Investigating Uptake and Subsequent Health Outcomes Associated With Pfizer-BioNTech Bivalent COVID-19/Influenza Vaccine Concomitant Administration Using a Claims-based Real-world Data Source in the US
Conditions: Influenza, Human, COVID-19
Sponsor: Pfizer

Pfizer-BioNTech BA.4/5 bivalent mRNA COVID-19 vaccine
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NON-INTERVENTIONAL (NI) STUDY PROTOCOL
Study Information
Title
Investigating uptake and subsequent health 
outcomes associated with Pfizer-BioNTech
bivalent COVID-19/Influenza vaccine 
concomitant administration using a 
claims-based real-world data source in the 
US
Protocol number
C4591061
Protocol version identifier
1.0
Date
26 April 2023
Active substance 
J07BN01
Medicinal product
Pfizer-BioNTech BA.4/5 bivalent mRNA 
COVID-19 vaccine
Research question and objectives
The primary objectives are: 

To describe the proportion of 
Pfizer-BioNTech bivalent mRNA
COVID-19/influenza vaccines that are 
concomitantly administered, and the 
characteristics of patients who receive 
concomitant administration.

To estimate vaccine effectiveness (VE) 
of concomitant administration of a single 
dose of the mRNA Pfizer-BioNTech 
bivalent COVID-19 vaccine and a 
licensed flu vaccine versus either vaccine
alone against all-cause hospitalization or 
acute respiratory illness requiring 
hospitalization, emergency 
department/urgent care or outpatient
encounters.
Author
, PhD, MHS
Pfizer Inc. 
PPD
PPD

Pfizer-BioNTech BA.4/5 bivalent mRNA COVID-19 vaccine
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Co-Author
, MS, MBA
Pfizer Inc. 
This document contains confidential information belonging to Pfizer. Except as otherwise agreed to in writing, 
by accepting or reviewing this document, you agree to hold this information in confidence and not copy or 
disclose it to others (except where required by applicable law) or use it for unauthorized purposes. In the event 
of any actual or suspected breach of this obligation, Pfizer must be promptly notified.
PPD
PPD
PPD

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1. TABLE OF CONTENTS
1. TABLE OF CONTENTS.......................................................................................................3
2. LIST OF ABBREVIATIONS................................................................................................5
3. RESPONSIBLE PARTIES....................................................................................................8
4. ABSTRACT...........................................................................................................................9
5. AMENDMENTS AND UPDATES.....................................................................................10
6. MILESTONES.....................................................................................................................11
7. RATIONALE AND BACKGROUND................................................................................11
8. RESEARCH QUESTION AND OBJECTIVES .................................................................12
9. RESEARCH METHODS ....................................................................................................12
9.1. Study Design ...........................................................................................................12
9.2. Setting......................................................................................................................13
9.2.1. Inclusion Criteria ........................................................................................13
9.2.2. Exclusion Criteria.......................................................................................13
9.3. Variables..................................................................................................................14
9.3.1. Exposure .....................................................................................................14
9.3.2. Outcomes ....................................................................................................16
9.3.3. Censoring....................................................................................................22
9.3.4. Covariates ...................................................................................................23
9.4. Data Sources............................................................................................................30
9.5. Study Size................................................................................................................30
9.6. Data Management ...................................................................................................32
9.7. Data Analysis ..........................................................................................................32
9.8. Quality Control........................................................................................................34
9.9. Limitations of the Research Methods......................................................................34
9.10. Other Aspects ........................................................................................................34
10. PROTECTION OF HUMAN SUBJECTS ........................................................................34
10.1. Patient Information................................................................................................34
10.2. Patient Consent......................................................................................................34
10.3. Institutional Review Board (IRB)/Independent Ethics Committee (IEC) ............35
10.4. Ethical Conduct of the Study ................................................................................35

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11. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS ......................................................................................................................35
12. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS........35
13. REFERENCES ..................................................................................................................36
14. LIST OF TABLES.............................................................................................................39
15. LIST OF FIGURES ...........................................................................................................39
ANNEX 1. LIST OF STAND ALONE DOCUMENTS .........................................................40
ANNEX 2 ADDITIONAL INFORMATION..........................................................................41

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2. LIST OF ABBREVIATIONS
Abbreviation
Definition
AE
Adverse Event
AIDS
Acquired Immunodeficiency Syndrome
CI
Confidence interval
CDC
Centers for Disease Control and Prevention
CFR.
Code of Federal Regulations
CIOMS
Council for International Organizations of Medical 
Sciences
CKD
Chronic Kidney Disease
CDM
Clinformatics Data Mart
COVID-19
Coronavirus Disease 2019
CPT
Current Procedural Terminology
ED
Emergency Department
EMA
European Medicines Agency
ENCePP
European Network of Centres for 
Pharmacoepidemiology and Pharmacovigilance
FDA
Food and Drug Administration
GPP
Guidelines for Good Pharmacoepidemiologic 
Practice
HCPCS
Healthcare Common Procedure Coding System
HIPAA
Health Insurance Portability and Accountability Act
HIV
Human Immunodeficiency Virus
HR
Hazard ratio
ICD-10-CM
International Classification of Diseases, 10th revision, 

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Abbreviation
Definition
Clinical Modification
IEC
Independent Ethics Committee
IPTW
Inverse Probability of Treatment Weights
IRB
Institutional Review Board
ISPOR
International Society for Pharmacoeconomics and 
Outcomes Research
ISPOR-ISPE
International Society for Pharmacoeconomics and 
Outcomes Research – International Society for 
Pharmacoepidemiology
mRNA
Messenger Ribonucleic Acid 
NDC
National Drug Code
NVX-CoV2373
Novavax COVID-19 vaccine
NY
New York
POS
Place of service
SARS-CoV-2
Severe Acute Respiratory Syndrome Coronavirus 2
SAS
Statistical Analysis System
SCD
Sickle cell disease
SD
Standard Deviation
SIV
Seasonal Influenza Vaccine
UC
Urgent care
UK
United Kingdom
US
United States
VAERS
Vaccine adverse event reporting system

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Abbreviation
Definition
VE
Vaccine effectiveness

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3. RESPONSIBLE PARTIES
Name, degree(s)
Job Title
Affiliation
Address
, PhD
SVP, Chief Medical 
Affairs Officer, 
Vaccines/Antivirals 
and Evidence 
Generation
Pfizer Inc.
, 
PhD, MHS
Director – Vaccines 
RWE Scientist, NI 
Study Lead
Pfizer Inc. 
, 
MS, MBA
Head, RWE 
Vaccines & Hospital
Pfizer Inc.
, 
MPH
Manager – Vaccines 
RWE Scientist
Pfizer Inc.
, 
PhD
VP, Global COVID-
19, Flu Vaccines & 
Antiviral Lead
Pfizer Inc.
, 
PhD, MPH
Global Influenza 
Medical Affairs Lead
Pfizer Inc.
, 
MSc
Senior Director –
mRNA Vaccines, 
Value & Evidence
Pfizer Inc.
, 
PhD, MSc
Director, Statistical 
Research and Data 
Science Center
Pfizer Inc.
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD

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4. ABSTRACT
Not applicable.

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5. AMENDMENTS AND UPDATES
None.

