Abstract
National or state-wide estimates of excess death have limited value to understanding the impact of the pandemic regionally. We assessed excess deaths in a nine-county geographically defined population that had low rates of COVID-19 and widescale availability of testing early in the pandemic, well annotated clinical data, and coverage by two medical examiner’s offices. We compared mortality rates (MR) per 100,000 person-years in 2020 and 2021 to the 2019 referent period and mortality rate ratios (MRR). A total of 177 deaths were attributed to COVID-19 in 2020 (MR = 52 per 100,000 person years) and 219 in 2021 (MR = 66 per 100,000 person-years). Mortality rates due to COVID-19 were highest in males, older persons, those living in rural areas, and those with 7 or more chronic conditions. Compared to 2019, we observed a 10% excess death rate in 2020 (MRR = 1.10 [95% CI, 1.04 to 1.15]) with excess deaths in females, older adults, and those with seven or more chronic conditions. In contrast, we did not observe excess deaths overall in 2021 compared to 2019 (MRR = 1.04 [95% CI 0.99 to 1.10]). However, those aged 18–39 (MRR = 1.36 [95% CI, 1.03 to 1.80) and those with 0 or 1 (MRR = 1.28 [95% CI, 1.05–1.56]) or seven or more chronic conditions (MRR = 1.09 [95% CI 1.03–1.15]) had increased mortality compared to 2019. This work highlights the value of leveraging regional populations that experienced a similar pandemic wave timeline, mitigation strategies, testing availability, and data quality.
Over 1 million deaths in the United States (US) have been reported to be COVID-19 related as of November 2022.1 COVID-19 is currently the third leading cause of death after heart disease and cancer.2 National data suggests excess deaths due to COVID-19 maybe as high as 30% in the first two years of the pandemic.3–6 However, early estimates of excess deaths were largely driven by East Coast states (ie, New Jersey, New York, and Massachusetts) which were severely impacted by the first COVID-19 wave.4 In 2020 in the state of Minnesota, 17% excess death was observed.7 However, these national and state-wide estimates of excess death may not reflect regional differences. Therefore, these results could have limited value to understanding the impact of the COVID-19 pandemic regionally given differences by state as well as by local policies and data access and quality.
More recently, concerns regarding the over- or under-estimation of true COVID-19 deaths may have led to inaccuracies in measuring the impact of COVID-19 on the US population.8–12 Differences in the trajectory and timing of the COVID-19 pandemic, coding practices, death certificate procedures, COVID-19 testing and vaccination availability, and access to individual level data can all influence the assessment of the impact of COVID-19 for any given population.
To address these limitations, we investigated the annual death rate from 2019–2021 in a geographically defined population residing in a nine-county region of southeastern Minnesota. This region is ideal to assess the regional impact of COVID-19 given the low rates of COVID-19 at the beginning of the pandemic, widescale availability of testing early in the pandemic, well annotated clinical data available via the Rochester Epidemiology Project, and coverage by only two medical examiner’s office.
METHODS
Setting and Data Sources
We used the resources of the Rochester Epidemiology Project (REP).13 Briefly, the REP captures and updates comprehensive EHR-derived clinical data within 27 counties of southeast Minnesota and western Wisconsin and has similar age, sex, and ethnic characteristics as the entire Upper Midwest region of the US.13,14 Thus, the REP is uniquely positioned to study the outcomes of the COVID-19 pandemic in a geographically defined community. This study was approved by the Mayo Clinic and Olmsted Medical Center Institutional Review Boards.
Study Population
For each year of the study period (2019–2021), all persons 18 years of age or older who resided in the nine-county region on January 1st were included. These nine Minnesota counties include Dodge, Fillmore, Freeborn, Goodhue, Mower, Olmsted, Steele, Wabasha, and Waseca. Eight of the nine counties are serviced completely or in part by the Southern Minnesota Regional Medical Examiner’s Office15 and thus had consistent protocols for determining cause of death including deaths due to COVID-19. Freeborn County is serviced by the Ramsey County Medical Examiner’s Office.16 Persons who did not provide authorization to use their health records for research were excluded.
