key: cord-1018299-f2jzz41d
authors: Ackley, Calvin A.; Lundberg, Dielle J.; Ma, Lei; Elo, Irma T.; Preston, Samuel H.; Stokes, Andrew C.
title: County-level estimates of excess mortality associated with COVID-19 in the United States
date: 2022-01-05
journal: SSM Popul Health
DOI: 10.1016/j.ssmph.2021.101021
sha: 6fc0e995394fc99177eaa74963defc94bed509e3
doc_id: 1018299
cord_uid: f2jzz41d

The COVID-19 pandemic in the U.S. has been largely monitored using death certificates containing reference to COVID-19. However, prior analyses reveal that a significant percentage of excess deaths associated with the pandemic were not directly assigned to COVID-19. In this study, we estimated a generalized linear model of expected mortality based on historical trends in deaths by county of residence between 2011 and 2019. We used the results of the model to generate estimates of excess mortality and excess deaths not assigned to COVID-19 in 2020 for 1470 county sets in the U.S. representing 3138 counties. Across the country, we estimated that 438,386 excess deaths occurred in 2020, among which 87.5% were assigned to COVID-19. Some regions (Mideast, Great Lakes, New England, and Far West) reported the most excess deaths in large central metros, whereas other regions (Southwest, Southeast, Plains, and Rocky Mountains) reported the highest excess mortality in nonmetro areas. The proportion assigned to COVID-19 was lowest in large central metro areas (79.3%). Regionally, the proportion of excess deaths assigned to COVID-19 was lowest in the Southeast (81.6%), Southwest (82.6%), Far West (83.7%), and Rocky Mountains (86.7%). Across the regions, the number of excess deaths exceeded the number of directly assigned COVID-19 deaths in most counties. The exception to this pattern occurred in New England, which reported more directly assigned COVID-19 deaths than excess deaths in metro and nonmetro areas. Many county sets had substantial numbers of excess deaths that were not accounted for in direct COVID-19 death counts. Estimates of excess mortality at the local level can inform the allocation of resources to areas most impacted by the pandemic and contribute to positive behavior feedback loops, such as increases in mask-wearing and vaccine uptake.

Estimates of excess deaths are critical for tracking the direct and indirect effects of the COVID-19 pandemic and for developing equitable policy responses. 1 Provisional estimates from the Center for Disease Control and Prevention (CDC) indicate that between 545,600 and 660,200 excess deaths occurred in the United States from January 26, 2020 to February 27, 2021. 2 The CDC further estimates that between 75 and 88% of excess deaths were directly assigned to COVID-19 on death certificates. 2 Other prior estimates of excess mortality have also found significant discrepancies between direct COVID-19 deaths and excess mortality. [2] [3] [4] [5] Weinberger et al. found that 95,235 deaths were assigned to COVID-19 between March 1 and May 30, 2020 and that 122,300 excess deaths occurred, meaning that 78% of excess deaths were assigned to COVID-19. 3 Woolf et al. identified that 72.4% of excess deaths were assigned to COVID-19 from March 1, 2020 to January 2, 2021. 4 Excess deaths not assigned to COVID-19 may reflect a variety of factors, including deaths that were ascribed to other causes of death due to limited testing, 6 indirect deaths caused by interruptions in the provision of health care services, 7, 8 or indirect deaths caused by the broader social and economic consequences of the pandemic. [9] [10] [11] At the state-level, the proportion of excess deaths not assigned to COVID-19 has been shown to vary significantly, suggesting that attribution of deaths to COVID-19 may not be uniform across the country. 4 For example, there were more COVID-19 deaths than excess deaths in the state of Massachusetts, whereas only 35.9% of excess deaths were assigned to COVID-19 in the state of Alaska. 4 Estimation of excess mortality at the local level may be valuable for several reasons. First, states are heterogeneous units. 12, 13 Prior studies have found that the proportion of excess deaths not assigned to COVID-19 differed significantly by county-level sociodemographic and health care factors. 5, 14 Second, deaths are registered at the county-level. [15] [16] [17] Thus, it is reasonable to assume that administrative differences may exist between counties in the processing of deaths. Third, countylevel data may be helpful for appreciating the full burden of the COVID-19 pandemic in an area and informing community and policy interventions. Fourth, providing accurate data to residents could J o u r n a l P r e -p r o o f result in a positive behavioral feedback loop, encouraging protective actions such as wearing masks and pursuing vaccination. 18 The objective of the present study is to generate estimates of excess mortality at the local level and examine geographic variation in excess mortality and the proportion of excess deaths not assigned to COVID-19. Examining excess deaths across local areas has the potential to identify jurisdictions that have been especially hard hit by the COVID-19 pandemic and whose excess mortality has been hidden. Such estimates can be used to inform pandemic preparedness and response at the county, state, and national levels.

