The 2001 GBD study uses a summary measure of population health, the DALY, that explicitly incorporates several important social values. This has the advantage that the effects of changing preferences can be readily explored through sensitivity analysis, as illustrated in this chapter. Another advantage of the burden of disease approach is that it entails a data audit, whereby the completeness, reliability, and consistency of routinely collected data are assessed and critical gaps in health data collection are identified. One implication is that periodic quality assessments of, say, routine cause of death data are needed to ensure their continued relevance and reliability for public policy (Mathers and others 2005). Another is the need for a more rational assessment of priority data for the health care sector that places greater emphasis on data collection and data linkage to facilitate burden of disease studies rather than on routine collection of statistics of limited relevance to public health. The burden of disease framework, based on the estimated distribution and duration of health states resulting from incident cases, would benefit greatly from wider availability of linked data sets on health outcomes and further longitudinal research into health state transition probabilities following on from specific disease or injury causes (Kelman and Bass 2002).
A major advance with GBD 2001 has been the systematic, though as yet incomplete, attempt to quantify uncertainty in both national and global assessments of the disease burden. This uncertainty must be taken into account when making cross-national comparisons, and needs to be carefully communicated and interpreted by epidemiologists and policy makers alike. Estimates of mortality in countries without functioning vital registration systems for causes of death will always be substantially more uncertain than those derived from systems where all deaths are registered and medically certified. The same may be said for the quantification of disability due to various conditions, where the gaps in data availability across countries are likely to be even more extreme than for mortality.
Despite the progress of the past decade, the incremental gains in advancing knowledge and understanding of global descriptive epidemiology have been modest. A globally coordinated research and development effort is urgently needed to devise and implement cost-effective approaches to data collection and analysis in poor countries that are targeted to their health development needs, and that can routinely yield comparable information of sufficient quality to establish how the disease and risk factor burden is changing in populations (Murray, Lopez, and Wibulpolprasert 2004).
Much can be learned about the health of populations from relatively modest investments in sample registration systems, provided these are designed to reliably measure the causes of death in sample areas and have sufficient resources to do so. China's Disease Surveillance Points system is a good example of what can be done to improve knowledge about disease and injury control priorities in low-income countries at a modest cost (Lopez 1998; Yang and others 2005). Greater investments in getting the descriptive epidemiology of diseases and injuries correct in poor countries will do vastly more to reduce uncertainty in disease burden assessments than philosophical debate about the appropriateness of social value choices. Just as the production of global and regional estimates should not create the impression that the descriptive epidemiology of disease and injury is reliably known, so the uncertainties around these estimates must not create the impression that not enough is known reliably enough to usefully inform health priorities and programs. Health intelligence is an essential ingredient of the health development process. Those engaged in collecting, analyzing, and disseminating population health information have a responsibility to develop this evidence base using novel methods that communicate what we do know, as well, if not more convincingly, than what we do not know.
Information for policy purposes will never be perfect, but good policy makers will want to benefit from all available information to guide priority setting and action. We might well take solace in the comments of a prominent medical statistician who once cautioned that "Making the best the enemy of the good is a sure way to hinder any statistical progress. The scientific purist who will wait for medical statistics until they are nosologically exact is no wiser than Horace's rustic waiting for the river to flow away" (Greenwood 1948, p. 28).