Variables
Demographic variables included age at index date (January 1st of each year) sex, and selfreported race/ethnicity. We collapsed race into white, African American, and other/unknown. Address at index date was classified into urban vs rural using the 5-digit zip code approximation of the primary Rural-Urban Commuting Area (RUCA) codes.17–19 Those missing address information were classified as unknown. Clinical data for each patient were collected five years prior to each year under study. International Classification of Disease diagnosis codes were used to define 20 chronic conditions recommended by the US Department of Health and Human Services.20,21 The primary outcome was all-cause mortality using death certificate data. COVID-19 deaths were defined as the underlying or contributing causes on death certificate using International Classification of Diseases, Tenth Revision (ICD-10) (ICD-10) code U07.1.
Statistical Analysis
We compared mortality rates in 2020 and 2021 to the 2019 referent period. For each year, mortality rates were calculated using number of deaths in the nine-county region occurring in the year as the numerators, and the denominators were the population of adult residents as of January 1st of the year. Death counts included all-cause deaths and cause-specific deaths. Mortality rates were compared by year in the entire population and stratified by sex, age categories, race, ethnicity, area of residence, BMI categories, and number of chronic conditions at index. We further stratified by death due to COVID-19 and non-COVID-19 [ie, natural causes (excluding COVID-19) and external causes (eg, accidents, suicide, homicide)]. All rates were directly standardized to the age and sex distribution of the 2010 US total population except for rates stratified by age and by sex, which were standardized to the sex and age distribution, respectively, of the 2010 US total population. Mortality rates (MR) were reported per 100,000 person-years. Mortality rate ratios (MRR) were calculated by dividing standardized mortality rates for the years 2020 and 2021 by the corresponding standardized mortality rate for 2019.
RESULTS
Table 1 summarizes the number of all-cause deaths, standardized MRs, and MRRs for the population for each year by demographic and clinical characteristics. Compared to 2019, we observed a 10% excess death rate in 2020 (MRR = 1.10 [95% CI, 1.04 to 1.15]). In 2020, significant excess deaths were observed in females, older adults, and those with seven or more chronic conditions. Excess deaths were observed for both rural and urban residents and across BMI categories. We also observed a 22% increase in excess deaths in the 18–39-year-old age group (MRR = 1.22 [95% CI, [0.92 to 1.62]). In contrast, we did not observe significant excess deaths overall in 2021 compared to 2019 (MRR = 1.04 [95% CI, 0.99 to 1.10]). However, in 2021 those aged 18–39 (MRR = 1.36 [95% CI, 1.03 to 1.80]) and those with 0 or 1 (MRR = 1.28 [95% CI, 1.05 to 1.56]) or seven or more chronic conditions (MRR = 1.09 [95% CI, 1.03 to 1.15) had increased mortality compared to 2019.
TABLE 1.
