A prediction model to estimate completeness of electronic physician claims databases

Electronic physician claims databases are widely used for chronic disease research and surveillance, but quality of the data may vary with a number of physician characteristics, including payment method. The objectives were to develop a prediction model for the number of prevalent diabetes cases in fee-for-service (FFS) electronic physician claims databases and apply it to estimate cases among non-FFS (NFFS) physicians, for whom claims data are often incomplete.

A retrospective observational cohort design was adopted.

Data from the Canadian province of Newfoundland and Labrador were used to construct the prediction model and data from the province of Manitoba were used to externally validate the model.

A cohort of diagnosed diabetes cases was ascertained from physician claims, insured resident registry and hospitalisation records. A cohort of FFS physicians who were responsible for the diagnosis was ascertained from physician claims and registry data.

A generalised linear model with a γ distribution was used to model the number of diabetes cases per FFS physician as a function of physician characteristics. The expected number of diabetes cases per NFFS physician was estimated.

The diabetes case cohort consisted of 31,714 individuals; the mean cases per FFS physician was 75.5 (median = 49.0). Sex and years since specialty licensure were significantly associated (p < 0.05) with the number of cases per physician. Applying the prediction model to NFFS physician registry data resulted in an estimate of 18,546 cases; only 411 were observed in claims data. The model demonstrated face validity in an independent data set.

Comparing observed and predicted disease cases is a useful and generalisable approach to assess the quality of electronic databases for population-based research and surveillance.