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SurVigilance: An Application for Accessing Global Pharmacovigilance Data

Raktim Mukhopadhyay, Marianthi Markatou

TL;DR

SurVigilance tackles the problem of accessing post-marketing pharmacovigilance data from multiple SRS databases by unifying retrieval into a modular, open-source tool. Built in Python/Streamlit, it provides both a GUI and programmatic API to download and store data from seven databases, enabling construction of $I \times J$ contingency tables with entries $n_{ij}$ for disproportionality analyses (e.g., $2 \times 2$ tables). Key contributions include cross-database data retrieval, contingency-table construction workflows, a practical demonstration with DMA and FAERS, and a timing study to characterize retrieval efficiency, all designed to integrate into existing workflows. The work significantly lowers technical barriers for researchers, enabling reproducible pharmacovigilance analyses and streamlined data pipelines across diverse data sources.

Abstract

Even though several publicly accessible pharmacovigilance databases are available, extracting data from them is a technically challenging process. Existing tools typically focus on a single database. We present SurVigilance, an open-source tool that streamlines the process of retrieving safety data from seven major pharmacovigilance databases. SurVigilance provides a graphical user interface as well as functions for programmatic access, thus enabling integration into existing research workflows. SurVigilance utilizes a modular architecture to provide access to the heterogeneous sources. By reducing the technical barriers to accessing safety data, SurVigilance aims to facilitate pharmacovigilance research.

SurVigilance: An Application for Accessing Global Pharmacovigilance Data

TL;DR

SurVigilance tackles the problem of accessing post-marketing pharmacovigilance data from multiple SRS databases by unifying retrieval into a modular, open-source tool. Built in Python/Streamlit, it provides both a GUI and programmatic API to download and store data from seven databases, enabling construction of contingency tables with entries for disproportionality analyses (e.g., tables). Key contributions include cross-database data retrieval, contingency-table construction workflows, a practical demonstration with DMA and FAERS, and a timing study to characterize retrieval efficiency, all designed to integrate into existing workflows. The work significantly lowers technical barriers for researchers, enabling reproducible pharmacovigilance analyses and streamlined data pipelines across diverse data sources.

Abstract

Even though several publicly accessible pharmacovigilance databases are available, extracting data from them is a technically challenging process. Existing tools typically focus on a single database. We present SurVigilance, an open-source tool that streamlines the process of retrieving safety data from seven major pharmacovigilance databases. SurVigilance provides a graphical user interface as well as functions for programmatic access, thus enabling integration into existing research workflows. SurVigilance utilizes a modular architecture to provide access to the heterogeneous sources. By reducing the technical barriers to accessing safety data, SurVigilance aims to facilitate pharmacovigilance research.
Paper Structure (16 sections, 4 figures, 8 tables)

This paper contains 16 sections, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Software description
  3. Web-automation/Scraping
  4. Related Software
  5. Illustrative examples
  6. Programmatic Data Retrieval from the DMA Database
  7. FAERS Data Download and Processing
  8. Data Download from FAERS
  9. Construction of a Contingency Table from Downloaded FAERS Data
  10. Computational time associated with accessing data
  11. Limitations
  12. Impact
  13. Conclusions
  14. Responsible Use
  15. Code for Processing Downloaded FAERS Data
  16. ...and 1 more sections

Figures (4)

  • Figure 1: A simplified overview of the main functionalities of SurVigilance is depicted. SurVigilance provides the graphical interface using the UI class of the ui module. The scraping functions implemented in scrapers sub-module of the ui module can also be accessed separately to perform data collection in a programmatic manner. Additionally, the process of data acquisition from each database is illustrated. The abbreviation DOM stands for Document Object Model, which represents the structure of a webpage.
  • Figure 2: The home page of SurVigilance displaying the available databases. Please note that the home page contains a button at the top which can be used to specify the location where data will be stored.
  • Figure 3: Data download page for FAERS database. After clicking the "List all FAERS years and available quarters" button, all available data files are shown from which a user can select the data to be downloaded.
  • Figure 4: After selecting the data files to be downloaded, the user needs to scroll to the download button at the bottom of the page. Once the download of the selected files is complete, a success message is shown.