Using semi-Markov process to model incremental change in HIV staging with cost effect

Abstract

Over the past years, parametric and non-parametric methods have been used in modelling cost and effectiveness according to one studied event or one health state. In this study we used semi-Markov model in which the distributions of sojourn times are explicitly defined. Weibull distribution was chosen and used in modelling the hazard function for each transition. Using a regression model for cost, a cumulative cost function of cost was developed enabling us to determine the estimated mean cost per patient in each state defined in the semi-Markov model. ICER was used for cost effectiveness analysis in comparing two strategies (Patients in DCM and patients not in DCM) of follow up. Using viral load, three states were defined; V L < 200ml, 200ml < V L < 1000ml, V L > 10000ml and an absorbing state death. The mean cost of the patients for each state 1, 2 and 3 was $765, $829 and $1395 respectively. The calculated ICER ratio was $483.8268/life-year-saved. The cost of keeping patients in state 1 (on DCM) was relatively cheaper and efficient compared to the other states.