Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and AdolescentsFREE
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Abstract
Background:
Objective:
Design:
Setting:
Participants:
Intervention:
Measurements:
Results:
Limitation:
Conclusion:
Primary Funding Source:
Methods
Data Sources
Specification of Hypothetical Trials and CER Studies
Statistical Analysis
Sensitivity Analyses
Missingness in Vaccine Records
Role of the Funding Source
Results
Study Population
Characteristic | Vaccinated (n = 45 007) | Unvaccinated (n = 32 385) | Overall (n = 77 392) |
---|---|---|---|
Median age (Q1–Q3), y | 14 (13–16) | 15 (13–17) | 15 (13–16) |
Distribution, n (%) | |||
12 y | 8922 (19.8) | 4926 (15.2) | 13 848 (17.9) |
13 y | 8099 (18.0) | 4864 (15.0) | 12 963 (16.7) |
14 y | 8037 (17.9) | 4923 (15.2) | 12 960 (16.7) |
15 y | 7311 (16.2) | 4749 (14.7) | 12 060 (15.6) |
16 y | 5450 (12.1) | 4546 (14.0) | 9996 (12.9) |
17 y | 4076 (9.1) | 3991 (12.3) | 8067 (10.4) |
18 y | 1674 (3.7) | 2075 (6.4) | 3749 (4.8) |
19 y | 942 (2.1) | 1461 (4.5) | 2403 (3.1) |
20 y | 496 (1.1) | 850 (2.6) | 1346 (1.7) |
Sex, n (%) | |||
Female | 23 589 (52.4) | 16 500 (50.9) | 40 089 (51.8) |
Male | 21 416 (47.6) | 15 880 (49.0) | 37 296 (48.2) |
Ethnicity, n (%) | |||
White | 16 446 (50.8) | 17 964 (39.9) | 34 410 (44.5) |
Black/African American | 6019 (18.6) | 12 012 (26.7) | 18 031 (23.3) |
Hispanic | 4925 (15.2) | 9629 (21.4) | 14 554 (18.8) |
Other/unknown | 4995 (15.4) | 5402 (12.0) | 10 397 (13.4) |
Hospital, n (%) | |||
A | 5424 (12.1) | 7385 (22.8) | 12 809 (16.6) |
B | 12 884 (28.6) | 5216 (16.1) | 18 100 (23.4) |
C | 6333 (14.1) | 3457 (10.7) | 9790 (12.6) |
D | 1723 (3.8) | 914 (2.8) | 2637 (3.4) |
E | 4369 (9.7) | 6063 (18.7) | 10 432 (13.5) |
F | 12 831 (28.5) | 3409 (10.5) | 16 240 (21.0) |
G | 1430 (3.2) | 1457 (4.5) | 2887 (3.7) |
H | 13 (0.0) | 4484 (13.8) | 4497 (5.8) |
Entry time, n (%) | |||
July–September 2021 | 38 335 (85.2) | 24 509 (75.7) | 62 844 (81.2) |
October–November 2021 | 6672 (14.8) | 7876 (24.3) | 14 548 (18.8) |
Obesity, n (%) | |||
0 | 28 029 (62.3) | 22 479 (69.4) | 50 508 (65.3) |
1 | 16 978 (37.7) | 9906 (30.6) | 26 884 (34.7) |
Pediatric Medical Complexity Algorithm, n (%) | |||
0 | 25 634 (57.0) | 19 916 (61.5) | 45 550 (58.9) |
1 | 10 915 (24.3) | 6417 (19.8) | 17 332 (22.4) |
2 | 8458 (18.8) | 6052 (18.7) | 14 510 (18.7) |
Negative tests before entry, n (%) | |||
0 | 1330 (3.0) | 2888 (8.9) | 4218 (5.5) |
1 | 34 272 (76.1) | 16 299 (50.3) | 50 571 (65.3) |
2 | 7388 (16.4) | 9739 (30.1) | 17 127 (22.1) |
≥3 | 2017 (4.5) | 3459 (10.7) | 5476 (7.1) |
Characteristic | Omicron Study in Children | Omicron Study in Adolescents | ||||
---|---|---|---|---|---|---|
Vaccinated (n = 50 398) | Unvaccinated (n = 61 141) | Overall (n = 111 539) | Vaccinated (n = 21 180) | Unvaccinated (n = 34 900) | Overall (n = 56 080) | |
Median age (Q1–Q3), y | 8 (6–10) | 7 (6–9) | 8 (6–10) | 14 (13–16) | 15 (13–17) | 15 (13–17) |
Distribution, n (%) | ||||||
5 y | 8165 (16.2) | 13 321 (21.8) | 21 486 (19.3) | – | – | – |
6 y | 7447 (14.8) | 11 314 (18.5) | 18 761 (16.8) | – | – | – |
7 y | 7090 (14.1) | 9151 (15.0) | 16 241 (14.6) | – | – | – |
8 y | 7028 (13.9) | 7922 (13.0) | 14 950 (13.4) | – | – | – |
9 y | 6773 (13.4) | 7085 (11.6) | 13 858 (12.4) | – | – | – |
