Assessing heterogeneity of electronic health-care databases: A case study of background incidence rates of venous thromboembolism

Abstract

Purpose: Heterogeneous results from multi-database studies have been observed, for example, in the context of generating background incidence rates (IRs) for adverse events of special interest for SARS-CoV-2 vaccines. In this study, we aimed to explore different between-database sources of heterogeneity influencing the estimated background IR of venous thromboembolism (VTE). Methods: Through forest plots and random-effects models, we performed a qualitative and quantitative assessment of heterogeneity of VTE background IR derived from 11 databases from 6 European countries, using age and gender stratified background IR for the years 2017–2019 estimated in two studies. Sensitivity analyses were performed to assess the impact of selection criteria on the variability of the reported IR. Results: A total of 54 257 284 subjects were included in this study. Age–gender pooled VTE IR varied from 5 to 421/100 000 person-years and IR increased with increasing age for both genders. Wide confidence intervals (CIs) demonstrated considerable within-data-source heterogeneity. Selecting databases with similar characteristics had only a minor impact on the variability as shown in forest plots and the magnitude of the I2 statistic, which remained large. Solely including databases with primary care and hospital data resulted in a noticeable decrease in heterogeneity. Conclusions: Large variability in IR between data sources and within age group and gender strata warrants the need for stratification and limits the feasibility of a meaningful pooled estimate. A more detailed knowledge of the data characteristics, operationalisation of case definitions and cohort population might support an informed choice of the adequate databases to calculate reliable estimates.