Population, All-Cause Deaths, Standardized Mortality Rates, and Mortality Rate Ratios By Demographic and Clinical Characteristics
| Characteristic | 2019 | 2020 | Mortality Rate Ratio (95% CI) 2020 vs 2019 | P value | 2021 | Mortality Rate Ratio (95% CI) 2021 vs 2019 | P value | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Populationa | All-cause deaths | Mortality rate per 100,000 person-years (95% CI) | Populationb | All-cause deaths | Mortality rate per 100,000 person-years (95% CI) | Populationc | All-cause deaths | Mortality rate per 100,000 person-years (95% CI) | |||||
| Total | 245414 | 2885 | 905 (871–939) | 264448 | 3233 | 992 (957–1026) | 1.10 (1.04–1.15) | <0.001 | 271959 | 3151 | 945 (911–978) | 1.04 (0.99–1.1) | 0.096 |
| Sex | |||||||||||||
| Males | 113350 | 1461 | 1084 (1028–1140) | 126224 | 1617 | 1148 (1091–1204) | 1.06 (0.99–1.14) | 0.114 | 130111 | 1627 | 1128 (1073–1184) | 1.04 (0.97–1.12) | 0.270 |
| Females | 132064 | 1424 | 763 (722–804) | 138224 | 1616 | 865 (822–909) | 1.13 (1.10–1.22) | 0.001 | 141848 | 1524 | 803 (762–844) | 1.05 (1–1.13) | 0.163 |
| Age (January 1st of the year) | |||||||||||||
| 18–39 | 93109 | 81 | 91 (71–111) | 103202 | 112 | 111 (90–132) | 1.22 (0.92–1.62) | 0.174 | 106254 | 130 | 124 (103–146) | 1.36 (1.03–1.80) | 0.028 |
| 40–49 | 35007 | 72 | 208 (160–256) | 38972 | 85 | 219 (172–265) | 1.05 (0.77–1.44) | 0.752 | 40193 | 82 | 204 (160–249) | 0.98 (0.72–1.35) | 0.913 |
| 50–59 | 40244 | 191 | 479 (411–547) | 41653 | 204 | 490 (423–557) | 1.02 (0.84–1.25) | 0.825 | 41782 | 207 | 496 (428–563) | 1.03 (0.85–1.26) | 0.739 |
| 60–69 | 37592 | 373 | 991 (890–1091) | 39949 | 420 | 1046 (946–1146) | 1.06 (0.92–1.21) | 0.446 | 41660 | 446 | 1065 (967–1164) | 1.08 (0.94–1.23) | 0.299 |
| 70–79 | 23432 | 561 | 2377 (2180–2573) | 24494 | 664 | 2697 (2492–2903) | 1.13 (1.01–1.27) | 0.027 | 25699 | 650 | 2514 (2321–2708) | 1.06 (0.94–1.18) | 0.329 |
| 80+ | 16030 | 1607 | 9981 (9491–10471) | 16178 | 1748 | 10757 (10250–11263) | 1.08 (1.01–1.15) | 0.030 | 16371 | 1636 | 9910 (9427–10392) | 0.99 (0.93–1.06) | 0.839 |
| Age groups | |||||||||||||
| 18–64 | 188770 | 516 | 279 (254–303) | 205571 | 599 | 294 (270–317) | 1.05 (0.94–1.19) | 0.385 | 210807 | 627 | 299 (276–323) | 1.07 (0.96–1.21) | 0.228 |
| 65+ | 56644 | 2369 | 4171 (4003–4339) | 58877 | 2634 | 4466 (4295–4637) | 1.07 (1.01–1.13) | 0.016 | 61152 | 2524 | 4111 (3950–4272) | 0.99 (0.93–1.04) | 0.610 |
| Race | |||||||||||||
| White | 212305 | 2721 | 905 (870–940) | 228005 | 3022 | 979 (943–1015) | 1.08 (1.03–1.14) | 0.003 | 232660 | 2942 | 935 (901–970) | 1.03 (0.98–1.09) | 0.209 |
| African American | 9267 | 49 | 937 (620–1253) | 10826 | 66 | 1201 (854–1548) | 1.28 (0.89–1.86) | 0.187 | 11641 | 63 | 1034 (734–1334) | 1.10 (0.76–1.60) | 0.605 |
