A simple case definition for osteoporosis case diagnosis is feasible based upon administrative health data. This may facilitate implementation of a population-based osteoporosis surveillance program, providing information that could help to inform and guide screening, prevention, and treatment resources.
Our aim was to construct and validate a simplified algorithm for osteoporosis case ascertainment from administrative databases that would be suitable for disease surveillance.
Multiple classification rules were applied to different sets of hospital diagnosis, physician claims diagnosis, and prescription drug variables from Manitoba, Canada. Algorithms were validated against results from a regional bone mineral density testing program that identified bone mineral density (BMD) measurements in 4,015 women age 50 years and older with at least one BMD test between April 1, 2000 and March 31, 2001.
Sensitivity as high as 93.3% was achieved with 3 years of data. Specificity ranged from 50.8% to 91.4% overall, and from 81.2% to 99.1% for discriminating osteoporotic from normal BMD. Sensitivity and overall accuracy were generally lower for algorithms based on diagnosis codes alone than for algorithms that included osteoporosis prescriptions. In the subgroup without prior osteoporotic fractures or chronic corticosteroid use, one simple algorithm (one hospital diagnosis, physician claims diagnosis, or osteoporosis prescription within 1 year) gave accuracy measures exceeding 90% for discriminating osteoporosis from normal BMD across a wide range of disease prevalence.
A relatively simple case definition for osteoporosis surveillance based upon administrative health data can achieve an acceptable level of sensitivity, specificity, and accuracy. Performance is enhanced when the case definition includes osteoporosis medication use in the formulation.