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6. MILESTONES
Milestone
Planned date
Start of data collection 
05 May 2023
End of data collection
12 May 2023
Final study report
12 December 2023
7. RATIONALE AND BACKGROUND
Data suggest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) likely follows 
a seasonality pattern as with other respiratory viral infections such as influenza; where 
activity peaks during the traditional winter viral respiratory season.1 CDC guidance for the 
2020 and 2021 flu seasons included a recommended time interval of at least 14 days between 
flu and COVID-19 vaccines.2 Based on updated data, the CDC has revised their
recommendations that COVID-19 vaccines may be co-administered with seasonal influenza 
vaccines (SIV).3 Specifically, COVID-19 and SIV can be administered concomitantly, and 
routine administration of all age-appropriate doses of vaccines simultaneously is 
recommended for children, adolescents, and adults.3 A systematic literature review of 
30 studies revealed that when SIV and COVID-19 booster vaccines are combined, there is 
potential for an increase in the uptake of the COVID-19 vaccine, predominately because 
many individuals are accustomed to taking SIV annually.4
Studies have reported similar safety and immunogenicity among those who received 
co-administrated COVID-19 vaccine and SIV compared to those who received these 
vaccines separately.5-11 Three of these studies were clinical trials comparing safety and 
immunogenicity between SIV and COVID-19 co-administration versus SIV or COVID-19 
alone. In these three clinical trials, no safety concerns or immune interferences were found 
regardless of the vaccines or the age of vaccinated subjects (18-64 or 65+).6,9-11 Hause et al in 
a retrospective cohort study of self-reported vaccine data found a significant increase in 
reports of systemic reactions 0-7 days following co-administration of a COVID-19 mRNA 
booster vaccines with an SIV compared to COVID-19 mRNA vaccine alone;5 whereas 
results from a VAERS database8 and the National Health Interview Survey7 analyses found
no unusual or unexpected patterns of AEs or differences in symptom severity. 
Regarding efficacy/effectiveness, one study was identified and reviewed. Novavax’s 
NVX-CoV2373 COVID-19 vaccine efficacy when co-administered with the quadrivalent 
influenza cell-based vaccine Flucelvax (Seqirus UK, Maidenhead) among individuals aged 
18 to <65 years was 87∙5% (95% CI –0∙2 to 98∙4); was slightly attenuated compared to when 
administered alone (89∙8% (95% CI 79∙7–95∙5).9

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Although the above studies evaluated safety, immunogenicity and efficacy, the proportion of 
individuals receiving concomitantly administered COVID-19/influenza vaccines in routine 
clinical practices is not well characterized and has the potential to be a large percentage of 
vaccine recipients. Further, it is unknown whether receiving COVID-19/influenza vaccines 
concomitantly alters subsequent health outcomes. Therefore, the aim of this study is to 
evaluate within the context of a large national healthcare claims database in the US the 
proportion of bivalent COVID-19/influenza vaccines concomitantly administered; the 
characteristics of patients who receive concomitant administration and subsequent health 
outcomes among groups defined by vaccine administration.
8. RESEARCH QUESTION AND OBJECTIVES
Primary Objective 1: To describe the proportion of Pfizer-BioNTech bivalent mRNA 
COVID-19/influenza vaccines that are concomitantly administered, and the characteristics of 
patients who receive concomitant administration.
Primary Objective 2: To estimate vaccine effectiveness (VE) of concomitant administration 
of a single dose of the mRNA Pfizer-BioNTech bivalent COVID-19 vaccine and a licensed 
flu vaccine versus either vaccine alone against all-cause hospitalization or acute respiratory 
illness requiring hospitalization, emergency department/urgent care or outpatient encounters.
9. RESEARCH METHODS 
9.1. Study Design
This structured secondary data collection study will be a retrospective, cohort design to 
describe concomitant vaccine uptake and subsequent outcomes during one influenza 
vaccination season. Individuals will be indexed on the date of their first Pfizer-BioNTech 
mRNA bivalent COVID-19 or influenza vaccine. Exposure groups will be assigned as 
follows: 

Group 1: Same day (Pfizer-BioNTech  mRNA bivalent COVID and flu on the same 
day); 

Group 2: COVID alone (Pfizer-BioNTech  bivalent COVID only); 

Group 3: flu alone (influenza vaccine of any type only). 

 
A one year lookback period prior to the index vaccination will be used to identify covariates. 
Individuals will be followed starting 15 days after vaccination until experiencing one of the 
following: 1) outcome 2) disenrollment 3) receipt of a second vaccine of the same type 4) 
receipt of the other vaccine or any type of COVID-19 vaccine 5) end of data or 6) death. 
Only Pfizer-BioNTech mRNA bivalent COVID-19 vaccine will be assessed. All types of 
influenza vaccines recommended for the eligible population will be eligible for inclusion, but 
for the 65+ age group only enhanced influenza vaccines will be included.
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Figure 1.
Overall Study Design
9.2. Setting
The study period will be 31 August 2022 through 14 February 2023, but data will be used 
going back to 31 August 2021 to ascertain baseline covariates. The study population will 
include adults who were enrolled in the Optum Clinformatics Database as of 31 August 2022
(the date when the Pfizer-BioNTech bivalent mRNA vaccine was authorized), and have 
received a Pfizer-BioNTech mRNA bivalent COVID-19 or influenza vaccine during the 
study period. 
9.2.1. Inclusion Criteria
Patients must meet all of the following inclusion criteria to be eligible for inclusion in the 
study:
1. Enrolled in Optum claims database as of 31 August 2022 (date bivalent authorized).
2. Has a Pfizer-BioNTech mRNA bivalent COVID-19 or influenza vaccine (any type) 
between 31 August 2022 and 30 January 2023 (earliest vaccine is index date).
3. Aged >=18 years on the index date.
4. 365 days of continuous enrolment prior to index date.
9.2.2. Exclusion Criteria
Patients meeting any of the following criteria will not be included in the study:
1. Patients with a second dose of any type of bivalent mRNA COVID-19 or flu vaccine, 
disenrolled or died within 14 days following the first dose.
2. Has prior COVID-19 diagnosis (U07.1, any setting, any position) within 90 days (per 
CDC guidance12 on length of time before receiving a vaccine) before and on index.
3. Has influenza vaccine between 01 August -30 August 2022 (before study period).

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4.
Has a COVID-19 or influenza diagnosis code in any setting within 14 days after 
index vaccination (excluded from all cohorts).
5. For patients 65+, had a standard dose influenza vaccine or unknown type influenza 
vaccine.
6. Has Moderna mRNA bivalent COVID-19 vaccine or monovalent vaccine of any type
on index date.
9.3. Variables
9.3.1. Exposure
Table 1.
Vaccination Status Variables
Variable
Role
Operational definition
Group 1: Same Day
(Concomitantly administered 
Pfizer-BioNTech COVID-19
mRNA bivalent vaccine + 
influenza vaccine)
Exposure
Patients meeting the following criteria will be included in 
the Same Day cohort: 
1.
NDC or CPT codes for both the Pfizer-BioNTech 
mRNA bivalent COVID-19 vaccine and an influenza 
vaccine during the study period.
2.
The first Pfizer-BioNTech mRNA bivalent COVID-
19 vaccine and influenza vaccine NDC or CPT codes
during the study period occurred on the same date.
3.
No influenza vaccine NDC or CPT code between 
01 August 2022 and 30 August 2022.
If the patient receives >1 mRNA bivalent COVID-19
vaccine or influenza vaccine during follow-up, the patient 
is censored on the date of the NDC or CPT code for the 
second vaccine of the same type. mRNA bivalent 
COVID-19 and influenza vaccine NDC and CPT codes 
are listed in Appendix Table 1.
Group 2: COVID alone
(COVID-19 mRNA bivalent 
vaccine alone)
Exposure
Patients meeting the following criteria will be included in 
the COVID-19 alone cohort: 
1.
Pfizer-BioNTech mRNA bivalent COVID-19
vaccine NDC or CPT code during the study period
and no influenza vaccine on that date.
2.
No influenza vaccine NDC or CPT code between 
01 August 2022 and 30 August 2022.
If the patient receives >1 mRNA bivalent COVID-19
vaccine (any type) during follow-up or an influenza 
vaccine, the patient is censored on the date of the NDC or 
CPT code for the second mRNA bivalent COVID-19