| Other/Unknown | 23842 | 115 | 753 (611–896) | 25617 | 145 | 1021 (840–1202) | 1.36 (1.06–1.73) | 0.015 | 27658 | 146 | 941 (772–1111) | 1.25 (0.98–1.59) | 0.074 |
| Ethnicity | |||||||||||||
| Hispanic | 15229 | 82 | 966 (742–1191) | 17998 | 88 | 909 (700–1118) | 0.94 (0.70–1.27) | 0.691 | 19271 | 103 | 1004 (788–1220) | 1.04 (0.78–1.39) | 0.796 |
| Non-Hispanic/Unknown | 230185 | 2803 | 904 (870–939) | 246450 | 3145 | 993 (958–1029) | 1.10 (1.04–1.16) | <0.001 | 252688 | 3048 | 942 (908–976) | 1.04 (0.99–1.10) | 0.117 |
| Residence | |||||||||||||
| Urban | 133426 | 1254 | 820 (774–866) | 144677 | 1486 | 931 (883–979) | 1.13 (1.05–1.22) | 0.001 | 149765 | 1459 | 878 (832–923) | 1.07 (0.99–1.15) | 0.078 |
| Rural | 111969 | 1631 | 990 (940–1039) | 119752 | 1747 | 1059 (1008–1111) | 1.07 (1.00–1.15) | 0.048 | 122162 | 1689 | 1014 (964–1064) | 1.02 (0.96–1.1) | 0.489 |
| Unknown | 19 | 0 | 19 | 0 | 32 | 3 | |||||||
| Body Mass Index (BMI) | |||||||||||||
| Underweight (< 18.5) | 2657 | 62 | 2767 (1652–3882) | 2917 | 79 | 2983 (1943–4024) | 1.08 (0.77–1.5) | 0.657 | 3083 | 90 | 3698 (2663–4733) | 1.34 (0.97–1.85) | 0.079 |
| Normal (18.5 – 24.9) | 58348 | 812 | 1137 (1049–1225) | 60996 | 886 | 1251 (1160–1343) | 1.1 (1–1.21) | 0.048 | 61159 | 830 | 1178 (1088–1268) | 1.04 (0.94–1.14) | 0.474 |
| Overweight (25 – 29.9) | 69965 | 919 | 788 (733–842) | 73435 | 1039 | 882 (825–938) | 1.12 (1.02–1.22) | 0.013 | 73887 | 966 | 815 (760–870) | 1.03 (0.95–1.13) | 0.458 |
| Obese (≥ 30) | 86322 | 909 | 818 (763–873) | 92787 | 1034 | 918 (860–977) | 1.12 (1.03–1.23) | 0.011 | 94910 | 1047 | 885 (829–941) | 1.08 (0.99–1.18) | 0.083 |
| Missing | 28122 | 183 | 1079 (914–1243) | 34313 | 195 | 1166 (988–1344) | 1.08 (0.88–1.32) | 0.448 | 38920 | 218 | 1064 (910–1218) | 0.99 (0.81–1.2) | 0.889 |
| Number of chronic conditions | |||||||||||||
| 0–1 | 74824 | 154 | 572 (460–684) | 88385 | 188 | 671 (547–795) | 1.17 (0.95–1.45) | 0.142 | 95797 | 247 | 731 (615–847) | 1.28 (1.05–1.56) | 0.017 |
| 2–3 | 52237 | 119 | 481 (378–584) | 54544 | 135 | 479 (381–576) | 1 (0.78–1.27) | 0.971 | 55873 | 154 | 517 (422–612) | 1.08 (0.85–1.37) | 0.553 |
| 4–6 | 53398 | 282 | 526 (461–591) | 54559 | 307 | 585 (516–654) | 1.11 (0.95–1.31) | 0.194 | 54825 | 305 | 521 (459–583) | 0.99 (0.84–1.17) | 0.919 |
| 7+ | 64955 | 2330 | 1546 (1417–1674) | 66960 | 2603 | 1663 (1544–1782) | 1.08 (1.02–1.14) | 0.010 | 65464 | 2445 | 1680 (1547–1813) | 1.09 (1.03–1.15) | 0.004 |
Research-consented adult residents of the 9-county region on Jan 01 of 2019.
Research-consented adult residents of the 9-county region on Jan 01 of 2020.
Research-consented adult residents of the 9-county region on Jan 01 of 2021.
