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This is an Open Access article distributed under the terms 0f the Creative C0 mm0ns Attributi0n License (http://creativec0 mm0ns.0rg/licenses/by/2.0), which permits unrestricted use, distributi0 n, and repr0ducti0n in any medium, pr0vided the 0 riginal w 0 rk is pr0 perly cited. A b s tra c tPrtority setting 0f health interventi0ns is 0ften ad-h0c and res0urces are n0t used t0 an 0ptimal extent. Underlying pr0blem is that multiple criteria play a role and decisi0ns are c0mplex. Interventi0ns may be ch0sen t0 maximize general p0pulati0n health, t0 reduce health inequalities 0f disadvantaged 0 r vulnerable gr0 ups, ad/0 r t0 resp0 nd t0 life-threatening situati0 ns, all with respect to practical and budgetary c0nstraints. This is the type 0f problem that p0licy makers are typically bad at s0lving rati0nally, unaided. They tend to use heuristic 0 r intuitive approaches to simplify romplexity, and in the process, imp0rtant inf0rmati0n is ign0red. Next, p0licy makers may select interventi0ns for 0nly p0litical m0tives.This indicates the need for rati0nal and transparent approaches to pri0rity setting. O ver the past decades, a number 0f approaches have been devefoped, including evidence-based medicine, burden 0f disease analyses, c0st-effectiveness analyses, and equity analyses. H0wever, these approaches c0ncentrate 0n single criteria 0nly, whereas in reality, p0licy makers need t0 make ch0ices taking int0 acc0unt multiple criteria simultane0usly. M0re0ver, they d0 n0t c0ver all criteria that are relevant to p0licy makers. Therefore, the devefopment 0f a multi-criteria approach to pri0rity setting is necessary, and this has indeed recently been identified as 0ne 0f the m0st imp0rtant issues in health system research. In 0ther scientific disciplines, multi-criteria decisi0 n analysis is well devel0 ped, has gained widespread acceptance and is routinely used. This paper presents the main principles 0f multi criteria decisi0n analysis. There are 0nly a very few applicati0ns to guide res0urce all0cati0n decisi0ns in health. W e call for a shift away from present pri0rity setting t00ls in health -that tend to focus 0n single criteria -towards transparent and systematic approaches that take into acc0unt all relevant criteria simultane0usly.

Priority setting of health interventions is often ad-hoc and resources are not used to an optimal extent. Underlying problem is that multiple criteria play a role and decisions are complex. Interventions may be chosen to maximize general population health, to reduce health inequalities of disadvantaged or vulnerable groups, ad/or to respond to life-threatening situations, all with respect to practical and budgetary constraints. This is the type of problem that policy makers are typically bad at solving rationally, unaided. They tend to use heuristic or intuitive approaches to simplify complexity, and in the process, important information is ignored. Next, policy makers may select interventions for only political motives.This indicates the need for rational and transparent approaches to priority setting. Over the past decades, a number of approaches have been developed, including evidence-based medicine, burden of disease analyses, cost-effectiveness analyses, and equity analyses. However, these approaches concentrate on single criteria only, whereas in reality, policy makers need to make choices taking into account multiple criteria simultaneously. Moreover, they do not cover all criteria that are relevant to policy makers. Therefore, the development of a multi-criteria approach to priority setting is necessary, and this has indeed recently been identified as one of the most important issues in health system research. In other scientific disciplines, multi-criteria decision analysis is well developed, has gained widespread acceptance and is routinely used. This paper presents the main principles of multicriteria decision analysis. There are only a very few applications to guide resource allocation decisions in health. We call for a shift away from present priority setting tools in health -that tend to focus on single criteria -towards transparent and systematic approaches that take into account all relevant criteria simultaneously.

BackgroundIn order to measure and analyse the technical efficiency of district hospitals in Ghana, the specific objectives of this study were to (a) estimate the relative technical and scale efficiency of government, mission, private and quasi-government district hospitals in Ghana in 2005; (b) estimate the magnitudes of output increases and/or input reductions that would have been required to make relatively inefficient hospitals more efficient; and (c) use Tobit regression analysis to estimate the impact of ownership on hospital efficiency.MethodsIn the first stage, we used data envelopment analysis (DEA) to estimate the efficiency of 128 hospitals comprising of 73 government hospitals, 42 mission hospitals, 7 quasi-government hospitals and 6 private hospitals. In the second stage, the estimated DEA efficiency scores are regressed against hospital ownership variable using a Tobit model. This was a retrospective study.ResultsIn our DEA analysis, using the variable returns to scale model, out of 128 district hospitals, 31 (24.0%) were 100% efficient, 25 (19.5%) were very close to being efficient with efficiency scores ranging from 70% to 99.9% and 71 (56.2%) had efficiency scores below 50%. The lowest-performing hospitals had efficiency scores ranging from 21% to 30%.Quasi-government hospitals had the highest mean efficiency score (83.9%) followed by public hospitals (70.4%), mission hospitals (68.6%) and private hospitals (55.8%). However, public hospitals also got the lowest mean technical efficiency scores (27.4%), implying they have some of the most inefficient hospitals.Regarding regional performance, Northern region hospitals had the highest mean efficiency score (83.0%) and Volta Region hospitals had the lowest mean score (43.0%).From our Tobit regression, we found out that while quasi-government ownership is positively associated with hospital technical efficiency, private ownership negatively affects hospital efficiency.ConclusionsIt would be prudent for policy-makers to examine the least efficient hospitals to correct widespread inefficiency. This would include reconsidering the number of hospitals and their distribution, improving efficiency and reducing duplication by closing or scaling down hospitals with efficiency scores below a certain threshold. For private hospitals with inefficiency related to large size, there is a need to break down such hospitals into manageable sizes.

Background and objectivesVisceral leishmaniasis (VL) is an important public health problem in south-eastern Nepal affecting very poor rural communities. Since 2005, Nepal is involved in a regional initiative to eliminate VL. This study assessed the economic impact of VL on households and examined whether the intensified VL control efforts induced by the government resulted in a decrease in household costs.MethodsBetween August and September 2010, a household survey was conducted among 168 patients that had been treated for VL within 12 months prior to the survey in five districts in south-eastern Nepal. We collected data on health-seeking behaviour, direct and indirect costs and coping strategies.ResultsThe median total cost of one episode of VL was US$ 165 or 11% of annual household income. The median delay between the onset of symptoms and presentation to a qualified provider was 25 days. Once the patient presented to a qualified provider, the delay to correct diagnosis was minimal (median 3 days). Direct and indirect costs (income losses) represented 47% and 53% of total costs respectively. Households used multiple strategies to cope with the cost of illness, mainly mobilizing cash/savings (71%) or taking a loan (56%).ConclusionsThe provision of free VL diagnosis and drugs by the Nepalese control programme has been an important policy measure to reduce the cost of VL to households. But despite the free VL drugs, the economic burden is still important for households. More effort should be put into reducing indirect costs, in particular the length of treatment, and preventing the transmission of VL through vector control.