| AUC | Area Under the receiver-operator Curve |
| CCAE | IBM MarketScan Commercial Claims and Encounters |
| CDM | Common Data Model |
| CIOMS | Council for International Organizations of Medical Sciences |
| COVID-19 | COronaVIrus Disease 2019 |
| CRAN | Comprehensive R Archive Network |
| EHR | Electronic Health Record |
| H1N1pdm | Hemagglutinin Type 1 and Neuraminidase Type 1 (2009 pandemic influenza) |
| HPV | Human PapillomaVirus |
| IRB | Institutional review board |
| LLR | Log Likelihood Ratio |
| MAP | Maximum A Posteriori |
| MaxSPRT | MAXimized Sequential Probability Ratio Test |
| MCMC | Markov Chain Monte Carlo |
| MDCR | IBM MarketScan Medicare Supplemental Database |
| MDCD | IBM MarketScan Multi-State Medicaid Database |
| MSE | Mean Squared Error |
| OHDSI | Observational Health Data Science and Informatics |
| OMOP | Observational Medical Outcomes Partnership |
| RCT | Randomized controlled trial |
| SCCS | Self-Controlled Case Series |
| SCRI | Self-Controlled Risk Interval |
| WHO | World Health Organization |
| Investigator | Institution/Affiliation |
|---|---|
| Fan Bu * | Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA |
| George Hripcsak | Department of Biomedical Informatics, Columbia University, New York, NY, USA |
| Patrick B. Ryan | Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA |
| Marc A. Suchard | Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA |
| * Principal Investigator |
This study is undertaken within Observational Health Data Sciences and Informatics (OHDSI), an open collaboration. GH receives grant funding from the US National Institutes of Health and the US Food & Drug Administration. PBR and is an employee of Janssen Research and Development and shareholders in John & Johnson. FB and MAS receive grant funding from the US National Institutes of Health and the US Food & Drug Administration and contracts from the US Department of Veterans Affairs and Janssen Research and Development.
Background and Significance
As various approved COVID-19 vaccines are rolled out globally, safety signals have been identified from spontaneous reports and other data sources. The current standard method of safety surveillance adopted by the FDA is MaxSPRT, which suffers from the inflexibility of a pre-specified sequential analysis schedule. We hope to develop and implement a more flexible Bayesian surveillance framework and compare its performance with MaxSPRT in real-world data.
Study Aims
To compare the real-data performance (testing errors, timeliness, precision and bias) of Bayesian and frequentist sequential analysis methods for the study of comparative vaccine safety.
We will also produce a reference table of Type I and II error rates and signal detection times for all combinations of design and threshold choices, as exploration of the operating characteristics of Bayesian sequential methods.
Study Description
Design: historical comparator & self-controlled studies
Exposures: previous viral vaccines including 2017-2018 flu, H1N1pdm flu, Human Papillomavirus (HPV), and Varicella-Zoster.
Outcomes: selected adverse events of special interest (e.g., Guillain-Barre Syndrome); negative control outcomes; imputed positive control outcomes
Analyses:
Decision rules:
Metrics:
Table 4.1 lists any protocol amendments made over time.
| Number | Date | Section of study protocol | Amendment or update | Reason |
|---|---|---|---|---|
Table 5.1 lists the study milestones.
| Milestone | Planned / actual date |
|---|---|
| Start of analysis | 02/01/2022 |
| End of analysis | 05/31/2022 |
| Results presentation | 06/30/2022 |
Mass vaccination against SARS-CoV-2 is critical to ending the current COVID-19 global pandemic. By the beginning of 2022, 9 vaccines have been approved under the WHO Emergency Use List, and more than 10 billion doses have been administered globally by February 2022 [1]. With the large-scale usage of vaccines under emergency approval, it is essential to ensure their safety and effectiveness through post-market surveillance, as rare but serious adverse events may not be identified in phase 3 clinical trials. In the US, messenger RNA (mRNA) vaccines (BNT162b2, Pfizer-BioNTech; and mRNA-1273, Moderna) were the first SARS-CoV-2 vaccines authorized and as of February 2022, more than 500 million doses of mRNA vaccines have been administered. [2] And yet, there is limited experience with mRNA platforms previous to SARS-CoV-2, and therefore safety surveillance is particularly important to inform public health policy and maintain public trust.
The design of a rapid and reliable vaccine safety surveillance system requires an efficient and robust statistical monitoring approach. The current standard approach used by regulatory agencies in the US is a frequentist sequential analysis method, MaxSPRT [3]. It is designed to control the overall analysis Type I error rate of a sequential analysis by allocating the allowed false positive error over sequential analysis stages. This method has long suffered from its inflexibility as it requires a pre-specified analysis schedule, and does not allow extended analysis after the pre-chosen analysis endpoint.
