INTRODUCTION
The United States has recorded substantial all-cause excess mortality during the COVID-19 pandemic.1 States had varying policies and strategies to address the pandemic. States also varied in their excess mortality, a metric that takes into account baseline health risks by determining how pandemic mortality compared with their predicted mortality based on pre-pandemic data and trends. While vaccination rates likely influenced all-cause mortality rate differences across states, other characteristics such as rates of receipt of effective therapeutics and non-pharmacologic interventions (policies and behaviors) may have influenced detected differences.2–4
Accordingly, we quantified state-level excess deaths and estimated the number of excess deaths that could have potentially been averted if all US states had excess death rates matching the 10 US states with the lowest excess death rates during the SARS-CoV-2 variant era (i.e., the Delta and Omicron periods), thereby encompassing the entire spectrum of pandemic-related interventions and behavioral changes.
METHODS
Excess deaths were defined as observed minus expected deaths. To determine expected deaths, we applied seasonal autoregressive integrated moving average (sARIMA) models to monthly all-cause death data from the Centers for Disease Control and Prevention (January 1, 2015- February 29, 2020, 62 months) and state population counts (2014–2020) to create monthly population and expected death estimates for the study period (July 1, 2021-September 30, 2022), correcting for population losses due to cumulative pandemic-associated excess deaths, as previously described. 5–7 For each state and the District of Columbia, we modeled three age groups of adults (18–49, 50–64, and ≥ 65 years), which were then combined, creating composite excess mortality estimate.
The study assessed five three-month periods based on dominant variants in sequence surveillance; July 1-September 30, 2021 (Early Delta), October 1-December 31, 2021 (Late Delta/Early Omicron), January 1-March 31, 2022 (Omicron BA.1), April 1-June 30, 2022 (Omicron BA.2), July 1-September 30, 2022 (Omicron BA.5). For each period and age group, we determined the ten states with the lowest and highest excess mortality. The number of potential excess deaths averted (had the recorded excess mortality rates observed among the ten states with the lowest rates also been observed by the remaining 41 jurisdictions included) was computed for each period and age group, and summed to determine an all-US estimate of potential adult deaths averted.2,8 Excess mortality was plotted against vaccination rates for each state during each of the five three-month periods.9 Pearson correlations and corresponding p values were calculated.
Analyses were conducted with R version 4.0.3. This study used publicly available data and was not subject to institutional review approval per HHS regulation 45-CFR-46.101(c).
RESULTS
During the five Delta and Omicron periods, the US observed 599,552 (95% CI 518,239–680,864) excess deaths. The ten US states with the highest excess mortality had a combined excess mortality rate over five times the rate of the ten lowest excess mortality states (505.8 versus 97.1 per 100,000, Table 1). Excess deaths per 100,000 people varied widely among states, from 81.4 per (North Dakota) to 482.1 (West Virginia) overall and within periods (Table 1). Among states with higher excess mortality than the 10 lowest excess mortality states, the percent of excess deaths that could have potentially been averted ranged from 36% (Maryland) to 78% (West Virginia) Had all states matched the excess death rate of the ten lowest excess mortality states, an estimated 355,259 excess deaths nationwide could potentially have been averted during the period, representing 59% of the nation’s total contemporaneous excess mortality. Excess deaths per 100,000 were higher during Early Delta (67.9), Late Delta/Early Omicron (61.9), and Omicron BA.1 (62.7) than during Omicron BA.2 (13.5) and Omicron BA.5 (25.6) (Table 1).
Table 1.