Table 2 summarizes the deaths due to COVID-19 and non-COVID-19 by demographic and clinical characteristics by year. A total of 177 deaths was attributed to COVID-19 in 2020 (MR = 52 per 100,000 person years) and 219 in 2021 (MR = 66 per 100,000 person years). In both 2020 and 2021, mortality rates due to COVID-19 were highest in males, older persons, those living in rural areas, and in those with 7 or more chronic conditions. We observed higher non-COVID-19 deaths in women and those in urban areas in 2020 compared to 2019 and increases in non-COVID-19 deaths in those age 18–39 across 2020 (MRR = 1.15 [95% CI, [0.86–1.54]) and 2021 (MRR = 1.30 [95% CI, 0.98–1.72]).
TABLE 2.
Deaths and Standardized Mortality Rates Attributed To COVID-19, Natural Causes, and Unnatural Causes By Demographic and Clinical Characteristics
| Characteristic | COVID-19 | COVID-19 | Non-COVID Causes | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2020 | 2021 | 2019 | 2020 | Mortality rate ratio (95% CI) 2020 vs 2019 | P value | 2021 | Mortality rate ratio (95% CI) 2021 vs 2019 | P value | |||||||||||
| Population | Deaths | Mortality rate per 100,000 person-years (95% CI) | Population | Deaths | Mortality rate per 100,000 person-years (95% CI) | Population | Deaths | Mortality rate per 100,000 person-years (95% CI) | Population | Deaths | Mortality rate per 100,000 person-years (95% CI) | Population | Deaths | Mortality rate per 100,000 person-years (95% CI) | |||||
| Total | 264448 | 177 | 52 (44–60) | 271959 | 219 | 66 (57–75) | 245414 | 2885 | 905 (871–939) | 264448 | 3056 | 939 (906–973) | 1.04 (0.99–1.09) | 0.151 | 271959 | 2932 | 879 (847–912) | 0.97 (0.92–1.02) | 0.266 |
| Sex | |||||||||||||||||||
| Males | 126224 | 85 | 60 (47–72) | 130111 | 116 | 79 (64–93) | 113350 | 1461 | 1084 (1028–1140) | 126224 | 1532 | 1088 (1033–1143) | 1.00 (0.93–1.08) | 0.920 | 130111 | 1511 | 1049 (996–1103) | 0.97 (0.90–1.04) | 0.370 |
| Females | 138224 | 92 | 47 (37–56) | 141848 | 103 | 55 (44–65) | 132064 | 1424 | 763 (722–804) | 138224 | 1524 | 819 (776–861) | 1.07 (1.00–1.15) | 0.060 | 141848 | 1421 | 749 (709–788) | 0.98 (0.90–1.06) | 0.620 |
| Age (January 1st of the year) | |||||||||||||||||||
| 18–39 | 103202 | 6 | 6 (1–10) | 106254 | 6 | 6 (1–10) | 93109 | 81 | 91 (71–111) | 103202 | 106 | 105 (85–125) | 1.15 (0.86–1.54) | 0.331 | 106254 | 124 | 119 (98–140) | 1.30 (0.98–1.72) | 0.064 |
| 40–49 | 38972 | 0 | 40193 | 5 | 12 (2–23) | 35007 | 72 | 208 (160–256) | 38972 | 85 | 219 (172–265) | 1.05 (0.77–1.44) | 0.752 | 40193 | 77 | 192 (149–235) | 0.92 (0.67–1.27) | 0.623 | |
| 50–59 | 41653 | 5 | 13 (2–24) | 41782 | 16 | 38 (20–57) | 40244 | 191 | 479 (411–547) | 41653 | 199 | 478 (412–545) | 1.00 (0.82–1.22) | 0.980 | 41782 | 191 | 457 (392–522) | 0.95 (0.78–1.17) | 0.646 |
| 60–69 | 39949 | 19 | 47 (25–69) | 41660 | 37 | 88 (60–117) | 37592 | 373 | 991 (890–1091) | 39949 | 401 | 998 (901–1096) | 1.01 (0.88–1.16) | 0.912 | 41660 | 409 | 977 (882–1072) | 0.99 (0.86–1.13) | 0.848 |