A more flexible sequential analysis method is, therefore, much desired. A promising candidate is a Bayesian sequential testing framework. Under a Bayesian framework, multiplicity of sequential analyses can be handled more elegantly, without the need for a rigid, pre-specified analysis schedule while allowing continued analyses beyond anticipated endpoints. It is also easier to incorporate historical information into current analyses using a prior distribution through Bayesian inference. With all its theoretical advantages, however, the performance and operating characteristics of Bayesian sequential testing methods have not yet been extensively studied on large-scale real-world data. In particular, with observational health data, Bayesian methods can potentially adjust for unmeasured confounding and sampling bias, but the performance and behavior have not been evaluated in a systematic manner.
The goal of this study is to compare the performance of a Bayesian testing framework with that of MaxSPRT (the current standard approach), in terms of both the hypothesis testing errors (sensitivity and specificity) and estimation accuracy (in estimating the relative risks of adverse events of interest). This study will be conducted on various large-scale health claims databases, in order to understand the operating characteristics in a real-world data-intensive setting. At the initial stage of the study, all analyses will be performed retrospectively using historical vaccines with more regular roll-out schedules. We believe the results of our comprehensive evaluations will help us better understand the performance and behavior of a Bayesian sequential testing framework and facilitate the design of a more flexible and reliable safety surveillance system for COVID-19 vaccines.
The overarching aim is to compare the performance of frequentist and Bayesian sequential analysis methods for the generation of evidence of vaccine safety in observational, real-world data. Specific aims:
The evaluation will center on six existing (groups of) vaccines, for specific time periods (start date to end date), as shown in Table 8.1.
| Exposure Name | Start Date | End Date | History Start Date | History End Date |
|---|---|---|---|---|
| H1N1pdm vaccination | 01-09-2009 | 31-05-2010 | 01-09-2008 | 31-05-2009 |
| Seasonal flu vaccination (Fluvirin) | 01-09-2017 | 31-05-2018 | 01-09-2016 | 31-05-2017 |
| Seasonal flu vaccination (Fluzone) | 01-09-2017 | 31-05-2018 | 01-09-2016 | 31-05-2017 |
| Seasonal flu vaccination (All) | 01-09-2017 | 31-05-2018 | 01-09-2016 | 31-05-2017 |
| Zoster vaccination (Shingrix) | 01-01-2018 | 31-12-2018 | 01-01-2017 | 31-12-2017 |
| HPV vaccination (Gardasil 9) | 01-01-2018 | 31-12-2018 | 01-01-2017 | 31-12-2017 |
For some methods the period between historical start and historical end date will be used to estimate the historic incidence rate. For analyses executed on data in the southern hemisphere (if any) the flu seasons are different, and the study periods will need to be adjusted accordingly. The formal cohort definitions of each exposure can be found in Appendix A.
Negative controls are outcomes believed not to be caused by any of the vaccines, and therefore ideally would not be flagged as a signal by a safety surveillance system. Any effect size estimates for negative control ideally should be close to the null.
A single set of negative control outcomes is defined for all four vaccine groups. To identify negative control outcomes that match the severity and prevalence of suspected vaccine adverse effects, a candidate list of negative controls was generated based on similarity of prevalence and percent of diagnoses that were recorded in an inpatient setting (as a proxy for severity). Manual review of this list by clinical experts created the final list of 93 negative control outcomes. The full list of negative control outcomes can be found in Appendix B
Negative control outcomes are defined as the first occurrence of the negative control concept or any of its descendants.
Positive controls are outcomes known to be caused by vaccines, and ideally would be detected as signals by a safety surveillance system as early as possible. For various reasons, real positive controls are problematic.[4] Instead, here we will rely on imputed positive controls, created by shifting the estimated effect sizes for the negative controls. We assume the negative controls have a true effect size of 1, so to simulate the estimated effect size when the true effect size is \(\theta\) we multiply the estimate by \(\theta\). For example, if for a negative control a method produces an effect size estimate of 1.1, for a positive control with true effect size of 2 the estimated effect size becomes 1.1 x 2 = 2.2. This approach makes strong assumptions on the nature of the systematic error, most importantly that systematic error does not change as a function of the true effect size. Although this assumption is likely not to hold in the real world, imputing positive controls allows us to provide some indication of what type 2 error to expect for various true effect sizes. For each negative control we will impute positive controls with true effect sizes of 1.5, 2, and 4, so using the 93 negative controls we are able to construct 93 \(\times\) 3 = 279 positive control outcomes. This increased true effect is applied both for the first and second injection of multi-dose vaccines.
