Letters

With vaccination rates lagging in areas with higher social
vulnerability, small financial incentives should be con-
sidered in conjunction with other equity-promoting
strategies.5,6 The social incentive of cash cards for drivers
may also encourage people to help get their friends and fam-
ily vaccinated, a powerful motivator for those undecided
about vaccination. With hundreds of millions of dollars
being spent to accelerate COVID-19 vaccine uptake, these
study findings suggest that this strategy for increasing vacci-
nation merits greater investment.

Charlene A. Wong, MD, MSHP
William Pilkington, DPA, MPA, PhD
Irene A. Doherty, PhD, MPH
Ziliang Zhu, PhD
Hattie Gawande, BA
Deepak Kumar, PhD
Noel T. Brewer, PhD

Author Affiliations: Office of the Secretary, North Carolina Department of
Health and Human Services, Raleigh (Wong, Gawande); Advanced Center for
COVID-19 Related Disparities (ACCORD), Julius L. Chambers Biomedical
Biotechnology Research Institute, North Carolina Central University, Durham
(Pilkington, Doherty, Kumar); Department of Biostatistics, Gillings School of
Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill
(Zhu); Department of Health Behavior, Gillings School of Global Public Health,
University of North Carolina at Chapel Hill, Chapel Hill (Brewer).

Accepted for Publication: September 4, 2021.

Published Online: October 25, 2021. doi:10.1001/jamainternmed.2021.6170

Corresponding Author: Charlene A. Wong, MD, MSHP, Office of the Secretary,
North Carolina Department of Health and Human Services, 101 Blair Dr, Raleigh,
NC 27603 (charlene.wong@dhhs.nc.gov).

Author Contributions: Dr Kumar had full access to all the data in the study and
takes responsibility for the integrity of the data and the accuracy of the data
analysis.
Concept and design: Wong, Gawande, Kumar, Brewer.
Acquisition, analysis, or interpretation of data: Wong, Pilkington, Doherty, Zhu,
Kumar, Brewer.
Drafting of the manuscript: Wong, Pilkington, Doherty, Gawande, Brewer.
Critical revision of the manuscript for important intellectual content: Wong,
Doherty, Zhu, Kumar, Brewer.
Statistical analysis: Wong, Pilkington, Doherty, Zhu, Brewer.
Obtained funding: Wong, Kumar, Brewer.
Administrative, technical, or material support: Pilkington, Gawande, Kumar,
Brewer.
Supervision: Wong, Gawande, Kumar.

Conflict of Interest Disclosures: Dr Brewer reported grants from the North
Carolina Department of Health and Human Services during the conduct of the
study, and personal fees from the World Health Organization, the US Centers for
Disease Control and Prevention, and Merck outside the submitted work. No
other disclosures were reported.

1. Brewer NT, Chapman GB, Rothman AJ, Leask J, Kempe A. Increasing
vaccination: putting psychological science into action. Psychol Sci Public Interest.
2017;18(3):149-207. doi:10.1177/1529100618760521

2. National Governors Association. COVID-19 vaccine incentives. 2021.
Accessed June 22, 2021. https://www.nga.org/center/publications/covid-19-
vaccine-incentives/

3. Volpp KG, Cannuscio CC. Incentives for immunity–strategies for increasing
COVID-19 vaccine uptake. N Engl J Med. 2021;385(1):e1. doi:10.1056/
NEJMp2107719

4. Walkey AJ, Law A, Bosch NA. Lottery-based incentive in ohio and COVID-19
vaccination rates. JAMA. 2021;326(8):766-767. doi:10.1001/jama.2021.11048

5. Barry V, Dasgupta S, Weller DL, et al. Patterns in COVID-19 vaccination
coverage, by social vulnerability and urbanicity–United States, December 14,

2020-May 1, 2021. MMWR Morb Mortal Wkly Rep. 2021;70(22):818-824. doi:10.
15585/mmwr.mm7022e1

6. Wong CA, Dowler S, Moore AF, et al. COVID-19 vaccine administration, by
race and ethnicity–North Carolina, December 14, 2020-April 6, 2021. MMWR
Morb Mortal Wkly Rep. 2021;70(28):991-996. doi:10.15585/mmwr.mm7028a2

