Download
1 / 15
150 likes | 170 Views
ROLE OF ECONOMIC MODELLING IN A COST-EFFECTIVENESS ANALYSIS. Evaluating the cost-effectiveness of antimalarials in South Africa Charlotte Muheki Zikusooka. INTRODUCTION.
E N D
ROLE OF ECONOMIC MODELLING IN A COST-EFFECTIVENESS ANALYSIS Evaluating the cost-effectiveness of antimalarials in South Africa Charlotte Muheki Zikusooka
INTRODUCTION • In a world with limited resources, it is necessary to have methods of determining the best way to allocate those resources among alternative uses. • Definition: Economic Evaluation is a technique that was developed by economists to assist decision-making when choices have to be made between several courses of action. • There are 4 economic evaluation techniques; each one involves the systematic identification, measurement and valuation of costs and consequences / benefits/ outcomes of the alternatives under review.
BACKGROUND – SOUTH AFRICA • Malaria in South Africa: in 3 out of 9 provinces • Development of resistance to antimalarials; to Chloroquine (CQ)& Sulphadoxine-pyremethamine (SP) • Sky-rocketing malaria cases as a result. • SP adult dose cost $0.10 compared to AL adult dose cost $2.50 (at that time) • Policy makers: need evidence-based information showing the effectiveness, costs and cost-effectiveness of a range of alternatives, in order to make choices. • Traditional economic evaluation approaches become inadequate in situations where the measurement of the impact of an intervention is methodologically complex.
BACKGROUND • Extended response to the malaria problem, including: improved vector control (using new insecticide for indoor residual spraying) and improved management of malaria cases (effective antimalarial) and IRS in neighboring districts in Mozambique • Dramatic impact on malaria transmission & cases • To which intervention do attribute the positive impact? • How do conduct a cost-effectiveness analysis in this complex situation? • A range of methods were used, but in this presentation we focus on modelling, using the decision-tree model
Economic Modelling • Modelling: a way of representing the complexity of the real world in a more simple and comprehensible form • In economic evaluation, modelling is “a systematic quantitative approach for assessing the relative value of one or more decision options” • The use of models in cost-effectiveness studies has been increasing over the years despite the concerns and debates • Decision-analytic models used in economic evaluation include, decision trees, Markov models, Monte Carlo (stochastic) simulations, and Discrete Event simulations. • A decision tree “graphically depicts components of a decision problem and relates actions to consequences” • Markov model is a decision-analytic model that involves a Markov process, i.e. is a modelling technique derived from matrix algebra, which describes the transitions a cohort of patients make among a number of health states during the series of short or long cycles
DEAD WELL Uncomplicated malaria (S) Uncomplicated malaria (R) Severe malaria MARKOV Model - illustration
COST-EFFECTIVES OF ANTIMALARIALS IN S. AFR • Comparing SP and ACTs in 2 provinces (KZN & MPM) • A decision tree was used to evaluate and compare the relative impact on costs and health outcomes associated with both uncomplicated and severe malaria • SP monotherapy & artemether-lumefantrine (in KZN) • SP monotherapy & Artesunate + SP (in MPUM) • Antimalarial effectiveness: • KZN: SP (12%) AL (99%) • MPUM: SP (95%); AS+SP (99%)
RESULTS • ACTs were clearly more cost-effective relative to SP monotherapy, in both KZN and MPUM. • Sensitivity analyses of the decision tree model consistently confirm that ACTs more cost-effective relative to SP monotherapy even when the values of different variables are varied over a wide range of values. • C/E ratios were found sensitive to changes in some of the variables. • C/E ratio for the SP option is sensitive to changes in the values of pHosp_SP (i.e. the probability that a patient who has failed to get cured with SP (first time) will seek care at a hospital and will be hospitalised to get second line treatment). • The lower the value of pHosp_SP, the lower the C/E ratio and vice versa for the SP option.
KEY FINDINGS AND LESSONS • Despite being relatively more expensive, ACTs can be cost-effective. • ACTs were not only more cost-effective than SP monotherapy, but also resulted in substantial cost savings in the Kwazulu Natal and Mpumalanga contexts. • The finding that ACTs are cost-saving in Mpumalanga is particularly important in two ways: • SP monotherapy was still highly effective in Mpumalanga (90%) unlike in Kwazulu Natal where its effectiveness had declined to only 12%; • There had been no changes in local vector control programme in Mpumalanga (as was the case in Kwazulu Natal); • Similar findings have been reported by others: Sudre, P., J.G. Breman, D. McFarland, and J.P. Koplan (1992); Wilkins, J.J., N. Valentine, and K. Barnes (2002); Institute of Medicine of the National Academies (2004); Coleman, P.G., C. Morel, S. Shillcutt, C.A. Goodman, and A.J. Mills (2004)
ROLE OF MODELLING IN ECONOMICS • Health outcomes are normally achievable in the very long run • analysts need to find a method for extending the evidence of effectiveness to cover the time interval for which effectiveness could logically apply • THUS, the need to use modelling in economic evaluation remains of critical importance • HOWEVER: Data needs for economic models are normally extensive and such data are not easily available, resulting in the need to rely on assumptions • ALSO: Limitations with how to present more realistic disease epidemiology and health system challenges in economic models • POLICY MAKERS: the ‘black-box’ effect of some models makes their results less acceptable by policy makers
FINAL REMARKS • Economist, epidemiologist, public health specialists not trained in economic/mathematical models. • Ability to use modelling techniques in analyses either requires prior training on these techniques (which is not always feasible) or working with specialists in modelling (who usually do not have any training in diseases). • Results of models are not very much trusted and accepted by policy-makers and other. • Use of models in disease modelling and economic evaluations is useful and cannot be completely avoided. • How can modelling techniques be improved and made more accessible to non-mathematicians?
