Estimating the completeness of physician billing claims: an application of three-source capture-recapture methods
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Date
2019
Authors
Monkman, Stephanie
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Abstract
Background: Physician billing claims data contain information about services provided to patients. Fee-for-service (FFS) and non-fee-for-service (NFFS) physicians both submit claims; however, physician billing claims data may not comprehensively capture patient contacts from NFFS physicians who do not submit parallel claims (i.e., shadow bill). Capture-recapture (CR) methods, which were first developed in ecology research to estimate animal population size, have been proposed to estimate the number of missed claims. Our objective was to use three-source CR methods to estimate the completeness of physician billing claims data in Manitoba.
Methods: Log-linear regression (LLR) and multinomial logistic regression (MLR) models for three-source CR methods were investigated. Using computer simulation, the LLR and MLR models were compared using percent bias (PB) and 95% confidence interval (CI) coverage for correctly specified and misspecified models in the presence of heterogeneity of capture probability and data source dependence. The methods were applied to Manitoba’s administrative health data to estimate the number of cancer cases diagnosed by FFS and NFFS physicians. The Manitoba Cancer Registry was used to validate the estimates.
Results: Both the LLR and MLR models had low PB and acceptable 95% CI coverage for the correctly specified model under all simulation scenarios. However, the MLR model had less bias and better coverage when there was dependence among sources and covariates. The numeric example, the study cohort was comprised of 3,331 individuals. A total of 1,747 (52.4%) individuals were seen by a FFS physician and 1,584 (47.6%) individuals seen by a NFFS physician. The best-fit model for the LLR model estimated FFS physicians missed 819 (31.9%) cases while the model estimated NFFS physicians missed 1,086 (40.4%) cases. The best-fit MLR model estimated FFS physician missed 798 (31.5%) cases and estimated NFFS physicians missed 976 (39.7%) cases.
Conclusion: There remains uncertainty as to whether physician billing claims data is complete due to missed capture of claims from NFFS physicians, which can have consequences for disease surveillance. This research demonstrated the feasibility of using three-source CR methods and observed that NFFS physicians were estimated to miss more cancer cases than FFS physicians in administrative data.
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Keywords
Capture-recapture, Disease surveillance, Completeness, Physician billing claims
Citation
Vancouver