reyes-Gibby C
   12. Flores M
   13. Khawaja F
   14. Pande M
   15. Ali N
   16. Rojo R
   17. Karp DD
   18. Chaftari P
   19. Hachem R
   20. Raad II

   (2023) Long COVID in cancer patients: Preponderance of symptoms in majority of patients over long time period

   eLife 12:e81182.

   https://doi.org/10.7554/eLife.81182

   • PubMed
   • Google Scholar
8. 1. Dhana K
   2. Beck T
   3. Desai P
   4. Wilson RS
   5. Evans DA
   6. Rajan KB

   (2023) Prevalence of Alzheimer’s disease dementia in the 50 US states and 3142 counties: A population estimate using the 2020 bridged-race postcensal from the National Center for Health Statistics

   Alzheimer’s & Dementia 19:4388–4395.

   https://doi.org/10.1002/alz.13081

   • PubMed
   • Google Scholar
9. 1. Di Felice G
   2. Visci G
   3. Teglia F
   4. Angelini M
   5. Boffetta P

   (2022) Effect of cancer on outcome of COVID-19 patients: A systematic review and meta-analysis of studies of unvaccinated patients

   eLife 11:e74634.

   https://doi.org/10.7554/eLife.74634

   • PubMed
   • Google Scholar
10. 1. Doan C
    2. Li S
    3. Goodwin JS

    (2023) Breast and lung cancer screening among medicare enrollees during the COVID-19 pandemic

    JAMA Network Open 6:e2255589.

    https://doi.org/10.1001/jamanetworkopen.2022.55589

    • PubMed
    • Google Scholar
11. 1. Fedeli U
    2. Barbiellini Amidei C
    3. Han X
    4. Jemal A

    (2024) Changes in cancer-related mortality during the COVID-19 pandemic in the United States

    Journal of the National Cancer Institute 116:167–169.

    https://doi.org/10.1093/jnci/djad191

    • PubMed
    • Google Scholar
12. 1. Fernandes GA
    2. Junior APN
    3. Azevedo E Silva G
    4. Feriani D

    (2021) Excess mortality by specific causes of deaths in the city of São Paulo

    PLOS ONE 16:e0252238.

    https://doi.org/10.1371/journal.pone.0252238

    • Google Scholar
13. 1. Figueroa-Pizano MD
    2. Campa-Mada AC
    3. Carvajal-Millan E
    4. Martinez-Robinson KG
    5. Chu AR

    (2021) The underlying mechanisms for severe COVID-19 progression in people with diabetes mellitus: a critical review

    AIMS Public Health 8:720–742.

    https://doi.org/10.3934/publichealth.2021057

    • PubMed
    • Google Scholar
14. 1. Gobiņa I
    2. Avotiņš A
    3. Kojalo U
    4. Strēle I
    5. Pildava S
    6. Villeruša A
    7. Briģis Ģ

    (2022) Excess mortality associated with the COVID-19 pandemic in Latvia: A population-level analysis of all-cause and noncommunicable disease deaths in 2020

    BMC Public Health 22:1109.

    https://doi.org/10.1186/s12889-022-13491-4

    • PubMed
    • Google Scholar
15. 1. Grande E
    2. Fedeli U
    3. Pappagallo M
    4. Crialesi R
    5. Marchetti S
    6. Minelli G
    7. Iavarone I
    8. Frova L
    9. Onder G
    10. Grippo F

    (2022) Variation in cause-specific mortality rates in Italy during the first wave of the COVID-19 pandemic: A study based on nationwide data

    International Journal of Environmental Research and Public Health 19:805.

    https://doi.org/10.3390/ijerph19020805

    • PubMed
    • Google Scholar
16. 1. Han X
    2. Hu X
    3. Zhao J
    4. Jemal A
    5. Yabroff KR

    (2022a) Identification of deaths caused by cancer and COVID-19 in the US during march to december 2020

    JAMA Oncology 8:1696–1698.

    https://doi.org/10.1001/jamaoncol.2022.4315

    • PubMed
    • Google Scholar
17. 1. Han S
    2. Zhuang Q
    3. Chiang J
    4. Tan SH
    5. Chua GWY
    6. Xie C
    7. Chua MLK
    8. Soon YY
    9. Yang VS

    (2022b) Impact of cancer diagnoses on the outcomes of patients with COVID-19: a systematic review and meta-analysis

    BMJ Open 12:e044661.

    https://doi.org/10.1136/bmjopen-2020-044661

    • PubMed
    • Google Scholar
18. 1. Han X
    2. Yang NN
    3. Nogueira L
    4. Jiang C
    5. Wagle NS
    6. Zhao J
    7. Shi KS
    8. Fan Q
    9. Schafer E
    10. Yabroff KR
    11. Jemal A

