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Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 Dec 20;41(3):844–854. doi: 10.1016/j.vaccine.2022.12.036

A safety study evaluating non-COVID-19 mortality risk following COVID-19 vaccination

Stanley Xu a,, Runxin Huang a, Lina S Sy a, Vennis Hong a, Sungching C Glenn a, Denison S Ryan a, Kerresa Morrissette a, Gabriela Vazquez-Benitez b, Jason M Glanz c, Nicola P Klein d, Bruce Fireman d, David McClure e, Elizabeth G Liles f, Eric S Weintraub g, Hung-Fu Tseng a, Lei Qian a
PMCID: PMC9763207  PMID: 36564276

Abstract

Background

The safety of COVID-19 vaccines plays an important role in addressing vaccine hesitancy. We conducted a large cohort study to evaluate the risk of non-COVID-19 mortality after COVID-19 vaccination while adjusting for confounders including individual-level demographics, clinical risk factors, health care utilization, and community-level socioeconomic risk factors.

Methods

The retrospective cohort study consisted of members from seven Vaccine Safety Datalink sites from December 14, 2020 through August 31, 2021. We conducted three separate analyses for each of the three COVID-19 vaccines used in the US. Crude non-COVID-19 mortality rates were reported by vaccine type, age, sex, and race/ethnicity. The counting process model for survival analyses was used to analyze non-COVID-19 mortality where a new observation period began when the vaccination status changed upon receipt of the first dose and the second dose. We used calendar time as the basic time scale in survival analyses to implicitly adjust for season and other temporal trend factors. A propensity score approach was used to adjust for the potential imbalance in confounders between the vaccinated and comparison groups.

Results

For each vaccine type and across age, sex, and race/ethnicity groups, crude non-COVID-19 mortality rates among COVID-19 vaccinees were lower than those among comparators. After adjusting for confounders with the propensity score approach, the adjusted hazard ratios (aHRs) were 0.46 (95% confidence interval [CI], 0.44–0.49) after dose 1 and 0.48 (95% CI, 0.46–0.50) after dose 2 of the BNT162b2 vaccine, 0.41 (95% CI, 0.39–0.44) after dose 1 and 0.38 (95% CI, 0.37–0.40) after dose 2 of the mRNA-1273 vaccine, and 0.55 (95% CI, 0.51–0.59) after receipt of Ad26.COV2.S.

Conclusion

While residual confounding bias remained after adjusting for several individual-level and community-level risk factors, no increased risk was found for non-COVID-19 mortality among recipients of three COVID-19 vaccines used in the US.

1. Introduction

Four COVID-19 vaccines have been authorized in the United States since December 14, 2020. The two mRNA COVID-19 vaccines, BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna), have been widely used while the adenoviral vector vaccine, Ad26.COV2.S (Janssen), has been available but used more sparingly compared to the mRNA vaccines. NVX-CoV2373 (Novavax) was authorized in the United States in July 2022, after the study period.

BNT162b2 and mRNA-1273 were initially authorized as a 2-dose primary series, and Ad26.COV2.S as a 1-dose primary series.[1], [2], [3], [4] Clinical trials showed that the three COVID-19 vaccines (mRNA vaccines and Ad26.COV2.S) were well-tolerated with local and systemic reactions such as injection site pain, fever, chills, muscle aches, joint pain, and headache commonly noted.[5], [6], [7] Post-emergency use authorization observational studies showed associations with some rare, clinically serious adverse events such as myocarditis or pericarditis following mRNA COVID-19 vaccination, Guillain-Barré Syndrome following Ad26.COV2.S vaccination,[8], [9], [10], [11], [12], [13] and thrombosis with thrombocytopenia syndrome following Ad26.COV2.S vaccination.[7], [14].

Several studies have examined mortality risk after COVID-19 vaccination, although they had limited sample size, were restricted to specialized populations (e.g., nursing home residents), lacked a comparison group, or did not comprehensively adjust for confounders. A moderate-sized cohort study of 21,222 nursing home residents compared all-cause mortality between COVID-19 mRNA vaccinees and unvaccinated residents and found that vaccinees had lower all-cause mortality after adjusting for some confounders.[15] A longitudinal study compared mortality rates over time among vaccinated patients in the U.S. Veterans Affairs health system with no history of COVID-19 and found no evidence of excess mortality associated with receipt of mRNA vaccines.[16] Preliminary results in a large cohort study showed that COVID-19 vaccine recipients had lower rates of non-COVID-19 mortality than did unvaccinated comparators after adjusting for age, sex, race/ethnicity, and study site,[17] suggesting possible effects of unmeasured confounders and healthy vaccinee effects (i.e., vaccinated persons tend to be healthier than unvaccinated persons).[18], [19].

This study aimed to evaluate the risk of non-COVID-19 mortality after COVID-19 vaccination in a large cohort of individuals using survival analyses and an improved inverse probability of treatment weighting (IPTW) approach to adjust for confounders including individual-level demographics, clinical risk factors, health care utilization, and community-level socioeconomic risk factors. We hypothesized that COVID-19 vaccines do not increase the risk for non-COVID-19 mortality despite their association with some rare severe adverse events.

2. Methods

2.1. Study design and population

We conducted a retrospective cohort study among health plan members aged ≥ 12 years enrolled in seven Vaccine Safety Datalink (VSD) sites (Kaiser Permanente [KP] Southern California, KP Northern California, KP Colorado, KP Northwest, KP Washington, HealthPartners, and Marshfield Clinic). The VSD population is socio-economically diverse and represents about 3% of the U.S. population.[20] Vaccination status was assessed from December 14, 2020 through June 30, 2021, and deaths were assessed until August 31, 2021 to allow at least two months of follow-up. Follow-up was censored upon any COVID-19 vaccination between July 1, 2021 and August 31, 2021.

2.2. Exposure

The exposure was vaccination with one of three authorized COVID-19 vaccines: BNT162b2, mRNA-1273, and Ad26.COV2.S. Three separate analyses were conducted for each of the three vaccines with separate comparator groups. We performed weekly frequency matching on age and sex within each VSD site.[17] For a given week and a pre-specified matching ratio, COVID-19 vaccine recipients of dose 1 during the week were identified and their vaccination dates were used to assign index dates to comparators who had not been vaccinated as of that date and were randomly selected according to the matching ratio. We allowed those comparators who were matched in a previous week to switch to being vaccinated upon receiving a COVID-19 vaccine. The matched “comparators” thus included both pre-vaccination person-time among COVID-19 vaccinees as well as unvaccinated person-time of individuals who did not receive any COVID-19 vaccines by June 30, 2021.

For the mRNA vaccines, individuals who received the vaccines from December 14, 2020 through June 30, 2021 were included in the vaccinated group. The weekly frequency matching ratio of vaccinated individuals and comparators was about 1:1. Exposure had three levels: pre-vaccination, after dose 1 and after dose 2. For Ad26.COV2.S recipients, individuals who received the vaccine from February 27, 2021 through June 30, 2021 were included in the vaccinated group, and the matching ratio was 1:4. Exposure had two levels: pre-vaccination and after dose 1.