10 y | 7011 (13.9) | 6434 (10.5) | 13 445 (12.1) | – | – | – |
11 y | 6884 (13.7) | 5914 (9.7) | 12 798 (11.5) | – | – | – |
12 y | – | – | – | 4754 (22.4) | 5760 (16.5) | 10 514 (18.7) |
13 y | – | – | – | 3421 (16.2) | 5520 (15.8) | 8941 (15.9) |
14 y | – | – | – | 3338 (15.8) | 5299 (15.2) | 8637 (15.4) |
15 y | – | – | – | 3123 (14.7) | 5315 (15.2) | 8438 (15.0) |
16 y | – | – | – | 2634 (12.4) | 4944 (14.2) | 7578 (13.5) |
17 y | – | – | – | 2201 (10.4) | 3836 (11.0) | 6037 (10.8) |
18 y | – | – | – | 986 (4.7) | 2114 (6.1) | 3100 (5.5) |
19 y | – | – | – | 475 (2.2) | 1400 (4.0) | 1875 (3.3) |
20 y | – | – | – | 248 (1.2) | 712 (2.0) | 960 (1.7) |
Sex, n (%) | ||||||
Female | 23 962 (47.5) | 28 669 (46.9) | 52 631 (47.2) | 11 402 (53.8) | 17 954 (51.4) | 29 356 (52.3) |
Male | 26 436 (52.5) | 32 468 (53.1) | 58 904 (52.8) | 9775 (46.2) | 16 939 (48.5) | 26 714 (47.6) |
Ethnicity, n (%) | ||||||
White | 14 399 (28.6) | 24 644 (40.3) | 39 043 (35.0) | 16 240 (46.5) | 6836 (32.3) | 23 076 (41.1) |
Black/African American | 13 711 (27.2) | 13 733 (22.5) | 27 444 (24.6) | 6154 (17.6) | 6157 (29.1) | 12 311 (22.0) |
Hispanic | 12 119 (24.0) | 12 781 (20.9) | 24 900 (22.3) | 6287 (18.0) | 3784 (17.9) | 10 071 (18.0) |
Other/unknown | 10 169 (20.2) | 9983 (16.3) | 20 152 (18.1) | 6219 (17.8) | 4403 (20.8) | 10 622 (18.9) |
Hospital, n (%) | ||||||
A | 5019 (10.0) | 9266 (15.2) | 14 285 (12.8) | 2131 (10.1) | 5183 (14.9) | 7314 (13.0) |
B | 15 229 (30.2) | 13 168 (21.5) | 28 397 (25.5) | 6397 (30.2) | 6556 (18.8) | 12 953 (23.1) |
C | 5482 (10.9) | 7409 (12.1) | 12 891 (11.6) | 1719 (8.1) | 4075 (11.7) | 5794 (10.3) |
D | 4766 (9.5) | 2878 (4.7) | 7644 (6.9) | 678 (3.2) | 1337 (3.8) | 2015 (3.6) |
E | 5843 (11.6) | 10 551 (17.3) | 16 394 (14.7) | 2047 (9.7) | 4263 (12.2) | 6310 (11.3) |
F | 9786 (19.4) | 11 348 (18.6) | 21 134 (18.9) | 3563 (16.8) | 5186 (14.9) | 8749 (15.6) |
G | 1250 (2.5) | 3239 (5.3) | 4489 (4.0) | 622 (2.9) | 2353 (6.7) | 2975 (5.3) |
H | 3023 (6.0) | 3282 (5.4) | 6305 (5.7) | 4023 (19.0) | 5947 (17.0) | 9970 (17.8) |
Entry time, n (%) | ||||||
January–March 2022 | 37 970 (75.3) | 32 523 (53.2) | 70 493 (63.2) | 14 684 (69.3) | 19 032 (54.5) | 33 716 (60.1) |
April–June 2022 | 5882 (11.7) | 11 919 (19.5) | 17 801 (16.0) | 3344 (15.8) | 7087 (20.3) | 10 431 (18.6) |
July–September 2022 | 4994 (9.9) | 10 329 (16.9) | 15 323 (13.7) | 2206 (10.4) | 5479 (15.7) | 7685 (13.7) |
October–November 2022 | 1552 (3.1) | 6370 (10.4) | 7922 (7.1) | 946 (4.5) | 3302 (9.5) | 4248 (7.6) |
Obesity, n (%) | ||||||
0 | 33 381 (66.2) | 42 165 (69.0) | 75 546 (67.7) | 13 832 (65.3) | 23 895 (68.5) | 37 727 (67.3) |
1 | 17 017 (33.8) | 18 976 (31.0) | 35 993 (32.3) | 7348 (34.7) | 11 005 (31.5) | 18 353 (32.7) |
Pediatric Medical Complexity Algorithm, n (%) | ||||||
0 | 33 870 (67.2) | 40 976 (67.0) | 74 846 (67.1) | 13 482 (63.7%) | 21 079 (60.4) | 34 561 (61.6) |
1 | 10 000 (19.8) | 11 189 (18.3) | 21 189 (19.0) | 4382 (20.7%) | 6764 (19.4) | 11 146 (19.9) |
2 | 6528 (13.0) | 8976 (14.7) | 15 504 (13.9) | 3316 (15.7%) | 7057 (20.2) | 10 373 (18.5) |
Negative tests before entry, n (%) | ||||||
0 | 2337 (4.6) | 5640 (9.2) | 7977 (7.2) | 768 (3.6) | 2966 (8.5) | 3734 (6.7) |
1 | 34 077 (67.6) | 28 417 (46.5) | 62 494 (56.0) | 15 707 (74.2) | 17 303 (49.6) | 33 010 (58.9) |