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Table 1.
Vaccination Status Variables
Variable
Role
Operational definition
vaccine/influenza vaccine. mRNA bivalent COVID-19
vaccine NDC and CPT codes are listed in Appendix 
Table 1.
Group 3: Flu alone (Influenza 
vaccine alone)
Exposure
Patients meeting the following criteria will be included in 
the Flu alone cohort: 
1.
Influenza vaccine NDC or CPT code during the 
study period and no mRNA bivalent COVID-19
vaccine on that date.
2.
No influenza vaccine NDC or CPT code between 
01 August 2022 and 30 August 2022.
If the patient receives >1 influenza vaccine or an mRNA 
bivalent COVID-19 vaccine (any type) during follow-up, 
the patient is censored on the date of the NDC or CPT 
code for the second influenza vaccine/COVID-19
vaccine. Influenza vaccine NDC and CPT codes are listed 
in Appendix Table 1.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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9.3.2. Outcomes
All outcomes will be assessed starting on Day 15. All hospitalization related outcomes in the 
table below will be ascertained using acute facility stays only. Inpatient stays classified as 
skilled nursing facility or rehabilitation will not be used. COVID-19 and flu hospitalizations 
will be assessed in any position and in the primary position to mitigate possibility of 
hospitalization with the disease versus for the disease. 
COVID-19 related endpoints will be identified using the following ICD-10-CM diagnosis 
code: U07.1.
Influenza will be identified using the following ICD-10-CM diagnosis codes: J09.X, J10.X, 
J11.X.
We will evaluate three negative control outcomes. These outcomes are unrelated to 
vaccination and likely share similar confounding patterns for health-seeking behaviors. These 
negative controls have been used in other studies of comparative VE of influenza vaccines or 
COVID-19 VE.13-15 Urinary tract infection and accidental injury are outcomes that could 
differentiate patients who seek medical care versus those who do not, and is likely a better 
choice of outcome for younger adults. Hospitalization in the first 14 days (ie, among recently 
vaccinated) uses time as a negative control, in that the vaccine likely could not biologically 
prevent health events until adequate levels of immunity has been achieved. 
Table 2.
Outcome Variables
Variable
Role
Operational definition
All-cause hospitalization 
Outcome
An inpatient (acute facility) claim that meets the 
following criteria:
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Place of service = 21.
3.
Non-null confinement ID.
4.
tos_cd = 'FAC_IP.ACUTE'.
Composite COVID-19 related 
hospitalization/ED/UC (any 
position)
Outcome
An inpatient (acute facility) claim that meets the 
following criteria:
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Place of service = 21.

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Table 2.
Outcome Variables
Variable
Role
Operational definition
3.
Non-null confinement ID.
4.
tos_cd = 'FAC_IP.ACUTE'.
5.
COVID-19 diagnosis code (ICD-10-CM: U07.1) in 
any position
OR
An ED/urgent care claim that meets the following 
criteria:
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Place of service = 23 or 20.
3.
Confinement id = null.
4.
COVID-19 diagnosis code (ICD-10-CM: U07.1) in 
any position
COVID-19 related 
hospitalization (any position)
Outcome
An inpatient (acute facility) claim that meets the 
following criteria:
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Place of service = 21.
3.
Non-null confinement ID.
4.
tos_cd = 'FAC_IP.ACUTE'.
5.
COVID-19 diagnosis code (ICD-10-CM: U07.1) in 
any position.
 
 
 
 
 
CCI

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Table 2.
Outcome Variables
Variable
Role
Operational definition
 
Composite influenza-related 
hospitalization/ED/UC (any 
position)
An inpatient (acute facility) claim that meets the 
following criteria:
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Place of service = 21.
3.
Non-null confinement ID.
4.
tos_cd = 'FAC_IP.ACUTE'
5.
Influenza diagnosis code (ICD-10-CM: J09.X, 
J10.X, J11.X) in any position.
OR
An ED/urgent care claim that meets the following 
criteria:
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Place of service = 23 or 20.
3.
Confinement id = null.
4.
Influenza diagnosis code (ICD-10-CM: J09.X, 
J10.X, J11.X) in any position.
Influenza-related 
hospitalization (any position)
Outcome
An inpatient (acute facility) claim that meets the 
following criteria:
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Place of service = 21.
CCI

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Table 2.
Outcome Variables
Variable
Role
Operational definition
3.
Non-null confinement ID.
4.
tos_cd = 'FAC_IP.ACUTE'.
5.
Influenza diagnosis code (ICD-10-CM: J09.X, 
J10.X, J11.X) in any position.
 
 
 
 
 
 
COVID-19 related ED/Urgent 
care visit (any position)
Outcome
An ED/urgent care claim that meets the following 
criteria:
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Place of service = 23 or 20.
3.
Confinement id = null.
4.
COVID-19 diagnosis code (ICD-10-CM: U07.1) in 
any position.
Influenza-related ED/Urgent 
care visit (any position)
Outcome
An ED/urgent care claim that meets the following 
criteria:
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Place of service = 23 or 20.
3.
Confinement id = null.
CCI

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Table 2.
Outcome Variables
Variable
Role
Operational definition
4.
Influenza diagnosis code (ICD-10-CM: J09.X, 
J10.X, J11.X) in any position.
COVID-19related outpatient 
visit (any position)
Outcome
An outpatient claim that meets the following criteria: 
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Confinement id = null.
3.
COVID-19 diagnosis code (ICD-10-CM: U07.1) in 
any position.
4.
Does not meet the definition of an ED/UC/hospital 
visit.
Influenza-related outpatient 
visit (any position)
Outcome
An outpatient claim that meets the following criteria: 
1.
Occurs >14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.
2.
Confinement id = null.
3.
Influenza diagnosis code (ICD-10-CM: J09.X, 
J10.X, J11.X) in any position.
4.
Does not meet the definition of an ED/UC/hospital 
visit.
Urinary tract infection
Negative 
control 
outcome
≥1 ICD-10-CM diagnosis code in any position/any 
setting for a urinary tract infection (that occurs >14 days 
after the patient’s first mRNA bivalent COVID-19 or 
influenza vaccine dose during the study period. 
Accidental injury
Negative 
control 
outcome
≥1 ICD-10-CM diagnosis code in any position/any 
setting for accidental injury that occurs >14 days after the 
patient’s first mRNA bivalent COVID-19 or influenza 
vaccine dose during the study period. 
Hospitalization in the 14 days 
following vaccination 
(recently vaccinated) Day 1-
14
Negative 
control  
outcome
An inpatient (acute facility) claim that meets the 
following criteria:
1.
Occurs ≤14 days after the patient’s first mRNA 
bivalent COVID-19 or influenza vaccine dose during 
the study period.

