Big data-based epidemiology of uveitis and related intraocular inflammation

Abstract

Large administrative health databases, nationwide surveys, and the widespread adoption of electronic medical records have led to an increasing availability of health-related data on ocular inflammatory disease, allowing us to elucidate the real-world epidemiology of uveitis and examine patient and systems-level risk factors for the incidence of specific etiologies of uveitis and its complications. Despite the many advantages to using big databases, there are also limitations that clinicians must be aware of when making conclusions and extrapolating to the general population, such as the lack of standardization of nomenclature and coding. As the availability of even more robust datasets increases, clinicians and scientists should be prepared to leverage these tools to improve our understanding of disease pathophysiology and our ability to manage patients with ocular inflammatory disease.