Individuals were followed until death, disenrollment, receipt of a COVID-19 vaccine for unvaccinated comparators, or the end of follow-up (August 31, 2021), whichever occurred first. When individuals received different vaccine products for dose 1 versus dose 2, their follow-up was censored upon receipt of the second mismatched dose. To be included in this study, individuals were required to have ≥ 1 year enrollment in the health system before their index dates for their confounders to be properly measured. To increase comparability of health care-seeking behavior between COVID-19 vaccinated and unvaccinated individuals, we required that comparators had received ≥ 1 dose of influenza vaccine in the two years prior to the index date.

2.3. Outcomes

Since this was a safety study of COVID-19 vaccines, the primary outcome was non-COVID-19-associated death during follow-up, as COVID-19 vaccination was expected to be protective against COVID-19-associated death. We first identified deaths through VSD data files capturing hospital deaths and deaths reported to health plans, and then excluded deaths occurring ≤ 30 days following a COVID-19 diagnosis or a positive SARS-CoV-2 test. Secondary outcomes included 30-day non-COVID-19 mortality in which follow-up was censored 30 days after the index date, and all-cause mortality which included deaths from all causes including COVID-19.

2.4. Confounders

We considered individual-level confounders including age, sex, race/ethnicity, Medicaid status, history of COVID-19, number of combined outpatient and virtual visits within one year prior to the index date, inpatient visit (yes/no) within one year prior to the index date, Emergency Department (ED) visit (yes/no) within one year prior to the index date, inpatient or ED visit within 7 days prior to the index date (yes/no), presence of frailty measured within one year prior to the index date (yes if frailty index ≥ 0.11; no, otherwise),[21] Charlson Comorbidity Index (CCI) within one year prior to the index date, receipt of another vaccine within 14 days before or after the index date, neighborhood median household income, and neighborhood education level. Healthcare Common Procedure Coding System (HCPCS) codes that were used in the development of frailty scores were not available in this study, resulting in lower frailty scores. Therefore, we chose a frailty score of 0.11 as the cut-off for the presence of frailty. Neighborhood-level education was defined as < 50% or ≥ 50% of the neighborhood attaining > high school education.

2.5. Statistical analyses

For each vaccine type and dose and comparator group, crude non-COVID-19 mortality rates per 100 person-years were calculated as (number of non-COVID-19 deaths/person-years) × 100.

To reduce confounding bias in this observational study, we employed a propensity score weighting approach to adjust for the potential imbalance in confounders between the vaccinated and the comparison groups.[22], [23] Separate propensity score models were created for the three vaccine cohorts. For the two mRNA vaccines, we fit a multinomial model because the dependent variable in the propensity score model, COVID-19 vaccination, had three levels.[24] For Ad26.COV2.S, we fit a logistic regression model because the dependent variable, COVID-19 vaccination, had two levels. Based on the propensity score models we calculated stabilized weights (SW),[25] an improved inverse probability weighting approach in survival analyses. SWs not only reduce the impact of some extreme weights but also preserve the original sample size.[26] Balance in measured confounders between vaccinated and comparison groups was assessed with absolute standardized mean differences (SMD) before and after applying SWs. An absolute standardized mean difference of <0.10 indicated good confounder balance.[27].

The counting process model for survival analyses was used. A new observation period began when the vaccination status changed upon receipt of the first dose and the second dose.[28], [29] We used calendar time as the basic time scale in survival analyses to implicitly adjust for season and other temporal trend factors.[30] We estimated both unadjusted and SW-adjusted hazard ratios (aHR) and 95% confidence intervals (CI) of vaccination effects on non-COVID-19 mortality, 30-day non-COVID-19 mortality, and all-cause mortality.

To detect possible bias from inadequate confounding adjustment, we also conducted exploratory negative control outcome analyses [31] separately for each of the three COVID-19 vaccines in which we replaced the outcome of death with first occurrence of trauma or injury hospitalization during the exposure follow-up period (i.e., vaccinated or unvaccinated). We hypothesize that the negative control outcome, hospitalization for trauma or injury, shares the same potential sources of bias with our primary outcome (death) but cannot plausibly be related to COVID-19 vaccination.[18], [32] Trauma or injury hospitalizations were identified with the following ICD-10 codes: S00-T88 for injury, poisoning and certain other consequences of external causes, and V00-Y99 for external causes of morbidity.[33] A similar analytic approach as for the primary outcome (death) was used in the negative control outcome analyses. SWs were estimated from propensity score models where the same covariates for the primary outcome were included, and the receipt of COVID-19 vaccination was the dependent variable. We analyzed time since the calendar date of receiving the first dose among vaccinees or the corresponding index date among comparators to an incident trauma or injury hospitalization during the exposure follow-up period with and without applying SWs.

3. Results

3.1. Characteristics of COVID-19 vaccine recipients and their comparators

In total, 6,974,817 unique individuals (vaccinated and unvaccinated) were included in the study, with 5,107,262 unique individuals for analyses of BNT162b2, 4,037,724 unique individuals for analyses of mRNA-1273, and 1,510,652 unique individuals for analyses of Ad26.COV2.S. Some comparators appeared in more than one analytic cohort. By June 30, 2021, 3.3 million individuals in the study received at least one dose of BNT162b2, and 93.4% of them received two doses (Table 1 ); 2.4 million individuals received at least one dose of mRNA-1273, and 95.0% of them received two doses (Table 2 ). There were 331,282 individuals who received Ad26.COV2.S by June 30, 2021 (Table 3 ). Across vaccine types and doses, vaccine recipients and their comparator groups were comparable, with a few minor differences between groups (SMD greater than 0.10). However, application of SWs to the cohorts reduced the absolute SMD for all confounders to below 0.01 (Fig. 1 ).

Table 1.

Characteristics of BNT162b2 recipients and their comparators during the period from December 14, 2020 to June 30, 2021.