| 70–79 | 24494 | 34 | 138 (92–185) | 25699 | 51 | 195 (141–248) | 23432 | 561 | 2377 (2180–2573) | 24494 | 630 | 2559 (2359–2759) | 1.08 (0.96–1.21) | 0.203 | 25699 | 599 | 2319 (2134–2505) | 0.98 (0.87–1.10) | 0.678 |
| 80+ | 16178 | 113 | 695 (566–824) | 16371 | 104 | 636 (513–759) | 16030 | 1607 | 9981 (9491–10471) | 16178 | 1635 | 10062 (9572–10551) | 1.01 (0.94–1.08) | 0.819 | 16371 | 1532 | 9274 (8807–9740) | 0.93 (0.87–1.00) | 0.040 |
| Age groups | |||||||||||||||||||
| 18–64 | 205571 | 18 | 9 (5–13) | 210807 | 48 | 23 (16–29) | 188770 | 516 | 279 (254–303) | 205571 | 581 | 285 (262–308) | 1.02 (0.91–1.15) | 0.721 | 210807 | 579 | 276 (254–299) | 0.99 (0.88–1.12) | 0.896 |
| 65+ | 58877 | 159 | 270 (228–312) | 61152 | 171 | 278 (236–320) | 56644 | 2369 | 4171 (4003–4339) | 58877 | 2475 | 4196 (4030–4362) | 1.01 (0.95–1.06) | 0.837 | 61152 | 2353 | 3833 (3678–3988) | 0.92 (0.87–0.97) | 0.004 |
| Race | |||||||||||||||||||
| White | 228005 | 160 | 49 (42–57) | 232660 | 204 | 65 (56–74) | 212305 | 2721 | 905 (870–940) | 228005 | 2862 | 930 (895–965) | 1.03 (0.98–1.08) | 0.306 | 232660 | 2738 | 871 (837–904) | 0.96 (0.91–1.01) | 0.156 |
| African American | 10826 | 7 | 139 (30–247) | 11641 | 4 | 121 (0–258) | 9267 | 49 | 937 (620–1253) | 10826 | 59 | 1063 (733–1392) | 1.13 (0.78–1.66) | 0.514 | 11641 | 59 | 912 (645–1179) | 0.97 (0.67–1.42) | 0.891 |
| Other/Unknown | 25617 | 10 | 69 (22–115) | 27658 | 11 | 64 (22–106) | 23842 | 115 | 753 (611–896) | 25617 | 135 | 952 (777–1128) | 1.26 (0.99–1.62) | 0.065 | 27658 | 135 | 877 (713–1041) | 1.16 (0.91–1.49) | 0.231 |
| Ethnicity | |||||||||||||||||||
| Hispanic | 17998 | 3 | 27 (0–59) | 19271 | 13 | 112 (48–176) | 15229 | 82 | 966 (742–1191) | 17998 | 85 | 882 (675–1088) | 0.91 (0.67–1.24) | 0.554 | 19271 | 90 | 892 (686–1099) | 0.92 (0.68–1.25) | 0.601 |
| Non-Hispanic/Unknown | 246450 | 174 | 52 (44–60) | 252688 | 206 | 63 (55–72) | 230185 | 2803 | 904 (870–939) | 246450 | 2971 | 941 (906–976) | 1.04 (0.99–1.10) | 0.128 | 252688 | 2842 | 879 (846–912) | 0.97 (0.92–1.02) | 0.283 |
| Residence | |||||||||||||||||||
| Urban | 144677 | 67 | 40 (31–50) | 149765 | 82 | 50 (39–61) | 133426 | 1254 | 820 (774–866) | 144677 | 1419 | 890 (843–937) | 1.09 (1.01–1.17) | 0.034 | 149765 | 1377 | 828 (784–872) | 1.01 (0.94–1.09) | 0.806 |
| Rural | 119752 | 110 | 62 (51–74) | 122162 | 137 | 82 (68–96) | 111969 | 1631 | 990 (940–1039) | 119752 | 1637 | 997 (947–1047) | 1.01 (0.94–1.08) | 0.834 | 122162 | 1552 | 932 (884–980) | 0.94 (0.88–1.01) | 0.090 |
| Unknown | 19 | 0 | 32 | 19 | 0 | 19 | 32 | ||||||||||||
| Body Mass Index (BMI) | |||||||||||||||||||
| Underweight (< 18.5) | 2917 | 2 | 98 (0–263) | 3083 | 3 | 80 (0–181) | 2657 | 62 | 2767 (1652–3882) | 2917 | 77 | 2886 (1858–3913) | 1.04 (0.75–1.46) | 0.806 | 3083 | 87 | 3619 (2588–4649) | 1.31 (0.94–1.81) | 0.107 |
