Physician-billing claims databases can be used to determine the incidence of fractures in the community. This study tested three algorithms designed to accurately and reliably identify fractures from a physician-billing claims database and concluded that they were useful for identifying all types of fractures, except vertebral, sacral, and coccyx fractures.
To develop and validate algorithms that identify fracture events from a physician-billing claims database (PCDs).
Three algorithms were developed using physician's specialty, diagnostic, and medical service codes used in a PCD from the province of Quebec. Algorithm validity was assessed via calculation of positive predictive values (PPV; via verification of a sample of algorithm-identified cases with hospitalization files) and sensitivities (via cross-referencing respective algorithm-identified fracture cases with a well-characterized fracture cohort).
PPV and sensitivity varied across fracture sites. For most fracture sites, the PPV with algorithm 3 was higher than with algorithms 1 or 2. Except for knee fracture, the PPVs ranged from 0.81 to 0.96. Sensitivities were low at the vertebral, sacral, and coccyx sites (0.40-0.50), but high at all other fracture sites. For 95% of fractures, the fracture site identified by algorithm agreed with the fracture site from patients' medical records. Fracture dates identified by algorithm were within 2 days of the actual fracture date in 88% of fracture cases. Among cases identified by algorithm 3 to have had an open reduction (N?=?461), 95% underwent surgery according to their respective medical charts.
Algorithms using PCDs are accurate and reliable for identifying incident fractures associated with osteoporosis-related fracture sites. The identification of these fractures in the community is important for helping to estimate the burden associated with osteoporosis and the utility of programs designed to reduce the rates of fragility fracture.