Work Productivity Losses in the United States Among High-risk Patients With COVID-19 During Acute and Longer-term Follow-up in an Omicron Predominant Period (PULSE-US)
Conditions: COVID-19 (Coronavirus Disease 2019)
Sponsor: Pfizer

Nir m atrel vir-rit o na vir
C 4 6 7 1 0 6 6 N O N -I N T E R V E N TI O N A L S T U D Y P R O T O C O L
1. 0, 2 9 A u g ust 2 0 2 4
C T 2 4- WI -G L 0 2 -R F 0 2 6 . 0 N o n-I nterve nti o n al St u dy Pr ot oc ol Te m pl ate F or Sec o n d ary D at a C ollecti o n St u dy  
Pa ge 1 of 2 7
St u d y i nf or m ati o n
Title
W or k pr o d ucti vit y  l osses i n t he U nite d States a m o n g hi g h -ris k 
patie nts wit h C O VI D -1 9 d uri n g ac ute a n d l o n ger -ter m f oll o w-u p i n 
a n o micr o n pre d o mi na nt peri o d ( P U LS E -U S)
Pr ot oc ol n u m ber
C 4 6 7 1 0 6 6
Pr ot oc ol versi o n 
i de ntifier
1. 0
D ate
2 9 A u g ust 2 0 2 4
Acti ve s u bst a nce
nir matrel vir -rit o na vir
A T C J 0 5 A E 3 0
Me dici n al pr o d uct
Pa xl o vi d
Rese arc h q uesti o n 
a n d o bjecti ves
Rese arc h q uesti o n : W hat are t he i n direct b ur de n of C O VI D -1 9 a n d 
t he p ote ntial be nefits of nir matrel vir-rit o na vir ( N M V/r) f or re d uci n g 
t he i n direct b ur de n of disease a m o n g patie nts i n t he U nite d States at 
hi g h ris k of pr o gressi o n t o se vere C O VI D- 1 9 (“ hi g h -ris k patie nts”)?
Pri m ar y o bjecti ves :

Descri be de m o gra p hic a n d cli nical c haracteristics of a d ult, 
hi g h -ris k patie nts dia g n ose d wit h C O VI D -1 9, i ncl u di n g 
patie nts w h o recei ve d N M V/r (treate d) a n d t h ose w h o di d n ot 
recei ve a ny C O VI D -1 9 a nti viral t hera p y ( u ntreate d)

Descri be meas u res of w or k place pr o d ucti vit y l oss, s uc h as 
t otal da ys a wa y fr o m w or k a n d use of disa bilit y lea ve, as well 
as i n direct c osts ass ociate d wit h w or k l oss, a m o n g hi g h -ris k 
treate d a n d u ntreate d patie nts, directly matc he d o n 
de m o gra p hic a n d cli nical c haracteris tics
E x pl or at or y o bjecti ve:

C o m pare pr o d ucti vit y  l oss a n d i n direct c ost o utc o mes 
bet wee n hi g h -ris k treate d a n d u ntreate d patie nts usi n g 
m ulti varia ble m o dels, c o ntr olli n g f or patie nt c haracteristics
C o u ntr y
U S A
A ut h or
, P h D
Merati ve
T his d oc u me nt c o ntai ns c o nfi de ntial i nf or mati o n bel o n gi n g t o Pfizer.  E xce pt as ot her w ise a gree d t o i n writi n g, 
b y acce pti n g or re vie wi n g t his d oc u me nt, y o u a gree t o h ol d t his i nf or mati o n i n c o nfi de nce a n d n ot c o p y or 
discl ose it t o ot hers (e xce pt w here re q uire d b y a p plica ble la w) or use it f or u na ut h orize d p ur p oses.  I n t he e ve nt 
of a n y act ual or s us pecte d breac h of t his o bli gati o n, Pfizer m ust be pr o m ptl y n otifie d.
 
P P D
P P D
P P D

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1. TABLE OF CONTENTS
1. TABLE OF CONTENTS.......................................................................................................2
2. LIST OF ABBREVIATIONS................................................................................................4
3. RESPONSIBLE PARTIES....................................................................................................4
4. AMENDMENTS AND UPDATES.......................................................................................6
5. MILESTONES.......................................................................................................................7
6. RATIONALE AND BACKGROUND..................................................................................7
7. RESEARCH QUESTION AND OBJECTIVES ...................................................................8
8. RESEARCH METHODS ......................................................................................................9
8.1. Study Design .............................................................................................................9
8.2. Setting......................................................................................................................10
8.2.1. Inclusion Criteria ........................................................................................10
8.2.2. Exclusion Criteria.......................................................................................13
8.3. Variables..................................................................................................................14
8.4. Data Sources............................................................................................................17
8.5. Study Size................................................................................................................17
8.6. Study Population .....................................................................................................19
8.6.1. Subgroups ...................................................................................................19
8.7. Data Management ...................................................................................................19
8.8. Missing Data ...........................................................................................................19
8.9. Data Analysis ..........................................................................................................19
8.10. Additional Analyses ..............................................................................................21
8.11. Quality Control......................................................................................................22
8.12. Limitations of the Research Methods....................................................................23
9. PROTECTION OF HUMAN PARTICIPANTS .................................................................24
9.1. Patient Information..................................................................................................24
9.2. Patient Consent........................................................................................................24
9.3. Institutional Review Board (IRB)/ Ethics Committee (EC)....................................25
9.4. Ethical Conduct of the Study ..................................................................................25
10. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS ......................................................................................................................25
11. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS........25

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12. REFERENCES ..................................................................................................................26
13. LIST OF TABLES.............................................................................................................27
14. LIST OF FIGURES ...........................................................................................................27

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2. LIST OF ABBREVIATIONS
Abbreviation
Definition
ADHD
Attention deficit/hyperactivity disorder
AHRQ
Agency for Healthcare Research and Quality
CCI
Charlson Comorbidity Index
CDC
Centers for Disease Control
CDHP
Consumer-driven health plan
CKD
Chronic kidney disease
COB
Coordination of benefits
COPD
Chronic obstructive pulmonary disease
COVID-19
Coronavirus disease 2019
CVD
Cardiovascular disease
ECI
Elixhauser Comorbidity Index
EPO
Exclusive provider organization
ER
Emergency room
EUA
Emergency use authorization
FDA
Food and Drug Administration
GLM
Generalized linear model
HDHP
High-deductible health plan
HIPAA
Health Insurance Portability and Accountability Act
HIV
Human immunodeficiency virus
HMO
Health maintenance organization
HPM
Health and Productivity Management Database
ICD-10
International Classification of Diseases, 10th Revision
IRB
Institutional review board
LTD
Long-term disability
MAFLD
Metabolic dysfunction-associated fatty liver disease
NMV/r
Nirmatrelvir-ritonavir
POS
Point-of-service
PPO
Preferred provider organization
PPPM
Per-patient-per-month
QA
Quality assurance
SD
Standard deviation
SMD
Standardized mean difference
STD
Short-term disability
US
United States
WC
Worker’s compensation
WPAI
Work productivity and activity impairment
3. RESPONSIBLE PARTIESPrincipal Investigator(s) of the Protocol
Name, Degree(s)
Job Title
Affiliation
Address

