Topic

Death Attribution

Fillmore and his collaborators have examined the evolving accuracy of death attribution during the COVID-19 pandemic, noting that standard surveillance methods based solely on positive test results became less precise as population immunity increased. By integrating clinical indicators such as hypoxemia and the administration of targeted therapeutics, he developed a more rigorous tool for distinguishing deaths directly caused by the virus from those occurring incidentally. His research demonstrates a significant longitudinal decrease in the proportion of deaths attributable to SARS-CoV-2 among US veterans, reflecting the impact of widespread vaccination and improved clinical management.

U.S. Veterans and VA Data 2 COVID-19 2 Real World Evidence 2 Retrospective Cohort Study 1 Electronic Health Records 1 Mortality Prediction 1 Case Definition Validation 1 Vaccination 1 Epidemiology 1 Logistic Regression 1 Model Validation 1 Pandemic Surveillance 1

Publications (2)

2024

Longitudinal trends in 30-day mortality attributable to SARS-CoV-2 among vaccinated and unvaccinated US veteran patients.

Caitlin A. Trottier^†, Jennifer La^†, Lucy Li, Nathanael R. Fillmore, Paul A. Monach, Shira Doron^‡, Westyn Branch-Elliman^‡^† Equal first authorship · ^‡ Equal senior authorship

Infection Control and Hospital Epidemiology45(3), 393-395

U.S. Veterans and VA Data Retrospective Cohort Study Electronic Health Records COVID-19 Mortality Prediction

2023

Maintaining the Utility of Coronavirus Disease 2019 Pandemic Severity Surveillance: Evaluation of Trends in Attributable Deaths and Development and Validation of a Measurement Tool.

Caitlin Trottier^†, Jennifer La^†, Lucy L. Li^†, Majd Alsoubani, Austin D. Vo, Nathanael R. Fillmore, Westyn Branch-Elliman, Shira Doron^‡, Paul A. Monach^‡^† Equal first authorship · ^‡ Equal senior authorship

Clinical Infectious Diseases77(9), 1247-1256

COVID-19 U.S. Veterans and VA Data Logistic Regression Model Validation Pandemic Surveillance