    (2023) Changes in cancer diagnoses and stage distribution during the first year of the COVID-19 pandemic in the USA: A cross-sectional nationwide assessment

    The Lancet. Oncology 24:855–867.

    https://doi.org/10.1016/S1470-2045(23)00293-0

    • PubMed
    • Google Scholar
19. Software

    1. Hansen CL

    (2024) Disentangling-the-relationship-between-cancer-mortality-and-COVID-19, software heritage, version swh:1:rev:1a94a1aee1d9326695e82c357bad65b3404561fa

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:b0e0fba6d9d46f90acc116d304af4a4e4cd0e4a7;origin=https://github.com/chelsea-hansen/Disentangling-the-relationship-between-cancer-mortality-and-COVID-19;visit=swh:1:snp:c4099c3588cfb454dbcc02fa65392cd20cf92ba3;anchor=swh:1:rev:1a94a1aee1d9326695e82c357bad65b3404561fa
20. 1. Haribhai S
    2. Bhatia K
    3. Shahmanesh M

    (2023) Global elective breast- and colorectal cancer surgery performance backlogs, attributable mortality and implemented health system responses during the COVID-19 pandemic: A scoping review

    PLOS Global Public Health 3:e0001413.

    https://doi.org/10.1371/journal.pgph.0001413

    • PubMed
    • Google Scholar
21. 1. Huang I
    2. Lim MA
    3. Pranata R

    (2020) Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia - A systematic review, meta-analysis, and meta-regression

    Diabetes & Metabolic Syndrome 14:395–403.

    https://doi.org/10.1016/j.dsx.2020.04.018

    • PubMed
    • Google Scholar
22. 1. Islam N
    2. Shkolnikov VM
    3. Acosta RJ
    4. Klimkin I
    5. Kawachi I
    6. Irizarry RA
    7. Alicandro G
    8. Khunti K
    9. Yates T
    10. Jdanov DA
    11. White M
    12. Lewington S
    13. Lacey B

    (2021) Excess deaths associated with covid-19 pandemic in 2020: age and sex disaggregated time series analysis in 29 high income countries

    BMJ 373:1137.

    https://doi.org/10.1136/bmj.n1137

    • PubMed
    • Google Scholar
23. 1. Karlinsky A
    2. Kobak D

    (2021) Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset

    eLife 10:e69336.

    https://doi.org/10.7554/eLife.69336

    • PubMed
    • Google Scholar
24. 1. Kastora S
    2. Patel M
    3. Carter B
    4. Delibegovic M
    5. Myint PK

    (2022) Impact of diabetes on COVID-19 mortality and hospital outcomes from a global perspective: An umbrella systematic review and meta-analysis

    Endocrinology, Diabetes & Metabolism 5:e00338.

    https://doi.org/10.1002/edm2.338

    • PubMed
    • Google Scholar
25. 1. Kontopantelis E
    2. Mamas MA
    3. Webb RT
    4. Castro A
    5. Rutter MK
    6. Gale CP
    7. Ashcroft DM
    8. Pierce M
    9. Abel KM
    10. Price G
    11. Faivre-Finn C
    12. Van Spall HGC
    13. Graham MM
    14. Morciano M
    15. Martin GP
    16. Sutton M
    17. Doran T

    (2022) Excess years of life lost to COVID-19 and other causes of death by sex, neighbourhood deprivation, and region in England and Wales during 2020: A registry-based study

    PLOS Medicine 19:e1003904.

    https://doi.org/10.1371/journal.pmed.1003904

    • PubMed
    • Google Scholar
26. 1. Lee WE
    2. Woo Park S
    3. Weinberger DM
    4. Olson D
    5. Simonsen L
    6. Grenfell BT
    7. Viboud C

    (2023a) Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022

    eLife 12:e77562.

    https://doi.org/10.7554/eLife.77562

    • PubMed
    • Google Scholar
27. 1. Lee R
    2. Xu W
    3. Dozier M
    4. McQuillan R
    5. Theodoratou E
    6. Figueroa J

    (2023b) UNCOVER and the International Partnership for Resilience in Cancer Systems (I-PaRCS), Breast Cancer Working Group 2. A rapid review on the COVID-19’s global impact on breast cancer screening participation rates and volumes from January-December 2020

    eLife 12:e85680.