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Table 2.
Outcome Variables
Variable
Role
Operational definition
2.
Place of service = 21.
3.
Non-null confinement ID.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
CCI
CCI
CCI
CCI

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Table 2.
Outcome Variables
Variable
Role
Operational definition
within 60 days following discharge. 
 
 
 
 
 
Follow-up time
Outcome 
descriptive
Measured from day 15 through the minimum date of
censoring (see follow-up time description in Section 
9.3.3). 
Mean, standard deviation (SD), median (Q1-Q3), 
minimum, and maximum, will also be summarized for 
the length of follow-up in each group.
9.3.3. Censoring
The censoring date will be the minimum date in follow-up (starting >14 days after 
vaccination) of the following variables. 
Table 3.
Censoring Variables
Variable
Role
Operational definition
Outcome under study
Censoring
See outcome table above. Table 2. 
Disenrollment
Censoring
Date a patient disenrolls from the insurance plan, as 
indicated in the enrollment file.
End of data
Censoring
14 February 2023
Receipt of second vaccine of 
the same type 
Censoring
Any kind of mRNA bivalent COVID-19 vaccine is 
considered as a censoring vaccine. See exposure table for 
vaccination definitions.
Receipt of the other vaccine
type
Censoring
Any kind of mRNA bivalent COVID-19 vaccine is 
considered as a censoring vaccine. See exposure table for 
vaccination definitions. 
Death
Censoring
Death date is assigned the midpoint of the month in 
which death is reported in the database.
Nirmatrelvir/ritonavir 
(sensitivity analysis for 
COVID-19 endpoints)
Censoring
First dispensing for Nirmatrelvir/ritonavir during follow-
up using NDC codes. 
Influenza treatment 
(sensitivity analysis for 
treatment endpoints)
Censoring
First dispensing of any influenza treatments (see outcome 
table above for definitions) during follow -up using NDC 
codes. 
CCI

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9.3.4. Covariates
Table 4.
Covariates
Variable
Role
Operational definition
Age
Patient 
Characteristics, 
Subgroup
The number of years between the index date and the 
patient birth year. Age will be reported in the following 
categories: 18-64 and 65+. 
Continuous statistics including mean, standard deviation 
(SD), median (Q1-Q3), minimum, and maximum, will 
also be summarized.
Model specification: continuous linear
Sex
Patient 
Characteristics
Sex will be reported as Male (‘M’), Female (‘F’), and 
Unknown
Model specification: categorical
US Geographic Region
Patient 
Characteristics
US geographic region will be derived from the patient 
state and categorized into the following: 
Northeast – Connecticut, Maine, Massachusetts, New 
Hampshire, Rhode Island, Vermont, New Jersey, New 
York, Pennsylvania
Midwest – Indiana, Illinois, Michigan, Ohio, Wisconsin, 
Iowa, Kansas, Minnesota, Missouri, Nebraska, North 
Dakota, South Dakota
South – Delaware, District of Columbia, Florida, 
Georgia, Maryland, North Carolina, South Carolina, 
Virginia, West Virginia, Alabama, Kentucky, 
Mississippi, Tennessee, Arkansas, Louisiana, 
Oklahoma, Texas
West – Arizona, Colorado, Idaho, New Mexico, 
Montana, Utah, Nevada, Wyoming, Alaska, California, 
Hawaii, Oregon, Washington
Other/unknown – Puerto Rico/unknown region
Model specification: categorical
Charleston Comorbidity 
Index (CCI) 
Patient 
Characteristics
Charleston Comorbidity Index score will be measured 
during the 12 month baseline period before index and 
will be reported in the following categories: 0, 1, and 
2+. Continuous statistics including mean, standard 
deviation (SD), median (Q1-Q3), minimum, and 
maximum, will also be summarized.
Model specification: categorical (0,1,2+)
Prior Pneumonia or 
respiratory failure
Patient 
Characteristics
Any code (any setting) within the 1 year baseline for 
any type of pneumonia or respiratory failure
Prior heart disease
Patient 
Characteristics
Any code (any setting) within the 1 year baseline for 
heart disease
Prior asthma
Patient 
Characteristics
Any code (any setting) within the 1 year baseline for
asthma

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Table 4.
Covariates
Variable
Role
Operational definition
Prior chronic lung disease
Patient 
Characteristics
Any code (any setting) within the 1 year baseline for 
chronic lung disease
Prior diabetes
Patient 
Characteristics
Any code (any setting) within the 1 year baseline for 
diabetes
Prior kidney disorders
Patient 
Characteristics
Any code (any setting) within the 1 year baseline for 
kidney disorders
Month of index date 
Patient
Characteristics
August/September, October, November, December, 
January (Note: August will be combined with 
September as only 1 day of August will be included in 
the cohort). 
Model specification: categorical
Prior Influenza vaccination 
Patient 
Characteristics
One claim (NDC or CPT) for influenza vaccine of any 
type measured between 31 August 2021 – 31 May 2022
Model specification: dichotomous (yes/no)
COVID-19 vaccination in 
the 1 year prior
Patient 
Characteristics
One claim (NCD, CPT, procedures) for COVID vaccine 
of any type in the 365 days before index separated into 
the following time periods:

Recent vaccination: ≤60 days prior to index

Older vaccination: Between 61 and 365 days 
prior to index 

No evidence of vaccination in the prior year
If there is evidence of >1 COVID vaccination in the 
365 days prior to index, patients are categorized using 
the date of the most recent dose administered during the 
baseline period. 
Model specification: categorical (3 level)
Time from last COVID-19
vaccine dose
(Due to the likelihood of 
misclassification, this 
variable will be used for 
descriptive table only, not 
for model)
Patient 
Characteristics
Number of days from last COVID-19 vaccine dose. 
Reported as categories:17,18 <60 days, 60-179, >=180, 
not applicable. Continuous statistics including mean,
standard deviation (SD), median (Q1-Q3), minimum, 
and maximum, among those with a prior dose. 
Monovalent mRNA 
COVID-19 vaccine in the 
60 days prior to index
(Note for descriptive table 
only, not for model due to 
collinearity with covid 
vaccine in the prior year 
above)
Patient 
Characteristics
Yes/No. One claim (NCD, CPT, procedures) for 
monovalent mRNA COVID-19 vaccine between 01 July 
2022 – 30 August 2022

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Table 4.
Covariates
Variable
Role
Operational definition
Type of index influenza 
vaccine administered (Note 
for descriptive table only, 
not for model)
Patient 
Characteristics
Categories include: Enhanced/Standard/Unknown, and 
each specific type of vaccine 

Standard

Egg-based standard dose injection 
(Afluria, Fluarix, FluLaval, Fluzone 
Standard Dose)

Fluzone intradermal

Flucelvax (cell-based)

FluMist

Multiple

Enhanced

Fluzone High dose (Enhanced)

Flublok (recombinant/Enhanced)

Fluad (adjuvanted/Enhanced)

Multiple

Unknown
See below for operational definition for assigning 
specific flu vaccines.
Concurrent Shingrix 
vaccination (any dose)
Patient 
Characteristics
NDC or CPT code for Shingrix on the same day as the 
index vaccination
Model specification: dichotomous (yes/no)
Prior Shingrix vaccination
Patient 
Characteristics
NDC or CPT code for Shingrix in the 365 days prior to 
index

1 prior dose

2 prior doses (measured on unique days)
Model specification: dichotomous (yes/no) (Note: 
number of doses used in descriptive table only)
Concurrent pneumococcal 
vaccination
Patient 
Characteristics
NDC or CPT code for polysaccharide or conjugate 
pneumonia vaccine on the same day as the index 
vaccination
Model specification: dichotomous (yes/no)

