Estimation of Seasonal Quality-Adjusted Life-Year Using Seemingly Unrelated Regression Equation Models With an Application to Orthopedic Data.

OBJECTIVES: The technological advancement in the field of orthopedics has initiated better healthcare service that equates to the need of cost-effectiveness approach. We propose a model for estimating the simultaneous effect of health and cost involved in an orthopedic surgery implants by using seemingly unrelated regression equations models. METHODS: The simultaneous equations represent a relationship between the health status of a group of individuals and their expenditures related to the cost of surgical procedure/treatment undertaken in an orthopedic department of a hospital. We define model specification, estimation, and statistical tests in simultaneous equation models. This is further used to estimate the utility function that indeed helps in the computation of quality-adjusted life-year (QALY) values. RESULTS: Using the seemingly unrelated regression equation models for the seasonal data in 2018 and 2019, we have obtained the seasonal QALY values. Furthermore, the measurement of seasonal changes in QALY values is done by using a method of simple averages. CONCLUSIONS: We analyze the health conditions in orthopedics by the formation of health and expenditure relationship for the inpatients and outpatients undertaking a treatment. A framework has been setup for computing quality of life-year values by including the direct and the indirect costs. The patient-reported outcome measures are also useful in detecting the change in disease states and important difference in minimal clinical changes that further adds value to the computation of quality of life.