Excess deaths and potential deaths averted during the Delta and Omicron periods
Excess Deaths per 100,000 | Deaths Averted, Number (%) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
State | Early Delta | Late Delta/Early Omicron | Omicron BA.1 | Omicron BA.2 | Omicron BA.5 | Total | Early Delta | Late Delta/Early Omicron | Omicron BA.1 | Omicron BA.2 | Omicron BA.5 | Total |
10 states with highest excess mortality | 141.2 | 136.9 | 130.9 | 30.6 | 66.2 | 505.8 | 64,194 (84%) | 25,144 (70%) | 27,037 (60%) | 7237 (113%) | 17,414 (75%) | 141,025 (75%) |
West Virginia | 121.3 | 164.2 | 123.4 | 27.1 | 46 | 482.1 | 1402 (82%) | 1762 (76%) | 1131 (65%) | 440 (115%) | 540 (84%) | 5274 (78%) |
New Mexico | 69.7 | 147.8 | 97 | 29.1 | 27.3 | 370.9 | 792 (70%) | 1808 (75%) | 929 (59%) | 539 (114%) | 335 (75%) | 4402 (73%) |
Mississippi | 152.6 | 55.2 | 108.6 | 11.2 | 41.2 | 368.8 | 2993 (87%) | 449 (36%) | 1599 (65%) | 340 (135%) | 733 (83%) | 6114 (74%) |
Oklahoma | 123.8 | 76.6 | 111.2 | 18.5 | 38.5 | 368.7 | 3158 (84%) | 1281 (55%) | 2262 (67%) | 679 (121%) | 1000 (85%) | 8380 (75%) |
Kentucky | 108.7 | 94 | 93.1 | 25.2 | 28.8 | 349.8 | 3079 (81%) | 2035 (62%) | 1918 (59%) | 1016 (116%) | 800 (79%) | 8849 (72%) |
Alaska | 93.4 | 130.5 | 56.4 | 30 | 35.2 | 345.6 | 414 (80%) | 546 (76%) | 130 (42%) | 185 (112%) | 169 (87%) | 1445 (76%) |
Tennessee | 118.9 | 76.6 | 99.1 | 13.3 | 32.7 | 340.5 | 5345 (83%) | 2241 (54%) | 3334 (62%) | 935 (129%) | 1470 (82%) | 13,325 (72%) |
Alabama | 139.4 | 59.1 | 98.8 | 10.5 | 29.3 | 337 | 4560 (85%) | 881 (39%) | 2304 (61%) | 559 (137%) | 891 (79%) | 9196 (71%) |
Arkansas | 120.5 | 59.2 | 84.4 | 22.5 | 34.6 | 321.1 | 2333 (83%) | 540 (39%) | 1068 (54%) | 617 (117%) | 666 (82%) | 5225 (70%) |
Montana | 88.9 | 134.3 | 54.4 | 6.9 | 33.9 | 318.4 | 578 (76%) | 829 (72%) | 91 (51%) | 103 (125%) | 232 (79%) | 1832 (74%) |
Wyoming | 96.8 | 131.7 | 40.2 | 19.3 | 24.7 | 312.7 | 343 (78%) | 429 (72%) | 48 (51%) | 105 (120%) | 81 (73%) | 1006 (76%) |
Louisiana | 144.7 | 38.8 | 76.5 | 17.3 | 31.5 | 308.8 | 4423 (86%) | 338 (67%) | 1390 (51%) | 679 (115%) | 912 (82%) | 7742 (77%) |
South Carolina | 109.8 | 65.5 | 80 | 14 | 34.4 | 303.7 | 3683 (81%) | 1183 (43%) | 1666 (50%) | 756 (129%) | 1168 (81%) | 8455 (67%) |
Arizona | 69.1 | 110.9 | 71.9 | 19 | 28.9 | 299.7 | 2841 (70%) | 4400 (67%) | 1937 (45%) | 1362 (121%) | 1336 (77%) | 11,876 (67%) |
Florida | 146 | 36.2 | 57.1 | 14.3 | 31.6 | 285.2 | 21,952 (85%) | 356 (33%) | 2652 (30%) | 3297 (129%) | 4336 (76%) | 32,592 (74%) |
Ohio | 51.7 | 123.2 | 83 | 5.6 | 19.4 | 282.9 | 2825 (60%) | 7947 (70%) | 4019 (53%) | 566 (166%) | 1205 (68%) | 16,563 (64%) |
Maine | 41 | 107.1 | 59.2 | 36.9 | 37.6 | 281.8 | 183 (45%) | 740 (62%) | 156 (29%) | 457 (111%) | 332 (80%) | 1868 (63%) |
Georgia | 112.2 | 52 | 73.1 | 14.8 | 29.6 | 281.7 | 7736 (83%) | 1594 (37%) | 3221 (53%) | 1554 (125%) | 2081 (84%) | 16,184 (69%) |
Oregon | 83.2 | 68.6 | 57.6 | 32.2 | 39.3 | 280.9 | 2130 (75%) | 1105 (47%) | 631 (32%) | 1239 (112%) | 1127 (83%) | 6232 (65%) |
Idaho | 84.5 | 107.7 | 50.8 | 4.5 | 29.7 | 277.2 | 909 (76%) | 1032 (67%) | 170 (25%) | 161 (127%) | 344 (80%) | 2616 (66%) |
Missouri | 89.4 | 63 | 80 | 15.3 | 24.8 | 272.5 | 3291 (77%) | 1239 (45%) | 1978 (52%) | 924 (126%) | 806 (70%) | 8238 (65%) |
Nevada | 86.4 | 74.1 | 73.8 | 13.3 | 19.4 | 267 | 1653 (77%) | 987 (53%) | 898 (56%) | 430 (129%) | 349 (71%) | 4317 (67%) |
Vermont | 70.5 | 64.8 | 49 | 40.6 | 37.9 | 262.8 | 248 (69%) | 133 (40%) | 50 (47%) | 229 (110%) | 157 (81%) | 816 (68%) |
Texas | 101.4 | 44.3 | 69 | 13.4 | 27.3 | 255.4 | 18,557 (82%) | 3009 (30%) | 8302 (54%) | 3816 (127%) | 5220 (85%) | 38,904 (68%) |
