Annual Meeting of the NCI Cohort Consortium (Abstract Submission): Submission #9
Submission information
Submission Number: 9
Submission ID: 127490
Submission UUID: 7dffcea5-6689-44ee-9337-8a22003166d3
Submission URI: /egrp/cohortconsortium/abstracts
Submission Update: /egrp/cohortconsortium/abstracts?token=-VMxBgtXDj1PD77Jodk0khdhjRuh60pT8uyoSmWIdcY
Created: Fri, 09/13/2024 - 06:04
Completed: Fri, 09/13/2024 - 06:11
Changed: Mon, 09/16/2024 - 16:40
Remote IP address: 10.208.24.118
Submitted by: Anonymous
Language: English
Is draft: No
Webform: Cohort 2024 (Abstracts Submission)
Lightning Talks Abstract
Martin
Lajous
Faculty-Researcher
MD, ScD
Instituto Nacional de Salud Publica
A casual framework to account for COVID-19 deaths in mortality analyses in large cohort studies.
Background. COVID-19 deaths pose a methodological challenge for epidemiologic cohorts aimed at identifying risk factors for cancer and chronic disease mortality. The pandemic introduced a competing event which can potentially obscure the signals between exposures and chronic disease mortality and complicate the interpretation of results.
Methods. Using a causal framework, we will characterize the causal effects of lifestyle and reproductive factors and the conditions under which they can be identified in a setting where COVID-19 deaths act as a competing event for cancer and chronic disease mortality. We will apply this framework to data from two large cohort studies: the Polish Cohort Study (PONS; n=13,148 women and men) and the Mexican Teachers' Cohort (MTC; n=115,275 women).
Results. By the end of 2022, the proportion of deaths attributed to COVID-19 was 8.3% in the PONS cohort and 10.1% in the MTC cohort. Causal diagrams were used to explicitly articulate causal effects estimated in the presence of COVID-19 deaths as well causal effects that can be estimated using contemporary causal inference methods. Different analytic approaches using mortality follow-up data up to 2023 for both cohorts will illustrate how risk factor mortality estimates vary and how these variations impact interpretation.
Conclusion. Our study will contribute to advancing epidemiological research methods in large-scale prospective cohorts by addressing the challenges posed by COVID-19 mortality as a competing risk. These findings will be particularly valuable to NCI’s Cancer Cohort Consortium and similar cohorts facing analytical challenges due to the ongoing impact of the COVID-19 pandemic.
Methods. Using a causal framework, we will characterize the causal effects of lifestyle and reproductive factors and the conditions under which they can be identified in a setting where COVID-19 deaths act as a competing event for cancer and chronic disease mortality. We will apply this framework to data from two large cohort studies: the Polish Cohort Study (PONS; n=13,148 women and men) and the Mexican Teachers' Cohort (MTC; n=115,275 women).
Results. By the end of 2022, the proportion of deaths attributed to COVID-19 was 8.3% in the PONS cohort and 10.1% in the MTC cohort. Causal diagrams were used to explicitly articulate causal effects estimated in the presence of COVID-19 deaths as well causal effects that can be estimated using contemporary causal inference methods. Different analytic approaches using mortality follow-up data up to 2023 for both cohorts will illustrate how risk factor mortality estimates vary and how these variations impact interpretation.
Conclusion. Our study will contribute to advancing epidemiological research methods in large-scale prospective cohorts by addressing the challenges posed by COVID-19 mortality as a competing risk. These findings will be particularly valuable to NCI’s Cancer Cohort Consortium and similar cohorts facing analytical challenges due to the ongoing impact of the COVID-19 pandemic.