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In assessing the cost-effectiveness of an intervention, the interpretation and handling of uncertainties of the traditional summary measure, the Incremental Cost Effectiveness Ratio (ICER), can be problematic. This is particularly the case with strategies towards universal health coverage in which the decision makers are typically concerned with coverage and equity issues. We explored the feasibility and relative advantages of the net-benefit framework (NBF) (compared to the more traditional Incremental Cost-Effectiveness Ratio, ICER) in presenting results of cost-effectiveness analysis of a community based health insurance (CBHI) scheme in Nouna, a rural district of Burkina Faso. Data were collected from April to December 2007 from Nouna’s longitudinal Demographic Surveillance System on utilization of health services, membership of the CBHI, covariates, and CBHI costs. The incremental cost of a 1 increase in utilization of health services by household members of the CBHI was 433,000 XOF ($1000 approximately). The incremental cost varies significantly by covariates. The probability of the CBHI achieving a 1% increase in utilization of health services, when the ceiling ratio is $1,000, is barely 30% for households in Nouna villages compared to 90% for households in Nouna town. Compared to the ICER, the NBF provides more useful information for policy making. © 2012 Hounton and Newlands; licensee BioMed Central Ltd.
The Nouna health district, also referred to as Kossi province, is a rural health district situated in the North West of Burkina Faso. The area is characterized by dry weather with a mean annual rainfall of about 800 mm resulting in dry savannah vegetation. In the early 1990s, a Demographic Surveillance System (DSS) was established by the Nouna Health Research Centre. The original DSS area covered 39 villages (~population about 26 000 inhabitants) and has been progressively extended to cover 58 villages and Nouna town, with a population of about 72 000 people. The density of population was about 35 individuals per square km. The population is distributed in roughly 9,500 households and composed of 65% rural dwellers and 35% Nouna semi-urban dwellers. The population is essentially young with children less than 15 years of age representing about 48% of the total population, and only 6.2% above 60 years of age. The inhabitants are mostly subsistence farmers and/or cattle keepers, and illiteracy is extremely high, over 80% (Figure (Figure22). Cost-effectiveness acceptability curves, Nouna CBHI, Burkina Faso. * Please note on X-axis actual values correspond to displayed values * 1000. The Nouna community based health insurance scheme (CBHI) was launched in 2004 and was developed by the Nouna Health Research Centre as an operational research project to study how to improve community access and uptake of health services and how to meet the need of the poor within Nouna health district. The intervention has been extensively described in the literature [9-13]. It is a voluntary community health insurance scheme which aims to reduce financial barriers (out-of-pocket payments) and improve quality of care, thus improving access and uptake of health care. The alternative intervention will be the status quo (no enrolment in the CBHI). Data were extracted from the longitudinal Nouna demographic and surveillance site on membership of the Nouna CBHI, utilization of health services, the average distance from village to health centre, assets ownership, age and education level of the head of household. In order to generate household-level costs of the CBHI scheme from a societal perspective we added, for every household member of the scheme, the household costs (enrolment fees and premium) to the average cost of enrolling in the Nouna CBHI scheme from the health system perspective. The latter was obtained by dividing the 2007 annual costs of running the Nouna CBHI scheme by the number of households, members (370) in 2007. For households which are not members of the scheme, there is obviously no cost incurred for membership fees. However, these households have to meet the cost of the utilization of health services out of pocket. We use the estimated cost of the benefit package of the Nouna CBHI which was 9630 West Africa francs (XOF), equivalent to approximately $20) in 2004 [12]. We computed ICER for extra additional utilization of health services, and cost effectiveness acceptability curves (CEAC) to illustrate the decision rule of cost effectiveness of the intervention. The ICER decision rule is that if the estimated ICER lies below the ceiling ratio, which represents the maximum decision makers are willing to pay for an incremental unit of the measure of effectiveness, then the intervention concerned is deemed cost-effective. By varying the ceiling ratio, the varying probability that the intervention is cost-effective can be identified. The CEAC shows the probability of the intervention being cost-effective for all potential values of the ceiling ratio. Unlike the ICER approach, the CEAC can also be employed to obtain a confidence interval of cost-effectiveness. It also avoids the problems of interpretation of a negative ICER [14,15]. There isa straightforward graphical representation and interpretation that a new treatment is not cost effective [7]. In this paper, the likelihood values that the Nouna CBHI is cost-effective compared to the status quo were obtained using the p-values on the Nouna CBHI dummy when running an Ordinary Least Square (OLS) regression. The p-values are 2-sided p-values; however only one-sided probability is needed to test whether the incremental