International Registries: U.K. National Joint Registry, Nordic Registries, and Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR).

National-level joint arthroplasty registries were among the first large orthopaedic surgery databases and represent some of the longest-running and most influential big databases in our profession. Nordic registries were among the first registries and were followed by excellent registries in the United Kingdom and Australia. In this article, we describe each of these registries and highlight the data elements collected, the data points that can be obtained by linking the national arthroplasty registries to other national registries or databases, the completeness of data, and the strengths and weaknesses of each database. Each of these registries publishes an annual report that is available online, and each also can do more detailed analysis of certain aspects of its data for special studies.When evaluating and interpreting data from national joint registries, the user should be aware that, despite the power of huge numbers, there remain numerous limitations to the observational data. Strong selection biases exist with regard to which patients are chosen for which procedure. Surgeons of different skill levels may use one technique or implant differentially compared with another. The end points that registries collect differ widely: for example, some report only hip dislocations leading to revision rather than all dislocations. Registries in countries in which there are long wait times for revision surgery may report artificially low revision rates compared with countries in which revision surgery is more easily accessible. Despite these limitations, registries have the merit of reporting the actual results of huge numbers of surgical procedures performed across the spectrum of hospitals, surgeons, and patients, making the data extremely powerful for identifying trends, identifying early signs of problems related to certain implants or implant classes, and identifying associations between variables that can be further studied to determine if the effect is causal.