2 | 10 514 (20.9) | 19 816 (32.4) | 30 330 (27.2) | 3654 (17.3) | 11 012 (31.6) | 14 666 (26.2) |
≥3 | 3470 (6.9) | 7268 (11.9) | 10 738 (9.6) | 1051 (5.0) | 3619 (10.4) | 4670 (8.3) |
Vaccine Effectiveness
Epidemiologic Measure | Vaccinated | Unvaccinated | Overall | Vaccine Effectiveness (95% CI), % |
---|---|---|---|---|
Delta study in adolescents | ||||
Follow-up | ||||
Total follow-up, n of person-weeks | 644 162 | 398 906 | 1 043 068 | – |
Median (Q1–Q3) | 16 (12–18) | 13 (9–17) | 15 (10–18) | – |
Absolute risk, % | ||||
Documented infection | 0.35 | 5.26 | 2.41 | 98.4 (98.1–98.7) |
Mild COVID-19 | 0.06 | 1.43 | 0.63 | 99.0 (98.5–99.3) |
Moderate or severe COVID-19 | 0.03 | 0.49 | 0.22 | 98.7 (97.4–99.3) |
ICU admission with COVID-19 | ≤0.01 | 0.05 | ≤0.03 | 99.0 (92.5–99.9) |
Cardiac complication | 0.02 | 0.03 | 0.02 | 1.22 (0.34–4.35)* |
Age 12–15 y | ||||
Total follow-up, n of person-weeks | 458 981 | 229 083 | 688 064 | – |
Documented infection, % | 0.34 | 5.52 | 2.28 | 99.0 (98.6–99.3) |
Age 16–21 y | ||||
Total follow-up, n of person-weeks | 185 181 | 169 823 | 355 003 | – |
Documented infection, % | 0.36 | 4.91 | 2.64 | 97.0 (95.9–97.8) |
Omicron study in children | ||||
Follow-up | ||||
Total follow-up, n of person-weeks | 1 925 686 | 1 911 599 | 3 837 285 | – |
Median (Q1–Q3) | 44 (35–46) | 36 (19–44) | 40 (25–45) | – |
Absolute risk, % | ||||
Documented infection | 1.89 | 5.46 | 3.85 | 74.3 (72.2–76.2) |
Mild COVID-19 | 0.54 | 1.55 | 1.09 | 73.5 (69.2–77.1) |
Moderate or severe COVID-19 | 0.19 | 0.67 | 0.45 | 75.5 (69.0–81.0) |
ICU admission with COVID-19 | 0.02 | 0.08 | 0.05 | 84.9 (64.8–93.5) |
Cardiac complication | ≤0.01 | 0.03 | ≤0.02 | 0.28 (0.08–0.95)* |
Age 5–8 y | ||||
Total follow-up, n of person-weeks | 1 101 418 | 1 254 819 | 2 356 236 | – |
Documented infection, % | 1.96 | 5.10 | 3.78 | 71.3 (68.2–74.1) |
Age 9–11 y | ||||
Total follow-up, n of person-weeks | 824 268 | 656 780 | 1 481 049 | – |
Documented infection, % | 1.80 | 6.11 | 3.95 | 77.9 (75.1–80.4) |
Omicron study in adolescents | ||||
Follow-up | ||||
Total follow-up, n of person-weeks | 772 176 | 1 113 561 | 1 885 736 | – |
Median (Q1–Q3) | 42 (30–45) | 37 (22–44) | 39 (25–45) | – |
Absolute risk, % | ||||
Documented infection | 1.82 | 8.17 | 5.77 | 85.5 (83.8–87.1) |
Mild COVID-19 | 0.43 | 2.07 | 1.45 | 87.0 (83.5–89.8) |
Moderate or severe COVID-19 | 0.15 | 0.88 | 0.60 | 84.8 (77.3–89.9) |
ICU admission with COVID-19 | ≤0.02 | 0.14 | ≤0.10 | 91.5 (69.5–97.6) |
Cardiac complication | ≤0.02 | 0.05 | ≤0.04 | 0.10 (0.02–0.57)* |
Age 12–15 y | ||||
Total follow-up, n of person-weeks | 524 053 | 654 315 | 1 178 368 | – |
Documented infection, % | 1.93 | 8.24 | 5.67 | 85.8 (83.6–87.7) |
Age 16–21 y | ||||
Total follow-up, n of person-weeks | 248 123 | 459 246 | 707 368 | – |
Documented infection, % | 1.60 | 8.07 | 5.93 | 85.9 (82.7–88.5) |
Vaccine Effectiveness: Severe Illness and Complications
Sensitivity Analyses and Addressing Misclassification Bias
Discussion
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Keywords
- Age groups
- Cardiology and cardiovascular diseases
- Cardiovascular diseases
- Children
- COVID-19
- Heart diseases
- Hospital medicine
- Pericardial diseases
- Pericarditis
- Population statistics
- Prevention, policy, and public health
- Preventive medicine
