Administrative-based case-finding algorithms to evaluate health inequities: a methodological framework and an application in progressive pulmonary fibrosis

The ability to study disease at the population level is required to understand the causes for delays in diagnosing a disease. Administrative data are collected when patients have an encounter with the healthcare system and are commonly used to identify individuals with disease. However, there is no way to specifically identify patients whose disease is getting worse over time and would benefit the most from treatment. My proposed research program will create a framework to develop algorithms that identify patients with worsening disease using administrative data. My research will start with people who have progressive pulmonary fibrosis (lung scarring conditions) and then extend to other common lung diseases. Using these algorithms to identify people with progressive lung diseases, I will then study health inequities that prevent people from receiving treatment. I will use these findings to inform health system and policy changes by working with patients, researchers, clinicians, and policymakers.