    https://doi.org/10.7554/eLife.85680

    • Google Scholar
28. 1. Lemanska A
    2. Andrews C
    3. Fisher L
    4. Bacon S
    5. Frampton AE
    6. Mehrkar A
    7. Inglesby P
    8. Davy S
    9. Roberts K
    10. Patalay P
    11. Goldacre B
    12. MacKenna B
    13. Collaborative O
    14. Walker AJ

    (2023) Healthcare in England was affected by the COVID-19 pandemic across the pancreatic cancer pathway: A cohort study using OpenSAFELY-TPP

    eLife 12:e85332.

    https://doi.org/10.7554/eLife.85332

    • PubMed
    • Google Scholar
29. 1. Lundberg CE
    2. Santosa A
    3. Björk J
    4. Brandén M
    5. Cronie O
    6. Lindgren M
    7. Edqvist J
    8. Åberg M
    9. Adiels M
    10. Rosengren A

    (2023) Age and sex differences in cause-specific excess mortality and years of life lost associated with COVID-19 infection in the Swedish population

    European Journal of Public Health 33:916–922.

    https://doi.org/10.1093/eurpub/ckad086

    • PubMed
    • Google Scholar
30. 1. Msemburi W
    2. Karlinsky A
    3. Knutson V
    4. Aleshin-Guendel S
    5. Chatterji S
    6. Wakefield J

    (2023) The WHO estimates of excess mortality associated with the COVID-19 pandemic

    Nature 613:130–137.

    https://doi.org/10.1038/s41586-022-05522-2

    • PubMed
    • Google Scholar
31. 1. Nascimento de Lima P
    2. van den Puttelaar R
    3. Hahn AI
    4. Harlass M
    5. Collier N
    6. Ozik J
    7. Zauber AG
    8. Lansdorp-Vogelaar I
    9. Rutter CM

    (2023) Projected long-term effects of colorectal cancer screening disruptions following the COVID-19 pandemic

    eLife 12:e85264.

    https://doi.org/10.7554/eLife.85264

    • PubMed
    • Google Scholar
32. Website

    1. National Cancer Institute

    (2024) Cancer stat facts

    SEER. Accessed May 6, 2024.

    https://seer.cancer.gov/statfacts/
33. 1. Nickson C
    2. Smith MA
    3. Feletto E
    4. Velentzis LS
    5. Broun K
    6. Deij S
    7. Grogan P
    8. Hall M
    9. He E
    10. St John DJ
    11. Lew J-B
    12. Procopio P
    13. Simms KT
    14. Worthington J
    15. Mann GB
    16. Canfell K

    (2023) A modelled evaluation of the impact of COVID-19 on breast, bowel, and cervical cancer screening programmes in Australia

    eLife 12:e82818.

    https://doi.org/10.7554/eLife.82818

    • PubMed
    • Google Scholar
34. 1. Nonboe MH
    2. Napolitano G
    3. Schroll JB
    4. Vejborg I
    5. Waldstrøm M
    6. Lynge E

    (2023) Impact of COVID-19 pandemic on breast and cervical cancer screening in Denmark: A register-based study

    eLife 12:e81605.

    https://doi.org/10.7554/eLife.81605

    • PubMed
    • Google Scholar
35. 1. Raad II
    2. Hachem R
    3. Masayuki N
    4. Datoguia T
    5. Dagher H
    6. Jiang Y
    7. Subbiah V
    8. Siddiqui B
    9. Bayle A
    10. Somer R
    11. Fernández Cruz A
    12. Gorak E
    13. Bhinder A
    14. Mori N
    15. Hamerschlak N
    16. Shelanski S
    17. Dragovich T
    18. Vong Kiat YE
    19. Fakhreddine S
    20. Pierre AH
    21. Chemaly RF
    22. Mulanovich V
    23. Adachi J
    24. Borjan J
    25. Khawaja F
    26. Granwehr B
    27. John T
    28. Yepez EY
    29. Torres HA
    30. Ammakkanavar NR
    31. Yibirin M
    32. Reyes-Gibby CC
    33. Pande M
    34. Ali N
    35. Rojo RD
    36. Ali SM
    37. Deeba RE
    38. Chaftari P
    39. Matsuo T
    40. Ishikawa K
    41. Hasegawa R
    42. Aguado-Noya R
    43. García AG
    44. Puchol CT
    45. Lee DG
    46. Slavin M
    47. Teh B
    48. Arias CA
    49. Kontoyiannis DP
    50. Malek AE
    51. Chaftari A-M
    52. Data-Driven Determinants for COVID-19 Oncology Discovery Effort (D3CODE) Team

    (2023) International multicenter study comparing COVID-19 in patients with cancer to patients without cancer: Impact of risk factors and treatment modalities on survivorship

    eLife 12:e81127.