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Ashley Cha-Silva, PharmD, MS
Director, 
Value & Evidence, 
COVID Antiviral
Pfizer
66 Hudson Blvd E
New York, NY 10001
Roshani Shah, MPH, PhD(c)
Director, 
Value & Evidence, 
COVID & Antivirals
Pfizer
66 Hudson Blvd E
New York, NY 10001
Brenna Brady, PhD
Practice Leader,
Real World Data 
Research & Analytics
Merative
100 Phoenix Drive
Ann Arbor, MI 48108

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3. AMENDMENTS AND UPDATES
None

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4. MILESTONES
Milestone
Planned Date
MSC Study Concept Endorsement
27 March 2024
Final analysis plan
29 August 2024
MSC Protocol Endorsement
11 September 2024
Patient selection
09 October 2024 (4 weeks after 
analysis plan approval)
Analytic file build
20 November 2024 (6 weeks after 
patient selection)
Descriptive analyses
20 December 2024 (4 weeks after 
completion of file build)
Multivariable analyses
31 January 2025 (4 weeks after 
completion of descriptive analyses)
Final study report
28 February 2025 (4 weeks after 
completion of multivariable analyses)
5. RATIONALE AND BACKGROUND
The SARS-CoV-2 virus emerged on the world stage in late 2019, resulting in a global pandemic of 
what became known as coronavirus disease-2019 (COVID-19); although the pandemic phase of the 
virus officially ended in the United States (US) in May 2023, COVID-19 remains an endemic 
disease. Vaccination is the primary method of COVID-19 prevention; however, antiviral therapies 
are also a critical piece of COVID-19 management post-infection, especially in the face of viral 
mutation and decreasing vaccination rates.
In the US, nirmatrelvir-ritonavir (NMV/r; Paxlovid) is an oral antiviral used for the treatment of 
mild-to-moderate COVID-19 within 5 days of symptom onset in patients aged ≥12 years at high risk 
of progression to severe disease, including hospitalization and death.[1] NMV/r received emergency 
use authorization (EUA) for use among patients ≥12 years old from the US Food and Drug 
Administration (FDA) on December 21, 2021, and was granted full FDA approval for use among 
adult patients on May 25, 2023. The EPIC-HR trial demonstrated that NMV/r reduced the risk of 
COVID-19 related hospitalization or death from any cause by 86%, when treated within 5 days of 
symptom onset, compared to placebo.[2] A later study of real-world effectiveness observed a 79.6% 
reduction in risk of all-cause hospitalization or death within 30 days when NMV/r was dispensed 
within 5 days of COVID-19 symptom onset [14], thus demonstrating a continued benefit of NMV/r
treatment into a period in which the omicron variant of COVID-19 has predominated (December 
2021 to the present).
Economic assessments have estimated the direct burden of COVID-19 to be substantial, with 
COVID-19 patients incurring costs 2-to-3-fold higher than matched controls in the 6 months 
following infection.[3] There is also some evidence of substantial work loss due to COVID-19, 
including a recent study showing COVID-19 to be the second most common reason for short-term 

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disability (STD) between 2020 and 2022, with an average of 24 days of work lost per COVID-
related STD claim among a sample including, but not limited to, high-risk patients; patients who 
went on long-term disability (LTD) leave due to COVID-19 lost an average of 153 days of work per 
claim [17]. COVID-related STD claims cost an average of approximately $3,500, while COVID-
related LTD costs averaged over $19,000 per claim. However, the overall burden of disease (direct 
healthcare plus indirect societal burden) among a population of exclusively high-risk patients, as 
well as the potential impact of antivirals in modulating said burden, remains unclear. Moreover, 
there is a lack of contemporary estimates of disease burden that are reflective of an Omicron-
predominant period among a population that is largely either vaccinated or gained natural immunity
due to prior infection. Indirect associated costs and the broader impact of COVID-19 have been 
evaluated in a macroeconomic study; however, overall societal and cost impacts are not reflective of 
the Omicron period (December 2021 to present) or stratified by either high-risk status or those 
treated with NMV/r.[4] Work productivity and activity impairment (WPAI) were captured in EPIC-
HR, and included both vaccinated and unvaccinated patients in acute COVID settings [2, 5]; 
however, these analyses were not able to elucidate the overall impact of antiviral therapy on work 
productivity loss. Similarly, recent studies examining population-level benefit and cost savings 
associated with Paxlovid treatment in patients with COVID-19 Omicron variant infections did not 
examine indirect cost savings.[6]
The current Paxlovid cost-effectiveness model only entails direct medical costs; therefore, an 
understanding of indirect disease burdens and potential offsets associated with Paxlovid therapy will 
be integral to inform and update associated indirect medical costs.[7] This study will utilize the 
MarketScan Commercial, Medicare, and Health and Productivity Management (HPM) Databases to 
investigate absence, STD, LTD, and estimated indirect costs among patients diagnosed with 
COVID-19 who are at high risk of progression to severe COVID-19 (“high-risk patients”) who do or 
do not receive treatment with Paxlovid. The MarketScan HPM Database is the gold standard choice 
for capturing work productivity among patients in the US due to large sample size and linkage to the 
claims databases. This data source has been previously leveraged by Pfizer HV&E teams supporting 
key brands including Ibrance and Xeljanz. [8, 9]
6. RESEARCH QUESTION AND OBJECTIVES
This study seeks to investigate the indirect burden of COVID-19 as measured by workplace 
productivity loss and associated costs among patients who receive NMV/r (Paxlovid) and those who 
do not receive any antiviral therapy during acute and longer-term follow-up among adult patients in 
the US with mild-to-moderate COVID-19 at high risk for progression to severe disease.1[10]
The study will address the following primary objectives:
1. Describe demographic and clinical characteristics of adult, high-risk patients diagnosed with 
COVID-19, including patients who received NMV/r (treated) and those who did not receive 
any antiviral therapy used to treat COVID-19 (untreated).
2. Describe measures of workplace productivity loss, such as total days away from work and 
use of disability leave, as well as indirect costs associated with work loss, among high-risk 
                                                
1 High risk for disease progression defined as either being ≥50 years old or having a condition identified by the CDC as 
having evidence of high risk

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treated and untreated patients, directly matched on demographic and clinical characteristics.
To account for remaining imbalances between treated and untreated patients after matching, 
the following exploratory objective will be addressed if sample size allows:
1. Compare productivity loss and indirect cost outcomes between high-risk treated and 
untreated patients using multivariable models to control for patient characteristics.
7. RESEARCH METHODS 
7.1. Study Design
This is a retrospective cohort study of adult, high-risk patients diagnosed with COVID-19 using data 
from the Merative™MarketScan® Commercial, Medicare, and Health and Productivity Management 
(HPM) Databases. Eligible patients will be stratified into treated and untreated cohorts, defined by 
receipt of NMV/r or no receipt of any antiviral therapy for COVID-19, respectively. The index date 
for both cohorts will be the date of the first COVID-19 diagnosis and patients will be followed over 
a 6-month pre-period and variable post-period. The post-period will be a minimum of 30 days to 
allow patients the opportunity to experience the primary study outcomes of work loss days (absence, 
STD, and LTD) along with associated indirect costs. Events marking the end of patients’ follow-up 
are detailed in section 8.2.1 and include end of enrollment, end of available work loss data, death, 
initiation of new treatments, and a new COVID-19 diagnosis. Work loss/productivity outcomes will 
be reported over the variable length post-period within separate subgroups of patients with eligibility 
for each type of productivity loss.
Figure 1. Study Schematic