Kansas | 59.9 | 72.6 | 88.3 | 8.8 | 23.4 | 253 | 882 (66%) | 832 (52%) | 1124 (57%) | 274 (125%) | 429 (73%) | 3541 (62%) |
Delaware | 40.7 | 64.8 | 86.5 | 30.4 | 28.9 | 251.3 | 148 (46%) | 206 (40%) | 350 (51%) | 275 (114%) | 173 (75%) | 1152 (58%) |
Michigan | 32.5 | 114 | 57.1 | 11.8 | 20.2 | 235.6 | 911 (36%) | 6104 (68%) | 1403 (31%) | 1245 (134%) | 1094 (69%) | 10,758 (58%) |
Indiana | 58.7 | 84.7 | 66.6 | 0.3 | 24.4 | 234.6 | 2012 (66%) | 2594 (59%) | 1488 (50%) | 240 (127%) | 999 (74%) | 7334 (61%) |
United States | 67.9 | 61.9 | 62.7 | 13.5 | 25.6 | 231.5 | 123,376 (70%) | 69,597 (43%) | 65,550 (40%) | 45,040 (129%) | 51,696 (78%) | 355,259 (59%) |
Pennsylvania | 35.9 | 93.2 | 66.8 | 13.2 | 20.5 | 229.7 | 1601 (47%) | 5579 (62%) | 2731 (41%) | 1977 (119%) | 1566 (67%) | 13,454 (59%) |
Virginia | 50.4 | 64.7 | 65.2 | 17.4 | 30.2 | 227.9 | 2065 (61%) | 2043 (47%) | 1931 (44%) | 1439 (122%) | 1681 (82%) | 9159 (59%) |
North Carolina | 78.5 | 57.2 | 49.2 | 12.2 | 30.7 | 227.8 | 4895 (74%) | 1838 (38%) | 790 (46%) | 1367 (132%) | 2112 (81%) | 11,003 (66%) |
Illinois | 44.2 | 60.3 | 69.4 | 17.8 | 27.9 | 219.6 | 2356 (55%) | 2483 (42%) | 3134 (46%) | 2102 (122%) | 2152 (80%) | 12,226 (57%) |
Washington | 60 | 49.1 | 42.8 | 22.9 | 36.2 | 210.9 | 2480 (67%) | 945 (31%) | 160 (50%) | 1649 (117%) | 1912 (85%) | 7146 (67%) |
Colorado | 38.6 | 83.6 | 45.6 | 21.2 | 20.4 | 209.4 | 919 (51%) | 2380 (61%) | 540 (25%) | 1164 (117%) | 731 (76%) | 5734 (59%) |
Iowa | 34 | 73.4 | 50.3 | 9.9 | 19.8 | 187.4 | 311 (45%) | 906 (51%) | 274 (22%) | 317 (124%) | 360 (66%) | 2168 (48%) |
Nebraska | 36.1 | 69.1 | 58.1 | 2.3 | 20 | 185.6 | 224 (48%) | 503 (50%) | 307 (36%) | 57 (159%) | 209 (71%) | 1300 (49%) |
Utah | 47.9 | 53.4 | 40.7 | 15.4 | 18.1 | 175.5 | 731 (64%) | 547 (49%) | 226 (26%) | 428 (115%) | 343 (79%) | 2276 (58%) |
New Hampshire | 14.7 | 60.2 | 48.3 | 21.8 | 24.9 | 170 | 254 (45%) | 26 (20%) | 275 (115%) | 177 (64%) | 732 (61%) | |
Minnesota | 23.7 | 76.8 | 40.6 | 12.7 | 15.9 | 169.7 | 149 (49%) | 1810 (54%) | 291 (38%) | 728 (131%) | 431 (128%) | 3410 (64%) |
California | 42.9 | 37.4 | 46.5 | 12.2 | 29.2 | 168.2 | 7391 (56%) | 884 (37%) | 3723 (31%) | 4934 (131%) | 7537 (83%) | 24,468 (60%) |
Wisconsin | 26.8 | 75.9 | 53.3 | 2 | 6.8 | 164.8 | 302 (30%) | 1804 (52%) | 637 (26%) | 290 (165%) | 221 (125%) | 3255 (45%) |
South Dakota | 32 | 68.8 | 54.1 | -11.9 | 9.6 | 152.6 | 60 (50%) | 218 (47%) | 100 (27%) | 38 (121%) | 63 (112%) | 479 (46%) |
New York | 25 | 42.8 | 51.3 | 12.8 | 18.4 | 150.4 | 1886 (29%) | 2559 (131%) | 1476 (129%) | 5921 (62%) | ||
Hawaii | 50.7 | 25.6 | 32.5 | 17.9 | 21.6 | 148.3 | 324 (58%) | 243 (116%) | 159 (64%) | 726 (71%) | ||
Massachusetts | 19.1 | 40 | 43.2 | 18.1 | 25.2 | 145.5 | 494 (29%) | 428 (22%) | 1131 (117%) | 996 (73%) | 3049 (51%) | |
Connecticut | 20.3 | 35.2 | 51.6 | 12.7 | 10.1 | 129.8 | 63 (28%) | 50 (15%) | 337 (24%) | 435 (125%) | 140 (133%) | 1026 (43%) |
New Jersey | 17.2 | 32.8 | 52.2 | 3.8 | 9.6 | 115.6 | 980 (28%) | 485 (130%) | 333 (136%) | 1799 (44%) | ||
Maryland | 20.8 | 34.1 | 51.9 | 1.6 | 6.9 | 115.2 | 619 (27%) | 275 (131%) | 894 (36%) | |||
10 states with lowest excess mortality | 19.5 | 41.7 | 32 | -3.7 | 7.7 | 97.1 | Reference | Reference | Reference | Reference | Reference | Reference |
Rhode Island | 11.8 | 49.4 | 35.4 | -9 | -2.6 | 85 | 20 (40%) | 64 (35%) | 71 (43%) | 36 (139%) | 190 (45%) | |
District of Columbia | 25.5 | 20.2 | 36.5 | 4.3 | -3.4 | 83 | 68 (49%) | 67 (31%) | 63 (110%) | 197 (48%) | ||
North Dakota | 13.2 | 67 | 20.4 | -25.1 | 5.8 | 81.4 | 39 (64%) | 193 (49%) | 44 (74%) | 65 (110%) | 74 (109%) | 415 (65%) |