    https://doi.org/10.7554/eLife.81127

    • PubMed
    • Google Scholar
36. 1. Rajan KB
    2. Weuve J
    3. Barnes LL
    4. McAninch EA
    5. Wilson RS
    6. Evans DA

    (2021) Population estimate of people with clinical Alzheimer’s disease and mild cognitive impairment in the United States (2020-2060)

    Alzheimer’s & Dementia 17:1966–1975.

    https://doi.org/10.1002/alz.12362

    • PubMed
    • Google Scholar
37. 1. Reichert TA
    2. Simonsen L
    3. Sharma A
    4. Pardo SA
    5. Fedson DS
    6. Miller MA

    (2004) Influenza and the winter increase in mortality in the United States, 1959-1999

    American Journal of Epidemiology 160:492–502.

    https://doi.org/10.1093/aje/kwh227

    • PubMed
    • Google Scholar
38. 1. Rüthrich MM
    2. Giessen-Jung C
    3. Borgmann S
    4. Classen AY
    5. Dolff S
    6. Grüner B
    7. Hanses F
    8. Isberner N
    9. Köhler P
    10. Lanznaster J
    11. Merle U
    12. Nadalin S
    13. Piepel C
    14. Schneider J
    15. Schons M
    16. Strauss R
    17. Tometten L
    18. Vehreschild JJ
    19. von Lilienfeld-Toal M
    20. Beutel G
    21. Wille K
    22. LEOSS Study Group

    (2021) COVID-19 in cancer patients: clinical characteristics and outcome-an analysis of the LEOSS registry

    Annals of Hematology 100:383–393.

    https://doi.org/10.1007/s00277-020-04328-4

    • PubMed
    • Google Scholar
39. 1. Seneviratne SL
    2. Yasawardene P
    3. Wijerathne W
    4. Somawardana B

    (2022) COVID-19 vaccination in cancer patients: A narrative review

    The Journal of International Medical Research 50:3000605221086155.

    https://doi.org/10.1177/03000605221086155

    • PubMed
    • Google Scholar
40. 1. Short KR
    2. Kedzierska K
    3. van de Sandt CE

    (2018) Back to the future: Lessons learned from the 1918 influenza pandemic

    Frontiers in Cellular and Infection Microbiology 8:343.

    https://doi.org/10.3389/fcimb.2018.00343

    • PubMed
    • Google Scholar
41. 1. Siegel RL
    2. Giaquinto AN
    3. Jemal A

    (2024) Cancer statistics, 2024

    CA 74:12–49.

    https://doi.org/10.3322/caac.21820

    • PubMed
    • Google Scholar
42. 1. Silversmit G
    2. Verdoodt F
    3. Van Damme N
    4. De Schutter H
    5. Van Eycken L

    (2021) Excess mortality in a nationwide cohort of cancer patients during the initial phase of the COVID-19 pandemic in Belgium

    Cancer Epidemiology, Biomarkers & Prevention 30:1615–1619.

    https://doi.org/10.1158/1055-9965.EPI-21-0230

    • PubMed
    • Google Scholar
43. 1. Stadlbauer D
    2. Tan J
    3. Jiang K
    4. Hernandez MM
    5. Fabre S
    6. Amanat F
    7. Teo C
    8. Arunkumar GA
    9. McMahon M
    10. Capuano C
    11. Twyman K
    12. Jhang J
    13. Nowak MD
    14. Simon V
    15. Sordillo EM
    16. van Bakel H
    17. Krammer F

    (2021) Repeated cross-sectional sero-monitoring of SARS-CoV-2 in New York city

    Nature 590:146–150.

    https://doi.org/10.1038/s41586-020-2912-6

    • PubMed
    • Google Scholar
44. 1. Starkey T
    2. Ionescu MC
    3. Tilby M
    4. Little M
    5. Burke E
    6. Fittall MW
    7. Khan S
    8. Liu JKH
    9. Platt JR
    10. Mew R
    11. Tripathy AR
    12. Watts I
    13. Williams ST
    14. Appanna N
    15. Al-Hajji Y
    16. Barnard M
    17. Benny L
    18. Burnett A
    19. Bytyci J
    20. Cattell EL
    21. Cheng V
    22. Clark JJ
    23. Eastlake L
    24. Gerrand K
    25. Ghafoor Q
    26. Grumett S
    27. Harper-Wynne C
    28. Kahn R
    29. Lee AJX
    30. Lomas O
    31. Lydon A
    32. Mckenzie H
    33. NCRI Consumer Forum
    34. Panneerselvam H
    35. Pascoe JS
    36. Patel G
    37. Patel V
    38. Potter VA
    39. Randle A
    40. Rigg AS
    41. Robinson TM
    42. Roylance R
    43. Roques TW
    44. Rozmanowski S
    45. Roux RL
    46. Shah K
    47. Sheehan R
    48. Sintler M
    49. Swarup S
    50. Taylor H
    51. Tillett T
    52. Tuthill M
    53. Williams S
    54. Ying Y
    55. Beggs A
    56. Iveson T
    57. Lee SM
    58. Middleton G
    59. Middleton M
    60. Protheroe A
    61. Fowler T
    62. Johnson P
    63. Lee LYW