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7.2. Setting
This study will utilize the secondary, retrospective MarketScan HPM Database. Merative will create 
an analytic file of data from the MarketScan HPM Database for patients meeting the criteria below 
between June 19, 2021 and December 31, 2022.
7.2.1. Inclusion Criteria
Patients must meet all of the following inclusion criteria to be eligible for inclusion in the study:
1. >1 non-diagnostic2 outpatient medical claim in the MarketScan Commercial or Medicare 
Database with the International Classification of Diseases, 10th Revision (ICD-10) diagnosis 
code for COVID-19 (U071) between December 16, 2021 (5 days prior to the date of emergency 
use authorization of NMV/r) and December 1, 2022 (30 days prior to the end of available HPM 
data); the date of the earliest qualifying claim is the index date.
2. Primary beneficiary on their insurance policy (i.e., eligible for inclusion in the MarketScan HPM 
Database)
3. >18 years old on the index date
4. >6 months of continuous enrollment with medical and pharmacy benefits in the MarketScan 
Commercial or Medicare Database before the index date (baseline period)
5. >30 days of continuous enrollment with medical and pharmacy benefits in the MarketScan 
Commercial or Medicare Database after and including the index date (minimum follow-up 
period)
-
The end of follow-up will be defined as the first of: 1) the end of continuous eligibility, 2) 
the end of available work loss data on December 31, 2022, 3) death,3 4) initiation of a 
non-Paxlovid COVID-19 antiviral in the treated cohort or initiation of a COVID-19 
antiviral in the untreated cohort, or 5) day prior to a new diagnosis of COVID-194
                                                
2 Non-diagnostic claims are those that are not for lab tests, radiology exams, or other tests/procedures that may be used 
to initially establish a diagnosis. A condition may be included on a “diagnostic” claim for reimbursement purposes when 
the diagnosis is suspected but not yet confirmed. Requiring non-diagnostic claims ensures that only patients with a 
confirmed diagnosis are identified. For example, a patient tested for COVID-19 where the test result is negative may 
have a COVID-19 diagnosis code on the claim for the test for reimbursement purposes; this patient would not be 
expected to have a COVID-19 diagnosis on the office visit (or other non-diagnostic) claim(s). Patients with a COVID-19 
diagnosis on a claim for a COVID-19 test will be required to also have a COVID-19 diagnosis on a separate claim that is 
not for a COVID-19 test (i.e., patients whose only COVID-19 diagnosis is on a claim for a COVID-19 test will not be 
included in the study).
3 Death will be based on inpatient discharge status, Social Security Administration data, or employer reported death
4 New diagnoses of COVID-19 will be defined as a COVID-19 diagnosis occurring ≥90 days following the last diagnosis 
on record (e.g., 90-day clean period between last COVID-19 diagnosis and start of new COVID-19 episode). Diagnoses 
occurring <90 days apart will be assumed to be part of the same COVID-19 episode. A new COVID-19 diagnosis may 
also be identified by a claim for a COVID-19 test ≥30 days post-index, followed by a non-diagnostic claim with a 
COVID-19 diagnosis within the next 7 days. The date of the test will be considered the date of the new COVID-19 
diagnosis.

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6. Either age >50 years at index OR >1 non-diagnostic medical claim with an ICD-10 diagnosis 
code, during the baseline period or on the index date, for one of the conditions below determined 
by the Centers for Disease Control and Prevention to have evidence of an association with high 
risk of progression to severe COVID-19: [10]
!
Cancer 
!
Cerebrovascular disease
!
Chronic lung disease, including:
o
Asthma
o
Bronchiectasis
o
Chronic obstructive pulmonary disease (COPD), including emphysema and chronic 
bronchitis
o
Interstitial lung disease and pulmonary fibrosis
o
Pulmonary embolism
o
Pulmonary hypertension
!
Chronic liver disease, including:
o
Alcoholic liver disease
o
Autoimmune hepatitis
o
Cirrhosis
o
Metabolic dysfunction-associated fatty liver disease (MAFLD)
!
Cystic fibrosis
!
Dementia
!
Diabetes mellitus
!
Disabilities, including:
o
Activities of daily living impairments and dependency
o
Attention deficit/hyperactivity disorder (ADHD)
o
Cerebral palsy
o
Congenital malformations
o
Down syndrome
o
Intellectual and developmental disabilities
o
Learning disabilities
o
Spinal cord injuries
!
Heart conditions, including:
o
Aneurysm and dissection of the aorta, heart, and coronary arteries

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o
Angina
o
Arrhythmias
o
Cardiac septal defect
o
Cardiomegaly
o
Cardiomyopathies
o
Congenital defects of the heart or great arteries/veins
o
Coronary artery disease/atherosclerosis and other chronic ischemia of the heart
o
Heart blocks and other conduction disorders
o
Heart failure
o
Hypertensive heart disease
o
Intracardiac thrombosis
o
Marfan syndrome
o
Myocardial degeneration
o
Myocardial infarction and other acute ischemic heart disease
o
Myocarditis
o
Non-ischemic (non-traumatic) myocardial injury
o
Pericarditis and other pericardial conditions
o
Rheumatic heart disease
o
Rupture of chordae tendineae or papillary muscle
o
Valve disorders
!
Human immunodeficiency virus (HIV)
!
Hypertension
!
Immunocompromised state
!
Primary immunodeficiencies
!
Prolonged use of corticosteroids or other immunosuppressive medications
!
Other immunocompromised state
!
Mental health conditions, including:
o
Mood disorders (e.g., depression)
o
Schizophrenia spectrum disorders
!
Overweight and obesity
!
Physical inactivity
!
Pregnancy (current or recent)