BNT162b2 recipients, no. (%)
Comparison group¥, no. (%)
Dose 1 Dose 2
Total 3,281,777 (100.0) 3,066,574 (100.0) 3,019,838 (100.0)
Age (years)
12–17 364,257 (11.1) 307,340 (10.0) 325,120 (10.8)
18–44 1,176,050 (35.8) 1,089,035 (35.5) 1,093,983 (36.2)
45–64 1,016,110 (31.0) 963,741 (31.4) 905,385 (30.0)
65–74 428,127 (13.0) 415,983 (13.6) 407,341 (13.5)
75–84 218,071 (6.6) 213,569 (7.0) 210,658 (7.0)
85+ 79,162 (2.4) 76,906 (2.5) 77,351 (2.6)
Sex
Female 1,776,526 (54.1) 1,663,975 (54.3) 1,672,856 (55.4)
Male 1,505,251 (45.9) 1,402,599 (45.7) 1,346,982 (44.6)
Race/ethnicity
Hispanic 732,464 (22.3) 667,054 (21.8) 769,843 (25.5)
Non-Hispanic White 1,419,254 (43.2) 1,347,867 (44.0) 1,333,749 (44.2)
Non-Hispanic Asian 553,048 (16.9) 522,556 (17.0) 437,603 (14.5)
Non-Hispanic Black 175,110 (5.3) 159,656 (5.2) 172,106 (5.7)
Missing 252,620 (7.7) 230,718 (7.5) 173,204 (5.7)
Multiple/Other 149,281 (4.5) 138,723 (4.5) 133,333 (4.4)
Number of outpatient and virtual visits in 1 year prior to index date
0 569,221 (17.3) 410,690 (13.4) 391,313 (13.0)
1–4 1,294,871 (39.5) 1,246,752 (40.7) 1,183,465 (39.2)
5–9 763,240 (23.3) 756,151 (24.7) 797,355 (26.4)
10+ 654,445 (19.9) 652,981 (21.3) 647,705 (21.4)
Had inpatient visit in 1 year prior to index date
No 3,075,590 (93.7) 2,874,205 (93.7) 2,776,341 (91.9)
Yes 206,187 (6.3) 192,369 (6.3) 243,497 (8.1)
Had Emergency Department visit in 1 year prior to index date
No 2,866,722 (87.4) 2,677,917 (87.3) 2,562,418 (84.9)
Yes 415,055 (12.6) 388,657 (12.7) 457,420 (15.1)
Had inpatient or Emergency Department visit within 7 days prior to index date
No 3,265,317 (99.5) 3,050,558 (99.5) 2,992,835 (99.1)
Yes 16,460 (0.5) 16,016 (0.5) 27,003 (0.9)
Medicaid enrollment in 2019
No 3,075,661 (93.7) 2,884,031 (94.0) 2,747,252 (91.0)
Yes 206,116 (6.3) 182,543 (6.0) 272,586 (9.0)
Receipt of another vaccine within 14 days before or after index date
No 3,262,268 (99.4) 3,051,535 (99.5) 2,958,648 (98.0)
Yes 19,509 (0.6) 15,039 (0.5) 61,190 (2.0)
Neighborhood median household income
<$40,000 141,861 (4.3) 128,552 (4.2) 157,358 (5.2)
$40,000-$59,999 563,553 (17.2) 517,023 (16.9) 588,752 (19.5)
$60,000-$79,999 775,073 (23.6) 720,936 (23.5) 745,303 (24.7)
$80,000-$99,999 686,974 (20.9) 643,751 (21.0) 620,156 (20.5)
$100,000+ 1,071,901 (32.7) 1,016,800 (33.2) 864,108 (28.6)
Missing 42,415 (1.3) 39,512 (1.3) 44,161 (1.5)
Charlson Comorbidity Index in 1 year prior to index date
0 2,446,561 (74.5) 2,269,703 (74.0) 2,160,227 (71.5)
1–2 564,342 (17.2) 535,881 (17.5) 569,323 (18.9)
3+ 270,874 (8.3) 260,990 (8.5) 290,288 (9.6)
Frailty score in 1 year prior to index date
<0.11 3,208,658 (97.8) 2,997,403 (97.7) 2,943,815 (97.5)
≥0.11 73,119 (2.2) 69,171 (2.3) 76,023 (2.5)
Incident COVID-19 diagnosis/lab test before index date
No 3,069,217 (93.5) 2,867,240 (93.5) 2,772,957 (91.8)
Yes 212,560 (6.5) 199,334 (6.5) 246,881 (8.2)
Neighborhood-level education
≤high school 562,993 (17.2) 510,978 (16.7) 614,676 (20.4)
>high school 2,676,180 (81.5) 2,515,919 (82.0) 2,360,727 (78.2)
Missing 42,604 (1.3) 39,677 (1.3) 44,435 (1.5)
¥

The matched comparators included both pre-vaccination person-time among COVID-19 vaccinees as well as unvaccinated person-time of individuals who did not receive any COVID-19 vaccines by June 30, 2021.

Frequency matching variable.

Table 2.

Characteristics of mRNA-1273 recipients and their comparators during the study from December 14, 2020 to June 30, 2021.

mRNA-1273 recipients, no. (%)
Comparison group¥, no. (%)
Dose 1 Dose 2
Total 2,393,784 (100.0) 2,274,079 (100.0) 2,360,007 (100.0)
Age (years)
18–44 825,774 (34.5) 764,853 (33.6) 823,644 (34.9)
45–64 849,745 (35.5) 809,990 (35.6) 845,919 (35.8)
65–74 437,465 (18.3) 424,837 (18.7) 413,925 (17.5)
75–84 213,918 (8.9) 209,339 (9.2) 209,650 (8.9)
85+ 66,882 (2.8) 65,060 (2.9) 66,869 (2.8)
Sex
Female 1,305,698 (54.5) 1,244,432 (54.7) 1,287,818 (54.6)
Male 1,088,086 (45.5) 1,029,647 (45.3) 1,072,189 (45.4)
Race/ethnicity
Hispanic 560,236 (23.4) 525,531 (23.1) 594,930 (25.2)
Non-Hispanic White 1,085,612 (45.4) 1,040,255 (45.7) 1,072,255 (45.4)
Non-Hispanic Asian 343,451 (14.3) 329,430 (14.5) 322,237 (13.7)
Non-Hispanic Black 137,479 (5.7) 128,875 (5.7) 139,891 (5.9)
Missing 163,089 (6.8) 151,443 (6.7) 130,782 (5.5)
Multiple/Other 103,917 (4.3) 98,545 (4.3) 99,912 (4.2)
Number of outpatient and virtual visits in 1 year prior to index date
0 349,156 (14.6) 234,730 (10.3) 283,802 (12.0)
1–4 881,265 (36.8) 862,508 (37.9) 871,239 (36.9)
5–9 647,182 (27.0) 653,009 (28.7) 677,812 (28.7)
10+ 516,181 (21.6) 523,832 (23.0) 527,154 (22.3)
Had inpatient visit in 1 year prior to index date
No 2,224,639 (92.9) 2,114,772 (93.0) 2,154,089 (91.3)
Yes 169,145 (7.1) 159,307 (7.0) 205,918 (8.7)
Had Emergency Department visit in 1 year prior to index date
No 2,058,786 (86.0) 1,956,838 (86.0) 1,974,117 (83.6)
Yes 334,998 (14.0) 317,241 (14.0) 385,890 (16.4)
Had inpatient or Emergency Department visit within 7 days prior to index date
No 2,381,537 (99.5) 2,261,038 (99.4) 2,337,408 (99.0)
Yes 12,247 (0.5) 13,041 (0.6) 22,599 (1.0)
Medicaid enrollment in 2019
No 2,264,951 (94.6) 2,154,562 (94.7) 2,184,722 (92.6)
Yes 128,833 (5.4) 119,517 (5.3) 175,285 (7.4)
Receipt of another vaccine within 14 days before or after index date
No 2,382,043 (99.5) 2,266,083 (99.6) 2,312,634 (98.0)
Yes 11,741 (0.5) 7,996 (0.4) 47,373 (2.0)
Neighborhood median household income
<$40,000 120,048 (5.0) 111,980 (4.9) 127,576 (5.4)
$40,000-$59,999 460,540 (19.2) 433,884 (19.1) 471,255 (20.0)
$60,000-$79,999 593,354 (24.8) 562,006 (24.7) 590,812 (25.0)
$80,000-$99,999 505,885 (21.1) 481,953 (21.2) 487,902 (20.7)
$100,000+ 682,916 (28.5) 654,963 (28.8) 650,839 (27.6)
Missing 31,041 (1.3) 29,293 (1.3) 31,623 (1.3)
Charlson Comorbidity Index in 1 year prior to index date
0 1,633,820 (68.3) 1,544,175 (67.9) 1,584,300 (67.1)
1–2 496,513 (20.7) 475,844 (20.9) 496,275 (21.0)
3+ 263,451 (11.0) 254,060 (11.2) 279,432 (11.8)
Frailty score in 1 year prior to index date
<0.11 2,329,733 (97.3) 2,213,223 (97.3) 2,292,285 (97.1)
≥0.11 64,051 (2.7) 60,856 (2.7) 67,722 (2.9)
Incident COVID-19 diagnosis/lab test before index date
No 2,226,925 (93.0) 2,113,743 (92.9) 2,156,274 (91.4)
Yes 166,859 (7.0) 160,336 (7.1) 203,733 (8.6)
Neighborhood-level education
≤high school 462,407 (19.3) 433,417 (19.1) 498,099 (21.1)
>high school 1,900,073 (79.4) 1,811,117 (79.6) 1,830,037 (77.5)
Missing 31,304 (1.3) 29,545 (1.3) 31,871 (1.4)
¥

