In the present study, we use a Bayesian hierarchical model to estimate all-cause excess mortality by month for 3127 counties for the period from March 2020 to February 2022. In addition to generating county-month level estimates of excess mortality, we examine spatial patterning of these estimates across Census divisions and metropolitan (metro) and nonmetropolitan (nonmetro) areas between the first and second years of the pandemic.
Prior studies of excess mortality in the U.S. have primarily focused on national- and state-level estimates ( 5 6 ), but estimating the full impact of the COVID-19 pandemic at the county level is necessary to understand finer-grained geographic patterns of excess mortality. Although a prior study generated predictions of excess mortality for 1470 county sets for all months of 2020 combined ( 4 ), to the best of our knowledge, there are no estimates of excess mortality at the county-month level across the first 2 years of the pandemic. In addition, expanding these estimates to the second year of the pandemic is critical because the geographic impact of the pandemic has changed markedly since the first year because of changing national- and state-level policies, the availability of vaccines, and the emergence of additional variants.
For these reasons, it is beneficial to use excess mortality as a measure of the pandemic’s impact, particularly when examining geographic patterns in mortality. Estimates of excess mortality are more comparable spatially than COVID-19–assigned deaths alone, because states use different procedures to assign COVID-19 deaths and local death investigation systems may have different policies and resources that affect assignment of COVID-19 deaths ( 9 26 ). Furthermore, because many COVID-19 deaths were not assigned to COVID-19 early in the pandemic, excess mortality is likely to provide a more accurate measure of the pandemic’s impact for purposes of resource allocation and evaluating health disparities ( 7 28 ). Thus, continued tracking of excess mortality across time and space helps to clarify the total impact of the pandemic, identify where its impacts have been greatest, and implement the most appropriate policy responses.
The coronavirus disease 2019 (COVID-19) pandemic has had a substantial impact on mortality in the United States (U.S.), leading to declines in life expectancy rarely observed since the end of World War II ( 1 2 ). Estimates of excess mortality, which compare observed deaths to those expected in the absence of the pandemic, suggest that the true death toll of the pandemic is much larger than indicated by official COVID-19 deaths alone ( 3 7 ). Deaths attributable to the pandemic may have been assigned to causes other than COVID-19 for several reasons. Lack of access to testing in the community, combined with the inconsistent use of postmortem testing for suspected cases, likely resulted in a large share of undiagnosed COVID-19 infections and deaths, especially early in the pandemic ( 8 12 ). In addition, persons with comorbid conditions may have had their cause of death assigned to the comorbid condition rather than to COVID-19 ( 13 ). Excess deaths not assigned to COVID-19 may also reflect deaths indirectly related to the pandemic, including deaths associated with reductions in access to health care, hospital avoidance due to fear of COVID-19 infection, increases in drug overdoses, and economic hardship leading to housing and food insecurity ( 14 20 ). Last, excess mortality may also capture the offsetting effects of pandemic-related declines in mortality, such as reductions in influenza mortality associated with COVID-19 mitigation measures, declines in air pollution and related mortality, and fewer deaths occurring because people who might have died later in the pandemic had already died from COVID-19 ( 3 25 ).
( A to D ) Each line in the four panels represents a county. For each line, the end point that is a vertical line reflects relative excess mortality in the first year of the pandemic (March 2020 to February 2021), while the end point that is a dot indicates relative excess mortality in the second year of the pandemic (March 2021 to February 2022). The color of the line distinguishes between counties that saw a decline in relative excess mortality (blue) and those that saw an increase (orange). The 30 most populous counties were selected for each metro-nonmetro category.
Each cell in the heatmaps represents a county-month. ( A ) The 50 most populous large metro counties. ( B ) The 50 most populous nonmetro counties. Large metro includes large central metros and large fringe metros. In the shaded heatmaps colored from white to dark red, darker and redder colors indicate higher relative excess mortality. In the white-and-gray heatmaps, gray cells indicate county-months with a greater than 95% probability of positive excess mortality. Counties were sorted vertically on the basis of the month when the highest peak of excess mortality occurred. Counties at the top of the heatmaps thus had their highest relative excess mortality earlier in the pandemic. In contrast, those at the bottom had their highest relative excess mortality later in the pandemic.
An emerging rural disadvantage is also visible when examining temporal trends for individual counties. Figure 6 shows temporal trends in relative excess mortality for the most populous counties among large metro and nonmetro counties. Among large metro counties, relative excess mortality was especially high in Northeastern counties in the early pandemic and in California counties during the Winter peak. In nonmetro counties, marked increases in mortality were observed during the second year of the pandemic, especially during the Delta peak. Figure 7 explores changes in excess mortality between the first and second years of the pandemic among the most populous counties in each metro-nonmetro category. In the most populous large metro counties, substantial declines in excess mortality were observed between the first and second years. For nonmetro counties, the opposite pattern was observed. These areas were generally spared in the first year, after which they experienced high excess mortality in the second year. Figure 8 displays temporal trends for each county alongside state trend lines. This figure reveals substantial variation in temporal trends in relative excess mortality across states along with substantial variation in relative excess mortality trends within states.