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Table 4.
Covariates
Variable
Role
Operational definition
Prior pneumococcal 
vaccination
Patient 
Characteristics
NDC or CPT code for polysaccharide or conjugate 
pneumonia vaccine in the 365 days prior to index
Model specification: dichotomous (yes/no)
Vaccine administration 
setting
Patient 
Characteristics
Vaccine administration setting will be determined based 
on the reported place of service for the claim. 
Prescription claims will be assumed to have occurred in 
the Pharmacy setting. The most common settings will be 
reported and patients with multiple conflicting 
administration setting codes on index will be classified
as multiple.
Model specification: pharmacy, office, other
Nursing home residence
Patient 
Characteristics
Any claim with care location (POS =32) of nursing 
home in the 365 days prior to index.
Model specification: dichotomous (yes/no)
Skilled nursing facility
Patient 
Characteristics
Any claim with care location of SNF (POS = 31) in the 
365 days prior to index.
Model specification: dichotomous (yes/no)
Functional status/mobility
Patient 
Characteristics
A procedure code indicating decreased functional status 
or mobility in the 365 days prior to index. 
Model specification: dichotomous (yes/no)
Wellness visit
Patient 
Characteristics
A CPT or diagnosis code for wellness visit in the 
365 days prior to index. 
Model specification: dichotomous (yes/no)
Immunocompromised 
status
Patient 
Characteristics
Assess this group as the following category (Yes/No): 
>=1 IC condition.
Any code within the 1 year baseline for the following 
conditions:

Hematologic malignancy

HIV/AIDS

Solid organ transplant

Bone marrow transplant

Primary immunodeficiencies

Immunosuppresive medications 
(eg, chemotherapy, oral corticosteroids ≥20mg 
of prednisone or equivalent for ≥14 days).
Model specification: dichotomous (yes/no)
Conditions that place or 
may place patients at risk 
of Severe COVID-19
Patient 
Characteristics
Assess this group as the following category (Yes/No):
>=1 high risk condition.
Baseline comorbid conditions that place or may place 

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Table 4.
Covariates
Variable
Role
Operational definition
patients at risk of Severe COVID-19 will be assessed in 
the 12 months prior to index date. Conditions will be 
assess by having at least 1 ICD-10-CM code in any 
position in any setting. The conditions include: 

Cancer history

Chronic kidney disease (CKD) at any stage

Chronic liver disease 

Chronic lung diseases 

Dementia or other neurological conditions

Diabetes (type 1 or type 2)

Disabilities; including Down Syndrome

Heart conditions (heart failure; coronary artery 
disease; or cardiomyopathies)

HIV infection

Hypertension

Immunocompromised state (primary caused by 
genetic defects; secondary/acquired from prolonged 
use of corticosteroids or other immune weakening 
medicines)

Mental health conditions 

Overweight and obesity

Physically inactive

Sickle cell disease (SCD) or thalassemia

Smoking; current or former

Solid organ or blood stem cell transplant (includes 
bone marrow transplants)

Stroke or cerebrovascular disease

Substance use disorders (alcohol; opioid; or cocaine 

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Table 4.
Covariates
Variable
Role
Operational definition
use disorder)

Tuberculosis

Pregnancy or recent pregnancy (for at least 42 days 
following end of pregnancy).
Model specification: dichotomous (yes/no)
Number of outpatient visits 
in the 180 days prior
Patient 
Characteristics
Count unique days with an outpatient visit in the 
180 days prior to index 

Dichotomous, yes/no

Continuous statistics including mean, standard 
deviation (SD), median (Q1-Q3), minimum, 
and maximum.
Model specification: number categories (0, 1, 2, 3+)
Number of hospitalizations 
in the 180 days prior
Patient 
Characteristics
Count of unique days with an acute hospital admission 
(POS = 21 and tos_cd = 'FAC_IP.ACUTE') in the 180 
days prior to index 

Dichotomous, yes/no

Continuous statistics including mean, standard 
deviation (SD), median (Q1-Q3), minimum, 
and maximum
Model specification: number categories (0, 1, 2+)
Number of prior COVID-
19 tests in the prior 365 
days
Patient 
Characteristics
Count of unique days with COVID-19 test (PCR or 
antigen test) ordered in the 365 days prior to index 

Dichotomous, yes/no

Continuous statistics including mean, standard 
deviation (SD), median (Q1-Q3), minimum, 
and maximum
Model specification: number categories (0, 1, 2+)
Flu test in the prior 365 
days
Patient 
Characteristics
Influenza test ordered in the 365 days prior to index 

Dichotomous, yes/no
Model specification: dichotomous (yes/no)
Number of prior telehealth 
visits in the prior 365 days
Patient 
Characteristics
Count of unique days with telehealth visit in the 365 
days prior to index 

Dichotomous, yes/no

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Table 4.
Covariates
Variable
Role
Operational definition

Continuous statistics including mean, standard 
deviation (SD), median (Q1-Q3), minimum, 
and maximum
Model specification: number categories (0, 1, 2+)
Number of labs ordered in 
the prior 365 days
Patient 
Characteristics
Count of unique days with any lab ordered in the 
365 days prior to index 

Dichotomous, yes/no

Continuous statistics including mean, standard 
deviation (SD), median (Q1 -Q3), minimum, 
and maximum
Model specification: number categories (0, 1, 2+)
Prior COVID-19 infection
Patient 
Characteristics
1 ICD-10-CM code (U07.1) in the 1 year baseline in any 
setting (>3 months prior to reflect exclusion criteria
Model specification: dichotomous (yes/no)
Prior influenza infection
Patient 
Characteristics
1 ICD-10-CM code for influenza in the 1 year baseline 
in any setting
Model specification: dichotomous (yes/no)
If there are conflicting records on the type of influenza vaccine received on index, influenza 
vaccine category (Enhanced, Standard, Unknown) will be assigned as follows: 
1. ≥1 NDC or CPT code for an enhanced flu vaccine.
2. Classified as enhanced.
3. ≥1 NDC or CPT code for a standard flu vaccine and no codes for enhanced.
4. Classified as standard.
5. ≥1 NDC or CPT code for an unknown type of flu vaccine and no codes for enhanced 
or standard.
a.
Classified as unknown.
For the influenza vaccine brand subcategories, patients with conflicting records will be 
classified as “Multiple”.