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!
Primary immunodeficiencies
!
Sickle cell disease or thalassemia
!
Smoking (current or former)
!
Solid organ or blood stem cell transplantation
!
Substance use disorder
!
Tuberculosis
7. Eligibility for absence, STD, or LTD during the first 30 days of the post-period
8. Treated cohort ONLY: ≥1 claim for Paxlovid within 5 days of index (index date and next 4 days)
7.2.2. Exclusion Criteria
Patients meeting any of the following criteria will not be included in the study:
1. Evidence of death on the index date or the following day [15]
2. Any inpatient admission on the index date or the following day [15]
3. ≥1 inpatient claim with a COVID-19 diagnosis in any position on the claim during the 30 days 
before the index date
4. >1 non-diagnostic claim with a diagnosis code for stage 4 or stage 5 chronic kidney disease, end 
stage renal disease, or a procedure code for dialysis, during the baseline period or on the index 
date [15]
5. Any claim for one of the following medications5 contraindicated for use with Paxlovid during the 
baseline period or on the index date: apalutamide, carbamazepine, phenobarbital, primidone, 
phenytoin, rifampin, rifapentine, lumacaftor/ivacaftor, St. John’s Wort, alfuzosin, ranolazine, 
amiodarone, dronedarone, flecainide, propafenone, quinidine, colchicine, lurasidone, pimozide, 
silodosin, eplerenone, ivabradine, dihydroergotamine, ergotamine, methylergonovine, lovastatin, 
simvastatin, voclosporin, lomitapide, eletriptan, ubrogepant, finerenone, naloxegol, sildenafil, 
triazolam, oral midazolam, fibanserin, tolvaptan [1]
6. Treated cohort ONLY: No use of other (non-Paxlovid) COVID-19 antivirals baseline through 30 
days post-index
7. Untreated cohort ONLY: No use of any COVID-19 antivirals baseline through 30 days post-
index
                                                
5 The MarketScan databases will only include claims for prescription medications paid at least in part by the insurance 
benefit; over the counter prescriptions will not be reflected in the data.

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7.3. Variables
Variable
Role
Data Source(s)
Assessment 
Period
Operational Definition
Treated
Exposure
Outpatient pharmacy 
claims
Study period
Claim for Paxlovid on the index date 
or in the 4 days after the index date
and no claims for other COVID-19 
antivirals during the baseline period or 
within 30 days post-index
Untreated
Exposure
Outpatient pharmacy 
and medical claims
Study period
No claims for any COVID-19 
antivirals during the baseline period or 
within 30 days post-index
Absence from 
work, presence
Outcome
HPM claims
Post-period
Presence of any absence records
Absence from 
work, days
Outcome
HPM claims
Post-period
Total number of days5 of absence, 
among all patients with absence 
eligibility and among patients with ≥1 
absence
Absence from 
work, type
Outcome
HPM claims
Post-period
Where available, absence days will be 
reported by absence type (e.g., sick, 
disability, recreational)
Absence from 
work, costs
Outcome
Estimated, HPM 
claims
Post-period
Estimated sum of wages associated 
with absence days; calculated as daily
wage multiplied by absence days; 
mean (SD) and median costs to be 
reported among all patients with 
absence eligibility and among patients 
with ≥1 absence
STD, presence
Outcome
HPM claims
Post-period
Presence of STD claim
STD, days
Outcome
HPM claims
Post-period
Sum of STD days, among all patients 
with STD eligibility and among 
patients with ≥1 STD claim
STD, reason
Outcome
HPM claims
Post-period
Where available, reason for STD will 
be classified as COVID-19 related 
(ICD-10: U071) or non-COVID-19 
related (other ICD-10 code)
STD, costs
Outcome
Estimated, HPM 
claims
Post-period
Estimated sum of wages associated 
with STD days; calculated as 70% of 
daily wage multiplied by STD days; 
mean (SD) and median costs to be 
reported among all patients with STD 
eligibility and among patients with ≥1 
STD claim
LTD, presence
Outcome
HPM claims
Post-period
Presence of LTD claim
LTD, days
Outcome
HPM claims
Post-period
Sum of LTD days, among all patients 
with LTD eligibility and among 
patients with ≥1 LTD claim
LTD, reason
Outcome
HPM claims
Post-period
Where available, reason for LTD will 
be classified as COVID-19 related 
(ICD-10: U071) or non-COVID-19 
related (other ICD-10 code)
LTD, costs
Outcome
Estimated, HPM 
claims
Post-period
Estimated sum of wages associated 
with STD days; calculated as 70% of 
daily wage multiplied by LTD days; 

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mean (SD) and median costs to be 
reported among all patients with LTD 
eligibility and among patients with ≥1 
LTD claim
Age
Demographic
covariate
Demographics table
Index date
Age, years
Age group
Demographic 
covariate
Demographics table
Index date
Age at index grouped into the 
following categories:
!
18-29
!
30-39
!
40-49
!
50-64
!
65-74
!
75+
Sex
Demographic
covariate
Demographics table
Index date
Sex (male or female) 
Region of 
residence
Demographic
covariate
Demographics table
Index date
Region (Northeast, North Central, 
South, West, or Other/missing) 
Payer type
Demographic 
covariate
Demographics table
Index date
Commercial, Medicare Advantage, 
Medicare Supplemental
Insurance plan 
type
Demographic
covariate
Demographics table
Index date
Health plan type 
(comprehensive/indemnity, 
exclusive/preferred provider 
organization [EPO/PPO], point-of-
service w/ or w/o capitation [POS], 
health maintenance organization
[HMO], consumer-driven/high-
deductible health plan
[CDHP/HDHP], other/missing) 
Industry type
Demographic
covariate
Demographics table
Index date
Employer industry type (oil & gas 
extraction/mining, manufacturing 
[durable goods], manufacturing [non-
durable goods], 
transportation/communications/utilize
s, retail trade, finance/insurance/real 
estate, services, 
agricultural/forestry/fishing, 
construction, wholesale, other, 
missing) 
Index quarter
Characteristic
covariate
Calculated
Index date
Year and quarter (Q1, Q2, Q3, or Q4) 
of the index date
Duration of 
follow-up
Characteristic 
covariate
Calculated
Post-period
Number of months from (and 
including) the index date to the end of 
patients’ variable-length follow-up 
period
Charlson 
Comorbidity 
Index (CCI)
[11]
Baseline 
characteristic
covariate
Medical claims
Pre-period
Mean, standard deviation, and median
CCI score
Elixhauser 
Comorbidity 
Index (ECI)
[16]
Baseline 
characteristic 
covariate
Medical claims
Pre-period
Mean, standard deviation, and median 
ECI score

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Comorbidity
groups, 
presence
Baseline 
characteristic
covariate
Medical claims
Pre-period
Number and percentage of patients 
with ≥1 claim with a diagnosis in the 
following groups of conditions:
!
Immunocompromised status 
(HIV, immunocompromised 
state, or solid organ/stem cell 
transplant)
!
Chronic kidney disease (CKD)
!
Chronic liver disease
!
Obesity and smoking (obesity, 
overweight, or smoking related 
diagnoses)6
!
Diabetes mellitus
!
Cardiovascular and 
cerebrovascular (heart 
conditions, hypertension, and 
stroke/cerebrovascular disease)
!
Respiratory illnesses (lung 
disease or tuberculosis)
!
Neurodevelopmental disorders 
and other medically complex 
conditions (substance abuse 
disorder, mood disorders, 
dementia, disability, and sickle 
cell disease/thalassemia
!
Pregnancy
!
Cancer (active disease and 
history of)
Number of 
high-risk 
conditions
Baseline 
characteristic 
covariate
Outpatient pharmacy 
and medical claims
Pre-period
Mean, standard deviation, and median 
number of high-risk conditions
COVID-19 
vaccination 
status
Baseline 
characteristic
Outpatient Pharmacy 
and Medical claims
Pre-period
Number and percentage of patients
with ≥1 claim for a COVID-19 
vaccination during the baseline 
period7
Prior 
hospitalization
Baseline 
characteristic
covariate
Medical claims
Pre-period
Indicates patients who had one or 
more inpatients visits in the baseline
period
Prior ER visit
Baseline 
characteristic 
covariate
Medical claims
Pre-period
Indicates patients who had one or 
more ER visits in the baseline period
                                                