The matched comparators included both pre-vaccination person-time among COVID-19 vaccinees as well as unvaccinated person-time of individuals who did not receive any COVID-19 vaccines by June 30, 2021.

Frequency matching variable.

Table 3.

Characteristics of Ad26.COV2.S recipients and their comparators during the period from December 14, 2020 to June 30, 2021.

Ad26.COV2.S recipients, no. (%) Comparison group¥, no. (%)
Total 331,282 (100.0) 1,258,599 (100.0)
Age (years)
18–44 131,599 (39.7) 511,250 (40.6)
45–64 155,104 (46.8) 577,371 (45.9)
65–74 29,468 (8.9) 112,122 (8.9)
75–84 10,617 (3.2) 40,310 (3.2)
85+ 4,494 (1.4) 17,546 (1.4)
Sex
Female 157,429 (47.5) 612,728 (48.7)
Male 173,853 (52.5) 645,871 (51.3)
Race/ethnicity
Hispanic 68,961 (20.8) 314,622 (25.0)
Non-Hispanic White 155,004 (46.8) 556,914 (44.2)
Non-Hispanic Asian 43,545 (13.1) 181,479 (14.4)
Non-Hispanic Black 20,991 (6.3) 71,427 (5.7)
Missing 29,517 (8.9) 81,437 (6.5)
Multiple/Other 13,264 (4.0) 52,720 (4.2)
Number of outpatient and virtual visits in 1 year prior to index date
0 67,668 (20.4) 189,753 (15.1)
1–4 125,334 (37.8) 477,118 (37.9)
5–9 75,508 (22.8) 328,337 (26.1)
10+ 62,772 (18.9) 263,391 (20.9)
Had inpatient visit in 1 year prior to index date
No 309,083 (93.3) 1,157,824 (92.0)
Yes 22,199 (6.7) 100,775 (8.0)
Had Emergency Department visit in 1 year prior to index date
No 286,692 (86.5) 1,060,411 (84.3)
Yes 44,590 (13.5) 198,188 (15.7)
Had inpatient or Emergency Department visit within 7 days prior to index date
No 327,438 (98.8) 1,246,839 (99.1)
Yes 3,844 (1.2) 11,760 (0.9)
Medicaid enrollment in 2019
No 311,840 (94.1) 1,150,608 (91.4)
Yes 19,442 (5.9) 107,991 (8.6)
Receipt of another vaccine within 14 days before or after index date
No 329,640 (99.5) 1,238,863 (98.4)
Yes 1,642 (0.5) 19,736 (1.6)
Neighborhood median household income
<$40,000 16,468 (5.0) 66,153 (5.3)
$40,000-$59,999 63,408 (19.1) 250,320 (19.9)
$60,000-$79,999 79,691 (24.1) 311,262 (24.7)
$80,000-$99,999 67,861 (20.5) 256,366 (20.4)
$100,000+ 97,945 (29.6) 351,414 (27.9)
Missing 5,909 (1.8) 23,084 (1.8)
Charlson Comorbidity Index in 1 year prior to index date
0 255,939 (77.3) 941,713 (74.8)
1–2 52,807 (15.9) 222,535 (17.7)
3+ 22,536 (6.8) 94,351 (7.5)
Frailty score in 1 year prior to index date
<0.11 322,938 (97.5) 1,224,293 (97.3)
≥0.11 8,344 (2.5) 34,306 (2.7)
Incident COVID-19 diagnosis/lab test before index date
No 304,817 (92.0) 1,136,111 (90.3)
Yes 26,465 (8.0) 122,488 (9.7)
Neighborhood-level education
≤high school 61,554 (18.6) 258,423 (20.5)
>high school 263,771 (79.6) 976,978 (77.6)
Missing 5,957 (1.8) 23,198 (1.8)
¥

The matched comparators included both pre-vaccination person-time among COVID-19 vaccinees as well as unvaccinated person-time of individuals who did not receive any COVID-19 vaccines by June 30, 2021.

Frequency matching variable.

Fig. 1.

Fig. 1

Absolute standardized mean difference in characteristics among BNT162b2, mRNA-1273, and Ad26.COV2.S recipients and their comparators before and after applying stabilized weights.

The composition, sample sizes, and person-years of the study population are presented in Supplemental Table 1 after allowing unvaccinated comparators to switch to being vaccinated upon receiving a COVID-19 vaccine. Compared to vaccinated individuals, the average of follow-up among comparators was shorter mainly due to censoring upon receipt of a COVID-19 vaccine. The ratios of sample size of those who were ever vaccinated to those never vaccinated as of June 30, 2021 were 3,281,777: 902,814 = 1:0.28 for BNT162b2, 2,393,784: 676,955 = 1:0.28 for mRNA-1273, and 331,282: 523,615 = 1:1.6 for Ad26.COV2.S.

3.2. Crude mortality rates

Across vaccine types and doses, the crude non-COVID-19 mortality rates in vaccine recipients were lower than those in the corresponding comparator group. For BNT162b2, the crude non-COVID-19 mortality rates were 0.76 and 0.66 per 100 person-years for dose 1 and dose 2, respectively, while the comparator group had a crude mortality rate of 1.76 per 100 person-years (Table 4 ). For mRNA-1273, the crude non-COVID-19 mortality rates were 0.76 and 0.67 per 100 person-years for dose 1 and dose 2, respectively, versus 2.04 in the comparator group (Table 5 ). Ad26.COV2.S recipients had a crude mortality rate of 0.82 per 100 person-years, versus 1.58 in the comparator group (Table 6 ).

Table 4.

Number of non-COVID-19 deaths and crude mortality rates, overall and by age, sex, and race/ethnicity among BNT162b2 recipients and their comparators during the period from December 14, 2020 to August 31, 2021.