    (2023) A population-scale temporal case-control evaluation of COVID-19 disease phenotype and related outcome rates in patients with cancer in England (UKCCP)

    Scientific Reports 13:11327.

    https://doi.org/10.1038/s41598-023-36990-9

    • PubMed
    • Google Scholar
45. 1. Tahira AC
    2. Verjovski-Almeida S
    3. Ferreira ST

    (2021) Dementia is an age-independent risk factor for severity and death in COVID-19 inpatients

    Alzheimer’s & Dementia 17:1818–1831.

    https://doi.org/10.1002/alz.12352

    • PubMed
    • Google Scholar
46. 1. Tope P
    2. Farah E
    3. Ali R
    4. El-Zein M
    5. Miller WH
    6. Franco EL

    (2023) The impact of lag time to cancer diagnosis and treatment on clinical outcomes prior to the COVID-19 pandemic: A scoping review of systematic reviews and meta-analyses

    eLife 12:e81354.

    https://doi.org/10.7554/eLife.81354

    • PubMed
    • Google Scholar
47. Website

    1. U.S. Cancer Statistics Working Group

    (2023) U.S. Cancer Statistics Data Visualizations Tool, based on 2022 submission data (1999-2020): U.S. Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute

    Accessed May 6, 2024.

    https://www.cdc.gov/cancer/dataviz
48. 1. Wang Q
    2. Davis PB
    3. Gurney ME
    4. Xu R

    (2021) COVID-19 and dementia: Analyses of risk, disparity, and outcomes from electronic health records in the US

    Alzheimer’s & Dementia 17:1297–1306.

    https://doi.org/10.1002/alz.12296

    • PubMed
    • Google Scholar
49. 1. Williamson EJ
    2. Walker AJ
    3. Bhaskaran K
    4. Bacon S
    5. Bates C
    6. Morton CE
    7. Curtis HJ
    8. Mehrkar A
    9. Evans D
    10. Inglesby P
    11. Cockburn J
    12. McDonald HI
    13. MacKenna B
    14. Tomlinson L
    15. Douglas IJ
    16. Rentsch CT
    17. Mathur R
    18. Wong AYS
    19. Grieve R
    20. Harrison D
    21. Forbes H
    22. Schultze A
    23. Croker R
    24. Parry J
    25. Hester F
    26. Harper S
    27. Perera R
    28. Evans SJW
    29. Smeeth L
    30. Goldacre B

    (2020) Factors associated with COVID-19-related death using OpenSAFELY

    Nature 584:430–436.

    https://doi.org/10.1038/s41586-020-2521-4

    • PubMed
    • Google Scholar
50. 1. Yeh HC
    2. Golozar A
    3. Brancati FL

    (2018)

    Diabetes in America

    Chapter 29: Cancer and diabetes, Diabetes in America, Bethesda, National Institute of Diabetes and Digestive and Kidney Diseases (US).

    • Google Scholar
51. 1. Zhang Q
    2. Schultz JL
    3. Aldridge GM
    4. Simmering JE
    5. Kim Y
    6. Ogilvie AC
    7. Narayanan NS

    (2021) COVID-19 case fatality and Alzheimer’s disease

    Journal of Alzheimer’s Disease 84:1447–1452.

    https://doi.org/10.3233/JAD-215161

    • PubMed
    • Google Scholar

Author details

1. Chelsea L Hansen

   1. Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, United States
   2. PandemiX Center, Dept of Science & Environment, Roskilde University, Roskilde, Denmark
   3. Brotman Baty Institute, University of Washington, Seattle, United States

   Contribution

   Data curation, Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   chelsea.hansen@nih.gov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4526-6772

2. Cécile Viboud

   Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3243-4711

3. Lone Simonsen

   1. Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, United States
   2. PandemiX Center, Dept of Science & Environment, Roskilde University, Roskilde, Denmark

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1535-8526

• 694

  views

• 50

  downloads

• 1

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.