In addition to the negative control and imputed positive control outcomes, we will further investigate the risk of Guillain-Barre Syndrome (GBS) following the zoster vaccine, as comparison to previous study findings [5]. The previous study by Goud et al. used the self-controlled case series design to analyze Medicare claims data, and found a significant elevated risk (risk ratio 2.34, 95% CI, 1.01-5.41). We will use both the historical comparator and self-controlled designs, apply both frequentist and Bayesian sequential testing methods, and run analyses on a variety of large-scale databases, in the hope of a more comprehensive analysis of the risk of GBS post zoster vaccination.
We will execute BETTER as an OHDSI network study. All data partners within OHDSI are encouraged to participate voluntarily and can do so conveniently, because of the community’s shared Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and OHDSI tool-stack. Many OHDSI community data partners have already committed to participate and we will recruit further data partners through OHDSI’s standard recruitment process, which includes protocol publication on OHDSI’s GitHub, an announcement in OHDSI’s research forum, presentation at the weekly OHDSI all-hands-on meeting and direct requests to data holders.
Table 8.2 lists the potential data sources for BETTER; these sources encompass a large variety of practice types and populations. For each data source, we report a brief description and size of the population it represents. All data sources will receive institutional review board approval or exemption for their participation before executing BETTER.
| Data source | Population | Patients | History | Data capture process and short description |
|---|---|---|---|---|
| IBM MarketScan Commercial Claims and Encounters (CCAE) | Commercially insured, < 65 years | 142M | 2000 – | Adjudicated health insurance claims (e.g. inpatient, outpatient, and outpatient pharmacy) from large employers and health plans who provide private healthcare coverage to employees, their spouses and dependents. |
| IBM MarketScan Medicare Supplemental Database (MDCR) | Commercially insured, 65\(+\) years | 10M | 2000 – | Adjudicated health insurance claims of retirees with primary or Medicare supplemental coverage through privately insured fee-for-service, point-of-service or capitated health plans. |
| IBM MarketScan Multi-State Medicaid Database (MDCD) | Medicaid enrollees, racially diverse | 26M | 2006 – | Adjudicated health insurance claims for Medicaid enrollees from multiple states and includes hospital discharge diagnoses, outpatient diagnoses and procedures, and outpatient pharmacy claims. |
| Optum Clinformatics Data Mart (Optum) | Commercially or Medicare insured | 85M | 2000 – | Inpatient and outpatient healthcare insurance claims. |
| Optum Electronic Health Records (OptumEHR) | US, general | 93M | 2006 – | Clinical information, prescriptions, lab results, vital signs, body measurements, diagnoses and procedures derived from clinical notes using natural language processing. |
Vaccine safety surveillance methods can be broken down into four components: construction of a counterfactual (often referred to as the ‘expected count’), a time-at-risk, the estimation outcome (an estimate or posterior distribution for the effect size), and a decision rule based on the estimation outcome to differentiate signals from non-signals.
In this study, we mainly focus on two designs for counterfactual construction: historical comparator and self-controlled case series. The former design is currently the standard design adopted by various regulatory agencies such as FDA and CDC in the US and the CDC in the EU, while the latter is a design of rising popularity that has shown satisfactory performance according to numerous recent studies.
Traditionally, vaccine surveillance methods compute an expected count based on an incidence rate estimated during some historic time period, for example, in the years prior to the initiation of the surveillance study. [6][7] We will use the historical period indicated in Table 8.1 and evaluate four variations:
The SCCS and SCRI designs are self-controlled, comparing the time-at-risk (the time shortly following the vaccination) to some other time in the same patient’s record. The SCCS design uses all patient time when not at risk as the control time. [8] The SCRI design uses a pre-specified control interval relative to the vaccination date as the control time. [9] This unexposed time can be both before or after the time at risk.
We will evaluate five variations:
The time-at-risk is the time window, relative to the vaccination date, when outcomes will potentially be attributed to the vaccine. We define three time-at-risk windows: 1-28 days, 1-42 days, and 0-1 days after vaccination.
Time-at-risk windows will be constructed both for the first and second dose. The time-at-risk for one dose will be censored at the time of the next dose.
The effect-size of interest for both the HC and SCCS designs is the (log) relative incidence rate ratio. We obtain slightly different estimation outcomes for the frequentist (i.e., MaxSPRT) and Bayesian methods.
For frequentist MaxSPRT, we obtain:
Effect-size estimate. This is typically a maximum likelihood estimate (MLE) obtained from the analysis.
Log likelihood ratio (LLR). The log of the ratio between the likelihood of the alternative hypothesis (that there is an effect) and the likelihood of the null hypothesis (of no effect).
The LLR is a convenient and commonly used statistic when performing sequential testing, where the LLR can be compared to a pre-computed critical value, as is done in the MaxSPRT method. [3] Although typically MaxSPRT uses a historic rate as counterfactual, any counterfactual can be used to compute the LLR and can be used in MaxSPRT; our use of either the HC or SCCS/SCRI design does not affect the validity of using the LLR as the test statistic.