- Pulmonary diseases
- Respiratory infections
- Upper respiratory tract infections
- Vaccination and immunization
- Vaccines
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Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents. Ann Intern Med.2024;177:165-176. [Epub 9 January 2024]. doi:10.7326/M23-1754
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Unjustified assumptions about vaccination status
In this study, positive COVID vaccination status was determined from the EHR and is expected to be nearly 100% accurate. However, determination of negative COVID vaccination status was defined to be the absence of an EHR record of COVID vaccination. As such, the authors correctly observe that negative COVID vaccination status may be less than 100% accurate. False negative vaccination status occurs, for example, when a study subject receives COVID vaccination in a pharmacy not associated with one of the study institutions. The authors provide evidence that the effect of uncertainty of vaccination status on estimates of vaccine efficacy are limited provided that the "sensitivity" of the EHR to positive vaccination is 0.8 or greater for children and 0.7 - 0.9 for adolescents. However, the authors do not justify those ranges, only stating: "a range of possible sensitivities based on our prior study was prespecified for each study." No reference to that study is provided. These unjustified assumptions raise doubts about the study methodology and should limit confidence in the study conclusions.
Author Response to Yim
Note that some of the EHR data partners have augmented their data quality by including the vaccine records from a Health Information Exchange (HIE) program specifically focused on vaccine registries. The range of possible sensitivities was prespecified based on a pilot study, which compared the vaccination rates estimated from vaccine data obtained from health systems against the Centers for Disease Control and Prevention (CDC) reports. Specifically, a reference vaccination rate was calculated by weighting the CDC's county-level vaccination statistics by the patients’ residential addresses. This comparison between the health system-derived vaccination rates and the CDC's reference rates allowed us to predefine a range of possible values of sensitivity. The research paper detailing this pilot study is being finalized and will be submitted soon. For the pediatric population who receive their care during the pandemic from the participating institutions (all of which provided vaccination programs to the communities as well as the primary care physician units). A sensitivity of lower than 70% would be found uncommon.
In addition, to evaluate the robustness of findings from the statistical methods we used to account for the underreporting, we have conducted sensitivity analyses using alternative methods, including the naive method (without adjusting for underreporting) and using different ranges of possible sensitivity values. Please refer to Section 16 of the Supplement for details.
Disclosures:
Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M23-1754
Response to authors
One question that may arise is the extent to which the EHR data partners with the augmented data sets overlapped with the study of the accuracy of COVID vaccination status classification.