To date, MCDA knows very few applications to guide resource allocation decisions in health care, in either western or developing countries. These applications have used MCDA to different extents: to only illustrate its principles, to identify the criteria for priority setting, to identify and weigh the criteria for priority setting, or more comprehensive approaches that result in a rank ordering of interventions.
James et al. [47
] illustrated the principles of MCDA
by demonstrating the potential impact of alternative weights for equity and efficiency criteria on the ranking of a number of hypothetical interventions.
for priority setting were identified by two merely qualitative studies in Uganda [4
], including medical (e.g. effectiveness, cost-effectiveness, quality of evidence, severity of disease) and non-medical criteria (e.g. age, gender, and area of residence). Yet, they did not establish the weights of these criteria in a way that allows a rank ordering of interventions. Recently, a number of tools have been developed that take into account various criteria, but these do not explicitly attach weights to these criteria. Tugwell et al. [49
] have proposed the 'equity effectiveness loop' to highlight equity issues inherent in assessing health needs, effectiveness and cost-effectiveness of interventions. The 'marginal budgeting for bottlenecks' tool aims to bridge between costing, cost-effectiveness and burden of disease analysis [50
]. 'District health accounts' is a tool designed to help districts analyze their budgets and expenditures so that budgets can be set against priorities as defined by the prevailing burden of disease, and as such integrates budgeting, costing and burden of disease analysis [51
]. In the Netherlands, Dunning identified a number of criteria for public reimbursement of health care. However, some of its criteria – especially medical need – were not well defined, and its application therefore suboptimal [52
Further studies have quantified the scores and weights of criteria
, but these are typically limited to two criteria only: e.g. on cost-effectiveness and equity [53
], or on age and severity of illness [54
Recently, two comprehensive MCDA approaches have been developed. Wilson et al. [56
] developed a prioritization framework in an English Primary Care Trust. Through group discussion with policy makers, a number of criteria
were identified (such as effectiveness, quality of life, access/equity, need, and prevention) and were weighed
into four broad 'levels of importance'. Next, the groups scored
four hypothetical interventions on those criteria on a scale from 0–10. A simple linear additive evaluation model was used to calculate overall scores, and interventions were rank ordered according to their 'cost-value' ratio (estimated by dividing the costs of an interventions by the overall score). The authors consider the framework as a promising tool for prioritizing interventions in the Primary Care Trust.
Baltussen et al. carried out explorative research to prioritize health interventions in Ghana and Nepal using discrete choice experiments [37
]. In Ghana, criteria
were identified through a series of group discussions with policy makers, and included 'cost-effectiveness', 'poverty reduction', 'age', 'severity of illness', 'budget impact' and 'burden of disease'. Intervention scores
on those criteria were based on poverty profiles, burden of disease and cost-effectiveness analysis as presented in the World Health Report 2002 [58
], and were expressed on a binary scale with arbitrary cut-off values. The relative weights
of the various criteria were estimated through the use of discrete choice experiments (DCE) [59
], with a large number of policy makers. In the DCE, respondents choose their preferred option from sets of hypothetical interventions, each consisting of a bundle of criteria that described the intervention in question, with each criterion varying over a range of scores (Figure ). The criteria were constant in each scenario, but the scores that described each criterion varied across interventions. Analysis of the options chosen by respondents in each set revealed the extent to which each criterion was important. The work in Ghana showed that policy makers give high value to interventions that are cost-effective (score of 1.42), reduce poverty (1.25), target the young (0.84), or target severe diseases (0.38). Using a simple linear additive evaluation model, total scores were calculated for a set of interventions, and rank ordered accordingly: high priority interventions in Ghana were prevention of mother to child transmission in HIV/AIDS control, and treatment of pneumonia and diarrhea in childhood. Lower priority interventions were certain interventions to control blood pressure, tobacco and alcohol abuse. Full details are reported elsewhere [37
Example of a question in a discrete choice experiment.