Age- and Sex-Specific Incidence of Cerebral Venous
Sinus Thrombosis Associated With Ad26.COV2.S
COVID-19 Vaccination
Recent reports1-4 suggest a possible association between
Ad26.COV2.S (Johnson & Johnson/Janssen) COVID-19 vacci-
nation and cerebral venous sinus thrombosis (CVST). Esti-
mates of postvaccination CVST risk require accurate age- and
sex-specific prepandemic
CVST incidence rates; how-
ever, reported rates vary
widely.5 We compared the age- and sex-specific CVST rates af-
ter Ad26.COV2.S vaccination with the prepandemic CVST rate
in the population.

Supplemental content

Methods | In this population-based cohort study, to estimate
the risk of CVST after Ad26.COV2.S vaccination, we first iden-
tified all incident cases of CVST in Olmsted County, Minnesota
from January 1, 2001, through December 31, 2015 (eMethods
in the Supplement). Sex-and age-adjusted incidence rates
were adjusted to the 2010 US census population. We used
CDC Vaccine Adverse Event Reporting System (VAERS) data
from February 28, 2021 (vaccine approval date) to May 7,
2021, to estimate the incidence of CVST after Ad26.COV2.S
vaccination assuming 3 (15, 30, and 92 days) plausible post-
vaccination periods during which individuals were con-
sidered to be at risk of CVST. We then compared post-
Ad26.COV2.S vaccination CVST rates with prepandemic
rates to estimate postvaccination CVST risk. This study was
approved by the Mayo Clinic institutional review board. Medi-
cal records of Olmsted County residents with CVST were
reviewed only if the residents had signed an authorization for
accessing their medical records for research purposes. SAS,
version 9.4 (SAS Institute Inc) and R, version 4.0.3 (R Project
for Statistical Computing) were used for statistical analyses.
Significance was set at a 2-sided P < .05.

Results | From 2001 through 2015, 39 Olmsted County resi-
dents developed acute incident CVST. A total of 29 patients
(74.4%) had a predisposing venous thromboembolism risk fac-
tor (eg, infection, active cancer, or oral contraceptives [for wom-
en]) within 92 days before the event. The median age at diag-
nosis was 41 years (range, 22-84 years); 22 residents with CVST
(56.4%) were female. The overall age- and sex-adjusted CVST
incidence was 2.34 per 100 000 person-years (PY) (95% CI,
1.60-3.08 per 100 000 PY). Age-adjusted CVST rates for
female and male individuals were 2.46 per 100 000 PY (95%
CI, 1.43-3.49 per 100 000 PY) and 2.34 per 100 000 PY (95%
CI, 1.22-3.46 per 100 000 PY), respectively. Men aged 65 years
or older had the highest CVST rate (6.22 per 100 000 PY; 95%
CI, 2.50-12.82 per 100 000 PY), followed by women aged 18
to 29 years (4.71 per 100 000 person-years; 95% CI, 2.26-8.66
per 100 000 PY) (Table 1).

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Letters

Table 1. Annual Incidence of CVST Among Residents of Olmsted County, Minnesota,
From 2001 to 2015 by Age and Sex

Sex and age, y

Female

CVST cases, No.

Population,
person-years

Incidence, per 100 000
person-years (95% CI)

Age-adjusted ratea

Male

Age-adjusted ratea

All

18-29

30-39

40-49

50-64

≥65

Total

18-29

30-39

40-49

50-64

≥65

Total

18-29

30-39

40-49

50-64

≥65

Total

Age-adjusted ratea

Age- and sex-adjusted ratea

10

22

NA

4

2

4

2

2

3

2

3

7

7

4

7

9

17

NA

12

39

NA

NA

212 441

156 541

157 433

195 110

148 204

869 729

NA

181 342

147 386

145 879

175 760

112 508

762 875

NA

393 783

303 927

303 312

370 870

260 712

1 632 604

NA

NA

4.71 (2.26-8.66)

2.56 (0.70-6.54)