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9.4. Data Sources
Optum’s Clinformatics Data Mart (CDM) comes from a database of administrative health 
claims for members of a large national managed care company affiliated with Optum. The 
database includes over 73 million unique lives (2007 through 2021). Clinformatics Data Mart 
is statistically de-identified under the Expert Determination method consistent with HIPAA
and managed according to Optum customer data use agreements. These administrative claims 
submitted for payment by providers and pharmacies are verified, adjudicated, adjusted, and 
de-identified prior to inclusion. The Claims data comprises both commercial/employer 
sponsored plans and Medicare Advantage health plan data. This data covers ~30% of the 
national Medicare Advantage population. The population is geographically diverse, spanning 
all 50 states. In addition to medical claims and pharmacy claims, the Claims data includes 
tables with member eligibility and inpatient confinements. The Claims data also includes 
standard pricing for all medical claims, pharmacy claims, and inpatient confinements. 
Mortality is provided as month and year of death and is sourced from Centers for Medicare 
and Medicaid Servies, Social Security Administration Death Master File, facility claims with 
inpatient death, discontinuation of coverage due to death and external obituary data. 
9.5. Study Size
The sample size for this study is fixed by the duration of the observation window. All 
subjects who meet the inclusion/exclusion criteria will be included in the analyses.
Feasibility analyses from 31 August 2022- 16 January 2023 confirmed the availability of data 
to identify vaccinated subjects. Table 5 below shows the estimated number of patients in 
each group.
Table 5.
Estimated Number of Patients in Each Vaccination Exposure Group
Group 1: 
Same Day
Group 2: COVID-
19 alone
Group 3: 
Flu alone
 
 
 
18-64
244,864
144,695
772,418
65+
382,806
225,842
1,351,093
Composite COVID-19 hospitalizations/UC/ED
For 65+, and the Group 1 vs Group 2 comparison of the COVID-19 related composite 
hospitalizations/ED/UC endpoint, we assume the percentage of patients in Group 1 will be 
63% and the expected percentage of patients in Group 1 and Group 2 with an event overall 
will be 0.56%. If we use a non-inferiority margin of 1.15 for the upper bound of the 95% CI, 
1-sided alpha of 0.025, and 80% power, and an expected HR of 1, then we would need 1,724 
events resulting in 193,548 patients in Group 1, and 113,671 in Group 2, which meets our 
current estimated sample size.19
CCI
CCI
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For 18-64, and the Group 1 vs Group 2 comparison of the COVID-related composite 
hospitalizations/ED/UC endpoint, we assume the percentage of patients in Group 1 will be 
63% and the expected percentage of patients in Group 1 and Group 2 with an event overall 
will be 0.066%. If we use a non-inferiority margin of 1.15 for the upper bound of the 
95% CI, 1-sided alpha of 0.025, and 80% power, and an expected HR of 1, then we would 
need 1,638,003 in Group 1, and 962,002 in Group 2, which does not meet our current 
estimated sample size. 
Composite influenza hospitalizations/UC/ED
For 65+ and for the Group 1 vs Group 3 comparison of the flu-related composite 
hospitalizations/UC/ED endpoint, we assume the percentage of patients in Group 1 will be 
23% and the expected percentage of patients in Group 1 and Group 3 with the endpoint 
overall will be 0.3%. If we use a non-inferiority margin of 1.15 for the upper bound of the 
95% CI, 1-sided alpha of 0.025, and 80% power, and an expected HR of 1, then we would 
need 2,269 events resulting in 176,179 in Group 1, and 589,817 in Group 3, which meets our 
current estimated sample size.
For 18-64 and for the Group 1 vs Group 3 comparison of the flu-related composite 
hospitalizations/UC/ED endpoint, we assume the percentage of patients in Group 1 will be 
23% and the expected percentage of patients in Group 1 and Group 3 with the endpoint 
overall will be 0.09%. If we use a non-inferiority margin of 1.15 for the upper bound of the 
95% CI, 1-sided alpha of 0.025, and 80% power, and an expected HR of 1, then we would 
need 574,716 in Group 1, and 1,924,050 in Group 3, which does not meet our current 
estimated sample size.
The inputs to the sample size calculations are based on unadjusted estimates across both 
exposure groups, and there is the possibility of confounding. After adjustment, it is likely that 
endpoint estimates may be closer together, thus increasing our power for non-inferiority 
analyses.
COVID-19 Outpatient Visits
For 65+, and the Group 1 vs Group 2 comparison of the COVID-19 related outpatient visits 
endpoint, we assume the expected percentage of patients in Group 1 and Group 2 with an 
event overall will be 1.77% . If we use a non-inferiority margin of 1.15 for the upper bound 
of the 95% CI, 1-sided alpha of 0.025, and 80% power, and an expected HR of 1, then we 
would need 61,218 in Group 1, and 35,954 in Group 2, which meets our current estimated 
sample size. 

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For 18-64, and the Group 1 vs Group 2 comparison of the COVID-19 related outpatient visits 
endpoint, we assume the expected percentage of patients in Group 1 and Group 2 with an 
event overall will be 1.47%. If we use a non-inferiority margin of 1.15 for the upper bound of 
the 95% CI, 1-sided alpha of 0.025, and 80% power, and an expected HR of 1, then we 
would need 73,767 in Group 1, and 43,324 in Group 2, which meets our current estimated 
sample size. 
Influenza Outpatient Visits
For 65+ and for the Group 1 vs Group 3 comparison of the flu-related outpatient visits 
endpoint, we assume the expected percentage of patients in Group 1 and Group 3 with the 
endpoint overall will be 0.47%,. If we use a non-inferiority margin of 1.15 for the upper 
bound of the 95% CI, 1-sided alpha of 0.025, and 80% power, and an expected HR of 1, then 
we would need 110,467 in Group 1, and 369,822 in Group 3, which meets our current 
estimated sample size.
For 18-64 and for the Group 1 vs Group 3 comparison of the flu-related outpatient visits 
endpoint, we assume the expected percentage of patients in Group 1 and Group 3 with the 
endpoint overall will be 0.76%. If we use a non-inferiority margin of 1.15 for the upper 
bound of the 95% CI, 1-sided alpha of 0.025, and 80% power, and an expected HR of 1, then 
we would need 68,854 in Group 1, and 230,511 in Group 3, which meets our current 
estimated sample size.
9.6. Data Management 
All Optum claims structured data described above are stored within Pfizer’s data 
infrastructure.  Data are queried and analyzed using SAS. The data are only accessible to 
Pfizer colleagues who have been trained and approved to access Pfizer’s data warehouse. 
9.7. Data Analysis 
There is not a separate Statistical Analysis Plan document for this study. All methodology for 
summary and statistical analyses of data collected in this study is contained in this section. 
All computations and generation of tables will be performed using Statistical Analysis 
System (SAS) version 9.4 or higher (SAS Institure, Cary, NC, US). 
All analyses will be conducted for the following age subgroups: 18-64 and 65+.
Descriptive statistics will be presented to describe baseline characteristics by vaccination 
exposure group. Baseline characteristics will be described by frequency distribution for 
categorical variables while continuous variables expressed in terms of their mean and 
standard deviation or median and interquartile range (IQR) as appropriate. In general, 
missing or unavailable data will not be imputed and data will be analyzed as they are 
recorded in the database.

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To account for imbalance in patient characteristics between the vaccine exposure groups, 
2 separate logistic regression models will be used to create a propensity score used to 
calculate an inverse probability of treatment weights (IPTW). The two models will be as 
follows: Same day vs COVID-19 alone, and separately Same day vs Flu alone. Weights will 
be stabilized to avoid exertion of outliers, and we will further evaluate whether there is a 
need to truncate at the 1st and 99th percentile. Covariate balance before and after weighting 
will be assessed using standardized mean differences, separately for the same day/COVID-19
alone and Same day/flu alone comparisons. All variables listed in “Covariates” section with 
model specification instructions will be included in the score. If there are model convergence 
issues, changes in model specification may occur (eg, dropping variables with very low 
prevalence, categories may be collapsed further etc). Variables with residual imbalance will 
be included in the model for further adjustment. 
Each outcome will be assessed separately. Experiencing one disease specific outcome is not a 
censoring criteria for the other disease (ie, having a COVID-19 outpatient diagnosis is not a 
censoring event for the influenza endpoints). Weighted cumulative incidence curves will be 
used to describe the occurrence of each outcome separately over the entire follow-up period. 
Given the relatively short follow-up time, we expect small numbers of deaths. Therefore, 
regular Cox proportional hazards models will be used to estimate HRs and 95% CI for each 
outcome separately. If we find that death is more common than anticipated, we will consider 
using Fine and Gray’s proportional subdistribution hazards models to account for the 
competing risk of death. Models for the following vaccine group/outcome combinations will 
be assessed:

Same day/COVID-19 only:

All-cause hospitalization, composite COVID-19 hospitalization/ED/UC (any 
position), COVID-19 hospitalization (any position, primary position), COVID-19
ED/UC, COVID-19 outpatient

Same day/Flu only:

All-cause hospitalization, composite flu-hospitalization/ED/UC (any position), 
flu-hospitalization (any position, primary position), flu ED/UC, flu outpatient.
 