Early in the study period, state-level excess mortality and vaccination rates were strongly inversely correlated (Fig. 1). The magnitude and strengths of these correlations decreased later in the pandemic.
Fig. 1.
All-cause excess mortality in each US state (and Washington DC) are graphed per 100,000 population against contemporaneous vaccination rate. Each dot corresponds to a state. Each panel shows a different three-month period during the pandemic. Pearson correlations between excess mortality and contemporaneous vaccination rates (with their corresponding p values) are displayed with each period.
DISCUSSION
There were vast differences in excess mortality across US states during from July 2021-September 2022. By using the top quintile of US states as best-case scenario benchmarks, estimates for deaths potentially averted represent results that would be achievable in other states. The inverse relationship between vaccination rates and excess mortality fell markedly during the study period, likely reflecting rising immune seroprevalence over time.
Strengths of this observational study include using excess mortality rather than COVID-19 mortality, which provides better assessments of state-level performance by minimizing cause of death coding biases and including all deaths potentially modified by the pandemic. In addition, the focus on excess mortality (i.e., changes from baseline expected deaths), inherently takes existing risk differences across states into account. Study limitations include use of provisional mortality data and lack of information on causality.
These results suggest that a sizeable fraction of excess mortality observed during the Delta and Omicron eras could have been avoided. Minimizing jurisdiction-level disparities during future outbreaks will depend upon efforts that decrease the prevalence of medical comorbidities (decreasing baseline population health differences between the states), as well as the successful deployment of just-in-time measures including higher vaccination rates (especially earlier in outbreaks, as our analysis shows), wider use of effective therapeutics, and the adaptation and adherence to effective jurisdiction-level non-pharmacologic best practices.
Author Contributions
Dr. Faust and Mr. Renton had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Faust, Du, Krumholz, Renton.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Renton, Faust.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Faust, Du, Li, Lin, Renton.
Administrative, technical, or material support: Faust, Du, Lin.
Supervision: Faust, Krumholz.
Data visualization: Renton.
Data Availability
All mortality and population data are publicly available from the United States CDC.
Declarations:
Conflict of Interest:
Dr. Krumholz reported receiving consulting fees from UnitedHealth, Element Science, Aetna, Reality Labs, F-Prime, and Tesseract/4Catalyst; serving as an expert witness for Martin/Baughman law firm, Arnold and Porter law firm, and Siegfried and Jensen law firm; being a cofounder of Hugo Health, a personal health information platform; being a cofounder of Refactor Health, an enterprise health care, artificial intelligence–augmented data management company; receiving contracts from the Centers for Medicare & Medicaid Services through Yale New Haven Hospital to develop and maintain performance measures that are publicly reported; and receiving grants from Johnson & Johnson outside the submitted work. No other disclosures were reported.
Footnotes
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All mortality and population data are publicly available from the United States CDC.