1.27 (0.15-4.59)

2.05 (0.56-5.25)

1.35 (0.16-4.88)

2.53 (1.59-3.83)

2.46 (1.43-3.49)

1.10 (0.13-3.99)

2.04 (0.42-5.95)

1.37 (0.16-4.96)

1.71 (0.35-4.99)

6.22 (2.50-12.82)

2.23 (1.30-3.57)

2.34 (1.22-3.46)

3.05 (1.57-5.32)

2.30 (0.92-4.74)

1.32 (0.36-3.38)

1.89 (0.76-3.89)

3.45 (1.58-5.53)

2.39 (0.61-1.67)

2.38 (1.63-3.13)

2.34 (1.60-3.08)

Abbreviations: CVST, cerebral venous
sinus thrombosis; NA, not applicable.
a Adjusted to the US census 2010

population.

As of May 7, 2021, 8 727 851 Ad26.COV2.S vaccine doses had
been administered in the US; 46 potential CVST events occur-
ring within 92 days after Ad26.COV2.S vaccination were re-
ported to VAERS. Eight events were excluded because they
were potentially duplicate reports (4) or were not objectively
diagnosed (4). Twenty-seven of 38 objectively diagnosed cases
of CVST after Ad26.COV2.S vaccination (71.1%) occurred in fe-
male individuals. The median patient age was 45 years (range,
19-75 years). The median time from vaccination to CVST was
9 days (IQR, 6-13 days; range, 1-51 days); 31 of 38 cases of CVST
(81.6%) occurred within 15 days after vaccination, and 36
(94.7%) occurred within 30 days.

The overall incidence rate of post–Ad26.COV2.S vaccina-
tion CVST was 8.65 per 100 000 PY (95% CI, 5.88-12.28 per
100 000 PY) at 15 days, 5.02 per 100 000 PY (95% CI, 3.52-
6.95 per 100 000 PY) at 30 days, and 1.73 per 100 000 PY (95%
CI, 1.22-2.37 per 100 000 PY) at 92 days (Table 2). The 15-day
postvaccination CVST incidence rates for female and male in-
dividuals were 13.01 per 100 000 PY (95% CI, 8.24-19.52 per
100 000 PY) and 4.41 per 100 000 PY (95% CI, 1.90-8.68 per
100 000 PY), respectively. The postvaccination CVST rate
among females was 5.1-fold higher compared with the pre-
COVID-19 pandemic rate (13.01 vs 2.53 per 100 000 PY; P < .001)
(Table 2). This risk was highest among women aged 40 to 49
years (29.50 per 100 000 PY; 95% CI, 13.50-55.95 per 100 000
PY), followed by women aged 30 to 39 years (26.50 per 100 000
PY; 10.65-54.63 per 100 000 PY).

Discussion | In this population-based cohort study, we found that
the CVST incidence rate 15 days after Ad26.COV2.S vaccina-
tion was significantly higher than the prepandemic rate. How-
ever, the higher rate of this rare adverse effect must be con-
sidered in the context of the effectiveness of the vaccine in
preventing COVID-19 (absolute reduction of severe or critical
COVID-19 of 940 per 100 000 PY).6

Most CVST events occurred within 15 days after vaccina-
tion, which is likely the highest at-risk period. The postvacci-
nation CVST rate among females was higher than the prepan-
demic rate among females. The highest risk was among women
aged 30 to 49 years, but the absolute CVST risk was still low
in this group (up to 29.5 per 100 000 PY among women aged
40-49 years). The reason that women had a higher incidence
of postvaccination CVST is unclear; concomitant CVST risk fac-
tors or autoantibody production might have been involved.2
The overall prepandemic CVST incidence rate was slightly
higher in our study than in other studies (0.22-1.57 per 100 000
PY)5 likely because we captured all objectively diagnosed in-
cident CVST cases in a well-defined population, including those
discovered at autopsy.