6 Patients’ total hours of absence will be divided by 8 to determine the number of absence days, and so the total 
days of absence may not be whole numbers for some patients
6 Lifestyle-related diagnoses (e.g., weight and health behaviors) are known to be under-reported in administrative 
claims
7 MarketScan data will only identify patients who receive a COVID-19 vaccination through their insurance benefit 
during the pre-period. Patients who receive a vaccination through other means (e.g., mass vaccination site) or received a 
COVID-19 vaccination prior to the pre-period will not be reflected as vaccinated in this study. 

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7.4. Data Sources
Merative will use the MarketScan Commercial, Medicare, and HPM Databases for this study. These
databases provide access to complete patient inpatient and outpatient medical and prescription drug 
claim records along with lost work productivity stemming from absence, short-term disability, or 
long-term disability. The HPM database is composed of the subset of the overall MarketScan
Commercial and Medicare Databases where patients have eligibility for work loss reporting. During 
the time period of this study (June 2021 through December 2022), data for approximately 23.6
million enrollees were contained in the Commercial and Medicare Databases, with approximately 
3.9 million enrollees having data in the HPM Database during that time. Descriptions of the 
MarketScan Commercial, Medicare, and HPM databases are below. 
The MarketScan Health and Productivity Management Database contains workplace absence, 
short- and long-term disability (STD, LTD), and workers’ compensation (WC) data for primary 
beneficiaries (i.e., employees), which can be linked to medical and pharmacy utilization data from 
large United States employers. Not all data contributors contribute all types of productivity data.
Data through December 2022 are currently available in the HPM Database.
The MarketScan Commercial Database contains the inpatient, outpatient, and outpatient 
prescription drug experience of employees and their dependents, covered under a variety of fee-for-
service and managed care health plans, including exclusive provider organizations, PPOs, POS 
plans, indemnity plans, and health maintenance organizations (HMOs). 
The MarketScan Medicare Database contains the healthcare experience (both medical and 
pharmacy) of retirees with Medicare Advantage and Medicare Supplemental insurance plans paid for 
by employers. The database includes the employer-paid portion and out-of-pocket patient expenses 
for both Advantage and Supplemental plans, as well as the Medicare-covered portion of payment 
(represented as Coordination of Benefits [COB] Amount) for Supplemental plans. 
The MarketScan Databases are closed system and include an eligibility file for medical and 
pharmacy benefits as well as each type of work loss event. Presence of these files facilitates 
imposition of continuous  eligibility over the study period, thus researchers can interpret the lack of 
services or productivity losses as an absence of events, as opposed to potentially missing data. 
Similarly, demographics of age and sex are available for all patients in the MarketScan databases; 
other demographics are available for the vast majority of patients. 
7.5. Study Size
There are no a priori hypotheses specified for this study, thus sample size calculations are not 
applicable. However, estimated counts within the MarketScan HPM databases for patients with a 
diagnosis of COVID-19 and workplace productivity eligibility are presented below

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Table 1. Estimated Sample Size
CRITERIA
N
All patients with any enrollment in the MarketScan Commercial or Medicare Database
between 12/16/2021 and 12/1/2022
21,722,414
Any enrollment in the MarketScan HPM Database between 12/16/2021 and 12/1/2022
3,643,363
Patients in the HPM Database with ≥1 non-diagnostic claim for COVID-19 between 
12/16/2021 and 12/1/2022 (first claim serves as index date)
357,274
Aged ≥18 on index
357,272
Continuous eligibility with medical and pharmacy benefits for ≥6 months prior to 
index
297,291
Continuous eligibility with medical and pharmacy benefits for ≥30 days after and 
including the index date
290,371
Aged ≥50 on index or evidence of conditions associated with high-risk of 
COVID-19 progression in the baseline period or on the index date
192,479
No evidence of inpatient admissions on the index date or the following day
188,867
No evidence of advanced CKD (stage 4 or above) or dialysis in the 
baseline period or on the index date
187,876
No evidence of fills for contraindicated medications in the baseline 
period or on the index date
175,808
No claims for other non-Paxlovid COVID-19 antivirals in the baseline 
period or in the 30 days after and including the index date, and no 
claims for Paxlovid in the baseline period
171,143
No inpatient claims with a COVID-19 diagnosis in any position on 
the claim during the 30 days before the index date
170,841
TREATED - Claim for Paxlovid on the index date or in the next 4 
days
28,532
Work loss eligibility* during the 30 days after and including the 
index date
Absence
1,959
STD
20,083
LTD
20,404
UNTREATED - No evidence of COVID-19 antiviral treatment 
during the baseline period or during the 30 days after and including 
the index date
142,120
Work loss eligibility during the 30 days after and including the 
index date
Absence
10,595
STD
101,145
LTD
106,705

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*Indicates patients for whom absence, STD, and/or LTD data would be available (e.g., collected)
during the 30 days after and including the index date
7.6. Study Population
The full study sample will be composed of patients who meet all inclusion and exclusion criteria 
above. The treated cohort will include all eligible treated patients for whom a direct match can be 
identified; it is assumed that untreated matches will be able to be identified for the vast majority if 
not all treated patients. The untreated cohort will be composed of those patients who met eligibly
criteria and were successfully matched to a treated patient. Given the larger number of untreated 
compared to treated patients, it is expected that there will be untreated patients who are eligible for 
analyses but not included in the final sample due to their not being matched to a treated patient. 
7.6.1. Subgroups
Given the differential availability of absence, STD, and LTD data, results will be reported in defined 
subgroups of patients with absence, STD, or LTD eligibility. Direct matching of the treated and 
untreated cohorts will occur at the subgroup (e.g., patients with absence eligibility) level. As a result, 
patients who are eligible for multiple subgroups (e.g., have STD and LTD eligibility) may have
different matches for the different work loss type analyses. Results will not be reported for the 
overall eligible cohort (e.g., patients with absence, STD, or LTD eligibility). 
7.7. Data Management 
The Merative MarketScan Commercial, Medicare, and HPM Databases are commercially available 
databases; rights for the use of the data for this study were purchased by Pfizer. The raw data from 
the MarketScan Databases, as well as the analytic data files created for this study, will be managed
by Merative using SAS software (Carey, NC). 
Study data will be extracted once, as part of the analytic file build. Data will be extracted for all 
patients meeting eligibility criteria for the treated or untreated cohorts (e.g., adults at high risk of 
COVID-19 complications diagnosed with COVID-19 between December 16, 2021 and December 1, 
2022. Extracted data include eligibility information, patient characteristics, healthcare resource 
utilization, and work productivity losses (See Section 8.3).
7.8. Missing Data
Analyses will only include patients with continuous eligibility during the study period. Continuous 
eligibility with medical and pharmacy benefits will be required during the pre- and post-periods, 
while continuous eligibility for the specific work loss type (e.g., absence in the absence sample) will 
be required during the post-period. The presence of continuous eligibility, either medical, pharmacy, 
or work loss, indicates that all data are being collected for that specific concept during the study 
period. As a result, the lack of services (e.g., lack of an inpatient visit) or lack of work loss events 
(e.g., lack of STD claims) will be interpreted as that event not having occurred, as opposed to 
missing data. 
7.9. Data Analysis 
The study sample will be composed of the eligible cohorts of treated and untreated patients who 
meet all study inclusion criteria. Untreated patients will be directly matched to treated patients with a 
ratio of up to 5:1 without replacement. Direct matching will initially be based on age (+/- 5 years) 