For the Bayesian method, we obtain:
Posterior distribution for the effect-size, approximated by MCMC posterior samples. This is obtained using the Bayes Rule by combining the likelihood function and the prior distribution. The end result is not a single point estimate but rather a distribution profile about our knowledge of the effect-size given accrued data.
Maximum A Posteriori (MAP) estimate for the effect-size. This is a point estimate obtained by extracting the maxima of the posterior density; this estimate can be regarded as a Bayesian counterpart of the frequentist effect-size estimate.
Posterior mean for the effect-size. This is a commonly adopted Bayesian estimate, and is, in fact, the optimal Bayesian estimate with squared loss.
Posterior median for the effect-size. This is another commonly adopted Bayesian estimate, and is also the optimal Bayesian estimate with absolute error loss.
For the Bayesian method, we also evaluate different prior distribution choices for the effect-size:
We choose to use log-normal priors for their simplicity and wide use, in order to focus mainly on comparison between Bayesian and frequentist testing methods. We will consider adopting other prior distributions (e.g., Laplace priors) in subsequent studies.
For the frequentist method (MaxSPRT), analyses will be conducted with and without empirical calibration. [10,11]. Empirical calibration will be done using leave-one-out: when calibrating the estimate for a control, the systematic error distribution will be fitted uses all controls except the one being calibrated.
For the Bayesian method, inference will be conducted with and without Bayesian bias adjustment using negative control analyses. Similarly, bias adjustment will be done using leave-one-out.
To identify ‘signals’ we need a decision rule, for example in the shape of a threshold value on one of the estimates statistics.
In our experiment, for the frequentist surveillance method, we will consider a decision rule using the critical value \(cv\) computed for the LLR at the \(\alpha = 0.05\) level.
That is, we will reject the null and claim a signal when \(LLR > cv\). Here all critical values will be computed using the Sequential package in CRAN.
For the Bayesian method, we will implement two sets of decision rules, one for signal (rejecting null) and one for futility/safety (accepting null), by examining the posterior probabilities of the null and alternative hypotheses simultaneously:
We will evaluate three choices of \(\delta_1\): \(0.80, 0.90, 0.95\); and also three choices of \(\delta_0\):\(0.90, 0.95, 0.99\).
As we will conduct both estimation and testing tasks at the same time, we will compute two sets of metrics based on the study outcomes: (1) metrics for testing, and (2) metrics for estimation. (Some of the following metrics are adapted from previous work [12].)
To understand the time it takes for a method the identify signals, the study period for each vaccine will be divided into calendar months. For each month the methods will be executed using the data that had accumulated up to the end of that month, and the performance metrics will be reported for each month.
For those vaccines requiring multiple doses (zoster, HPV), metrics will be computed three times:
In total, we will evaluate:
For the frequentist MaxSPRT method, this will result in a total of 9 \(\times\) 3 \(\times\) [(9 + 9 + 9 + 9) \(\times\) 1 + (12 + 12) \(\times\) 3] \(\times\) (93 + 279 + 1) = 1,087,668 effect-size estimates, where each estimate will contain:
For the Bayesian method, this will result in a total of 9 \(\times\) 3 \(\times\) [(9 + 9 + 9 + 9) \(\times\) 1 + (12 + 12) \(\times\) 3] \(\times\) (93 + 279 + 1) \(\times\) 4 = 4,350,672 estimation outcomes, where each outcome will include:
These analysis results will be computed for each database.
BETTER does not involve human subjects research. The project does, however, use de-identified human data collected during routine healthcare provision. All data partners executing the BETTER studies within their data sources will have received institutional review board (IRB) approval or waiver for participation in accordance to their institutional governance prior to execution (see Table 10.1). BETTER executes across a federated and distributed data network, where analysis code is sent to participating data partners and only aggregate summary statistics are returned, with no sharing of patient-level data between organizations.
| Data source | Statement |
|---|---|
| IBM MarketScan Commercial Claims and Encounters (CCAE) | New England Institutional Review Board and was determined to be exempt from broad IRB approval, as this research project did not involve human subject research. |
| IBM MarketScan Medicare Supplemental Database (MDCR) | New England Institutional Review Board and was determined to be exempt from broad IRB approval, as this research project did not involve human subject research. |
| IBM MarketScan Multi-State Medicaid Database (MDCD) | New England Institutional Review Board and was determined to be exempt from broad IRB approval, as this research project did not involve human subject research. |
| Optum Clinformatics Data Mart (Optum) | New England Institutional Review Board and was determined to be exempt from broad IRB approval, as this research project did not involve human subject research. |
| Optum Electronic Health Records (OptumEHR) | New England Institutional Review Board and was determined to be exempt from broad IRB approval, as this research project did not involve human subject research. |
BETTER uses coded data that already exist in electronic databases. In these types of databases, it is not possible to link (i.e., identify a potential causal association between) a particular product and medical event for any specific individual. Thus, the minimum criteria for reporting an adverse event (i.e., identifiable patient, identifiable reporter, a suspect product and event) are not available and adverse events are not reportable as individual adverse event reports. The study results will be assessed for medically important findings.
