Improvements and Further Applications
What would improve the kind of estimates and conclusions reported in this volume? Most crucially, more and better data are needed in low- and middle-income countries to reduce reliance on extrapolation from high-income countries and on expert judgments. The need for information starts, in some cases, with better estimates of incidence and prevalence, but even where the epidemiology is well known, data on coverage and outcomes of existing interventions are scarce. Evidence of what it would cost to change coverage of existing interventions or add new interventions, and with what results, is particularly scarce and depends heavily on assumptions. This situation is sometimes true even for activities that have been conducted widely for many years and have been extensively analyzed, notably the EPI (chapter 20). Analyses should when possible be conducted at the level of a country or even smaller units, to take full account of all the reasons cost-effectiveness varies from place to place and to develop priorities on the basis of analyses appropriate to local circumstances. The methods used here are intended to help guide such efforts, and they can and should be refined through research to provide more robust help to policy.
Finally, a more concerted approach is needed for clarifying the options facing different decision makers and incorporating the results from systematic literature reviews into analytic models that compare the costs and effects of alternative interventions (Buxton and others 1997; Kuntz and Weinstein 2001). Modeling encourages explicit decision making and can deal comprehensively with the inputs and outcomes of decision options, which allows a range of uncertainties to be reflected. Thus, hypotheses about interventions can be formulated and tested statistically. Specifying models explicitly (as in chapter 16, for example) can also help identify gaps in current evidence and can capture details specific to particular populations and settings.