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sex, CCI category (0-1, 2, 3+), index date (+/-14 days), and the presence/absence of any 
hospitalization or ER visit during the baseline period. In the event of sample size issues using this 
matching procedure, the matching criteria may be modified or relaxed (e.g., matching on age group, 
allowing a +/- 30-day difference in index date, replacing CCI categories with categories for the 
number of high-risk conditions). Matching will be conducted within cohorts based on work loss 
eligibility (e.g., a treated patient with absence data will only be matched to an untreated patient with 
absence data) to facilitate comparison of primary outcomes across the treated and untreated cohorts; 
patients will not be required to have eligibility for all three types of work loss data in the post-period.
Matching methods will be consistent across the work loss types. Standardized mean differences 
(SMD) will be reported between the treated and untreated cohorts prior to and following matching to 
evaluate imbalances between cohorts; SMDs >0.1 are generally considered to represent imbalances. 
To address the first primary objective, demographics on the index date and clinical characteristics
during the six-month baseline period will be reported among treated and untreated patients within 
cohorts based on work loss eligibility; these characteristics will be reported for both the unmatched 
and matched samples. To address the second primary objective, absence, STD, and LTD outcomes
will be reported within the matched groups of treated and untreated patients with post-period 
eligibility for the relevant work loss type (e.g., absence results among patients with post-period 
absence eligibility). Indirect costs associated with each work loss type will also be estimated within 
the matched groups of treated and untreated patients, to address the third primary objective. Indirect 
costs will be reported within the subset of patients with evidence of a work loss event as well as 
within the overall eligible population; the latter reporting is intended to provide Pfizer with data that 
could be combined to estimate overall indirect costs of COVID-19. For descriptive reporting,
continuous variables will be reported as mean, median, and standard deviation, while categorical 
variables will be reported as frequency and percent. During the variable length post-period, 
continuous variables will be reported in a per-patient-per-month format to address the differing 
durations of follow-up across patients. Outcomes will be compared between the treated and 
untreated cohorts using relevant statistical tests (e.g., Student’s T-test, Chi-square test, Kruskal-
Wallis test, etc.). 
All study outcomes, including unadjusted absence days, STD days, LTD days, and associated costs 
will be reported over the variable length post-period within relevant subgroups of matched treated 
and untreated patients based on work loss productivity type eligibility. Data for absence, STD, and 
LTD days will be obtained directly from fields in the MarketScan HPM Database; indirect costs (in 
2022 US dollars) associated with work loss will be calculated based on median daily wage in the US
in 2022 (employed full time wage and salary workers), as defined by the Bureau of Labor Statistics,
and number of lost productivity days; resultant STD and LTD costs will be multiplied by 0.70, since 
STD and LTD benefits typically pay about 70% of an employee’s wages [12,13]. Indirect costs 
associated with each work loss type will be defined as:
!
Absence: median daily wage * total absence days
!
STD: (median daily wage * total absence days) * 0.7
!
LTD: (median daily wage * total absence days) * 0.7
To address the exploratory objective, up to six multivariable models may be constructed to assess the 
relationship between NMV/r treatment and the work loss and indirect cost outcomes from the second 
and third primary objectives. These models will adjust for any potential remaining differences in 

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demographics and baseline characteristics between the treated and untreated cohorts after matching. 
It is intended to use generalized linear model (GLM) methods to compare work loss and indirect cost 
outcomes between the treated and untreated cohorts. Each outcome type (absence, STD, LTD) 
consists of a pair of variables i.e., a count (day) and a continuous variable (cost).  Because of issues 
with counts and costs having values of zero, two-part models are likely to be used. The first part for 
both counts and costs will be a GLM model (logistic regression of binary data) comparing the 
probability of having zero (or non-zero) counts or costs between the treated and untreated cohorts. 
The second part will be a GLM regression (gamma distribution, log link) of the greater than zero 
counts expressed in per-patient-per-month (PPPM) form; and a GLM regression (gamma 
distribution, log link) for greater than zero cost. The PPPM form is preferred because of the variable 
length of follow-up and use of the gamma distribution and log link because of probable skewed data 
for both PPPM counts and costs. It is anticipated that model covariates will include demographics 
and baseline clinical characteristics, potentially some of the same covariates used in the match. 
Covariates of interest will be selected based on both remaining imbalance between cohorts following 
matching as well as clinical relevance. More specifics of the models, including choice in model type 
and covariates, will be determined in collaboration with Pfizer following assessment of the 
descriptive results. At this time the six models will include the following outcomes:
!
Number of absence days
!
Indirect costs related to absence
!
Number of STD days
!
Indirect costs related to STD
!
Number of LTD Days
!
Indirect costs related to LTD
7.10. Additional Analyses
The team may elect to conduct subset analyses to further investigate the burden of COVID-19 within 
select patient subgroups. At this time the team has identified the following subgroups as those of 
potential interest:
!
Sex
!
Immunocompromised status
!
Age (≥50 vs. <50)
!
Presence of specific conditions in the baseline period (e.g., CVD, respiratory conditions)
Decisions on whether to proceed with subgroup reporting, the specific subgroups of interest, and 
exact methods for reporting will be determined following review of the results of the planned 
analyses outlined in this document. 
It is important to note that some of the above subgroups are based on variables not included as direct 
matching variables. Therefore, the above subgroups are unlikely to maintain balance imposed by the 