Dose 1 Dose 2 Comparators
Number of deaths 100 person-years Crude mortality rate per 100 person-years Number of deaths 100 person-years Crude mortality rate per 100 person-years Number of deaths 100 person-years Crude mortality rate per 100
person-years
Overall 1,674 2,210 0.76 7,809 11,900 0.66 7,852 4465 1.76
Age (in years)
1217 3 233 0.01 5 771 0.01 8 474 0.02
1844 27 814 0.03 97 4,042 0.02 173 1,856 0.09
4564 141 687 0.21 570 3,803 0.15 974 1,339 0.73
6574 317 281 1.13 1,504 1,903 0.79 1,600 482 3.32
7584 528 141 3.75 2,458 1,014 2.42 2,229 218 10.20
85+ 658 54 12.08 3,175 367 8.66 2,868 95 30.22
Sex
Female 830 1,193 0.70 3,866 6,602 0.59 4,061 2,504 1.62
Male 844 1,017 0.83 3,943 5,298 0.74 3,791 1,961 1.93
Race/ethnicity
Hispanic 199 510 0.39 951 2,485 0.38 1,319 1,345 0.98
Non-Hispanic White 1,138 942 1.21 5,311 5,402 0.98 4,775 1,872 2.55
Non-Hispanic Asian 124 363 0.34 667 2,036 0.33 621 496 1.25
Non-Hispanic Black 123 120 1.03 459 615 0.75 707 293 2.41
Missing 26 175 0.15 133 827 0.16 105 262 0.40
Multiple/Other 64 101 0.64 288 534 0.54 325 197 1.65

Table 5.

Number of non-COVID-19 deaths and crude mortality rates, overall and by age, sex, and race/ethnicity among mRNA-1273 recipients and their comparators during the period from December 14, 2020 to August 31, 2021.

Dose 1 Dose 2 Comparators
Number of deaths 100 person-years Crude mortality rate per 100 person-years Number of deaths 100 person-years Crude mortality rate per 100 person-years Number of deaths 100 person-years Crude mortality rate per 100 person-years
Overall 1,577 2077 0.76 6,152 9132 0.67 7,732 3800 2.04
Age (in years)
1844 19 732 0.03 74 2839 0.03 122 1662 0.07
4564 151 729 0.21 549 3124 0.18 907 1286 0.71
6574 325 374 0.87 1,363 1895 0.72 1,672 528 3.16
7584 486 183 2.66 2,019 975 2.07 2,287 232 9.85
85+ 596 59 10.08 2,147 298 7.19 2,744 92 29.87
Sex
Female 736 1129 0.65 2,837 5094 0.56 3,977 2134 1.86
Male 841 948 0.89 3,315 4038 0.82 3,755 1666 2.25
Race/ethnicity
Hispanic 244 497 0.49 943 2032 0.46 1,354 1106 1.22
Non-Hispanic White 967 931 1.04 3,932 4281 0.92 4,682 1638 2.86
Non-Hispanic Asian 117 293 0.40 490 1342 0.37 591 425 1.39
Non-Hispanic Black 155 121 1.28 455 511 0.89 713 250 2.85
Missing 29 144 0.20 113 569 0.20 102 217 0.47
Multiple/Other 65 91 0.72 219 396 0.55 290 163 1.78

Table 6.

Number of non-COVID-19 deaths and crude mortality rates, overall and by age, sex, and race/ethnicity among Ad26.COV2.S recipients and their comparators during the period from December 14, 2020 to August 31, 2021.

After Ad26.COV2.S vaccination Comparators
Number of deaths 100 person-years Crude mortality rate per 100 person-years Number of deaths 100 person-years Crude mortality rate per 100 person-years
Overall 1,048 1272 0.82 3,339 2112 1.58
Age (in years)
1844 28 491 0.06 73 936 0.08
4564 187 604 0.31 620 841 0.74
6574 227 118 1.92 695 215 3.24
7584 278 42 6.70 824 82 10.02
85+ 328 17 19.26 1,127 38 30.00
Sex
Female 544 617 0.88 1,719 1093 1.57
Male 504 655 0.77 1,620 1019 1.59
Race/ethnicity
Hispanic 147 267 0.55 510 603 0.85
Non-Hispanic White 658 592 1.11 2,097 923 2.27
Non-Hispanic Asian 78 173 0.45 253 226 1.12
Non-Hispanic Black 108 81 1.34 269 139 1.93
Missing 15 109 0.14 66 131 0.51
Multiple/Other 42 51 0.83 144 90 1.60

3.3. Primary and secondary analyses

For each vaccine type, unadjusted HRs of non-COVID-19 mortality were significantly below 1, demonstrating reduced mortality in the vaccinated group (Table 7 ). Adjusting for confounders with the propensity score approach resulted in slight increases in the aHRs, but no overall change in direction or magnitude of the effect. For the BNT162b2 vaccine, the aHRs were 0.46 (95% CI, 0.44–0.49) after dose 1 and 0.48 (95% CI, 0.46–0.50) after dose 2. For the mRNA-1273 vaccine, the aHRs were 0.41 (95% CI, 0.39–0.44) after dose 1 and 0.38 (95% CI, 0.37–0.40) after dose 2. The aHR was 0.55 (95% CI, 0.51–0.59) following receipt of Ad26.COV2.S.

Table 7.

Unadjusted and adjusted hazard ratios (95%CI) of non-COVID-19 mortality, 30-day non-COVID-19 mortality, and all-cause mortality during the period from December 14, 2020 to August 31, 2021.



Unadjusted hazard ratios (95% CI)
Adjusted hazard ratios (95% CI)¥
Outcome Vaccines Dose 1 Dose 2 Dose 1 Dose 2
Non-COVID-19 mortality BNT162b2 0.38 (0.36–0.40) 0.41 (0.40–0.43) 0.46 (0.44–0.49) 0.48 (0.46–0.50)
mRNA-1273 0.35 (0.33–0.37) 0.35 (0.33–0.36) 0.41 (0.39–0.44) 0.38 (0.37–0.40)
Ad26.COV2.S 0.53 (0.50–0.57) N/A 0.55 (0.51–0.59) N/A
30-day non-COVID-19 mortality BNT162b2 0.21 (0.20–0.23) 0.23 (0.22–0.25) 0.27 (0.25–0.29) 0.30 (0.28–0.33)
mRNA-1273 0.16 (0.15–0.17) 0.21 (0.19–0.22) 0.19 (0.18–0.21) 0.25 (0.23–0.27)
Ad26.COV2.S 0.44 (0.37–0.51) N/A 0.43 (0.37–0.50) N/A
All-cause mortality BNT162b2 0.36 (0.34–0.38) 0.38 (0.37–0.40) 0.45 (0.43–0.47) 0.45 (0.43–0.46)
mRNA-1273 0.32 (0.30–0.34) 0.32 (0.31–0.33) 0.38 (0.37–0.41) 0.36 (0.34–0.37)
Ad26.COV2.S 0.50 (0.47,0.54) N/A 0.52 (0.49–0.56) N/A
¥

Hazard ratios were adjusted using stabilized weights for age, sex, race/ethnicity, Medicaid status, history of COVID-19, number of combined outpatient and virtual visits in one year prior to index date, inpatient visit (yes/no) in one year prior to index date, Emergency Department visit (yes/no) in one year prior to index date, inpatient or Emergency Department visit within 7 days prior to index date (yes/no), presence of frailty measured in one year prior to index date (yes if frailty index ≥ 0.11; no, otherwise), Charlson Comorbidity Index measured in one year prior to index date, receipt of another vaccine within 14 days before or after index date, neighborhood median household income, and neighborhood education level.

