A Study to Learn About How Well Yearly Updates to the COVID-19 Vaccine Work to Protect People From COVID-19 and How Much Money People Spend on Healthcare for COVID-19

Rationale and Background

On April 6, 2022, members of the United States (US) Vaccines and Related Biological Products Advisory Committee (VRBPAC) of the Food and Drug Administration (FDA) voted for the first time to update COVID-19 vaccine strain compositions from the original wildtype formulation to a formulation that better matched currently circulating variants. In a commentary in the Journal of the American Medical Association (JAMA) Network Open one month later, Dr Peter Marks (director of the Center for Biologics Evaluation and Research), Dr Janet Woodcock (principal deputy FDA commissioner) and Dr Robert Califf (FDA commissioner) argued

"Better alignment between the variant(s) covered by the vaccine and circulating variant(s) of SARS-CoV-2 might be expected to prevent a broader spectrum of disease, potentially for a longer time".

The US Centers for Disease Control and Prevention (CDC)'s Advisory Committee on Immunization Practices (ACIP) voted in each of 2022, 2023 and 2024 to recommend updated COVID-19 vaccines as authorized or approved by FDA in persons ≥ 6 months. There is therefore a need to evaluate effectiveness of these updated products on an annual basis, against a variety of health outcomes.

Research Question and Objectives

The research question for this study is "What is the real-world effectiveness of BNT162b2 formulations?"

Primary objective:

1. To evaluate vaccine effectiveness (VE) of BNT162b2 formulations in non-immunocompromised adults (age 18+) against COVID-19 related endpoints, by age, comorbid conditions of interest and adapted vaccine formulation. Secondary objective:
2. To measure COVID-19 VE of BNT162b2 formulations in pediatrics (age < 18) against COVID-19 related endpoints, by age and adapted vaccine formulation. Research Methods Study Design For all aims of this study, vaccination status will be measured as a time-varying exposure. Exposed person-time begins 14 days after receipt of a BNT162b2 formulation. Unexposed person-time is unvaccinated person-time, as well as 0-13 days after vaccination. The unexposed group will be further stratified by previous status. Data from one year prior to updated formulation availability will be used as the look-back period to define patient's characteristics, clinical history, risk factors, and healthcare utilization. All available data prior to this period may be used as well. Setting The youngest possible age for any person in any aim of the study is 6 months old. This protocol will be conducted among persons who have all of the following:

• At least one year of continuous medical enrollment prior to index date in an insurance plan that contributes to the HealthVerity claims database in the United States. Medical claims are used to measure inpatient, outpatient, emergency department and urgent care encounters. A gap of up to 30 days will be allowed.
• At least one year of continuous pharmacy enrollment prior to index date in an insurance plan that contributes to the HealthVerity claims database in the United States. Pharmacy claims are used to measure outpatient dispensations of drugs, vaccines, and other biological products. A gap of up to 30 days will be allowed.
• At least one year of continuous state residency in a state with mandatory COVID-19 vaccination reporting that contributes the entire state vaccine registry to HealthVerity.
• Does not have discrepancies in sex and/or year of birth between any of the available datasets. These are critical covariates in models as they are amongst the most important risk factors for severe disease.
• Did not have documented episode of COVID-19 and/or receipt of any COVID-19 vaccine during the 90 days prior to index date. These persons likely have lower risk of vaccination (the study's exposure of interest) as well as temporarily lower risk of COVID-19 (the study's outcome of interest), which would introduce heterogeneity in estimates were they not excluded. Variables Primary and secondary objectives, exposure: BNT162b2 mRNA vaccine, enumerated using Clinical Vaccines Administered (CVX), National Drug Code (NDC) and Current Procedural Terminology (CPT) codes. Primary and secondary objectives, outcomes: COVID-19 related endpoints:
• Outpatient encounters with an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code U07.1 "COVID-19"
• Incidence, as the number of people experiencing an event divided by the number of people at risk of an event (i.e. proportions)
• Incidence rate, as the number of people experiencing an event divided by the total person time at risk of an event (i.e. X events per 100,000 person-months)
• Adjusted hazard ratios
• Cost associated with these encounters, enumerated as mean (standard deviation) and median (first quartile - third quartile) among all non-zero costs.
• Emergency department encounters with an ICD-10-CM code U07.1
• Incidence, as the number of people experiencing an event divided by the number of people at risk of an event (i.e. proportions)
• Incidence rate, as the number of people experiencing an event divided by the total person time at risk of an event (i.e. X events per 100,000 person-months)
• Adjusted hazard ratios
• Cost associated with these encounters, enumerated as mean (standard deviation) and median (first quartile - third quartile) among all non-zero costs.
• Urgent care encounters with an ICD-10-CM code U07.1
• Incidence, as the number of people experiencing an event divided by the number of people at risk of an event (i.e. proportions)
• Incidence rate, as the number of people experiencing an event divided by the total person time at risk of an event (i.e. X events per 100,000 person-months)
• Adjusted hazard ratios
• Cost associated with these encounters, enumerated as mean (standard deviation) and median (first quartile - third quartile) among all non-zero costs.
• Inpatient encounters with an ICD-10-CM code U07.1. Of note, potentially incidental findings of COVID-19, such as those during the course of a major psychiatric or physical trauma associated admission, will be excluded using previously described methodology.
• Length of stay, as the number of days from admission to discharge expressed as mean (standard deviation) and median (first quartile - third quartile)
• Incidence, as the number of people experiencing an event divided by the number of people at risk of an event (i.e. proportions)
• Incidence rate, as the number of people experiencing an event divided by the total person time at risk of an event (i.e. X events per 100,000 person-months)
• Adjusted hazard ratios
• Cost associated with these encounters, enumerated as mean (standard deviation) and median (first quartile - third quartile) among all non-zero costs.
• If sample size permits, the above outcomes will be further stratified into cases that did and did not require intensive care unit (ICU) admission, invasive mechanical ventilation (IMV) or resulted in death. Potential confounders: a diverse set of variables will be used to balance baseline characteristics between those who are vs are not vaccinated. In brief, these will include sociodemographic and clinical features known to be associated with the risk of exposure and outcome and may need to be tailored based on the structure of confounding assumed to be present.5,6 The balanced set of covariates will contain a sample where standardized mean differences (SMD) between measured confounders are ≤ 0.1.7. Data Sources Medical and pharmacy claims Insurers contributing to closed claims include a mix of commercial payers, Medicare Advantage/Part C plans, and Medicaid Managed Care plans. Data elements in medical claims include patient demographic information, inpatient/outpatient visit-level information such as diagnoses, procedures, and length of stay, hospital characteristics, and medication information. Owing to the nature of claims, the data represent the final set of diagnoses over the course of the hospitalization sent to the patient's insurer for reimbursement, with diagnosis prioritization assigned by clinicians or hospital staff. Data elements in pharmacy claims include patient demographic information, NDC, date of service, dispensed quantity, and days' supply. State vaccine registries Since December 2020, vaccinators in many jurisdictions and states have been mandated to report COVID-19 vaccine administered to their respective state health authorities. State registry files have unique person identifiers, vaccination event date, and CVX codes, which are unique to each vaccine. Linkage methods between data sources The administrative insurance claims data and the state vaccine registry data were independently tokenized using HealthVerity's tokenization software, meaning patient identifiers are passed through software to create a unique de-identified identification number for each person. Identifying information is removed by the vaccine registries before sending the data to HealthVerity for data aggregation. This process ensures Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule compliance via expert determination. The insurance claims data were then linked to the state vaccination registries via the tokenized identification number. Study Size This study uses retrospective deidentified data without ability to recruit to a target, sample size calculations are not applicable. The expected number of people eligible for the study is expected to be approximately 30% of a state's population, given HealthVerity's marketplace coverage. In order to maintain confidentiality as well as causal inference principles of consistency, positivity and exchangeability, outcomes, covariates and subgroups will have at least 10 people in each category for inclusion in outputs. Throughout the protocol, this is referred to as "If sample size allows". Data Management Claims and vaccine registry data refreshes are delivered by HealthVerity monthly. All HealthVerity structured data described above are stored within Pfizer's data infrastructure. Data are queried and analyzed using Statistical Analysis System (SAS) and/or R. The HealthVerity claims, mortality, and vaccine registries are stored as separate databases and, owing to the nature of the data use agreement, on a different server than all other enterprise-wide data. The data are only accessible to Pfizer colleagues who have been trained and approved to access Pfizer's data warehouse. In brief, all data in the year prior to index through 6 months after index for persons who meet inclusion and exclusion criteria detailed in section 9.1 will be extracted. Data Analysis Data are extracted using standardized macro scripts by two experienced programmers, with all discrepancies resolved before final results delivery. Descriptive analyses will compare the distribution of sociodemographic and clinical characteristics in final cohorts. Time-dependent Cox proportional hazards models will be used to estimate the hazard of each outcome of interest with time-varying exposure and time-fixed covariates, resulting in adjusted hazard ratios (aHR) and 95% confidence intervals (95% CI). Detailed methodology for summary and statistical analyses of data collected in this study will be documented in a SAP, which will be dated, filed, and maintained by the sponsor. The SAP may modify the plans outlined in the protocol; any major modifications of primary endpoint definitions or their analyses would be reflected in a protocol amendment. Quality Control Data in HealthVerity's database are provided monthly in an electronic format. HealthVerity employs its tokenization product to match patients between different data sources with high accuracy. Each monthly delivery of data contains a quality control report, which was co-created between HealthVerity and Pfizer subject matter experts to ensure continued quality. All analyses will be performed internally and according to Pfizer analytic standards, including double programming to ensure quality control. Limitations of the Research Methods
• Some, but not all, states have COVID-19 vaccine reporting mandates, which are required for ascertainment of unvaccinated status. Therefore, the results of this study using states with mandatory reporting jurisdictions may not generalize to other areas of the country. However, a variety of geographic, sociodemographic, and health characteristics are included amongst the included populations.
• There is no laboratory confirmation of the COVID-19 diagnosis. It is difficult to know the direction of bias that this limitation may create. It is possible that some patients are being diagnosed with symptoms alone (over-ascertainment of outcomes of interest) or that we are missing positive cases due to at-home testing (under-ascertainment of outcomes of interest).
• The results from this study may differ from those of VE studies using other data sources, such as Kaiser Permanente of Southern California or the Veterans Administration.
• Attempts to address confounding may not be able to fully account for confounders.