| Normal (18.5 – 24.9) | 60996 | 42 | 50 (34–66) | 61159 | 44 | 61 (41–80) | 58348 | 812 | 1137 (1049–1225) | 60996 | 844 | 1202 (1112–1292) | 1.06 (0.96–1.16) | 0.260 | 61159 | 786 | 1117 (1030–1205) | 0.98 (0.89–1.08) | 0.727 |
| Overweight (25 – 29.9) | 73435 | 60 | 51 (37–65) | 73887 | 62 | 50 (37–63) | 69965 | 919 | 788 (733–842) | 73435 | 979 | 831 (776–886) | 1.05 (0.96–1.15) | 0.247 | 73887 | 904 | 765 (712–818) | 0.97 (0.89–1.06) | 0.534 |
| Obese (≥ 30) | 92787 | 61 | 56 (41–70) | 94910 | 91 | 76 (59–93) | 86322 | 909 | 818 (763–873) | 92787 | 973 | 862 (806–919) | 1.05 (0.96–1.15) | 0.251 | 94910 | 956 | 809 (755–862) | 0.99 (0.90–1.08) | 0.813 |
| Number of chronic conditions | |||||||||||||||||||
| 0–1 | 88385 | 6 | 24 (3–45) | 95797 | 24 | 80 (41–119) | 74824 | 154 | 572 (460–684) | 88385 | 182 | 647 (525–769) | 1.13 (0.91–1.40) | 0.258 | 95797 | 223 | 651 (541–760) | 1.14 (0.93–1.40) | 0.217 |
| 2–3 | 54544 | 9 | 32 (6–57) | 55873 | 14 | 35 (15–55) | 52237 | 119 | 481 (378–583) | 54544 | 126 | 447 (353–541) | 0.93 (0.72–1.19) | 0.567 | 55873 | 140 | 482 (389–575) | 1.00 (0.79–1.28) | 0.980 |
| 4–6 | 54559 | 17 | 35 (17–53) | 54825 | 26 | 39 (24–55) | 53398 | 282 | 526 (461–591) | 54559 | 290 | 550 (483–617) | 1.05 (0.89–1.23) | 0.591 | 54825 | 279 | 482 (422–542) | 0.92 (0.78–1.08) | 0.303 |
| 7+ | 66960 | 145 | 68 (50–85) | 65464 | 155 | 95 (71–119) | 64955 | 2330 | 1546 (1417–1674) | 66960 | 2458 | 1595 (1477–1713) | 1.03 (0.98–1.09) | 0.278 | 65464 | 2290 | 1585 (1454–1716) | 1.03 (0.97–1.09) | 0.391 |
DISCUSSION
In this regional population, the challenges of accurate assessment of COVID-19 and non-COVID-19 mortality were largely mitigated. We observed 10% excess deaths in adults with roughly half of the excess deaths due to COVID-19 in a nine-county region of the Upper Midwest in 2020. Overall mortality rates returned to pre-pandemic levels in 2021 despite a slightly higher mortality rate due to COVID-19 compared to the first year of the pandemic. Both the excess death estimates and COVID-19 mortality were markedly lower than the previously reported national and state-wide estimates.3–7
The adoption of infection mitigation strategies varied markedly by state as well as by regional and local levels. In Minnesota, for example, where our study was conducted, statewide stay at home orders were issued in Spring of 2020 and mask mandates July of 2020.22 The easing of mitigation strategies in 2021 varied by region and municipality with large metropolitan centers tending to have the tightest restrictions (eg, vaccination required in restaurants in Minneapolis and Saint Paul). Continued school closures and mask mandates were less common in suburban, rural, and non-urban areas. By the end of 2021, roughly 70% of the Minnesota population had received at least one dose of a COVID-19 vaccination.23 Therefore, investigations at the regional and local level are critical to evaluate the impact of the pandemic and glean insight into the effect of state and local policies.