Across vaccine types and doses, aHRs of 30-day non-COVID-19 mortality and of all-cause mortality were lower than those from the analyses of non-COVID-19 mortality (Table 7).

3.4. Exploratory negative control outcome analyses

Compared to unvaccinated comparators, the aHR for trauma or injury hospitalization after receipt of BNT162b2 and mRNA-1273 was 1.06 (95% CI, 1.02–1.10) and 1.08 (95% CI, 1.04–1.12), respectively; the aHR for Ad26.COV2.S was 0.93 (95% CI, 0.85–1.00) (Table 8 ).

Table 8.

Unadjusted and adjusted hazard ratios (95% CI) of trauma or injury hospitalization during the period from December 14, 2020 to August 31, 2021.

Vaccine Unadjusted hazard ratios (95% CI) Adjusted hazard ratios (95% CI)a
BNT162b2 0.91 (0.88–0.94) 1.06 (1.02–1.10)
mRNA-1273 0.95 (0.92–0.99) 1.08 (1.04–1.12)
Ad26.COV2.S 0.86 (0.79–0.93) 0.93 (0.85–1.00)
a

Hazard ratios were adjusted using stabilized weights for age, sex, race/ethnicity, Medicaid status, history of COVID-19, number of combined outpatient and virtual visits in one year prior to index date, inpatient visit (yes/no) in one year prior to index date, Emergency Department visit (yes/no) in one year prior to index date, inpatient or Emergency Department visit within 7 days prior to index date (yes/no), presence of frailty measured in one year prior to index date (yes if frailty index ≥ 0.11; no, otherwise), Charlson Comorbidity Index measured in one year prior to index date, receipt of another vaccine within 14 days before or after index date, neighborhood median household income, and neighborhood education level.

4. Discussion

In this study of more than 6 million recipients of COVID-19 vaccines and their unvaccinated comparators, we found that recipients of BNT162b2, mRNA-1273, and Ad26.COV2.S vaccines had lower non-COVID-19 mortality risk than their comparator groups. For mRNA vaccines, the aHRs of dose 1 and dose 2 ranged from 0.38 to 0.48. These primary analysis findings of no increased mortality risk among COVID-19 vaccine recipients are consistent with existing knowledge about mortality risk after COVID-19 vaccination.[15], [16], [17] The aHRs of all-cause mortality were lower than those from the analyses of non-COVID-19 mortality, likely due to the protection of COVID-19 vaccines against COVID-19 infection, severe illness, and deaths. The findings suggested some all-cause mortality benefit of COVID-19 vaccines for unknown causes in addition to their known protection against COVID-19 infection, severity of the disease and death. While previous studies have suggested that live attenuated vaccines may be associated with lower risk of non-vaccine-targeted infections,[34], [35], [36] it is unclear whether trained immunity might also be induced by mRNA and adenoviral vector COVID-19 vaccines. If so, such non-specific protection against heterologous infection could lead to decreased mortality due to non-COVID-19 causes.

A recent study in Hungary demonstrated the effectiveness of COVID-19 vaccination in reducing all-cause mortality after adjusting for measured confounders and potential healthy vaccinee effect when compared to unvaccinated individuals.[37] A VSD study found that the mortality rates were lower in the days immediately following vaccination in a cohort of adults and children between January 1, 2005 and December 31, 2008, indicating a healthy vaccinee effect.[38] Another VSD study included individuals aged 9 to 26 years with deaths between January 1, 2005 and December 31, 2011. A case-centered method was used to estimate a relative risk (RR) for death in days 0 to 30 after vaccination. It was shown that RRs after any vaccination and influenza vaccination were significantly lower for deaths due to nonexternal causes and all causes. The authors suggested that vaccination would be less probable in individuals whose death was imminent. Also, since the population was relatively unhealthy, this bias might not be from the traditional healthy vaccinee effect, but rather from unmeasured confounding related to the timing of vaccination by indication or disease severity.[39].

Jackson et al [18] used trauma or injury hospitalization as a negative control outcome in investigating the protective effect of influenza vaccination against influenza hospitalization and all-cause mortality in the elderly. They found that influenza vaccination appeared to be associated with a lower risk for both influenza hospitalization and all-cause mortality as well as trauma or injury hospitalization, indicating inadequate confounding adjustment. In our negative control outcome analyses, the aHR for trauma or injury hospitalization was close to the null for the three COVID-19 vaccines, suggesting that the negative association between COVID-19 vaccines and non-COVID-19 mortality was not likely biased by the pathways examined through the negative control outcome.

The associations that we found between COVID-19 vaccination and non-COVID-19 mortality are stronger than can plausibly be attributed to any real protective effect of vaccination. A more convincing explanation is selection bias as has been reported in studies of influenza vaccination and mortality.[18], [19], [40], [41] Selection bias can arise as patients who anticipate that they are near death “give up” on vaccinations as they are near death and they tend to become less willing and able to seek vaccinations and other preventive services. Although we have extensive data on diagnoses, demographics, and use of health services in the study population, this source of bias is not well measured, and we have not been able to adequately adjust for it. In the context of widespread suggestions on social media that COVID-19 vaccines are unsafe, it is reassuring that we found no evidence of any association of COVID-19 vaccination with increased risk of death. We think our analyses would yield more convincing hazard ratio estimates if we could better adjust for selection bias. Future analyses using a modified self-controlled case series design might be able to mitigate the healthy vaccinee effect by controlling for unmeasured fixed risk factors through within-person comparisons.[42].

In addition to unmeasured confounding, this study had at least two additional limitations. First, causes of death were not available and were not included in the analyses. A temporal relationship between a COVID-19 diagnosis or a positive SARS-CoV-2 test and death was used as a proxy for defining COVID-19-related death. We could have missed COVID-19 related diagnoses and misclassified some non-COVID-19 deaths, especially among unvaccinated individuals because they were more likely to be infected with COVID-19. The potential differential misclassification of non-COVID-19 deaths may have overestimated the non-COVID-19 mortality rates among unvaccinated individuals, leading to lower hazard ratios for vaccinees. Further, without knowing causes of death, we could not estimate and compare the proportions of deaths due to various causes. Second, the VSD population is an insured population and the findings in the current study may not be generalizable to the general population.