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direct matching of the overall eligible treated and untreated cohorts (i.e., matches will be broken). At 
this time, the Merative team is suggesting the following potential methods to address likely 
imbalances between subgroups:
!
Conduct a purely descriptive analysis of subgroups derived from the directly matched treated 
and untreated cohorts with the caveat that groups may no longer be comparable due to 
‘broken matches’
!
Utilize multivariate modeling to control for differences (resulting from ‘broken matches’) in 
subgroups derived from the directly matched treated and untreated cohorts
!
Identify patients eligible for subgroups from either the pre-match or post-match eligible 
samples and conduct a second direct matching to establish the final subgroups for analyses
Scope of the subgroup analysis would be defined once the specific subgroups of interest and analytic 
method is defined. 
Additionally, since the timing of COVID symptom onset is lacking in claims data, and because 
Paxlovid is intended to be prescribed within 5 days of symptom onset, a sensitivity analysis will be 
conducted to examine the sample size of treated patients using a 3-day time period after and 
including the index date to look for the first claim for Paxlovid. Depending on the difference in 
sample sizes between using the 3-day and 5-day windows, patient characteristics may be 
descriptively compared between the two samples.
7.11. Quality Control
The MarketScan database build QA process begins when the individual client databases are created 
and continues as the databases are combined and enhanced to form the MarketScan Research 
Databases. 
Quality checks completed when the databases are updated include:
!
Reasonableness of data: We ensure relationships between two or more fields are reasonable 
by comparing them with normative data. For example, we check to see if the age and gender 
distribution of our database continues to follow the distribution in AHRQ’s Medical
Expenditure Panel Survey.
!
Validity: We ensure that selected fields are valid by comparing records with lists of possible 
valid values. Data require additional processing if invalid results are present more than one 
percent of the time.
!
Claims lag: We do not release annual data files until close to 100 percent of claims have 
been paid. This process takes approximately six months after the close of a calendar year or a 
quarter and ensures that researchers have the most complete and accurate financial data. 
These data are used primarily for outcomes research projects, which typically require this 
type of financial accuracy. However, our Early View Database has a 90-day lag that includes 
paid amounts for 100 percent of prescription drugs, approximately 85 percent of physician 
office visits, and approximately 70 percent of hospital claims. These data are used when data 
timeliness is paramount to track product uptake, market share, etc.

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Merative’s specific quality assurance (QA) practices as they relate to individual studies are described 
below: 
!
Project team buy-in to analysis plan: The research leader and analyst are responsible for 
creating the analysis plan and detailed specifications for a project. The entire project team 
reviews these documents to make sure that all of Pfizer’s needs outlined in the proposal are 
included in the analysis plan and specification. They also confirm that every project team 
member is interpreting the analysis in the same manner.
!
Documentation and diagnostics applied to computer code development: The primary 
programmer is responsible for creating and documenting all project-specific SAS code, 
including comments as to why changes are made over the course of the project. In addition to 
evaluating diagnostic output independently, the programmer evaluates this output with the 
entire project team before results tables are generated.
!
Independent code review: A second programmer is assigned to a project for the express 
purpose of performing code review. The code review programmer works with the primary 
programmer in order to confirm that SAS code is written with correct syntax and generates 
results as specified in the analysis plan. Issues identified by the code reviewer are 
documented and resolved.
!
Validation of study results: The research leader and analyst are responsible for validating 
the results tables to confirm that results are consistent across analysis tables and that the 
results make sense from a “real world” perspective. If applicable, results are compared with 
previous trend reports to ensure consistency.
!
Peer review of final deliverables: In addition to the research leader and analyst reviewing 
all final reports and manuscripts before they go to Pfizer, an internal peer reviewer is also 
assigned to evaluate final products. This peer reviewer is an analyst who examines the final 
results and reports to ensure concordance among all final documents as well as to evaluate 
content for consistency.
!
Overall QA process: The research leader has ultimate responsibility for making sure that all 
facets of the QA process have been completed and are documented. All project-related 
materials are stored in a central location to maximize efficiency in case additional, follow-on 
analyses or project updates are required.
7.12. Limitations of the Research Methods
Limitations of this study include those associated with retrospective administrative claims analyses 
in general and include the potential for misclassification due to coding errors or limitations of coding 
taxonomy. Additionally, as the HPM database is composed of employed individuals with employer 
sponsored commercial or Medicare insurance whose employers contribute work loss data to the 
MarketScan Databases; therefore, results from this analysis may not generalize to unemployed 
patients or individuals with other forms of insurance (e.g., Medicaid) or those whose employers do 
not contribute work loss data. Results are also likely to be more reflective of outcomes among full-
time employees, as many employers do not offer medical benefits to part-time employees. Although 
specific information around sick or vacation time benefits are not known, it is likely that the 
employees reflected in this analysis have some form of available benefit; as a result, individuals in 

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this database may be more likely to take an absence from work due to illness than a part-time hourly 
worker with no benefits. 
Specifically for this study, the coding taxonomy around COVID-19 does not include information on 
severity of presentation. It’s also worth noting that only medically attended cases of COVID-19 
would ever generate a claim on the medical record. As a result, this analysis will be reflective of 
work loss associated with disease that is severe enough to spur an interaction with a healthcare 
provider. Towards the point of severity, patients are required to maintain eligibility for 30 days 
following COVID-19 diagnosis and patients with evidence of a hospitalization or death in the 2 days 
following index will be excluded from the study sample. Therefore, results of this analysis are likely 
to be reflective of patients with symptomatic, but not severe COVID-19 – consistent with the 
prescribing indications for Paxlovid. However, data regarding the timing of symptom onset will not 
be available, and therefore the receipt of Paxlovid within 5 days after and including the date of 
diagnosis will be used a proxy for the 5-day window after symptom onset in which Paxlovid should 
be prescribed. A sensitivity analysis of the number of patients included using a 3-day vs. 5-day 
window will be conducted, and patient characteristics may be compared between the two groups
depending on the difference in the sample sizes. Additionally, though imposing any minimum 
amount of follow-up time introduces survival bias, the nature of the study outcomes (work 
productivity loss) requires that patients have enough follow-up time to have the opportunity to 
experience work loss; therefore, patients are required to have at least 30 days of post-index database 
enrollment and so results may not be generalizable to treated or untreated high-risk patients who die 
within 30 days of diagnosis. Given the limitations of claims data information on relevant clinical 
factors such as the specific COVID-19 variant, will not be available; vaccination status is also likely 
to be unknown or incomplete given the use of a 6-month pre-period and the prevalence non-
insurance-based vaccination sites (e.g., mass vaccination sites) early in the pandemic. 
The study design addresses issues around severity through the requirement for patients to be at high 
risk for COVID-19 progression, matching of the treated and untreated cohorts, and use of 
multivariable modeling to control for remaining measured differences between cohorts; however, 
differences between cohorts, especially those not able to be assessed via administrative claims data,
may remain. Patients in the treated cohort are also required to receive Paxlovid within 5 days of the 
first COVID-19 diagnosis, consistent with prescribing guidelines; however, the study team will have 
no way of knowing when symptoms first appeared. As such, some patients in the treated cohort may 
be initiating Paxlovid more than five days after the start of symptoms; if anything, this point would 
be expected to reduce effect sizes associated with Paxlovid therapy. Similarly, a claim for Paxlovid 
in the MarketScan Databases indicates that a patient received the medication; it is assumed that 
patients took the medication as prescribed. 
8. PROTECTION OF HUMAN PARTICIPANTS
8.1. Patient Information 
This study involves data that exist in deidentified/anonymized structured format and contain no 
patient personal information. 
8.2. Patient Consent
As this study involves deidentified/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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8.3. Institutional Review Board (IRB)/ Ethics Committee (EC)
All database records are de-identified and fully compliant with United States patient confidentiality 
requirements, including the Health Insurance Portability and Accountability Act (HIPAA) of 1996. 
The databases have been evaluated and certified by an independent third party to be in compliance 
with the HIPAA statistical de-identification standard. The databases were certified to satisfy the 
conditions set forth in Sections 164.514 (a)-(b)1ii of the HIPAA privacy rule regarding the 
determination and documentation of statistically de-identified data.8 Because the study proposed 
herein uses only de-identified patient records and does not involve the collection, use, or transmittal 
of individually identifiable data, Institutional Review Board (IRB) approval to conduct this study is 
not necessary. An IRB exemption was obtained on 17 October 2024.
8.4. 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
CT24-WI-GL02-RF04 External Guidance Document for the Design and Conduct and Non-
Interventional Studies.
9. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE REACTIONS 
As this is an analysis of de-identified retrospective data, adverse event/adverse event reporting is not 
applicable. . In these data sources, individual patient data are not retrieved or validated, and it is not 
possible to link (i.e., identify a potential association between) a particular product and medical event 
for any individual. Thus, the minimum criteria for reporting an adverse event (AE) (i.e., identifiable 
patient, identifiable reporter, a suspect product, and event) cannot be met.
10. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS
This study will include a final report for consumption of results by the Pfizer team. External 
publications (e.g., abstracts, posters, manuscripts) may also be developed for dissemination of results 
to a broader audience. 
In the event of any prohibition or restriction imposed (e.g., 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.  
                                                