net-benefit is positive (Nouna CBHI is preferred) or negative (the status quo is preferred). The one–sided p-values were thus obtained by dividing the 2-sided p-values by 2. For negative incremental net-benefits the probabilities that Nouna CBHI is preferred equals the one sided p-values, and for positive incremental net-benefits, the probabilities that the Nouna CBHI is preferred equals 1 minus the one sided p-values (Hoch JS et al, 2006). A major strength of this technique when it comes to resource allocation is that for a given budget, one can model the different probabilities that the Nouna CBHI is preferred to the status quo. However, whilst this technique could be sufficient in clinical care decisions about the choice of preferred medication, technology, or screening exam, in the public health field, a decision maker is often concerned about issues beyond the optimality of one intervention over another, especially with issues of equity. This is where the net-benefit framework could potentially be very useful in assessing the effect of significant determinants on the marginal cost-effectiveness of a universal health coverage intervention such as the Nouna community based health insurance. Given access to health care is influenced by major determinants (such as the distance to health facilities, education, or assets ownership), a net-benefit framework, applied to the cost effectiveness of the Nouna CBHI, could be effective through the joint probability distribution in identifying the most important determinants that affect the cost-effectiveness results. The net-benefit framework employs linear regression techniques, and to date, has been most often used alongside clinical trials of health care regimens or technology devices [2-5]. Thus, it has the potential even for observational studies with patient-level effect and cost data, for the better presentation and interpretation of cost-effectiveness results and better evidence based decision making. The traditional equation ΔC/ΔE (ICER) can be re-arranged by multiplying each arm of the equation by ΔE. The result is ΔC = ΔE * ICER and for any ceiling ratio Ro, ΔC = ΔE * Ro. Thus, a net-benefit statistic can be computed as follows: ΔE * Ro − ΔC = ΔNB. We computed for each observation (household) in the household survey an individual net-benefit statistic. The expression of an individual net-benefit NBi = ΔEi * Ro − ΔCi is similar to a traditional linear regression equation Y = α + δXi + εi where Y is the dependent variable, α is the intercept, δ the coefficient on an explanatory variable (continuous variable or dummy variable taking the value 1 for a positive outcome and 0 for a negative outcome for example) and εi is the standard error. Thus, for the Nouna community based health insurance scheme, the household net-benefit could be modeled as NBi = α + δCBHIi + εi where NBi is the net-benefit for each subject (or household), α is the intercept, CBHIi, is the intervention (taking the value zero if a household is not a member of the scheme and 1 for a member), δti, is the incremental net benefit and εi is the standard error. The interpretation is straightforward and when this difference is greater than zero, it means that the incremental cost for one additional unit of effectiveness (in this case utilization of health services) is below the Ro (the maximum the provider is willing to pay). The CBHI will be deemed cost-effective in relation to the status quo. Similarly, if the coefficient is negative, then the incremental cost for one additional unit of effectiveness is above the Ro and the status quo will be deemed cost-effective. The basic model above (NBi = α + δCBHIi + εi) could then be expanded to include important covariates and thereby allow the examination of the marginal impact of these covariates on incremental cost effectiveness. The final model may look like: NBi = α + ∑j=1P βj xij + δti + ∑j=1P ýj xij + ϵi where NBi is the summation of the interaction between the treatment dummy (Community Based Health Insurance for example, coded yes or no) and the covariates. ý’s magnitude and significance indicates how the cost effectiveness of CBHI is expected to vary at the margin. Thus the use of the net-benefit model for presenting and interpreting cost-effectiveness analysis results has the potential to overcome the double dilemma of not being able to access progress using outcomes measures (for example, computing maternal or perinatal mortality) and not being able to reliably assess cost-effectiveness using incremental cost-effectiveness ratios. As indicated in the background section, cost effectiveness analysis traditionally relies on use of an incremental cost effectiveness ratio (ICER) to indicate, among a set of alternative strategies, which is the most cost effective. Not only does the ICER as a ratio not indicate what to do, how to do it or where to do it, the decision rule is not straightforward when there is no clear dominance of one alternative over another [2,6,14]. Moreover, there are very few situations in which decision makers decide to solely go with one strategy over another. Rather, they are more likely to allocate resources across a range of complementary strategies for maximum health gains and thus the net-benefit framework offers an advantage over the traditional ICER approach in presenting and interpreting results for public health interventions (Table (Table11). Relative advantages of net-benefit framework and incremental cost-effectiveness ratio for presenting and interpreting results of cost-effectiveness analysis The study was approved by the ethical review board of Nouna Health Research Centre.
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