Open science aims to make scientific research, including its data process and software, and its dissemination, through publication and presentation, accessible to all levels of an inquiring society, amateur or professional [13] and is a governing principle of BETTER. Open science delivers reproducible, transparent and reliable evidence. All aspects of BETTER (except private patient data) will be open and we will actively encourage other interested researchers, clinicians and patients to participate. This differs fundamentally from traditional studies that rarely open their analytic tools or share all result artifacts, and inform the community about hard-to-verify conclusions at completion.
We will publicly register this protocol and announce its availability for feedback from stakeholders, the OHDSI community and within clinical professional societies. This protocol will link to open source code for all steps to generating diagnostics, effect estimates, figures and tables. Such transparency is possible because we will construct our studies on top of the OHDSI toolstack of open source software tools that are community developed and rigorously tested [12]. We will publicly host BETTER source code at URL TBD, allowing public contribution and review, and free re-use for anyone’s future research.
BETTER embodies a new approach to generating evidence from healthcare data that overcome weaknesses in the current process of answering and publishing (or not) one question at a time. Generating evidence for thousands of research and control questions using a systematic process enables us to not only evaluate that process and the coherence and consistency of the evidence, but also to avoid \(p\)-hacking and publication bias [4]. We will store and openly communicate all of these results as they become available using a user-friendly web-based app that serves up all descriptive statistics, study diagnostics and effect estimates for each cohort comparison and outcome. Open access to this app will be through a general public facing BETTER web-page.
We will deliver multiple presentations at scientific venues and will also prepare multiple scientific publications for clinical, informatics and statistical journals.
We believe in sharing our findings that will guide clinical care with the general public. BETTER will use social-media (Twitter) to facilitate this. With dedicated support from the OHDSI communications specialist, we will deliver regular press releases at key project stages, distributed via the extensive media networks of UCLA and Columbia.
People enter the cohort when observing any of the following:
Limit cohort entry events to the earliest event per person.
The cohort end date will be offset from index event’s start date plus 0 days.
Entry events will be combined into cohort eras if they are within 0 days of each other.
| Concept ID | Concept Name | Code | Vocabulary | Excluded | Descendants | Mapped |
|---|---|---|---|---|---|---|
| 40213187 | Novel influenza-H1N1-09, all formulations | 128 | CVX | NO | YES | NO| |
| 40166607 | influenza A-California-7-2009-(H1N1)v-like virus vaccine 0.03 MG/ML Injectable Suspension | 864704 | RxNorm | NO | YES | NO| |
| 40166130 | 0.25 ML influenza A-California-7-2009-(H1N1)v-like virus vaccine 0.03 MG/ML Prefilled Syringe | 864781 | RxNorm | NO | YES | NO| |
| 40166144 | 0.5 ML influenza A-California-7-2009-(H1N1)v-like virus vaccine 0.03 MG/ML Prefilled Syringe | 864797 | RxNorm | NO | YES | NO| |
| 42902936 | influenza A-California-7-2009-(H1N1)v-like virus vaccine 0.03 MG/ML Prefilled Syringe | 1360049 | RxNorm | NO | YES | NO| |
| 40240135 | influenza A-California-7-2009-(H1N1)v-like virus vaccine 0.09 MG/ML | 1111367 | RxNorm | NO | YES | NO| |
| 40225009 | influenza A-California-7-2009-(H1N1)v-like virus vaccine 0.12 MG/ML | 1005949 | RxNorm | NO | YES | NO| |
| 40166608 | influenza A-California-7-2009-(H1N1)v-like virus vaccine 158000000 UNT/ML | 864812 | RxNorm | NO | YES | NO| |
| 45776785 | influenza A-California-7-2009-(H1N1)v-like virus vaccine 50000000 MG/ML | 1543758 | RxNorm | NO | YES | NO| |
| 40166609 | influenza A-California-7-2009-(H1N1)v-like virus vaccine Injectable Suspension | 864703 | RxNorm | NO | YES | NO| |
| 40166611 | influenza A-California-7-2009-(H1N1)v-like virus vaccine Prefilled Syringe | 864780 | RxNorm | NO | YES | NO| |
People enter the cohort when observing any of the following:
Limit cohort entry events to the earliest event per person.
The cohort end date will be offset from index event’s start date plus 0 days.
