Drug Safety

, Volume 36, Issue 2, pp 119–134 | Cite as

An Evaluation of the THIN Database in the OMOP Common Data Model for Active Drug Safety Surveillance

  • Xiaofeng Zhou
  • Sundaresan Murugesan
  • Harshvinder Bhullar
  • Qing Liu
  • Bing Cai
  • Chuck Wentworth
  • Andrew Bate
Original Research Article

Abstract

Background

There has been increased interest in using multiple observational databases to understand the safety profile of medical products during the postmarketing period. However, it is challenging to perform analyses across these heterogeneous data sources. The Observational Medical Outcome Partnership (OMOP) provides a Common Data Model (CDM) for organizing and standardizing databases. OMOP’s work with the CDM has primarily focused on US databases. As a participant in the OMOP Extended Consortium, we implemented the OMOP CDM on the UK Electronic Healthcare Record database—The Health Improvement Network (THIN).

Objective

The aim of the study was to evaluate the implementation of the THIN database in the OMOP CDM and explore its use for active drug safety surveillance.

Methods

Following the OMOP CDM specification, the raw THIN database was mapped into a CDM THIN database. Ten Drugs of Interest (DOI) and nine Health Outcomes of Interest (HOI), defined and focused by the OMOP, were created using the CDM THIN database. Quantitative comparison of raw THIN to CDM THIN was performed by execution and analysis of OMOP standardized reports and additional analyses. The practical value of CDM THIN for drug safety and pharmacoepidemiological research was assessed by implementing three analysis methods: Proportional Reporting Ratio (PRR), Univariate Self-Case Control Series (USCCS) and High-Dimensional Propensity Score (HDPS). A published study using raw THIN data was selected to examine the external validity of CDM THIN.

Results

Overall demographic characteristics were the same in both databases. Mapping medical and drug codes into the OMOP terminology dictionary was incomplete: 25 % medical codes and 55 % drug codes in raw THIN were not listed in the OMOP terminology dictionary, representing 6 % condition occurrence counts, 4 % procedure occurrence counts and 7 % drug exposure counts in raw THIN. Seven DOIs had <0.3 % and three DOIs had 1 % of unmapped drug exposure counts; each HOI had at least one definition with no or minimal (≤0.2 %) issues with unmapped condition occurrence counts, except for the upper gastrointestinal (UGI) ulcer hospitalization cohort. The application of PRR, USCCS and HDPS found, respectively, a sensitivity of 67, 78 and 50 %, and a specificity of 68, 59 and 76 %, suggesting that safety issues defined as known by the OMOP could be identified in CDM THIN, with imperfect performance. Similar PRR scores were produced using both CDM THIN and raw THIN, while the execution time was twice as fast on CDM THIN. There was close replication of demographic distribution, death rate and prescription pattern and trend in the published study population and the cohort of CDM THIN.

Conclusions

This research demonstrated that information loss due to incomplete mapping of medical and drug codes as well as data structure in the current CDM THIN limits its use for all possible epidemiological evaluation studies. Current HOIs and DOIs predefined by the OMOP were constructed with minimal loss of information and can be used for active surveillance methodological research. The OMOP CDM THIN can be a valuable tool for multiple aspects of pharmacoepidemiological research when the unique features of UK Electronic Health Records are incorporated in the OMOP library.

Supplementary material

40264_2012_9_MOESM1_ESM.pdf (400 kb)
Supplementary material 1 (PDF 400 kb)

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Copyright information

© Springer International Publishing Switzerland 2012

Authors and Affiliations

  • Xiaofeng Zhou
    • 1
  • Sundaresan Murugesan
    • 1
  • Harshvinder Bhullar
    • 2
  • Qing Liu
    • 1
  • Bing Cai
    • 1
  • Chuck Wentworth
    • 3
  • Andrew Bate
    • 1
    • 4
    • 5
  1. 1.Epidemiology, Worldwide Safety Strategy, PfizerNew YorkUSA
  2. 2.Cegedim Strategic Data Medical Research LtdLondonUK
  3. 3.Analytic Consulting SolutionsWakefieldUSA
  4. 4.School of Information Systems, Computing and MathematicsBrunel UniversityLondonUK
  5. 5.Division of Clinical PharmacologyNYU School of MedicineNew YorkUSA

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