Methodological issues for using a common data model (CDM) of COVID-19 vaccine uptake and important adverse events of interest (AEIs): the Data and Connectivity COVID-19 Vaccines Pharmacovigilance (DaC-VaP) United Kingdom feasibility study. (Preprint)
Authors: Delanerolle Gayathri , Williams Robert , Stipancic Ana , Byford Rachel , Forbes Anna , Anand Sneha , Bradley Declan , Tsang Ruby , Murphy Siobhán , Akbari Ashley , Bedston Stuart , Lyons Ronan , Owen Rhiannon , Beggs Jillian , Chuter Antony , Balharry Domnique , Joy Mark , Sheikh Aziz , Hobbs F.D. Richard , de Lusignan Simon 
Published: March 2022

BACKGROUND

The Data and Connectivity COVID-19 Vaccines Pharmacovigilance (DaC-VaP) UK-wide collaboration was created to monitor vaccine uptake and effectiveness and provide pharmacovigilance using routine clinical and administrative data. To monitor these, pooled analyses may be needed. However, variation in terminologies present a barrier as, England uses the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), while the rest of the UK uses the Readv2 terminology in primary care. The availability of data sources is not uniform across the UK.

OBJECTIVE

To use the concept mappings in the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to identify common concepts recorded and to report these in a repeated cross-sectional study. We plan to do this for vaccine coverage and two adverse events of interest (AEIs), cerebral venous sinus thrombosis (CVST) and anaphylaxis. We identified concept mappings to SNOMED CT, Readv2, the World Health Organisation’s International Classification of Disease version 10 (ICD-10) terminology and the UK’s Dictionary of Medicines and Devices (dm+d).

METHODS

Exposures and outcomes of interest to DaC-VaP for pharmacovigilance studies were selected. Mappings of these variables to different terminologies used across the UK devolved nations health services were identified from the Observational Health Data Sciences and Informatics (OHDSI) Athena online browser. Lead analysts from each nation then confirm or add to the mappings identified. These mappings will then be used to report AEIs in a common format. We will report rates for windows of 0-2 days and 3-28 days post-vaccine every 28 days.

RESULTS

We list the mappings between Read v2, SNOMED CT, ICD-10 and dm+d. For vaccine exposure, we found clear mapping from OMOP to our clinical terminologies, though dm+d had codes not listed by OMOP at the time of searching. We found a list of CVST and anaphylaxis codes. For CVST we had to use a broader cerebral venous thrombosis conceptual approach to include Read v2. We identified 56 SNOMED clinical terms from which we selected 47, and 15 Read v2 codes. For anaphylaxis, our refined search identified 60 SNOMED codes and 9 from Read v2, from which we selected 10 and 4 clinical terms to include in our repeated cross-sectional studies.

CONCLUSIONS

This approach enables the use of mappings to different terminologies within the OMOP CDM without the need to catalogue an entire database. However, Read v2 has less granular concepts than some terminologies such as SNOMED CT. Additionally, the OMOP CDM cannot compensate for limitations in the clinical coding system. Neither Read v2 nor ICD-10 are sufficiently granular to enable CVST to be specifically flagged. Hence, any pooled analysis will have to be at the less specific level of cerebrovascular venous thrombosis. Overall the mappings within this CDM are useful, and our method could be used for rapid collaborations where there are only a limited number of concepts to pool.