Each line represents the rolling cumulative relative excess mortality for a combination of metro-nonmetro category and Census region. Large metro includes large central metros and large fringe metros. Each Census region is represented by a different line color: dark blue for Midwest, teal for Northeast, yellow-green for South, and black for West. Each metro-nonmetro category is represented by a different line type: solid for large metro, dashed for medium or small metro, and dotted for nonmetro. The y axis greater than 1 has been log-transformed (base 10) to facilitate comparisons between categories at lower values of relative excess mortality. Rolling cumulative relative excess mortality is calculated as the sum of excess deaths divided by the sum of expected deaths for all months from March 2020 through a given month. Mean estimates for expected deaths were used for these calculations. For example, values for February 2022 reflect total excess deaths for 24 months of the pandemic, from March 2020 through February 2022. Decreasing cumulative relative excess mortality indicates months with relative excess mortality below average to date for a given combination of Census region and metro-nonmetro category. Increasing cumulative relative excess mortality indicates months with relative excess mortality above average to date for a given combination of Census region and metro-nonmetro category.
Each point in the graph represents a county and reflects its relative excess mortality from March 2020 to February 2021 (horizontal axis) and its relative excess mortality from March 2021 to February 2022 (vertical axis). We excluded counties with less than 10,000 residents to make the relationship between the two variables clearer. The 45° line separates the plot into two parts. Points above the line saw higher excess mortality in the second year of the pandemic compared to the first. Points falling below the line instead saw lower excess mortality in the second year compared to the first.
The large metro category includes large central metros and large fringe metros. All nonlarge metro counties are classified as medium metros, small metros, and nonmetro areas. The shaded intervals behind the bars separate the different waves of the COVID-19 pandemic as follows: Initial (March 2020 to Aug 2020), Winter (October 2020 to February 2021), Delta (August 2021 to October 2021), and Omicron (November 2021 to February 2022). The height of each bar reflects relative excess mortality (excess deaths as a percentage of expected deaths). The color of the bars reflects each division-month position (percentile) in the overall distribution of relative excess mortality. Black, solid segments below the bars indicate units for which the posterior probability of positive excess mortality is above 95%.
Throughout the pandemic, national trends in excess mortality reflect the aggregation of heterogeneous trends across disparate regions and metro and nonmetro areas. To explore subnational patterns, Fig. 3 shows temporal trends in relative excess mortality across combinations of Census divisions (New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, and Pacific) and metro-nonmetro categories (large metro versus medium metro, small metro, and nonmetro areas). The initial peak in excess mortality nationally was mostly driven by high excess mortality in large metros within the Middle Atlantic division. In contrast, the Winter peak spared this region and affected counties across the metro-nonmetro continuum in other divisions. As the pandemic progressed, there was a higher degree of concordance in temporal patterns across areas, which was especially evident during Delta and Omicron. Figure 4 further illustrates differences in the geography of the pandemic between the first and second years by directly comparing relative excess mortality in the 2 years across divisions and metro-nonmetro categories. Large metro counties mostly had greater relative excess mortality in the first year of the pandemic than they did in the second year. In contrast, nonmetro counties were more likely to have greater relative excess mortality in the second year compared with the first year. This pattern is indicative of the emergence of a rural mortality disadvantage in the second year of the pandemic.
( A to D ) Each county in the map is colored according to the posterior probability that the observed death count is higher than the expected one. We highlight counties where the probability of positive relative excess mortality is higher than 0.75. The four maps refer to the four peak periods of the pandemic, months of particularly high excess mortality.
( A to D ) Each county in the map is colored according to its relative excess mortality (the ratio of excess deaths over expected deaths). Each of the four maps refers to one of the four peak periods of the pandemic, months of particularly high excess mortality. Relative excess mortality for counties with 0 expected deaths was classified as “Undefined.”
DISCUSSION
This study produced monthly estimates of excess mortality for 3127 counties in the U.S. from March 2020 through February 2022, identifying 1,179,024 excess deaths during the first 2 years of the pandemic. Between the first and second years of the pandemic, relative excess mortality decreased in large metros and increased in nonmetro areas. The increases in excess mortality in nonmetro areas occurred most markedly during the Delta wave of the pandemic. By the end of February 2022, nonmetro areas in the South had the highest cumulative relative excess mortality, surpassing large metros in the Northeast and other areas that were affected heavily in the early pandemic.
6, Prior studies of excess mortality have largely produced estimates for the year 2020 ( 3 29 ), leaving patterns of excess mortality during 2021 and 2022 understudied. The Centers for Disease Control and Prevention (CDC) has reported an estimate of approximately 1.1 million excess deaths in the U.S. from March 2020 to February 2022, which is in line with our estimate ( 30 ).