Entry events will be combined into cohort eras if they are within 0 days of each other.
| Concept ID | Concept Name | Code | Vocabulary | Excluded | Descendants | Mapped |
|---|---|---|---|---|---|---|
| 1593906 | influenza A virus A/Hong Kong/4801/2014 (H3N2) antigen 0.03 MG/ML / influenza A virus A/Singapore/GP1908/2015 (H1N1) antigen 0.03 MG/ML / influenza B virus B/Brisbane/60/2008 antigen 0.03 MG/ML [Fluvirin 2017-2018] | 1928971 | RxNorm | NO | YES | NO| |
People enter the cohort when observing any of the following:
Limit cohort entry events to the earliest event per person.
The cohort end date will be offset from index event’s start date plus 0 days.
Entry events will be combined into cohort eras if they are within 0 days of each other.
| Concept ID | Concept Name | Code | Vocabulary | Excluded | Descendants | Mapped |
|---|---|---|---|---|---|---|
| 1593354 | influenza A virus A/Hong Kong/4801/2014 (H3N2) antigen 0.12 MG/ML / influenza A virus A/Michigan/45/2015 (H1N1) antigen 0.12 MG/ML / influenza B virus B/Brisbane/60/2008 antigen 0.12 MG/ML [Fluzone 2017-2018] | 1928341 | RxNorm | NO | YES | NO| |
People enter the cohort when observing any of the following:
Limit cohort entry events to the earliest event per person.
The cohort end date will be offset from index event’s start date plus 0 days.
Entry events will be combined into cohort eras if they are within 0 days of each other.
| Concept ID | Concept Name | Code | Vocabulary | Excluded | Descendants | Mapped |
|---|---|---|---|---|---|---|
| 40213145 | influenza, injectable, quadrivalent, contains preservative | 158 | CVX | NO | YES | NO| |
| 42903442 | influenza B virus | 1312376 | RxNorm | NO | YES | NO| |
| 40213150 | influenza, live, intranasal, quadrivalent | 149 | CVX | NO | YES | NO| |
| 40213159 | influenza virus vaccine, whole virus | 16 | CVX | NO | YES | NO| |
| 40225028 | influenza virus vaccine, inactivated A-Victoria-210-2009 X-187 (H3N2) (A-Perth-16-2009) strain | 1005931 | RxNorm | NO | YES | NO| |
| 40213156 | influenza virus vaccine, split virus (incl. purified surface antigen)-retired CODE | 15 | CVX | NO | YES | NO| |
| 40213151 | Seasonal, trivalent, recombinant, injectable influenza vaccine, preservative free | 155 | CVX | NO | YES | NO| |
| 40213327 | influenza nasal, unspecified formulation | 151 | CVX | NO | YES | NO| |
| 40213148 | influenza, intradermal, quadrivalent, preservative free, injectable | 166 | CVX | NO | YES | NO| |
| 40213158 | influenza virus vaccine, unspecified formulation | 88 | CVX | NO | YES | NO| |
| 36878713 | Influenza Virus Fragmented, Inactivated, Strain B / Phuket / 3073/2013 | OMOP989577 | RxNorm Extension | NO | YES | NO| |
| 42873961 | influenza B virus vaccine, B-Wisconsin-1-2010-like virus | 1303855 | RxNorm | NO | YES | NO| |
| 40225038 | influenza virus vaccine, live attenuated, A-Perth-16-2009 (H3N2) strain | 1005911 | RxNorm | NO | YES | NO| |
| 40213146 | Influenza, injectable, quadrivalent, preservative free | 150 | CVX | NO | YES | NO| |
| 40213143 | Influenza, injectable, Madin Darby Canine Kidney, preservative free, quadrivalent | 171 | CVX | NO | YES | NO| |
| 36879025 | Influenza Virus Surface Antigens, strain A / Switzerland / 9715293/2013 H3N2 - Analogue Strain Nib-88 | OMOP991645 | RxNorm Extension | NO | YES | NO| |
| 40213157 | Seasonal trivalent influenza vaccine, adjuvanted, preservative free | 168 | CVX | NO | YES | NO| |
| 45776076 | influenza A virus vaccine, A-Texas-50-2012 (H3N2)-like virus | 1541617 | RxNorm | NO | YES | NO| |
| 40213149 | influenza virus vaccine, live, attenuated, for intranasal use | 111 | CVX | NO | YES | NO| |
| 40213147 | Influenza, injectable,quadrivalent, preservative free, pediatric | 161 | CVX | NO | YES | NO| |
| 40213152 | Seasonal, quadrivalent, recombinant, injectable influenza vaccine, preservative free | 185 | CVX | NO | YES | NO| |
| 42903441 | influenza A virus | 1312375 | RxNorm | NO | YES | NO| |
| 40213141 | influenza, high dose seasonal, preservative-free | 135 | CVX | NO | YES | NO| |
| 40213153 | Influenza, seasonal, injectable | 141 | CVX | NO | YES | NO| |
| 40213144 | Influenza, injectable, Madin Darby Canine Kidney, quadrivalent with preservative | 186 | CVX | NO | YES | NO| |
| 40213142 | Influenza, injectable, Madin Darby Canine Kidney, preservative free | 153 | CVX | NO | YES | NO| |
| 40213155 | seasonal influenza, intradermal, preservative free | 144 | CVX | NO | YES | NO| |
| 40164828 | influenza B virus vaccine B/Brisbane/60/2008 antigen | 857921 | RxNorm | NO | YES | NO| |
People enter the cohort when observing any of the following:
The cohort end date will be offset from index event’s start date plus 0 days.
