open access publication

Preprint, 2024

Disentangling the relationship between cancer mortality and COVID-19 in the US

medRxiv, Page 2024.01.02.24300715, 10.1101/2024.01.02.24300715

Contributors

Hansen, Chelsea L 0000-0002-4526-6772 (Corresponding author) [1] [2] [3] Viboud, Cecile G [2] Simonsen, Lone 0000-0003-1535-8526 [2] [3]

Affiliations

  1. [1] University of Washington
  2. [NORA names: United States; America, North; OECD];
  3. [2] Fogarty International Center
  4. [NORA names: United States; America, North; OECD];
  5. [3] Roskilde University
  6. [NORA names: RUC Roskilde University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

Abstract Several countries have reported that deaths with a primary code of cancer did not rise during COVID-19 pandemic waves compared to baseline pre-pandemic levels. This is in apparent conflict with findings from cohort studies where cancer has been identified as a risk factor for COVID-19 mortality. Here we further elucidate the relationship between cancer mortality and COVID-19 on a population level in the US by testing the impact of death certificate coding changes during the pandemic and leveraging heterogeneity in pandemic intensity across US states. We computed excess mortality from weekly deaths during 2014-2020 nationally and for three states with distinct COVID-19 wave timing (NY, TX, and CA). We compared pandemic-related mortality patterns from underlying and multiple cause (MC) death data for six types of cancer and compared to that seen for chronic conditions such as diabetes and Alzheimer’s. Any death certificate coding changes should be eliminated by study of MC data. Nationally in 2020, we found only modest excess MC cancer mortality (∼13,600 deaths), representing a 3% elevation over baseline level. Mortality elevation was measurably higher for less deadly cancers (breast, colorectal, and hematologic, 2-7%) than cancers with a poor 5-year survival (lung and pancreatic, 0-1%). In comparison, there was substantial elevation in MC deaths from diabetes (37%) and Alzheimer’s (19%). Homing in on the intense spring 2020 COVID-19 wave in NY, mortality elevation was 1-16% for different types of cancer and 128% and 49% for diabetes and Alzheimer’s, respectively. To investigate the peculiar absence of excess mortality on deadly cancers, we implemented a demographic model and simulated the expected covid-related mortality using COVID-19 attack rates, life expectancy, population size and mean age for each chronic condition. This model indicates that these factors largely explain the considerable differences in observed excess mortality between these chronic conditions during the COVID-19 pandemic, even if cancer had increased the relative risk of mortality by a factor of 2 or 5. In conclusion, we found limited elevation in cancer mortality during COVID-19 waves, even after considering MC mortality, and this was especially pronounced for the deadliest cancers. Our demographic model predicted low expected excess mortality in populations living with certain types of cancer, even if cancer is a risk factor for COVID-19 fatality, due to competing mortality risk. We also find a moderate increase in excess mortality from hematological cancers, aligned with other types of observational studies. While our study concentrates on the immediate consequences of the COVID-19 pandemic on cancer mortality in 2020, further research should consider excess mortality in the complete pandemic period. Also, a study of the delayed impact of the pandemic on cancer mortality due to delayed diagnosis and treatment during the pandemic period is warranted.

Keywords

Abstract, Abstract Several countries, Alzheimer, COVID-19, COVID-19 attack rate, COVID-19 fatalities, COVID-19 mortality, COVID-19 pandemic, COVID-19 pandemic wave, COVID-19 wave, COVID-related mortality, MC data, MC death, NY, US, US states, absence, absence of excess mortality, age, apparent conflict, attack rate, baseline, baseline levels, cancer, cancer mortality, cause, changes, chronic conditions, code changes, cohort, cohort study, comparison, conditions, conflict, consequences, countries, data, deadliest cancers, death, death data, delayed diagnosis, delayed impact, demographic models, diabetes, diagnosis, elevation, excess mortality, expectations, factors, fatalities, findings, hematological cancers, heterogeneity, immediate consequences, impact, increase, intensity, intensive spring, levels, leverage heterogeneity, life, life expectancy, model, moderate increase, mortality, mortality patterns, mortality risk, multiple causes, observational study, pandemic, pandemic intensity, pandemic period, pandemic wave, patterns, period, population, population level, population size, pre-pandemic levels, rate, relationship, relative risk, relative risk of mortality, research, risk, risk factors, risk of mortality, several countries, size, spring, state, study, time, treatment, wave, wave time, weekly deaths

Funders

  • Danish National Research Foundation
  • Carlsberg Foundation

Data Provider: Digital Science