Vaccines have been one of the most successful public health interventions to date. They are, however, pharmaceutical products that carry risks. Effective analyses of post-vaccination adverse events (AEs) is vital to assuring the safety of vaccines, a key public health intervention for reducing the frequency of vaccine- preventable illnesses. The CDC/FDA Vaccine Adverse Event Reporting System (VAERS) contains up to 30,000 reports per year over the past 25 years. VAERS reports include both structured data (e.g., vaccination date, first onset date, age, and gender) and unstructured narratives that often provide detailed clinical information about the clinical events and the temporal relationship of the series of event occurrences post vaccination. The structured data only provide one onsite date whereas temporal information of the sequence of events post vaccination is contained in the unstructured narratives. Current status ?While structured data in the VAERS are widely used, the narratives are generally ignored because of the challenges inherent in working with unstructured data. Without these narratives, potentially valuable information is lost. Goals - In response to the FOA, PA-15-312, this proposed project focuses on the specific objective on ?creation/evaluation of statistical methodologies for analyzing data on vaccine safety, including data available from existing data sources such as passive reporting systems or healthcare databases?. We propose to develop a novel framework to extract and accurately interpret the temporal information contained in the narratives through informatics approaches, and to develop prediction models for risk of severe AEs. Specifically, built upon the state-of-art ontology and natural language processing technologies, we will develop and validate a Temporal Information Modeling, Extraction and Reasoning system for Vaccine data (TIMER-V), which will automatically extract post-vaccination events and their temporal relationships from VAERS reports, semantically infer temporal relations, and integrate the exacted unstructured data with the structured data. Furthermore, we will provide and maintain a publicly available data access interface to query the new integrated data repository, which will facilitate vaccine safety research, casual inference, and other temporal related discovery. We will also develop and validate models to predict severe AEs using the co-occurrence or temporal patterns of the series of AEs post vaccination. To the best of our knowledge, this is the first attempt to make use of the unstructured narratives in the VAERS reports to facilitate the temporal related discovery to a broad community of investigators in pharmacology, pharmacoepidemiology, vaccine safety research, among others.

Public Health Relevance

Effective analyses of post-vaccination adverse events (AEs) is vital to assuring the safety of vaccines, a key public health intervention for reducing the frequency of vaccine-preventable illnesses. In response to the FOA, PA-15-312, this proposed project focuses on the specific objective on ?creation/evaluation of statistical methodologies for analyzing data on vaccine safety, including data available from existing data sources such as passive reporting systems or healthcare databases?. Currently the FDA/CDC Vaccine Adverse Event Reporting System (VAERS) only includes one onsite date in its database. The textual narratives in the reports are generally ignored primarily due to their unstructured nature. These narratives, however, contain more detailed information about the series of events that happened after vaccination, which could be valuable for more informed clinical studies. We propose to develop a novel framework to extract and accurately interpret the temporal information contained in the narratives through informatics approaches, and to develop prediction models for risk of severe AEs. Our new methods, their applications to VAERS database, and their dissemination will facilitate the entire research network for pursuing temporal related discovery with high methodological rigor.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI130460-05
Application #
10097968
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Brown, Liliana L
Project Start
2017-02-01
Project End
2022-01-31
Budget Start
2021-02-01
Budget End
2022-01-31
Support Year
5
Fiscal Year
2021
Total Cost
Indirect Cost
Name
University of Texas Health Science Center Houston
Department
Type
Sch Allied Health Professions
DUNS #
800771594
City
Houston
State
TX
Country
United States
Zip Code
77030
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