The present study avoided referral bias and included
only objectively diagnosed and confirmed cases. Only cases
with adequate details or imaging findings reported on
VAERS were used. Study limitations include possible ascer-
tainment bias by including only objectively diagnosed CVST
cases. VAERS reporting is voluntary and subject to reporting

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Table 2. Incidence Rate of CVST Occurring After the Administration of the Ad26.COV2.S Vaccine

Doses
administered, No.

Person-years
at risk

Incidence, per 100 000
person-years (95% CI)

Expected
CVST cases,
No.a

Incidence rate ratio
(95% CI)b

P valuec

Letters

Risk period,
sex, and
age, y

Observed
CVST
cases, No.

15 d

Female

Male

Male

30 d

Female

18-29

30-39

40-49

50-64

≥65

Total

18-29

30-39

40-49

50-64

≥65

Total

18-29

30-39

40-49

50-64

≥65

Total

18-29

30-39

40-49

50-64

≥65

Total

18-29

30-39

40-49

50-64

≥65

Total

18-29

30-39

40-49

50-64

≥65

Total

92 d

Female

Male

23

2

7

9

5

0

1

0

3

3

1

8

2

8

6

0

1

0

3

5

1

2

8

6

1

1

0

3

6

1

10

26

10

10

27

11

641 510

642 745

743 256

1 463 416

814 947

4 305 874

714 458

728 699

775 390

1 505 505

697 925

4 421 977

641 510

642 745

743 256

1 463 416

814 947

4 305 874

714 458

728 699

775 390

1 505 505

697 925

4 421 977

641 510

642 745

743 256

1 463 416

814 947

4 305 874

714 458

728 699

775 390

1 505 505

697 925

4 421 977

176 837

13.01 (8.24-19.52)

26 346

26 397

30 525

60 101

33 469

29 342

29 927

31 844

61 829

28 663

7.59 (0.91-27.40)

26.52 (10.65-54.63)

29.48 (13.50-55.95)

8.32 (2.70-19.42)

0.00 (0.00-11.03)

3.41 (0.10-18.98)

0.00 (0.00-12.33)

9.42 (1.95-27.54)

4.85 (1.00-14.18)

3.49 (0.10-19.43)

181 606

4.41 (1.90-8.68)

52 692

52 794

61 049

3.80 (0.46-13.70)

15.15 (6.53-29.85)

16.38 (7.86-30.12)

120 202

4.99 (1.83-10.87)

66 938

0.00 (0.00-5.51)

353 675

7.35 (4.80-10.77)

58 684

59 854

63 689

1.70 (0.05-9.49)

0.00 (0.00-6.17)

4.71 (0.97-13.77)

123 659

4.04 (1.31-9.44)

57 326

1.74 (0.05-9.72)

363 211

2.75 (1.32-5.06)

161 589

161 901

187 218

368 619

205 276

179 964

183 551

195 312

379 220

175 800

1.24 (0.15-4.47)

4.94 (2.13-9.73)

5.34 (2.56-9.82)

1.63 (0.60-3.54)

0.49 (0.01-2.71)

0.56 (0.02-3.10)

0.00 (0.00-2.01)

1.54 (0.32-4.49)

1.58 (0.58-3.44)

0.57 (0.02-3.17)

1 113 848

0.99 (0.49-1.77)

1.24

0.67

0.39

1.23

0.45

4.47

0.32

0.61

0.44

1.06

1.78

4.05

2.48

1.35

0.78

2.46

0.90

8.95

0.65

1.22

0.87

2.11

3.57

8.09

7.61

4.14

2.38

7.56

2.77

1.98

3.74

2.68

6.47

10.94

24.82

1.61 (0.17-7.57)

10.38 (2.64-48.33)

23.21 (4.80-221.05)

4.06 (0.87-20.45)

0.00 (0.004-23.58)

5.14 (2.74-9.68)

3.09 (0.05-59.36)

0.00 (0.00-11.92)

6.87 (0.79-82.34)

2.84 (0.38-21.32)

0.56 (0.01-4.36)

1.98 (0.74-4.83)

0.81 (0.09-3.78)

5.93 (1.59-26.93)

12.89 (2.75-120.81)

2.43 (0.58-11.73)

0.00 (0.00-11.79)

2.91 (1.58-5.38)

