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

Overview

abstract

  • BACKGROUND: Death within a specified time window following a positive SARS-CoV-2 test is used by some agencies for attributing death to COVID-19. With Omicron variants, widespread immunity, and asymptomatic screening, there is cause to re-evaluate COVID-19 death attribution methods and develop tools to improve case ascertainment. METHODS: All patients who died following microbiologically confirmed SARS-CoV-2 in the Veterans Health Administration (VA) and at Tufts Medical Center (TMC) were identified. Records of selected vaccinated VA patients with positive tests in 2022, and of all TMC patients with positive tests in 2021-2022, were manually reviewed to classify deaths as COVID-19-related (either directly caused by or contributed to), focused on deaths within 30 days. Logistic regression was used to develop and validate a surveillance model for identifying deaths in which COVID-19 was causal or contributory. RESULTS: Among vaccinated VA patients who died ≤30 days after a positive test in January-February 2022, death was COVID-19-related in 103/150 cases (69%) (55% causal, 14% contributory). In June-August 2022, death was COVID-19-related in 70/150 cases (47%) (22% causal, 25% contributory). Similar results were seen among the 71 patients who died at TMC. A model including hypoxemia, remdesivir, and anti-inflammatory drugs had positive and negative predictive values of 0.82-0.95 and 0.64-0.83, respectively. CONCLUSIONS: By mid-2022, "death within 30 days" did not provide an accurate estimate of COVID-19-related death in 2 US healthcare systems with routine admission screening. Hypoxemia and use of antiviral and anti-inflammatory drugs-variables feasible for reporting to public health agencies-would improve classification of death as COVID-19-related.

publication date

  • November 11, 2023

Research

keywords

  • COVID-19

Identity

PubMed Central ID

  • PMC10640692

Scopus Document Identifier

  • 85176964270

Digital Object Identifier (DOI)

  • 10.1093/cid/ciad381

PubMed ID

  • 37348870

Additional Document Info

volume

  • 77

issue

  • 9