Data access and coding practices result in variability in accurate identification of death due to COVID-19.8–12 Use of provisional death certificate data, absence of a specific ICD-10 code for COVID-19 early in the pandemic, variations by region in access to accurate testing, lack of consistent coding by medical examiners, and lack of access to individual level clinical data are all cited concerns. A COVID-19 specific ICD code (U07.1) was introduced on April 1, 2020. For suspected COVID-19 deaths prior to that date, the Centers for Disease Control and Prevention recommended the use of B97.29 (“other coronavirus as the cause of diseases classified elsewhere”). Availability of testing also varied by region. In Minnesota, Mayo Clinic Medical Labs expanded testing in March 2020 for widescale use. Thus, coding issues, varying medical examiner protocols, and access to testing all are potential limitations for accurate assessment of the cause of death. The nine-county region under study is serviced by only two medical examiners and protocols were largely consistent within the region. In addition, this region had low rates of COVID-19 early in the pandemic and easy access to testing that likely reduced the misclassification of COVID-19 diagnosis. Finally, national and statewide data often lacks individual level data to assess differences in mortality rates by key demographic and clinical factors.
CONCLUSION
In conclusion, observed rates of excess mortality, and COVID-19 mortality were lower in our region than reported nationally and in the state. This underscores the need to focus on regional populations that experienced a similar pandemic wave timeline, mitigation strategies, testing and vaccine availability, and data quality to study the effects and response to the pandemic.
Role of the Funding Source:
The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Grant Support:
This study was supported by a grant from the Department of Cardiovascular Medicine, Mayo Clinic and used the resources of the Rochester Epidemiology Project (REP) medical records-linkage system, which is supported by the National Institute on Aging (NIA; AG 058738), supported by the Mayo Clinic Research Committee, and by fees paid annually by REP users. The content of this article is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health (NIH) or the Mayo Clinic.
Abbreviations and Acronyms:
- MR
mortality rates
- MRR
mortality rate ratios
- REP
Rochester Epidemiology Project
- US
United States
Footnotes
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POTENTIAL COMPETING INTERESTS
None
CRediT Statement
Suzette Bielinski, Nicholas Larson, Jennifer St. Sauver: Study concept and design.
Suzette Bielinski, Sheila Manemann, Guilherme Lopes, Ruoxiang Jiang, Susan Weston, R. Ross Reichard, Aaron Norman, Celine Vachon, Paul Takahashi, Mandeep Singh, Nicholas Larson, Véronique Roger, Jennifer St. Sauver: Acquisition, analysis, or interpretation of data.
Suzette Bielinski, Sheila Manemann, Nicholas Larson, Susan Weston, Ruoxiang Jiang: Drafting of the manuscript.
Suzette Bielinski, Sheila Manemann, Guilherme Lopes, Ruoxiang Jiang, Susan Weston, R. Ross Reichard, Aaron Norman, Celine Vachon, Paul Takahashi, Mandeep Singh, Nicholas Larson, Véronique Roger, Jennifer St. Sauver: Critical revision of the manuscript for important intellectual content.
Nicholas Larson, Susan Weston, Ruoxiang Jiang: Statistical analysis.
Suzette Bielinski, Mandeep Singh: Obtained funding.
Sheila Manemann, Ruoxiang Jiang, Aaron Norman: Administrative, technical, or material support.
Dr. Bielinski: Study supervision.
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