 
 
 
 
 
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Sensitivity Analysis
For disease specific endpoints, we will conduct a sensitivity analysis whereby an additional 
censoring criteria will be applied. For COVID-19 specific endpoints, we will censor for 
Paxlovid dispensing. For influenza specific endpoints, we will censor for any of the 
following influenza treatment dispensing (Oseltamivir phosphate, Zanamivir, Peramivir, 
Baloxavir marboxil).
9.8. Quality Control
Data in Optum’s claims database are collected monthly in an electronic format. Analyses are 
programmed according to the specifications in the protocol and documented in a 
programming plan. All analyses will be performed internally and according to Pfizer analytic 
standards, including double programming to ensure quality control. All quality checks are 
documented in the programming plan.
9.9. Limitations of the Research Methods
There are several limitations associated with this study. This analysis is from one insurer that 
provides employer-sponsored and Medicare Advantage coverage and thus may not generalize 
to other populations, such as traditional Medicare fee-for-service, other government 
insurance or uninsured. It is possible that some vaccines may be missing if patients received 
vaccine in a setting where an insurance claim was not filed (ie employer sponsored clinic or 
paid out of pocket). This may be more problematic for influenza vaccine for younger ages. 
This will only impact the singular vaccine groups (ie, COVID-19 alone or influenza alone). 
The healthcare claims data are collected for billing purposes and may be subject to 
misclassification, misdiagnosis, and underreporting. Additionally, for our medically attended 
COVID-19 endpoints, we do not have laboratory confirmation of the COVID-19 diagnosis, 
therefore it is possible that some patients are being diagnosed with symptoms alone or that 
we are missing positive cases due to at-home testing. 
9.10. Other Aspects
Not applicable.
10. PROTECTION OF HUMAN SUBJECTS
10.1. Patient Information 
This study involves data that exist in anonymized structured format and contain no patient 
personal information. 
10.2. Patient Consent
As this study involves anonymized structured data, which according to applicable legal 
requirements do not contain data subject to privacy laws, obtaining informed consent from 
patients by Pfizer is not required.

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10.3. Institutional Review Board (IRB)/Independent Ethics Committee (IEC)
This study uses de-identified data. As such this study will be submitted to an IRB for 
exemption under category 4, pursuant to the terms of the US Department of Health and 
Human Service’s Policy for Protection of human Subjects at 45 C.F.R 46.104(d).
10.4. Ethical Conduct of the Study
The study will be conducted in accordance with legal and regulatory requirements, as well as 
with scientific purpose, value and rigor and follow generally accepted research practices 
described in Guidelines for Good Pharmacoepidemiologic Practices (GPP),20 Good Practices 
for Outcomes Research issued by the International Society for Pharmacoeconomics and 
Outcomes Research (ISPOR),21 and Food and Drug Administration (FDA) Guidance for 
Industry: Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment.22
11. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS 
This study involves data that exist as structured data by the time of study start. In these data 
sources, individual patient data are not retrieved or validated, and it is not possible to link 
(ie, identify a potential association between) a particular product and medical event for any 
individual. Thus, the minimum criteria for reporting an adverse event (AE) (ie, identifiable 
patient, identifiable reporter, a suspect product, and event) cannot be met.
12. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS
For all publications relating to the study, Pfizer will comply with recognized ethical
standards concerning publications and authorship, including Section II - “Ethical
Considerations in the Conduct and Reporting of Research” of the Uniform Requirements for 
Manuscripts Submitted to Biomedical Journals, http://www.icmje.org/index.html#authorship, 
established by the International Committee of Medical Journal Editors.
In the event of any prohibition or restriction imposed (eg, clinical hold) by an applicable 
competent authority in any area of the world, or if the party responsible for collecting data 
from the participant is aware of any new information which might influence the evaluation of 
the benefits and risks of a Pfizer product, Pfizer should be informed immediately. 

Pfizer-BioNTech BA.4/5 bivalent mRNA COVID-19 vaccine
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13. REFERENCES
1.
Wiemken TL, Khan F, Nguyen JL, Jodar L, McLaughlin JM. Is COVID-19 seasonal? A 
time series modeling approach. medRxiv. 2022:2022.06.17.22276570.
2.
Centers for Disease Control and Prevention. 2020-2021 Flu Season Summary 
[Available from: https://www.cdc.gov/flu/season/faq-flu-season-2020-2021.htm.
3.
Centers for Disease Control and Prevention. Interim Clinical Considerations for Use of 
COVID-19 Vaccines Currently Approved or Authorized in the United States 2023 
[Available from: https://www.cdc.gov/vaccines/covid-19/clinical-
considerations/interim-considerations-us.html#recommendations.
4.
Tzenios N, Tazanios ME, Chahine M. Combining Influenza and COVID-19 Booster 
Vaccination Strategy to Improve Vaccination Uptake Necessary for Managing the 
Health Pandemic: A Systematic Review and Meta-Analysis. Vaccines. 2023;11(1):16.
5.
Hause AM, Zhang B, Yue X, Marquez P, Myers TR, Parker C, et al. Reactogenicity of 
Simultaneous COVID-19 mRNA Booster and Influenza Vaccination in the US. JAMA 
Netw Open. 2022;5(7):e2222241.
6.
Lazarus R, Baos S, Cappel-Porter H, Carson-Stevens A, Clout M, Culliford L, et al. 
Safety and immunogenicity of concomitant administration of COVID-19 vaccines 
(ChAdOx1 or BNT162b2) with seasonal influenza vaccines in adults in the UK 
(ComFluCOV): a multicentre, randomised, controlled, phase 4 trial. The Lancet. 
2021;398(10318):2277-87.
7.
Xie Z, Hamadi HY, Mainous AG, Hong Y-R. Association of dual COVID-19 and 
seasonal influenza vaccination with COVID-19 infection and disease severity. Vaccine. 
2023;41(4):875-8.
8.
Moro PL, Zhang B, Ennulat C, Harris M, McVey R, Woody G, et al. Safety of co-
administration of mRNA COVID-19 and seasonal inactivated influenza vaccines in the 
vaccine adverse event reporting system (VAERS) during July 1, 2021–June 30, 2022.
Vaccine. 2023;41(11):1859-63.
9.
Toback S, Galiza E, Cosgrove C, Galloway J, Goodman AL, Swift PA, et al. Safety, 
immunogenicity, and efficacy of a COVID-19 vaccine (NVX-CoV2373) co-
administered with seasonal influenza vaccines: an exploratory substudy of a 
randomised, observer-blinded, placebo-controlled, phase 3 trial. The Lancet Respiratory 
Medicine. 2022;10(2):167-79.