Our study had several strengths. First, individual-level and community-level socioeconomic confounders were adjusted for in the survival analyses for estimating the association between COVID-19 vaccination and non-COVID-19 mortality and all-cause mortality. In particular, we included inpatient and ED visits within 7 days prior to the index date (yes/no) and a frailty score in the propensity score models to control for healthy vaccinee effects. Second, we used a rigorous propensity score approach to adjust for the measured confounders. After applying stabilized weights to the cohorts, all measured confounders were well balanced between recipients of COVID-19 vaccines and their comparator groups. Third, the frequency matching of vaccinated individuals during a given week with comparators who had not been vaccinated yet aligned the start of the comparators’ follow-up with that of vaccinated individuals. Because of the proper alignment of start of follow-up, the frequency matching helped to mitigate immortal time bias. [43], [44], [45] Fourth, the assignment of index dates for unvaccinated comparators that corresponded to the vaccination dates of their matched vaccinees, and the use of calendar time as the basic time scale in survival analyses ensured control for temporal factors. Finally, the study had a large, demographically diverse study population with up to 8 months of follow-up.

We conclude that, while residual confounding bias remained after adjusting for several individual-level and community-level risk factors, no increased risk was found for non-COVID-19 mortality and all-cause mortality among recipients of three widely used COVID-19 vaccines in the US. The findings in this study of individuals 12 years and older support CDC’s recommendation of COVID-19 vaccination for this age group. Future studies will include children<12 years of age.

Disclaimer

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

This study was approved by institutional review boards of all participating health care organization sites with a waiver of informed consent and was conducted consistent with federal law and CDC policy.§.

§ See e.g., 45C.F.R. part 46.102(l)(2), 21C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was supported by the Centers for Disease Control and Prevention through the Vaccine Safety Datalink under contract 75D30122D15429.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.vaccine.2022.12.036.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary data 1
mmc1.docx (43.4KB, docx)

Data availability

Data will be made available on request.

