The limited value of cost-effectiveness league tables – the need for a third dimension? Authors: Warren Stevens & David Jeffries Population Services International BACKGROUND Traditionally, cost effectiveness studies and the policy choices they intimate are made under the assumption that disease burden, need and utilization are randomly if unevenly distributed. Both burden of disease and utilization of health systems is known not to be randomly distributed. This issue has been ignored by most cost effectiveness studies due to three reasons; the difficulty in measuring it, the assumption that universal coverage will solve any inequalities, and the assumption that the issue of ICL is incorporated into the results of cost-effectiveness studies by default. THE INVERSE CARE LAW The inverse care law (ICL) was first described in a paper in The Lancet in 1970 by John Tudor Hart. Its basic presumption is that the the more a person or group of people are likely to benefit from an intervention, the less likely they are to access it; the greater the need, the poorer the access. The evidence for the existence of the ICL is strong and is apparent both over time and across multiple settings; developed and developing countries setting, public health, primary and tertiary care. Suggested causes of the ICL have centered around information asymmetry, and economic and social exclusion. As a result, the ICL has traditionally been measured with reference to household wealth indices. DATA 2006 India Demographic and Health Survey (n= 30,034); two 2003 mortality surveys from India (n=1,298); and a 1996 mortality survey from Bangladesh (n= 1,677) METHODS and RESULTS An age stratified model of ARI mortality was constructed from the survey data for India; using the same data, a utilization index was created from composite markers of access to healthcare. These were then applied across the 5-95 percentiles of the WFA z-score to obtain the figures below The opposite slopes of the death probability and the utilization index are clear in Figure 1, with a lower index for those subjects with the greatest need. For an ARI intervention with coverage ranging from 20 to 100%, the most deaths averted occur in subjects with higher z-scores for higher coverage. This is reflected in Figure 3, where there is a non-linear increase in the deaths averted, expressed as a percentage of deaths averted with full coverage. RESULTS Figure 3: Percent of deaths averted against coverage CONCLUSIONS The relative cost effectiveness of an intervention is often as reliant on who it reaches as much as how many it reaches Models that assume that the the impact of an intervention is not influenced by the scale of delivery or coverage level risks overestimating or underestimating the actual cost-effectiveness of any intervention in practice For a copy of this poster go to www.psi.org