The Effects of Diagnostic Definitions in Claims Data on Healthcare Cost Estimates: Evidence from a Large-Scale Panel Data Analysis of Diabetes Care in Japan

Background: Inaccurate estimates of diabetes-related healthcare costs can undermine the efficiency of resource allocation for diabetes care. The quantification of these costs using claims data may be affected by the method for defining diagnoses. Objectives: The aims were to use panel data analysis to estimate diabetes-related healthcare costs and to comparatively evaluate the effects of diagnostic definitions on cost estimates. Research design: Monthly panel data analysis of Japanese claims data. Subjects: The study included a maximum of 141,673 patients with type 2 diabetes who received treatment between 2005 and 2013. Measures: Additional healthcare costs associated with diabetes and diabetes-related complications were estimated for various diagnostic definition methods using fixed-effects panel data regression models. Results: The average follow-up period per patient ranged from 49.4 to 52.3 months. The number of patients identified as having type 2 diabetes varied widely among the diagnostic definition methods, ranging from 14,743 patients to 141,673 patients. The fixed-effects models showed that the additional costs per patient per month associated with diabetes ranged from US$180 [95 % confidence interval (CI) 178–181] to US$223 (95 % CI 221–224). When the diagnostic definition excluded rule-out diagnoses, the diabetes-related complications associated with higher additional healthcare costs were ischemic heart disease with surgery (US$13,595; 95 % CI 13,568–13,622), neuropathy/extremity disease with surgery (US$4594; 95 % CI 3979–5208), and diabetic nephropathy with dialysis (US$3689; 95 % CI 3667–3711). Conclusions: Diabetes-related healthcare costs are sensitive to diagnostic definition methods. Determining appropriate diagnostic definitions can further advance healthcare cost research for diabetes and its applications in healthcare policies.