Helping everyone do better: a call for validation studies of routinely recorded health data

Identifying multiple myeloma patients using data from the French health insurance databases

“…Validation of case identification algorithms represents an important issue, as demonstrated by recent calls, [6] and also by several initiatives from Mini Sentinel and OMOP (Observational Medical Outcomes Partnership) in United States or EU-ADR in Europe. [24] An important series of systematic review on methods for validating a wide range of disease, including lymphoma for instance, [25] has been published since 2012.…”

“…[4–6] As algorithms’ performance could be in many ways database-specific, there was a need to implement this validation study in French health insurance databases. A lot of previous validations were made with the ICD-9 in databases in the United States and validation studies are lacking for European and Nordic databases, in which ICD-10 is more frequent.…”

“…A lot of previous validations were made with the ICD-9 in databases in the United States and validation studies are lacking for European and Nordic databases, in which ICD-10 is more frequent. [6] Although several studies have measured the validity of cancer cases ascertainment in France, [7–9] none focused on hematological diseases. Then, the validity of identification of multiple myeloma cases through these databases has not been previously established.…”

Lessons learned on the design and the conduct of Post‐Authorization Safety Studies: review of 3 years of PRAC oversight

“…An ongoing EMA initiative will aim to increase visibility of existing data sources and support their use in a cross‐border setting for both public health and research needs. While the use of databases is increasingly popular, validation studies have been encouraged to ensure validity of study results . In addition, since PASS are often multicountry studies, additional considerations on how to combine different databases are important and have been subject to intense research over the past years .…”

Clinical epidemiology in the era of big data: new opportunities, familiar challenges

“…At the same time, in multinational database studies, validity concerns are increased proportional to the number of the databases, with the need of several valid operational definitions for the same clinical characteristic or event, to avoid propagating a systematic error on a large scale 53,56. Validation of algorithms in large secondary databases remains imperative for valid inference 15,56,57. The NARA collaboration has contributed to improvement of data validity in all four participating countries through regular meetings, where differences in registration practice have been discussed.…”