Entry events will be combined into cohort eras if they are within 0 days of each other.
| Concept ID | Concept Name | Code | Vocabulary | Excluded | Descendants | Mapped |
|---|---|---|---|---|---|---|
| 36248866 | Gardasil 9 Injectable Product | 1597098 | RxNorm | NO | YES | NO| |
| 45892513 | L1 protein, human papillomavirus type 11 vaccine / L1 protein, human papillomavirus type 16 vaccine / L1 protein, human papillomavirus type 18 vaccine / L1 protein, human papillomavirus type 31 vaccine / L1 protein, human papillomavirus type 33 vaccine / | 1597102 | RxNorm | NO | YES | NO| |
| 45892514 | 0.5 ML L1 protein, human papillomavirus type 11 vaccine 0.08 MG/ML / L1 protein, human papillomavirus type 16 vaccine 0.12 MG/ML / L1 protein, human papillomavirus type 18 vaccine 0.08 MG/ML / L1 protein, human papillomavirus type 31 vaccine 0.04 MG/ML / | 1597103 | RxNorm | NO | YES | NO| |
| 45892510 | 0.5 ML L1 protein, human papillomavirus type 11 vaccine 0.08 MG/ML / L1 protein, human papillomavirus type 16 vaccine 0.12 MG/ML / L1 protein, human papillomavirus type 18 vaccine 0.08 MG/ML / L1 protein, human papillomavirus type 31 vaccine 0.04 MG/ML / | 1597099 | RxNorm | NO | YES | NO| |
| 40213322 | Human Papillomavirus 9-valent vaccine | 165 | CVX | NO | YES | NO| |
People enter the cohort when observing any of the following:
The cohort end date will be offset from index event’s start date plus 0 days.
Entry events will be combined into cohort eras if they are within 0 days of each other.
| Concept ID | Concept Name | Code | Vocabulary | Excluded | Descendants | Mapped |
|---|---|---|---|---|---|---|
| 792784 | varicella zoster virus glycoprotein E Injection [Shingrix] | 1986828 | RxNorm | NO | YES | NO| |
| 792783 | varicella zoster virus glycoprotein E, recombinant 0.1 MG/ML [Shingrix] | 1986827 | RxNorm | NO | YES | NO| |
| 792788 | varicella zoster virus glycoprotein E, recombinant 0.1 MG/ML Injection [Shingrix] | 1986832 | RxNorm | NO | YES | NO| |
| 36421491 | Varicella-Zoster Virus Vaccine Live (Oka-Merck) strain Injectable Solution [Shingrix] | OMOP4763774 | RxNorm Extension | NO | YES | NO| |
| 792785 | Shingrix Injectable Product | 1986829 | RxNorm | NO | YES | NO| |
| 706103 | zoster vaccine recombinant | 187 | CVX | NO | YES | NO| |
| Outcome Id | Outcome Name |
|---|---|
| 438945 | Accidental poisoning by benzodiazepine-based tranquilizer |
| 434455 | Acquired claw toes |
| 316211 | Acquired spondylolisthesis |
| 201612 | Alcoholic liver damage |
| 438730 | Alkalosis |
| 441258 | Anemia in neoplastic disease |
| 432513 | Animal bite wound |
| 4171556 | Ankle ulcer |
| 4098292 | Antiphospholipid syndrome |
| 77650 | Aseptic necrosis of bone |
| 4239873 | Benign neoplasm of ciliary body |
| 23731 | Benign neoplasm of larynx |
| 199764 | Benign neoplasm of ovary |
| 195500 | Benign neoplasm of uterus |
| 4145627 | Biliary calculus |
| 4108471 | Burn of digit of hand |
| 75121 | Burn of lower leg |
| 4284982 | Calculus of bile duct without obstruction |
| 434327 | Cannabis abuse |
| 78497 | Cellulitis and abscess of toe |
| 4001454 | Cervical spine ankylosis |
| 4068241 | Chronic instability of knee |
| 195596 | Chronic pancreatitis |
| 4206338 | Chronic salpingitis |
| 4058397 | Claustrophobia |
| 74816 | Contusion of toe |
| 73302 | Curvature of spine |
| 4151134 | Cyst of pancreas |
| 77638 | Displacement of intervertebral disc without myelopathy |
| 195864 | Diverticulum of bladder |
| 201346 | Edema of penis |
| 200461 | Endometriosis of uterus |
| 377877 | Esotropia |
| 193530 | Follicular cyst of ovary |
| 4094822 | Foreign body in respiratory tract |
| 443421 | Gallbladder and bile duct calculi |
