COVID-19 and excess mortality in the United States: A county-level analysis

Abstract

Background: Coronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics.

Methods and findings: In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.

Conclusions: In this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.

Fig 1. Difference between the 2020 all-cause mortality rate and 2013–2018 historical mortality rate vs.

2020 direct COVID-19 mortality rate (n = 2,096)^a,b,c. ^a The solid blue line represents a linear model of best fit weighted by population size. ^b The dashed reference line represents a slope of 1. ^c Six counties with a direct COVID-19 death rate greater than 6 deaths per 1,000 person-years or a difference between 2020 all-cause and 2013–2018 all-cause death rates greater than 10 deaths per 1,000 person-years were excluded from the figure for the purpose of visualization. These counties were included in all regressions. COVID-19, Coronavirus Disease 2019.

Fig 2. Relationship between all-cause mortality and direct COVID-19 mortality across strata of sociodemographic and health factors^a,b,c,d,e.

^a n = 2,096 counties. ^b β₂ coefficients generated from primary model: M(i) = α+β₁M*(i)+β₂C(i), where M(i) = Death rate from all causes in county i in 2020, M*(i) = Death rate from all causes, county i in 2013–2018, and C(i) = COVID-19 death rate in county i in 2020. The model was weighted by the 2020 population and fully stratified into population-weighted quartiles for each sociodemographic or health factor. The coefficients for the upper and lower 25% of values for each factor are presented in this figure. ^c Sample interpretation: In counties with lower household income, for every 1 directly assigned COVID-19 death, there was an increase in 1.31 all-cause deaths, suggesting there were 0.31 deaths not assigned to COVID-19 for every 1 directly assigned COVID-19 death in these counties. ^d Indirectly age-standardized models are presented in S3 Fig. ^e The dashed line represents the overall estimate. The solid line represents a coefficient of 1, which indicates that for every 1 directly assigned COVID-19 deaths, 0 deaths not assigned to COVID-19 occurred. COVID-19, Coronavirus Disease 2019.

Fig 3. Decomposition of 2020 excess death rates across strata of sociodemographic and health factors^a,b,c.

^a n = 2,096 counties. ^b Predicted death rates generated from primary model: M(i) = α+β₁M*(i)+β₂C(i), where M(i) = Death rate from all causes in county i in 2020, M*(i) = Death rate from all causes, county i in 2013–2018, and C(i) = COVID-19 death rate in county i in 2020. The model was weighted by the 2020 population and fully stratified into population-weighted quartiles for each sociodemographic or health factor. The death rates for the upper and lower 25% of values for each factor are presented in this figure. ^c Indirectly age-standardized death rates are presented in S4 Fig. COVID-19, Coronavirus Disease 2019.