We used provisional data from the National Center for Health Statistics (NCHS) on COVID-19 mortality and all-cause mortality by county of residence from January 1 to December 31, 2020 reported by June 3, 2021. We used data with a twenty-two week lag (December 31, 2020 to June 3, 2021) to improve the completeness of data, since prior analysis of provisional NCHS vital statistics reveal low completeness within the month following a death but more than 75 percent completeness after eight-weeks. 19 COVID-19 deaths were identified using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) code U07.1 and included deaths assigned to COVID-19 as the underlying cause as well as deaths in which COVID-19 was reported as a cause that contributed to death on the death certificate. Prior reports indicate that COVID-19 was assigned as the underlying cause on death certificate in 92% of deaths that NCHS attributed to For our historical comparison period, we used CDC Wonder data on all-cause mortality by county of residence from 2011 to 2019. To compute death rates, we used data on estimated midyear populations from the U.S. Census Bureau for the years 2011 through 2020. For counties with between 1 and 9 all-cause or COVID-19 deaths in 2020 or fewer than 10 all-cause deaths in a particular year between 2011 and 2019, NCHS censored the exact number of deaths, so we used a value of 5.

To assess geographic patterns in mortality, we classified counties into 4 metropolitannonmetropolitan categories and also examined patterns by Bureau of Economic Analysis (BEA)

Regions (Appendix C). 21 We then stratified the regions by metropolitan-nonmetropolitan categories to yield 32 distinct geographic units.

To provide precise estimates reflecting each county area, we aggregated small counties according to the United States Census Bureau's County-Sets classification. Counties with a population of 50,000 or more stood alone, and smaller groups of counties were combined to form sets with a total population exceeding 50,000. Counties within these sets were contiguous and did not cross state borders. Our data included 3,138 counties with reported all-cause mortality for 2011-2020.

This translated into a final analytic sample of 1,470 county sets.

The present investigation relied on de-identified publicly available data and was therefore exempted from review by the Boston University Medical Center Institutional Review Board. Analyses were conducted using R, version 3.6.3 (R Project for Statistical Computing). Additional details about the data along with programming code for replicating the analyses are available from the linked GitHub repository.

To generate a prediction of expected mortality in 2020, we estimated a statistical model of mortality using historical mortality data from 2011-2019. Specifically, we modeled mortality at the county-set-year level using a quasi-poisson generalized linear model (QP-GLM) of the following form 22,23 (Appendix B):

Here, Yit denoted the number of all-cause deaths divided by the population of county-set i in year t. In the exponent, we included a county-set-specific intercept term, ⍺, which captured latent characteristics of each county-set that may be correlated with mortality beyond the county-level variability in COVID-19 deaths and the proportion of excess deaths assigned to COVID-19. We included one lag of the dependent variable, Yt-1, to capture potential serial correlation in mortality. We 

We defined excess deaths as the difference between the number of predicted all-cause deaths in 2020 and the number of observed all-cause deaths in 2020. For each county set in our sample, we produced an excess death rate for 2020 as well as a ratio of observed to expected deaths.

We defined excess deaths not assigned to COVID-19 as the difference between the number of excess deaths and the number of observed directly assigned COVID-19 deaths in 2020. Next, we defined the proportion of excess deaths assigned to COVID-19 as the ratio of the direct COVID-19 death rate to the excess death rate.

Across 1,470 county-sets in the U.S. representing 3,138 counties, we estimated that 438,386 excess deaths occurred in 2020. All-cause mortality was higher in most county sets in 2020 compared to predictions based on years 2011 through 2019, leading to substantial estimates of excess mortality (Appendix Figure A1) . For most county sets, excess mortality exceeded directly J o u r n a l P r e -p r o o f assigned COVID-19 mortality. Across all county sets, 87.5% of excess deaths were assigned to COVID-19, indicating that 12.5% of excess deaths were not assigned to COVID-19. Figure A2) . Table A1 presents summary statistics on excess mortality for each state.

There was also significant variation in the proportion of excess deaths assigned to COVID-19 across county sets in the U.S. Across the country, 91.2 million U.S. residents lived in counties where less than 75% of excess deaths were assigned to COVID-19 (Appendix Table A2 ). On average, Counties with large, negative excess mortality not assigned to COVID-19 were mostly in the New England region. Appendix Figure A3 and Appendix Figure A4 Only 48 county-sets, representing less than 2% of the U.S. population (5.5 million residents) experienced negative excess mortality (observed mortality was less than expected). Among these county-sets, none had statistically significant negative excess death rates. Appendix Table D1 provides estimates of excess death rates and their uncertainty intervals for 1,470 county-sets across the U.S.

In this study, we produced county-set level estimates of excess mortality and the proportion of excess deaths not assigned to COVID-19 and examined geographic variation in mortality across the United States. In total, we estimated that 439,698 total excess deaths occurred in 2020, of which 86.7% were assigned to COVID-19. This number is similar to the 458,000 excess deaths estimated by Islam et al.. 26 Our study reveals substantial heterogeneity in excess deaths and the proportion of excess deaths not assigned to COVID-19 across counties, which are the administrative unit for death registration. This result highlights the value of local-area estimates of excess mortality as state and national-estimates mask significant variability.

