Enhancing the comparability of costing methods: cross-country variability in the prices of non-traded inputs to health programmes

BACKGROUND National and international policy makers have been increasing their focus on developing strategies to enable poor countries achieve the millennium development goals. This requires information on the costs of different types of health interventions and the resources needed to scale them up, either singly or in combinations. Cost data also guides decisions about the most appropriate mix of interventions in different settings, in view of the increasing, but still limited, resources available to improve health. Many cost and cost-effectiveness studies include only the costs incurred at the point of delivery to beneficiaries, omitting those incurred at other levels of the system such as administration, media, training and overall management. The few studies that have measured them directly suggest that they can sometimes account for a substantial proportion of total costs, so that their omission can result in biased estimates of the resources needed to run a programme or the relative cost-effectiveness of different choices. However, prices of different inputs used in the production of health interventions can vary substantially within a country. Basing cost estimates on a single price observation runs the risk that the results are based on an outlier observation rather than the typical costs of the input. METHODS We first explore the determinants of the observed variation in the prices of selected "non-traded" intermediate inputs to health programmes--printed matter and media advertising, and water and electricity--accounting for variation within and across countries. We then use the estimated relationship to impute average prices for countries where limited data are available with uncertainty intervals. RESULTS Prices vary across countries with GDP per capita and a number of determinants of supply and demand. Media and printing were inelastic with respect to GDP per capita, with a positive correlation, while the utilities had a surprisingly negative relationship. All equations had relatively good fits with the data. CONCLUSION While the preferred option is to derive costs from a random sample of prices in each setting, this option is often not available to analysts. In this case, we suggest that the approach described in this paper could represent a better option than basing policy recommendations on results that are built on the basis of a single, or a few, price observations.