Developing model-based algorithms to identify screening colonoscopies using administrative health databases

BACKGROUND: Algorithms to identify screening colonoscopies in administrative databases would be useful for monitoring colorectal cancer (CRC) screening uptake, tracking health resource utilization, and quality assurance. Previously developed algorithms based on expert opinion were insufficiently accurate. The purpose of this study was to develop and evaluate the accuracy of model-based algorithms to identify screening colonoscopies in health administrative databases. METHODS: Patients aged 50-75 were recruited from endoscopy units in Montreal, Quebec, and Calgary, Alberta. Physician billing records and hospitalization data were obtained for each patient from the provincial administrative health databases. Indication for colonoscopy was derived using Bayesian latent class analysis informed by endoscopist and patient questionnaire responses. Two modeling methods were used to fit the data, multivariate logistic regression and recursive partitioning. The accuracies of these models were assessed. RESULTS: 689 patients from Montreal and 541 from Calgary participated (January to March 2007). The latent class model identified 554 screening exams. Multivariate logistic regression predictions yielded an area under the curve of 0.786. Recursive partitioning using the latent outcome had sensitivity and specificity of 84.5% (95% CI: 81.5-87.5) and 63.3% (95% CI: 59.7-67.0), respectively. CONCLUSIONS: Model-based algorithms using administrative data failed to identify screening colonoscopies with sufficient accuracy. Nevertheless, the approach of constructing a latent reference standard against which model-based algorithms were evaluated may be useful for validating administrative data in other contexts where there lacks a gold standard.