References

  • 1.CDC. Use of COVID-19 Vaccines in the United States. https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html. Accessed May 30, 2022.
  • 2.Oliver S., Gargano J., Marin M., Wallace M., Curran K.G., Chamberland M., et al. The Advisory Committee on Immunization Practices' Interim Recommendation for Use of Pfizer-BioNTech COVID-19 Vaccine - United States, December 2020. MMWR Morb Mortal Wkly Rep. 2020;69(50):1922–1924. doi: 10.15585/mmwr.mm6950e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Oliver S.E., Gargano J.W., Scobie H., Wallace M., Hadler S.C., Leung J., et al. The Advisory Committee on Immunization Practices' Interim Recommendation for Use of Janssen COVID-19 Vaccine - United States, February 2021. MMWR Morb Mortal Wkly Rep. 2021;70(9):329–332. doi: 10.15585/mmwr.mm7009e4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Oliver S., Gargano J., Marin M., Wallace M., Curran K.G., Chamberland M., et al. The Advisory Committee on Immunization Practices' Interim Recommendation for Use of Moderna COVID-19 Vaccine - United States, December 2020. MMWR Morb Mortal Wkly Rep. 2021;69(5152):1653–1656. doi: 10.15585/mmwr.mm695152e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Polack F.P., Thomas S.J., Kitchin N., Absalon J., Gurtman A., Lockhart S., et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. N Engl J Med. 2020;383(27):2603–2615. doi: 10.1056/NEJMoa2034577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Baden L.R., El Sahly H.M., Essink B., Kotloff K., Frey S., Novak R., et al. Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine. N Engl J Med. 2021;384(5):403–416. doi: 10.1056/NEJMoa2035389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.FDA. Janssen COVID-19 Vaccine EUA Fact Sheet for Healthcare Providers. 2022.
  • 8.Husby A, Hansen JV, Fosbøl E, Thiesson EM, Madsen M, Thomsen RW, et al. SARS-CoV-2 vaccination and myocarditis or myopericarditis: population based cohort study. BMJ (Clinical research ed). 2021;375:e068665. DOI:10.1136/bmj-2021-068665. [DOI] [PMC free article] [PubMed]
  • 9.Witberg G., Barda N., Hoss S., Richter I., Wiessman M., Aviv Y., et al. Myocarditis after Covid-19 Vaccination in a Large Health Care Organization. N Engl J Med. 2021;385(23):2132–2139. doi: 10.1056/NEJMoa2110737. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Klein N.P., Lewis N., Goddard K., Fireman B., Zerbo O., Hanson K.E., et al. Surveillance for Adverse Events After COVID-19 mRNA Vaccination. JAMA. 2021;326(14):1390. doi: 10.1001/jama.2021.15072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Oster M.E., Shay D.K., Su J.R., Gee J., Creech C.B., Broder K.R., et al. Myocarditis Cases Reported After mRNA-Based COVID-19 Vaccination in the US From December 2020 to August 2021. JAMA. 2022;327(4):331. doi: 10.1001/jama.2021.24110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Woo E.J., Mba-Jonas A., Dimova R.B., Alimchandani M., Zinderman C.E., Nair N. Association of Receipt of the Ad26.COV2.S COVID-19 Vaccine With Presumptive Guillain-Barré Syndrome, February-July 2021. JAMA. 2021;326:1606–1613. doi: 10.1001/jama.2021.16496. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Hanson K.E., Goddard K., Lewis N., Fireman B., Myers T.R., Bakshi N., et al. Incidence of Guillain-Barré Syndrome After COVID-19 Vaccination in the Vaccine Safety Datalink. JAMA Netw Open. 2022;5(4):e228879. doi: 10.1001/jamanetworkopen.2022.8879. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Long B., Bridwell R., Gottlieb M. Thrombosis with thrombocytopenia syndrome associated with COVID-19 vaccines. Am J Emerg Med. 2021;49:58–61. doi: 10.1016/j.ajem.2021.05.054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Bardenheier B.H., Gravenstein S., Blackman C., Gutman R., Sarkar I.N., Feifer R.A., et al. Adverse events following mRNA SARS-CoV-2 vaccination among U.S. nursing home residents. Vaccine. 2021;39(29):3844–3851. doi: 10.1016/j.vaccine.2021.05.088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Li L.L., Zheng C., La J., Do N.V., Monach P.A., Strymish J.M., et al. Impact of prior SARS-CoV-2 infection on incidence of hospitalization and adverse events following mRNA SARS-CoV-2 vaccination: A nationwide, retrospective cohort study. Vaccine. 2022;40(8):1082–1089. doi: 10.1016/j.vaccine.2022.01.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Xu S., Huang R., Sy L.S., Glenn S.C., Ryan D.S., Morrissette K., et al. COVID-19 Vaccination and Non-COVID-19 Mortality Risk - Seven Integrated Health Care Organizations, United States, December 14, 2020-July 31, 2021. MMWR Morb Mortal Wkly Rep. 2021;70(43):1520–1524. doi: 10.15585/mmwr.mm7043e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Jackson L.A., Jackson M.L., Nelson J.C., Neuzil K.M., Weiss N.S. Evidence of bias in estimates of influenza vaccine effectiveness in seniors. Int J Epidemiol. 2006;35:337–344. doi: 10.1093/ije/dyi274. [DOI] [PubMed] [Google Scholar]
  • 19.Simonsen L., Taylor R.J., Viboud C., Miller M.A., Jackson L.A. Mortality benefits of influenza vaccination in elderly people: an ongoing controversy. Lancet Infect Dis. 2007;7:658–666. doi: 10.1016/s1473-3099(07)70236-0. [DOI] [PubMed] [Google Scholar]
  • 20.Sukumaran L., McCarthy N.L., Li R., Weintraub E.S., Jacobsen S.J., Hambidge S.J., et al. Demographic characteristics of members of the Vaccine Safety Datalink (VSD): A comparison with the United States population. Vaccine. 2015;33(36):4446–4450. doi: 10.1016/j.vaccine.2015.07.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Kim D.H., Schneeweiss S., Glynn R.J., Lipsitz L.A., Rockwood K., Avorn J. Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index. The journals of gerontology Series A, Biological sciences and medical sciences. 2018;73:980–987. doi: 10.1093/gerona/glx229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Rosenbaum P., Rubin D. The Central Role of the Propensity Score in Observational Studies For Causal Effects. Biometrika. 1983;70:41–55. doi: 10.1093/biomet/70.1.41. [DOI] [Google Scholar]
  • 23.Rosenbaum P.R., Rubin D.B. Reducing Bias in Observational Studies Using Sub-Classification on the Propensity Score. J Am Stat Assoc. 1984;79(387):516. doi: 10.2307/2288398. [DOI] [Google Scholar]
  • 24.Imbens G. The Role of the Propensity Score in Estimating Dose-Response Functions. Biometrika. 2000;87(3):706–710. [Google Scholar]
  • 25.Robins J.M., Hernán M.A., Brumback B. Marginal structural models and causal inference in epidemiology. Epidimiology. 2000;11:550–560. doi: 10.1097/00001648-200009000-00011. [DOI] [PubMed] [Google Scholar]
  • 26.Xu S., Ross C., Raebel M.A., Shetterly S., Blanchette C., Smith D. Use of stabilized inverse propensity scores as weights to directly estimate relative risk and its confidence intervals. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2010;13:273–277. doi: 10.1111/j.1524-4733.2009.00671.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Austin P.C. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28:3083–3107. doi: 10.1002/sim.3697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Andersen P., Gill R. Cox's Regression Model for Counting Processes: A Large Sample Study. Ann Stat. 1982:10. doi: 10.1214/aos/1176345976. [DOI] [Google Scholar]
  • 29.Coolen F., Andersen P.K., Borgan O., Gill R.D., Keiding N. Statistical Models Based On Counting Processes. Journal of the Royal Statistical Society Series D (The Statistician) 1996;45(3):384. doi: 10.2307/2988475. [DOI] [Google Scholar]
  • 30.Wolkewitz M., Cooper B.S., Palomar-Martinez M., Alvarez-Lerma F., Olaechea-Astigarraga P., Barnett A.G., et al. Multiple time scales in modeling the incidence of infections acquired in intensive care units. BMC Med Res Method. 2016;16:116. doi: 10.1186/s12874-016-0199-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Shi X., Miao W., Tchetgen E.T. A Selective Review of Negative Control Methods in Epidemiology. Current epidemiology reports. 2020;7:190–202. doi: 10.1007/s40471-020-00243-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Lipsitch M., Tchetgen Tchetgen E., Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidimiology. 2010;21:383–388. doi: 10.1097/EDE.0b013e3181d61eeb. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.CMS. 2021 ICD-10-PCS. https://www.cms.gov/medicare/icd-10/2021-icd-10-pcs. Accessed May 30, 2022.
  • 34.Bardenheier B.H., McNeil M.M., Wodi A.P., McNicholl J.M., DeStefano F. Risk of Nontargeted Infectious Disease Hospitalizations Among US Children Following Inactivated and Live Vaccines, 2005–2014. Clin Infect Dis. 2017;1(65):729–737. doi: 10.1093/cid/cix442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Newcomer S.R., Daley M.F., Narwaney K.J., Xu S., DeStefano F., Groom H.C., et al. Order of Live and Inactivated Vaccines and Risk of Non-vaccine-targeted Infections in US Children 11–23 Months of Age. Pediatr Infect Dis J. 2020;39:247–253. doi: 10.1097/INF.0000000000002550. [DOI] [PubMed] [Google Scholar]
  • 36.Aaby P, Netea MG, Benn CS. Beneficial non-specific effects of live vaccines against COVID-19 and other unrelated infections. Lancet Infect Dis. 2022; 26:S1473-3099(22)00498-4. doi: 10.1016/S1473-3099(22)00498-4. [DOI] [PMC free article] [PubMed]
  • 37.Pálinkás A., Sándor J. Effectiveness of COVID-19 Vaccination in Preventing All-Cause Mortality among Adults during the Third Wave of the Epidemic in Hungary: Nationwide Retrospective Cohort Study. Vaccines. 2022:10. doi: 10.3390/vaccines10071009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.McCarthy N.L., Weintraub E., Vellozzi C., Duffy J., Gee J., Donahue J.G., et al. Mortality rates and cause-of-death patterns in a vaccinated population. Am J Prev Med. 2013;45(1):91–97. doi: 10.1016/j.amepre.2013.02.020. [DOI] [PubMed] [Google Scholar]
  • 39.McCarthy N.L., Gee J., Sukumaran L., Weintraub E., Duffy J., Kharbanda E.O., et al. Vaccination and 30-Day Mortality Risk in Children, Adolescents, and Young Adults. Pediatrics. 2016;137:e20152970. doi: 10.1542/peds.2015-2970. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Simonsen L., Reichert T.A., Viboud C., Blackwelder W.C., Taylor R.J., Miller M.A. Impact of influenza vaccination on seasonal mortality in the US elderly population. Arch Intern Med. 2005;165:265–272. doi: 10.1001/archinte.165.3.265. [DOI] [PubMed] [Google Scholar]
  • 41.Jackson L.A., Nelson J.C., Benson P., Neuzil K.M., Reid R.J., Psaty B.M., et al. Functional status is a confounder of the association of influenza vaccine and risk of all cause mortality in seniors. Int J Epidemiol. 2006;35(2):345–352. doi: 10.1093/ije/dyi275. [DOI] [PubMed] [Google Scholar]
  • 42.Farrington C.P., Whitaker H.J., Hocine M.N. Case series analysis for censored, perturbed, or curtailed post-event exposures. Biostatistics. 2009;10:3–16. doi: 10.1093/biostatistics/kxn013. [DOI] [PubMed] [Google Scholar]
  • 43.Suissa S., Azoulay L. Metformin and the risk of cancer: time-related biases in observational studies. Diabetes Care. 2012;35:2665–2673. doi: 10.2337/dc12-0788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Hernán M.A., Sauer B.C, Hernández-Díaz S, Platt R., Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016;79:70–75. doi: 10.1016/j.jclinepi.2016.04.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Lund JL., Horváth-Puhó E., Komjáthiné Szépligeti S., Sørensen HT., Pedersen L., Ehrenstein V., et al. Conditioning on future exposure to define study cohorts can induce bias: the case of low-dose acetylsalicylic acid and risk of major bleeding. Clin Epidemiol. 2017;9:611–626. doi: 10.2147/CLEP.S147175. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data 1
mmc1.docx (43.4KB, docx)

Data Availability Statement

Data will be made available on request.


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