Pfizer-BioNTech BA.4/5 bivalent mRNA COVID-19 vaccine
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10.
Izikson R, Brune D, Bolduc JS, Bourron P, Fournier M, Moore TM, et al. Safety and 
immunogenicity of a high-dose quadrivalent influenza vaccine administered 
concomitantly with a third dose of the mRNA-1273 SARS-CoV-2 vaccine in adults 
aged ≥65 years: a phase 2, randomised, open-label study. Lancet Respir Med. 
2022;10(4):392-402.
11.
Janssen C, Mosnier A, Gavazzi G, Combadière B, Crépey P, Gaillat J, et al. 
Coadministration of seasonal influenza and COVID-19 vaccines: A systematic review 
of clinical studies. Human Vaccines & Immunotherapeutics. 2022;18(6):2131166.
12.
Centers for Disease Control and Prevention. FAQs for the Interim Clinical 
Considerations for COVID-19 Vaccination  [Available from: 
https://www.cdc.gov/vaccines/covid-19/clinical-considerations/faq.html.
13.
Izurieta HS, Lu M, Kelman J, Lu Y, Lindaas A, Loc J, et al. Comparative Effectiveness 
of Influenza Vaccines Among US Medicare Beneficiaries Ages 65 Years and Older 
During the 2019–2020 Season. Clinical Infectious Diseases. 2020;73(11):e4251-e9.
14.
Hitchings MDT, Lewnard JA, Dean NE, Ko AI, Ranzani OT, Andrews JR, et al. Use of 
Recently Vaccinated Individuals to Detect Bias in Test-Negative Case-Control Studies 
of COVID-19 Vaccine Effectiveness. Epidemiology. 2022;33(4):450-6.
15.
Al-Aly Z, Bowe B, Xie Y. Long COVID after breakthrough SARS-CoV-2 infection. 
Nat Med. 2022;28(7):1461-7.
16.
Centers for Disease Control and Prevention. COVID Data Tracker, Summary of Variant 
Surveillance.  [Available from: https://covid.cdc.gov/covid-data-
tracker/?ACSTrackingID=USCDC_2145-
DM92477&ACSTrackingLabel=10.21.2022%20-%20COVID-
19%20Data%20Tracker%20Weekly%20Review&deliveryName=USCDC_2145-
DM92477#variant-summary.
17.
Surie D, DeCluir J, Zhu Y, Gaglani M, Ginde A, Douin D, et al. Early Estimates of 
Bivalent mRNA Vaccine Effectiveness in Preventing COVID-19–Associated 
Hospitalization Among Immunocompetent Adults Aged ≥65 Years — IVY Network, 18 
States, September 8–November 30, 2022. MMWR Morb Mortal Wkly Rep. 
2022;71:1625-30.
18.
Link-Gelles R, Ciesla A, Fleming-Dutra K, Smith Z, Britton A, Wiegand R, et al. 
Effectiveness of Bivalent mRNA Vaccines in Preventing Symptomatic SARS-CoV-2 
Infection — Increasing Community Access to Testing Program, United States, 
September–November 2022. MMWR Morb Mortal Wkly Rep. 2022;71:1526-30.
19.
Chow S-C, Shao J, Wang H, Lokhnygina Y. Sample Size Calculations in Clinical 
Research. 3rd ed. New York: CRC Press; 2017.

Pfizer-BioNTech BA.4/5 bivalent mRNA COVID-19 vaccine
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20.
Guidelines for Good Pharmacoepidemiology Practices (GPP). Public Policy Committee, 
International Society of Pharmacoepidemiology. Pharmacoepidemiology and Drug 
Safety 2015; 25:2-10. https://onlinelibrary.wiley.com/doi/full/10.1002/pds.3891.
21.
Good Practices for Outcomes Research issued by the International Society for 
Pharmacoeconomics and Outcomes Research (ISPOR) 
http://www.ispor.org/workpaper/practices_index.asp.
22.
Food and Drug Administration (FDA) Guidance for Industry: Good Pharmacovigilance 
Practices and Pharmacoepidemiologic Assessment 
https://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guida
nces/ ucm071696.pdf.

Pfizer-BioNTech BA.4/5 bivalent mRNA COVID-19 vaccine
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14. LIST OF TABLES 
Table 1.
Vaccination Status Variables
Table 2.
Outcome Variables
Table 3.
Censoring Variables
Table 4.
Covariates
Table 5.
Estimated Number of Patients in Each Vaccination Exposure Group
15. LIST OF FIGURES
Figure 1.
Overall Study Design

Pfizer-BioNTech BA.4/5 bivalent mRNA COVID-19 vaccine
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Version 1.0, 26 April 2023
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01-Jun-2022
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ANNEX 1. LIST OF STAND ALONE DOCUMENTS
None.

Pfizer-BioNTech BA.4/5 bivalent mRNA COVID-19 vaccine
C4591061 NON-INTERVENTIONAL STUDY PROTOCOL
Version 1.0, 26 April 2023
PFIZER CONFIDENTIAL
01-Jun-2022
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ANNEX 2 ADDITIONAL INFORMATION
Appendix Table 1. Excel File Containing all Bivalent mRNA Codes and Influenza Codes
Appendix Table 2. COVID-19 Variant Proportions in the US
Collection Week 
End Date
Predominant 
Variant1
% of Viral Lineages 
among Infections2
03 Sep 2022
BA.4/BA.5
97.6%
10 Sep 2022
BA.4/BA.5
96.8%
17 Sep 2022
BA.4/BA.5
95.5%
24 Sep 2022
BA.4/BA.5
93.9%
01 Oct 2022
BA.4/BA.5
91.6%
08 Oct 2022
BA.4/BA.5
86.7%
15 Oct 2022
BA.4/BA.5
81.5%
22 Oct 2022
BA.4/BA.5
76.4%
29 Oct 2022
BA.4/BA.5
65.8%
05 Nov 2022
BA.4/BA.5
58.3%
12 Nov 2022
BA.4/BA.5
49.6%
19 Nov 2022
BQ.1/BQ.1.1
42.8%
26 Nov 2022
BQ.1/BQ.1.1
47.6%
03 Dec 2022
BQ.1/BQ.1.1
52.8%
10 Dec 2022
BQ.1/BQ.1.1
57.1%
17 Dec 2022
BQ.1/BQ.1.1
58.9%
24 Dec 2022
BQ.1/BQ.1.1
59.3%
31 Dec 2022
BQ.1/BQ.1.1
58.5%
07 Jan 2023
BQ.1/BQ.1.1
53.0%

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Collection Week 
End Date
Predominant 
Variant1
% of Viral Lineages 
among Infections2
14 Jan 2023
BQ.1/BQ.1.1
47.5%
21 Jan 2023
XBB
45.7%
28 Jan 2023
XBB
55.1%
04 Feb 2023
XBB
64.7%
11 Feb 2023
XBB
71.5%
18 Feb 2023
XBB
78.1%
1.
The predominant variant was defined as the variant representing highest estimated percent of viral lineages among 
infections in a given week. Variant sublineage proportions were combined with the parent lineages. 
2.
The percent of viral lineages among infections was calculated using weekly weighted estimates from the CDC’s 
national genomic surveillance system. The CDC’s weighted estimates are based on empirical (observed) genomic 
sequencing data from the National SARS-CoV-2 Strain Surveillance (NS3) program, commercial or academic 
laboratories contracted by CDC, and state/local public health laboratories. Estimated percentages were calculated using 
data as of 14 April 2023. 

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