8 Regulations state:
§ 164.514 Other requirements relating to uses and disclosures of protected health information.
(a) Standard: De-identification of protected health information. Health information that does not identify an individual and with 
respect to which there is no reasonable basis to believe that the information can be used to identify an individual is not individually 
identifiable health information.
(b) Implementation specifications: Requirements for de-identification of protected health information. A covered entity may 
determine that health information is not individually identifiable health information only if:
(1) A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods 
for rendering information not individually identifiable: (i) Applying such principles and methods, determines that the risk is very small 
that the information could be used, alone or in combination with other reasonably available information, by an anticipated recipient to 
identify an individual who is a subject of the information; and (ii) Documents the methods and results of the analysis that justify such 
determination

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11. REFERENCES
1.
Pfizer. PAXLOVID™ (nirmatrelvir tablets; ritonavir tablets), co-packaged for oral use: 
Prescribing Information. 2023.
2.
Pfizer. EPIC-HR Final CSR v2.0: An Interventional Efficacy and Safety, Phase 2/3, Double-
Blind, 2-Arm Study to Investigate Orally Administered PF-07321332/Ritonavir Compared 
With Placebo in Nonhospitalized Symptomatic Adult Participants With COVID-19 Who are 
at Increased Risk of Progressing to Severe Illness. 8 March 2023.
3.
DeMartino JK, Swallow E, Goldschmidt D, et al. Direct health care costs associated with 
COVID-19 in the United States. J Manag Care Spec Pharm. 2022;28(9):936-947.
4.
Walmsley T, Rose A, John R, et al. Macroeconomic consequences of the COVID-19 
pandemic. Econ Model. 2023;120:106147.
5.
Di Fusco, M., Sun, X., Moran, M.M. et al. Impact of COVID-19 and effects of BNT162b2 
on patient-reported outcomes: quality of life, symptoms, and work productivity among US 
adult outpatients. J Patient Rep Outcomes 6, 123 (2022). 
6.
Bai Y, Du Z, Wang L, et al. The public health impact of Paxlovid COVID-19 treatment in 
the United States. Preprint. medRxiv. 2023;2023.06.16.23288870. Published 2023 Sep 7.
7.
Carlson J, Foos V, Kasle A, et al. Cost-Effectiveness of Oral Nirmatrelvir/Ritonavir in 
Patients at High Risk for Progression to Severe COVID-19 in the United States. Value 
Health. 2024;27(2):164-172.
8.
Engel-Nitz NM, Johnson MG, Johnson MP, Cha-Silva AS, Kurosky SK, Liu X. Palbociclib 
Adherence and Persistence in Patients with Hormone Receptor Positive/Human Epidermal 
Growth Factor Receptor 2 Negative (HR+/HER2-) Metastatic Breast Cancer. Patient Prefer 
Adherence. 2023;17:1049-1062. Published 2023 Apr 18.
9. Navarro-Compán V, Wei JC, Van den Bosch F, et al. Effect of tofacitinib on pain, fatigue, 
health-related quality of life and work productivity in patients with active ankylosing 
spondylitis: results from a phase III, randomised, double-blind, placebo-controlled trial. RMD 
Open. 2022;8(2):e002253.
10. Centers for Disease Control and Prevention. 2024. Underlying Conditions and the Higher 
Risk for Severe COVID-19. Accessed 19 July 2024. <Underlying Conditions and the Higher 
Risk for Severe COVID-19 | COVID-19 | CDC>
11. Beyrer J, Manjelievskaia J, Bonafede M, Lenhart G, Nolot S, Haldane D, Johnson J. 
Validation of an International Classification of Disease, 10th revision coding adaptation for 
the Charlson Comorbidity Index in United States healthcare claims data. Pharmacoepidemiol 
Drug Saf. 2021;30:582-593.
12. Bonafede M, Mehta R, Kim G, Sruti I, Tian M, Pelletier C, Goldfarb N. Productivity loss and 
associated costs among employed patients receiving disease-modifying treatment for 
multiple sclerosis. PharmacoEconomics. 2021;5:23-34.
13. Jerry M, Arcona S, McMorrow D, Schwartz H, Princic N, Sasane R. Work loss and direct 
and indirect costs associated with Parkinson’s disease. Clincoecon Outcomes Res. 
2023;15:309-319.
14. Lewnard JA, McLaughlin JM, Malden D, et al. Effectiveness of nirmatrelvir-ritonavir in 
preventing hospital admissions and deaths in people with COVID-19: a cohort study in a 
large US health-care system. Lancet Infect Dis. 2023;23(7):806-815.

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15. Dryden-Peterson S, Kim A, Kim AY, Caniglia EC, et al. Nirmatrelvir plus ritonavir for early 
COVID-19 in a large U.S. health system: a population-based cohort study. Ann Intern Med. 
2023;176(1):77-84.
16. Elixhauser A, Steiner C, Harris D, et al. Comorbidity measures for use with administrative 
data.  Medical Care. 1998;36(1):8-27.
17. Judy J, Yehoshua A, Gouvela-Pisano J, et al. Impact of COVID-19 on work loss in the 
United States – a retrospective database analysis. J Med Econ. 2024;27(1):941-951.
12. LIST OF TABLES 
Table 1. Estimated Sample Sizes
13. LIST OF FIGURES
Figure 1. Study Schematic

D o c u m e nt A p pr o v al R e c or d
D o c u m e nt N a m e:
D o c u m e nt Titl e:
Si g n e d B y:
D at e( G M T)
Si g ni n g C a p a cit y
 
P P D