1.55 (0.03-29.68)

0.00 (0.00-5.96)

3.44 (0.39-41.17)

2.37 (0.46-15.26)

0.28 (0.01-2.18)

1.24 (0.51-2.86)

0.26 (0.03-1.23)

1.93 (0.52-8.78)

4.20 (0.90-39.39)

0.79 (0.19-3.82)

0.36 (0.01-6.93)

0.50 (0.01-9.68)

0.00 (0.00-1.94)

1.12 (0.13-13.43)

0.93 (0.20-5.72)

0.09 (0.00-0.71)

0.44 (0.19-1.00)

1 084 603

2.49 (1.64-3.62)

27.44

0.98 (0.54-1.81)

.63

<.001

<.001

.04

>.99

<.001

.36

>.99

.04

.19

>.99

.12

>.99

.003

<.001

.20

>.99

<.001

.56

.56

.16

.29

.28

.54

.08

.39

.05

.75

.58

>.99

>.99

.09

>.99

>.99

.007

.05

Abbreviation: CVST, cerebral venous sinus thrombosis.
a Based on the population of Olmsted County, Minnesota.
b Observed vs expected.
c The exact binomial test P value was calculated using the binom.test() function

in the R, version 4.0.3, with the probability of an observed CVST case being
calculated as K2/(K1+K2), where K1 was the number of prepandemic
CVST cases in the general population (expected) and K2 was the number of
postvaccination CVST cases (observed).

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biases. VAERS monitors vaccine adverse events but does not
prove causality.

Aneel A. Ashrani, MD, MS
Daniel J. Crusan, BS
Tanya Petterson, MS
Kent Bailey, PhD
John A. Heit, MD

Author Affiliations: Division of Hematology, Department of Internal Medicine,
Mayo Clinic, Rochester, Minnesota (Ashrani, Heit); Divisions of Clinical Trials and
Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic,
Rochester, Minnesota (Crusan, Petterson, Bailey); Department of
Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota (Heit); Division of
Epidemiology, Mayo Clinic, Rochester, Minnesota (Heit).

Accepted for Publication: September 12, 2021.

Published Online: November 1, 2021. doi:10.1001/jamainternmed.2021.6352

Corresponding Author: Aneel A. Ashrani, MD, MS, Division of Hematology,
Department of Internal Medicine, Mayo Clinic, 200 First St SW, Rochester, MN
55905 (ashrani.aneel@mayo.edu).

Author Contributions: Dr Ashrani and Mr Crusan had full access to all of the
data in the study and takes responsibility for the integrity of the data and the
accuracy of the data analysis.
Concept and design: Ashrani, Petterson, Bailey, Heit.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Ashrani, Crusan, Petterson.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Crusan, Petterson, Bailey.
Obtained funding: Ashrani, Heit.
Administrative, technical, or material support: Ashrani, Heit.
Supervision: Ashrani, Petterson, Bailey, Heit.

Conflict of Interest Disclosures: Dr Ashrani reported receiving grants from the
National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health
(NIH) during the conduct of the study. Mr Crusan reported receiving grants from
the NIH during the conduct of the study. Dr Heit reported receiving grants from
the NHLBI, NIH during the conduct of the study. No other disclosures were
reported.

Funding/Support: This study was supported in part by grant R01HL66216 from
the NHLBI, NIH (Drs Ashrani and Bailey), the Rochester Epidemiology Project
(grant R01AG034676 from the National Institute on Aging, NIH), and the Mayo
Foundation.

Role of the Funder/Sponsor: The funders 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.

Disclaimer: The content is solely the responsibility of the authors and does not
necessarily represent the official views of the NIH.