Generating estimates of excess mortality at the county level has several potential benefits. First, because counties are the administrative unit for death investigation, excess mortality estimates have the potential to help identify counties where COVID-19 death rates differ from excess mortality rates and who might benefit from additional training and other resources around cause-of-death certification ( 31 ). These estimates may also be valuable for informing local public health workers, community organizations, and residents of the true impact of the pandemic, thus potentially increasing vaccination and uptake of other protective measures ( 32 ). These estimates may also be used to target federal and state emergency resources, such as funeral assistance support from the Federal Emergency Management Agency. Last, estimating excess mortality at the county level also enables analyses of social, structural, and policy factors affecting mortality associated with the pandemic, including across metro-nonmetro categories.
One major finding of this study is that the number of excess deaths in the second year of the pandemic was not substantially lower than the first year, which is noteworthy as vaccinations were available for much of 2021 and 2022. Despite the strong efficacy of vaccines, gaps in uptake likely contributed to high excess mortality in 2021 and 2022, which may persist into the future if these vaccination gaps are not closed. This finding may also reflect federal and state governments’ failure to invest in population-based strategies designed to protect the communities at greatest risk for COVID-19 death, such as financial support for family and medical leave especially for essential workers, improved ventilation of schools and workplaces, and vaccine and booster delivery programs organized in coordination with community partners ( 33 ).
A second major and related finding of this study is that excess mortality moved substantially from large metros in the first year of the pandemic to nonmetro areas during the second year. One factor that likely contributed to this change is vaccination. In urban areas, 75% of people aged 5 years and older were vaccinated as of January 2022 compared to only 59% of people aged 5 years and older in rural areas ( 34 35 ). This urban-rural difference in vaccination rates more than doubled since April 2021, suggesting that differences in vaccination rates across metro-nonmetro categories may be playing an increasingly important role in the rural mortality disadvantage observed in the second year of the pandemic. Another factor that may be contributing to high rural excess mortality is insufficient rural health infrastructure related to funding gaps and workforce shortages ( 36 ). This may have affected access to COVID-19 vaccination and treatment, including oral antivirals and monoclonal antibody treatments ( 37 38 ). Another consideration is the high prevalence of comorbidities among rural residents that likely increased risk for severe COVID-19 outcomes ( 39 ). Each of these factors may have contributed to the rural mortality disadvantage observed in this study. Additional research is needed to understand how the rural mortality disadvantage in excess mortality may differ by race and ethnicity and other demographic factors.
The study had several limitations. First, the study relied on publicly available data, which were subject to suppression of death counts fewer than 10 in a given county-month. We addressed this limitation by pooling information across different geographical levels through the use of hierarchical models and by taking advantage of the additional information provided by yearly death counts. However, our estimates remain uncertain in areas with small populations and few deaths. Second, our study examined all-cause mortality and did not explore differences in trends using cause-specific death rates. Assessing geographic and temporal differences in excess death rates by cause of death would allow for a deeper understanding of the mechanisms driving trends in excess mortality overall and is an important direction for future research. Similarly, our study assessed all-cause mortality in the overall population. Demographic stratification was outside of the scope of the current study, but we plan to examine county-level differences in excess mortality by gender, race and ethnicity, and education in future work. At this time, the estimates produced in this study could be linked to data on county-level demographic characteristics to examine differences in excess mortality associated with these factors. Third, we were not able to model age-specific or age-adjusted excess mortality at the county-month level because of suppression in public CDC data. However, the number of excess deaths across all ages combined is an important metric of the impact of the pandemic in a given area—even when its magnitude is partially explained by age distribution—because it captures the actual increases in mortality rather than the increases that would have occurred under a hypothetical standard age distribution ( 40 ). Furthermore, the effect of differing age structures across counties is at least partially mitigated in our study by (i) our statistical model of expected mortality, which captures differences in prepandemic mortality rates due to age and other time-invariant factors in a county-specific random intercept term and (ii) the use of a relative excess mortality metric, in which heightened mortality rates among older populations prepandemic and during the pandemic may offset one another when this ratio is calculated. Future research should investigate how age composition may contribute to county-level differences in excess mortality. Fourth, while we use the most up-to-date population estimates available from the U.S. Census Bureau, which take into consideration migration across counties, it is possible that migration during the COVID-19 pandemic altered counties’ sociodemographic makeup in mortality-relevant ways. The extent to which these changes would bias estimates of excess mortality depends on the differences between the mortality profile of in-migrating and out-migrating populations. Last, the primary objective of the present study was to generate descriptive estimates of excess mortality for each county over the course of the pandemic. Hence, we did not model the determinants of spatial-temporal variation in excess mortality. An important direction for future research will be to identify the key social, structural, and policy factors that contributed to differences in county excess mortality over time to gain insight into why some counties experienced more substantial mortality burdens during the pandemic than others.
In conclusion, this study provides the first county-level estimates of excess mortality by month in the U.S. during the first 2 years of the COVID-19 pandemic (March 2020 to February 2022). It reveals that the burden of excess mortality has moved substantially from large metros in the first year to nonmetro areas in the second year. Future research should use the estimates generated here to examine the factors associated with excess mortality throughout the pandemic, identify counties where COVID-19 death rates differ substantially from excess death rates, and study the mechanisms contributing to growing rural health disparities during the pandemic.