| 4299408 | Gouty tophus |
| 135215 | Hashimoto thyroiditis |
| 442190 | Hemorrhage of colon |
| 43020475 | High risk heterosexual behavior |
| 194149 | Hirschsprung’s disease |
| 443204 | Human ehrlichiosis |
| 4226238 | Hyperosmolar coma due to diabetes mellitus |
| 4032787 | Hyperosmolarity |
| 197032 | Hyperplasia of prostate |
| 140362 | Hypoparathyroidism |
| 435371 | Hypothermia |
| 138690 | Infestation by Pediculus |
| 4152376 | Intentional self poisoning |
| 192953 | Intestinal adhesions with obstruction |
| 196347 | Intestinal parasitism |
| 137977 | Jaundice |
| 317510 | Leukemia |
| 765053 | Lump in right breast |
| 378165 | Nystagmus |
| 434085 | Obstruction of duodenum |
| 4147016 | Open wound of buttock |
| 4129404 | Open wound of upper arm |
| 438120 | Opioid dependence |
| 75924 | Osteodystrophy |
| 432594 | Osteomalacia |
| 30365 | Panhypopituitarism |
| 4108371 | Peripheral gangrene |
| 440367 | Plasmacytosis |
| 439233 | Poisoning by antidiabetic agent |
| 442149 | Poisoning by bee sting |
| 4314086 | Poisoning due to sting of ant |
| 4147660 | Postural kyphosis |
| 434319 | Premature ejaculation |
| 199754 | Primary malignant neoplasm of pancreas |
| 4311499 | Primary malignant neoplasm of respiratory tract |
| 436635 | Primary malignant neoplasm of sigmoid colon |
| 196044 | Primary malignant neoplasm of stomach |
| 433716 | Primary malignant neoplasm of testis |
| 133424 | Primary malignant neoplasm of thyroid gland |
| 194997 | Prostatitis |
| 80286 | Prosthetic joint loosening |
| 443274 | Psychostimulant dependence |
| 314962 | Raynaud’s disease |
| 37018294 | Residual osteitis |
| 4288241 | Salmonella enterica subspecies arizonae infection |
| 45757269 | Sclerosing mesenteritis |
| 74722 | Secondary localized osteoarthrosis of pelvic region |
| 200348 | Secondary malignant neoplasm of large intestine |
| 43020446 | Sedative withdrawal |
| 74194 | Sprain of spinal ligament |
| 4194207 | Tailor’s bunion |
| 193521 | Tropical sprue |
| 40482801 | Type II diabetes mellitus uncontrolled |
| 74719 | Ulcer of foot |
| 196625 | Viral hepatitis A without hepatic coma |
| 197494 | Viral hepatitis C |
| 4284533 | Vitamin D-dependent rickets |
People may enter the cohort when observing any of the following:
Restrict entry events to having at least 1 visit occurrence of ‘Inpatient or Inpatient/ER visit,’ starting anytime on or before cohort entry start date and ending between 0 days before and all days after cohort entry start date.
Entry events having no condition occurrences of ‘Guillian-Barre syndrome,’ starting in the 365 days prior to cohort entry start date; allow events outside observation period; having at least 1 visit occurrence of ‘Inpatient or Inpatient/ER visit,’ starting anytime on or before ‘Guillian-Barre syndrome’ start date and ending between 0 days before and all days after ‘Guillian-Barre syndrome’ start date.
The cohort end date will be offset from index event’s start date plus 1 day.
Entry events will be combined into cohort eras if they are within 0 days of each other.
| Concept ID | Concept Name | Code | Vocabulary | Excluded | Descendants | Mapped |
|---|---|---|---|---|---|---|
| 374925 | Acute infective polyneuritis | 129131007 | SNOMED | NO | YES | NO| |
| 4164770 | Guillain-Barre syndrome | 40956001 | SNOMED | NO | YES | NO| |
| 4070552 | Fisher’s syndrome | 1767005 | SNOMED | NO | YES | NO| |
| Concept ID | Concept Name | Code | Vocabulary | Excluded | Descendants | Mapped |
|---|---|---|---|---|---|---|
| 262 | Emergency Room and Inpatient Visit | ERIP | Visit | NO | YES | NO| |
| 9201 | Inpatient Visit | IP | Visit | NO | YES | NO| |