There are several potential explanations for the discrepancy between excess mortality and directly assigned COVID-19 mortality observed in this study. One explanation is that the gap reflects underreporting of COVID-19 deaths. Especially early in the pandemic, testing was severely limited.

Underreporting may have also related to a lack of awareness of the clinical manifestations of COVID-19 as well as various social, health care, and political factors. 5, 14, 29 The gaps may also be explained by the indirect effects of the pandemic on mortality levels. Indirect effects could relate to interruptions or delays in health care or the broader social and economic upheaval caused by the pandemic, including loss of employment, social isolation and loneliness, and other factors. [30] [31] [32] Wolfson et al.

found that food insecurity has increased substantially during the COVID-19 pandemic, which could contribute to a range of health concerns. 30 Lange et al. identified a 42% decline in emergency department visits during the early pandemic, suggesting that life-threatening health conditions may have gone untreated and resulted in indirect deaths. 31 Wu argued that social isolation and loneliness were major risk factors for older adults during the pandemic and may have contributed to poor physical health status, including mortality. 32 NCHS data suggest that approximately 19,000 more deaths from unintentional injuries occurred in 2020 than in 2019. 28 We also observed numerous counties in which the direct COVID-19 death rate exceeded our estimates of excess mortality, especially in New England. A similar pattern was observed internationally, with countries such as New Zealand and Taiwan having few direct COVID-19 deaths and negative excess mortality while countries such as Luxembourg, France, Belgium, and Costa Rica had positive excess mortality but their direct COVID-19 death rates exceeded their excess death rates. 33 This finding could have occurred for several reasons. First, increases in mortality in 2020 due to COVID-19 may have been offset by declines in deaths from other causes (e.g. influenza, traffic deaths, and suicide). 28, 34, 35 Shelter-in-place policies may have also been associated with reductions in non-natural deaths. 36 Several of the counties with negative excess death rates not assigned to COVID-19 stood out as economically privileged areas that may have been isolated from the pandemic through an ability to work-from-home and avoid household crowding. It is also possible that medical certifiers in some counties over-assigned COVID-19 to death certificates. Differences could also relate to how directly assigned COVID-19 deaths were counted by NCHS. In about 8% of cases, COVID-19 was listed as something other than the underlying. 20 

This analysis had several limitations. First, the present study used cumulative data for all of 2020, and thus, it was not possible to examine changes in excess mortality over time. Examining trends in excess mortality using small-area data is a priority for future research. Second, an important caveat of this study is that age structure differs across counties. Since COVID-19 mortality is more common in older populations, some of the patterns observed across counties may simply reflect differences in age structure. Thus, an important future direction is to age standardize county-level estimates when age-specific mortality data become available. Indirect age standardization procedures could be used in the absence of county-level age-specific death rates. Demographic and socioeconomic variables could also be included in future versions of the model as explanatory variables. Third, the provisional county-level mortality files released by the NCHS did not include information on cause of death, and therefore it was not possible to disentangle the sources of excess deaths in 2020. Decomposing excess deaths by cause of death will be critical to understanding why some counties have a higher fraction of deaths unassigned to COVID-19 than others and the extent to which the discrepancies are explained by COVID-19 death undercounts versus indirect pandemic effects. Finally, the data used in the present study are provisional in nature and may be subject to further corrections by the NCHS.

In conclusion, the present study builds on prior work by extending estimates of excess mortality and excess deaths not assigned to COVID-19 to US county sets. The added geographic J o u r n a l P r e -p r o o f Page 12 detail of these estimates compared to prior studies may facilitate research on the causes and consequences of the COVID-19 pandemic on population health and provide useful data for local area health policy and planning. Estimates of excess mortality at the local level can also inform the allocation of resources to areas most impacted by the pandemic and contribute to positive protective behavior feedback loops, such as increases in mask-wearing and vaccine uptake. In doing so, they can inform the response to the COVID-19 pandemic and to any future pandemics that the country may face.

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Counties with the Highest Excess Death Rates Not Assigned to COVID-19 and Statistically Significant Negative Excess Death Rates Not Assigned

Notes: (top) U.S. county-sets with the highest, statistically significant positive excess death rates not assigned to COVID-19

county-sets with statistically significant negative excess death rates not assigned to COVID-19. The 95% confidence intervals indicate uncertainty in the excess death estimates as calculated by the bootstrapping procedure

Notes: Aggregated results by various geographic regions. Aggregate rates are computed by summing actual and predicted counts over counties in a particular region and dividing by the summed population. Note that this is equivalent to the population-weighted means of the county-level rates. COVID-19 deaths refer to deaths that appeared as either an underlying or contributing cause on the death certificate. Notes: Counties above the 45° line represent areas where excess deaths exceeded direct COVID-19 deaths, meaning the excess death rate not assigned to COVID-19 was positive. Counties below the 45° line represent areas where direct COVID-19 deaths exceeded excess deaths, meaning that the excess death rate not assigned to COVID-19 was negative. Excluded counties with direct COVID-19 death rates or excess death rates that were above the 99th percentile for each BEA region.