1. Centers for Disease Control and Prevention. Cases of cerebral venous sinus
thrombosis with thrombocytopenia after receipt of the Johnson & Johnson
COVID-19 vaccine New release. April 13, 2021. Accessed April 21, 2021. https://
emergency.cdc.gov/han/2021/han00442.asp

2. See I, Su JR, Lale A, et al. US case reports of cerebral venous sinus
thrombosis with thrombocytopenia after Ad26.COV2.S vaccination, March 2 to
April 21, 2021. JAMA. 2021;325(24):2448-2456. doi:10.1001/jama.2021.7517

3. Shay DK, Gee J, Su JR, et al. Safety monitoring of the Janssen (Johnson &
Johnson) COVID-19 vaccine—United States, March-April 2021. MMWR Morb
Mortal Wkly Rep. 2021;70(18):680-684. doi:10.15585/mmwr.mm7018e2

4. Shimabukuro T. Update: thrombosis with thrombocytopenia syndrome
(TTS) following COVID-19 vaccination. Paper presented at: Advisory Committee
on Immunization Practices; May 12, 2021.

5. Devasagayam S, Wyatt B, Leyden J, Kleinig T. Cerebral venous sinus
thrombosis incidence is higher than previously thought: a retrospective
population-based study. Stroke. 2016;47(9):2180-2182. doi:10.1161/
STROKEAHA.116.013617

6. Sadoff J, Gray G, Vandebosch A, et al; ENSEMBLE Study Group. Safety and
efficacy of single-dose Ad26.COV2.S vaccine against COVID-19. N Engl J Med.
2021;384(23):2187-2201. doi:10.1056/NEJMoa2101544

Letters

Payer-Specific Negotiated Prices for Prescription
Drugs at Top-Performing US Hospitals
Nearly one-third of pharmaceutical spending in the US is for
clinician-administered drugs (eg, infusions).1 Medicare Part B
reimbursement for these drugs is set at the average sales price
(ASP)—the average price charged by manufacturers to whole-
salers net of any rebates or
discounts—plus a 6% markup
(or 4.3% during budget se-
questration). By contrast, hospitals and physician offices charge
commercial insurers whatever price they negotiate, and they
retain any difference between the negotiated price and cost of
acquisition.

Supplemental content

While these negotiated prices have long been confiden-
tial, a transparency rule that took effect on January 1, 2021, re-
quires hospitals to post payer-specific negotiated prices for all
items and services, including clinician-administered drugs. We
analyzed a set of top-performing hospitals to quantify drug
pricing variation across insurers.

Methods | We searched the websites of the 20 top-rated hospi-
tals by US News and World Report rankings for pricing files
from January 1 through September 15, 2021. We selected
these hospitals because they were likely to have sufficient
resources to comply with reporting requirements and would
serve as a model for other hospitals that were deciding on
how to comply (eTable 1 in the Supplement). We extracted
commercial insurer-negotiated prices and self-pay cash prices
(the discounted prices for patients paying without insurance)
for the 10 drugs with the highest 2019 Medicare Part B expen-
ditures (eTable 2 in the Supplement).2 We evaluated median
prices relative to the Medicare payment limit to enable com-
parisons of hospital markups across drugs. We used Stata,
release 16.1 (StataCorp LLC) and Excel, version 16.16.27
(Microsoft) for the study analysis, which was performed from
July 1 to September 15, 2021. Institutional review board
approval was not required because we used only publicly
available data on prices of prescription drugs and did not use
patient information.

Results | Seventeen of the 20 hospitals (85%) posted files
aimed at complying with the new transparency rule. Eleven
(55%) included payer-specific pharmaceutical prices. Of the
hospitals that released pharmaceutical data, 82% (and
85% of hospitals overall) were 340B entities, which entitled
them to acquire drugs from manufacturers at prices below
the ASP.3

Prices varied between and within hospitals (Figure). Me-
dian negotiated prices for the 10 drugs in the study sample
ranged from 169% (IQR, 137%-264%) of the Medicare pay-
ment limit at Rush University Medical Center to 344% (IQR,
307- 368%) at the Mayo Clinic Hospital–Arizona, and median
self-pay cash prices ranged from 149% (IQR, 124%-203%) of the
Medicare payment limit at Rush to 306% at Brigham and Wo-
men’s Hospital (IQR, 273%-327%) and Massachusetts General
Hospital (IQR, 283%-327%; Table). There was also substantial
variation by drug, with the lowest median negotiated prices
relative to the Medicare payment limit observed for abata-

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