In the absence of a national mortality dataset that is recent, accessible and linkable, Flatiron has developed a composite mortality variable, amalgamated from multiple data sources, and benchmarked it to understand its quality. Mortality data are critical for determining health outcomes, but are frequently incomplete when derived from electronic health records. By optimally linking different death datasets, this composite variable is of improved quality and high recency, which makes it suitable for evaluating outcomes in studies that leverage real-world evidence.
Why this matters
For real-world research in potentially fatal diseases, sensitive mortality surveillance is key to the generation of reliable results, namely accurate overall survival estimates. In this study, the Flatiron Health team developed a multi-source composite mortality variable of high sensitivity, thus unlocking the ability to conduct reliable outcome studies in oncology populations.