Estimating under-recognized COVID-19 deaths, United States, march 2020-may 2021 using an excess mortality modelling approach

Abstract

Background In the United States, Coronavirus Disease 2019 (COVID-19) deaths are captured through the National Notifiable Disease Surveillance System and death certificates reported to the National Vital Statistics System (NVSS). However, not all COVID-19 deaths are recognized and reported because of limitations in testing, exacerbation of chronic health conditions that are listed as the cause of death, or delays in reporting. Estimating deaths may provide a more comprehensive understanding of total COVID-19–attributable deaths. Methods We estimated COVID-19 unrecognized attributable deaths, from March 2020—April 2021, using all-cause deaths reported to NVSS by week and six age groups (0–17, 18–49, 50–64, 65–74, 75–84, and ≥85 years) for 50 states, New York City, and the District of Columbia using a linear time series regression model. Reported COVID-19 deaths were subtracted from all-cause deaths before applying the model. Weekly expected deaths, assuming no SARS-CoV-2 circulation and predicted all-cause deaths using SARS-CoV-2 weekly percent positive as a covariate were modelled by age group and including state as a random intercept. COVID-19–attributable unrecognized deaths were calculated for each state and age group by subtracting the expected all-cause deaths from the predicted deaths. Findings We estimated that 766,611 deaths attributable to COVID-19 occurred in the United States from March 8, 2020—May 29, 2021. Of these, 184,477 (24%) deaths were not documented on death certificates. Eighty-two percent of unrecognized deaths were among persons aged ≥65 years; the proportion of unrecognized deaths were 0•24–0•31 times lower among those 0–17 years relative to all other age groups. More COVID-19–attributable deaths were not captured during the early months of the pandemic (March–May 2020) and during increases in SARS-CoV-2 activity (July 2020, November 2020—February 2021). Discussion Estimating COVID-19–attributable unrecognized deaths provides a better understanding of the COVID-19 mortality burden and may better quantify the severity of the COVID-19 pandemic. Funding None

Monthly excess mortality across counties in the United States during the Covid-19 pandemic, March 2020 to February 2022

“…Although our study does not distinguish between uncounted Covid-19 deaths and deaths indirectly related to the pandemic, many of the unassigned excess deaths identified in this study likely represent uncounted Covid-19 deaths (10, 11, 40). Discrepancies between Covid-19 death rates and excess death rates are problematic because they have the potential to mislead scientists and policymakers about which areas were most heavily affected during the pandemic.…”

Variation in COVID-19 Mortality in the US by Race and Ethnicity and Educational Attainment

“… 21 It is unclear whether similar underestimation of mortality also applies to Native Hawaiian and Other Pacific Islander individuals, as they have not been disaggregated from non-Hispanic Asian individuals in prior research on misclassification of race and ethnicity in mortality data. In addition, COVID-19 is likely misclassified in mortality data, and the CDC estimates that the true number of COVID-19 deaths was 30% higher than reported for the period from March 2020 to May 2021, 22 although whether misclassification varies by sociodemographic groups is unknown. As this was a cross-sectional study, we were also unable to assess whether the magnitude of inequalities changed over time.…”

Population Immunity Against COVID-19 in the United States

“…Finally, we used daily deaths reported by the CDC to calculate the total number of infections. Recent estimates indicate that 24% of the total deaths attributable to COVID-19 in the United States have not been reported ( 25 , 26 ), of which more than a third have occurred among persons aged 80 years or older. Although the unreported deaths may skew our estimates toward lower population immunity, IFR estimates are also subject to similar constraints, minimizing the potential bias in our estimates.…”