Finally, let us consider the leading causes of disease burden from different estimates. Residual cause groups with percentage of burden higher than the last cause included in ten leading causes are shown at the lower panel. Five out of the ten leading causes are common to all five estimates. These are: Lower respiratory infections (LRI), diarrhoeal diseases, tuberculosis (TB), ischaemic heart disease (IHD) and falls. If we compare GBD96 with the local cause of death anchored estimate, another three leading causes are found to be common. These additional leading causes common to minimally anchored GBD96 and local mortality anchored estimates are: low birth weight, road traffic accidents, and fires. The top ten causes of burden produced by the two estimates differ by two conditions. The GBD96 estimate has protein energy malnutrition, and birth asphyxia. The local cause of death anchored estimate does not show this. Instead, self-inflicted injury, and cerebrovascular disease (Cer. VD) appear in the list of leading causes.

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Periodontal disease shows up as the leading cause of burden in case of the local HSV-VAS anchored estimate. It goes to the second position, in case of the HSV-Torrance-TTO anchored estimate. The problem of protein energy malnutrition is highlighted by the two HSV anchored estimates. However, considering the present mortality level in the state, ranking of these disabilities as the top two causes of disease burden may meet with popular rejection. For example, tuberculosis is viewed as a serious public health problem in the state. The National Tuberculosis Control Program is being implemented in the state from 1962 (Mahapatra and Ramana, 1994). The Tuberculosis control program has continuously received political and professional support in view of the widely shared concern about the adverse public health impact of the disease. A School Health Project was started in the state in 1993, with assistance from the British Overseas Development Agency (ODA). Dental health of school children was a major component of this project which was subsequently discontinued in 1999. The British ODA did not renew funding, in view of less-than-expected project performance. Although many factors would have contributed to discontinuation of the School Health Program, the limited inference we draw is from a comparative review of support for the two programs described above is that the popular concern for tuberculosis control is much more sustained and stronger compared to a program with dental health as a major component. Based on this experience, we feel that is that people will be quick to point out that the two HSV anchored estimate puts the estimate of tuberculosis at the lower end of the ten leading causes of burden and highlights periodontal disease as the fore most cause of burden. This is not to deny the importance of disease burden due to periodontal disease and protein energy malnutrition. The argument here is about choice of the primary NBD estimate. The two HSV anchored estimate can be used to demonstrate sensitivity of disease burden estimates to an alternate health state valuation.

We have compared disease burden estimates from two versions of minimally anchored estimates (WDR93 and GBD96) and three intermediately anchored estimates (COD, HSV-VAS, and HSV-Torrance-TTO), with local data on causes of death and health state valuation. It would have been useful to look at changes in burden of disease estimates with local data on descriptive epidemiological parameters namely, incidence, age at onset, and duration of different diseases. Unfortunately descriptive epidemiological data are hard to come by. It needs co-ordinated efforts on the part of many epidemiologists to build up the descriptive epidemiological profile of a population. As and when such data are available, it will be useful to examine, how NBD estimates change with incorporation of local data on disease incidence and prevalence.

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However, on the basis of limited comparisons made above, certain inferences can be made. Firstly, Murray and Lopez have made substantive revisions between the GBD estimates published in WDR 1993 and the final version published in 1996. As we have seen here, the revisions for the India estimates were in the desirable direction tending to match local mortality levels and cause of death patterns. The cause of death anchored estimates are only marginally different from the GBD96 estimates, mainly because the later had already incorporated local data on urban cause of death and had gained some insights from the pilot study on rural cause of death. Hence it would be wrong to infer that collection of local cause of death would not improve a NBD estimate over the GBD estimate for the corresponding region. Rather the opposite inference is due. Recall the substantial difference between the WDR93 and the GBD96 estimates for India. The improvement can be attributed to availability of local cause of death information to the GBD96 team.

Addition of local health state valuations highlights the disability component to various extents. The two HSV anchored estimates blur general mortality level in Andhra Pradesh and the age pattern of mortality. The HSV- anchored estimates highlight disability as the major source of disease burden, almost to the exclusion of premature mortality. If disability is the major source of burden, the life expectancy in Andhra Pradesh would have been higher and the infant mortality rate, lower. It is doubtful whether the people in Andhra Pradesh are ready to ignore premature mortality and focus on the disability component of the burden to the extent the HSV estimate would recommend. Since we have not presented this estimate to people in Andhra Pradesh for serious consideration by policy makers, there is no evidence to support the above conjecture. But some insights are available from a somewhat similar situation elsewhere in the world. In 1989, the Oregon state in US set up the Oregon Health Services Commission and charged it with the responsibility of preparing a list of health services ranked by priority from the most important to the least important, representing comparative benefits of each service to the entire population (OHSC Website, 2000; Brown, 1991, Tengs and others, 1996). The commission produced a priority list in 1990. There was a lot of criticism and popular outrage about the ordering of various condition treatment pairs. One of the contrasts chosen by some people was to point out that routine dental care had received priority over life- saving procedures like appendectomy (Hadorn, 1991). OHSC responded to the popular outrage and revised the priority list altogether. Now let's compare the life expectancy at birth in Oregon and Andhra Pradesh. In 1990, Oregon had a life expectancy at birth of 76.6 years (males = 73.4 and females = 79.8). My estimate of life expectancy in Andhra Pradesh around the same time (1991) is between 56 to 60 years. Oregon was already experiencing a much lower level of mortality at the time of these developments.

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How do we then account for the difference between the priority implicit in the community valuation of health states and popular support for public health interventions to reduce mortality? One reason could be the difference in states of the world visualised by persons while valuing health states as an individual and while considering priorities for public health intervention by the community. For example, valuers may give higher importance to their own suffering while valuing a health state. They may be able to take a more detached view in the context of public policy. Another reason could be measurement error in health state valuation. While it is clear that further research on health state valuation is required, the lesson for NBD estimation is that investments on health state valuation may not give immediate returns to inform policy. Instead, NBD estimates seeking to inform current policy should invest in cause of death, and descriptive epidemiological studies. However, research on health state valuation in different settings will be important for methodological advancement of summary measures of population health.

Finally what is the information value of a National Burden of Disease estimate? Anchoring NBD estimates to local data on cause of death, incidence and prevalence of diseases gives added confidence to the validity of those estimates and encourages policy makers to give more weightage to the evidence produced by NBD estimates. Policy maker's confidence and reliance on NBD estimates will depend on the quality of local data to which the estimates are anchored. Another important contribution of NBD estimates is usually in allowing for disaggregated analysis for different population groups within the national or sub-national entity. For example, differences in needs and organisation of health care for rural and urban areas is an important issue. The Andhra Pradesh Disease Burden estimates, prepared for the rural and urban populations separately, allowed for rural - urban analyses, wherever necessary.