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analyses can be broken down further to incorporate who bears the costs or benefits of theprogram and hence the net benefits for the individual, Government and so on.Different modelling techniques exist that allow these types of CBA and CEA to be completed. Markov modelling utilises a probability matrix for the transition of a cohort ofindividuals over a defined period of time. Markov models do not have any memory i.e.they do not record whether or not a particular event has occurred for any one individualin the past. As a result, policy makers can have difficulty interpreting the methodologyused. That said, they are a commonly used technique.Discrete event simulation (DES) has greater complexity and a far greater ability tomodel variations in a population. DES uses a random walk methodology (or MonteCarlo simulation) that is based on a probability distribution of certain events occurring.This type of model is able to record the occurrence of an event for a particularindividual, as well as alter probabilities based on the characteristics of a particularindividual or environment.Both of these models use a discrete timeframe in which the model is run (for example, over atheoretical period of 30 years). This is completed by cycling through smaller units of time (forexample, one year) where the probability of an event occurring based on the characteristicsand past history of an individual is calculated.The structure of an intervention model would vary depending on the point where anintervention was made and which aspects of the longitudinal pathways were affected. Thebase information (that is, the inter-relationships) determined from the mapping exercisewould remain the same, however.Interventions can then be evaluated through two main techniques. Cost effectiveness analysis (CEA): this form of economic evaluation measures theconsequences of a program in the most appropriate natural effects or physical units(such as years of life gained or cases correctly diagnosed). This technique does notattempt to value the consequences.Cost utility analysis (CUA): is a subset of CEA, where the consequences ofprograms are adjusted by health state preferences or utility weights (QALYs orDALYs).Cost benefit analysis (CBA): this form of economic evaluation attempts to measurethe consequences of programs in monetary terms, to make the program outcomesrelative to the costs.The CEA and CUA use similar methods of measuring results, for example dollars per lifeyear gained or DALYs averted. Results from a CEA or CUA can be presented on a costeffectiveness plane where the relativities between the economic costs and effectiveness ofthe intervention can be clearly seen. Outcomes that are more effective are towards the rightof the graph while those that are more costly are towards the top of the cost effectivenessgraph.In the example shown in Figure 3-4, the blue circle is the current situation, while the whitecircles are alternative interventions that have been evaluated through CEA or CUA relative tothe current situation. Interventions that are to the right and lower relative to the ‘base case’are defined as being dominant i.e. they are more effective and less costly than theircomparator. Conversely, those to the left and higher are defined as being dominatedi.e. they are less effective and more costly than their comparator. In Figure 3-4 these areascorrespond to the blue and red quadrants respectively.99