PRM253 - An Epidemiologic Modeling Application To Pharmacoeconomics for Improved Health Care Planning

Abstract

PRM253 An EPidEMiologic ModEling APPlicAtion to PhARMAcoEconoMics foR iMPRovEd hEAlth cARE PlAnning Cid Ruzafa J., Cox A., Merinopoulou E., Baggaley R., Leighton P., Desai K. Evidera, London, UK Epidemiologic and pharmacoeconomic models differ in terms of populations considered, mathematical techniques used, and questions addressed. A typical pharmacoeconomic model assesses chronic or acute conditions, uses Markov techniques, and considers a closed patient group receiving a defined therapy to assess incremental costs needed to achieve gains in quality adjusted life years. A typical epidemiologic model assesses vaccination or public health interventions for infectious disease using differential equations and considers open populations representing communities to estimate prevalence or numbers of disease cases averted. The manner of conducting sensitivity analyses also differs. In oncology, in which multiple lines of treatment are available, the epidemiologic approach has application to estimate the patient point prevalence or the number of patients who can start on a line of therapy over a certain time period, when this cannot be determined from clinical trials or registers (which usually focus on single lines of therapy or limited types of patients that are not representative of the overall patient population). The approach consists of conceptualizing an open population that incorporates incidence of the condition and the transition of patients through various lines of treatment until death, and uses systems of difference/differential equations. Parameterization is challenging if there are several prognostic factors to describe the patient population, multiple or complex treatment pathways, and a wide range of variability. Parameters are obtained from the published literature, analyses of database information, and/ or surveys to experts in the field. Steady state solutions of the model equations estimate point and period prevalence. This approach is applicable to gastrointestinal stromal tumours and multiple myeloma. Resulting estimates are important for budget impact analysis and health care services planning by reducing uncertainty associated with identifying the patient numbers eligible for a given treatment. Epidemiologic modelling permits a framework to estimate disease prevalence that is little used in pharmacoeconomics.