Various attempts have been made to quantify the global burden of cancer and to estimate site-specific cancer mortality and morbidity [
1,
12-
15]. More recently, the efforts made by the International Agency for Research on Cancer (IARC) have led to the Globocan 2000 estimates, which has also used information on incidence and survival to estimate cancer death for the year 2000 from various sources including cancer registries [
12,
13]. The analyses reported here have built extensively on the IARC work to synthesis and estimate cancer mortality and incidence distributions by site for all regions of the world. Compared to the estimates of the Globocan 2000, the GBD 2000 estimates for global cancer mortality and incidence are 11% and 3% higher, respectively. Although the overall difference in terms of proportion is small, the absolute difference between the two estimates remains relatively large. This difference is predominantly due to a substantially large difference in the AFRO, EMRO, and SEARO regions.
Some researchers suggest that model-based estimates of cancer mortality in GBD 1990 bear little relation to the actual profile of cancer recorded at the regional registries [
1,
16]. The proposed approach here is substantially different from the previous estimates and, although broader cause of death to estimate cancer envelope is still based on cause of death models in some countries, majority of the data sources of cause of death is now vital registration and/or sampling data. The total number of mortality from cancer is not extrapolated by model alone and the survival model was used to estimate the distribution of death by site, not the actual magnitude of cancer mortality in regions where no or little data on detailed cause of death is available. The model estimates were consistent with mortality distribution of vital records and the Globocan 2000 [
7]. Therefore, the major source of discrepancy is not the estimated cancer mortality distribution but the overall mortality envelope applied to each region.
The Globocan 2000 estimates are based on either cancer incidence data from cancer registries in the region (with a survival model used to estimate deaths) or on mortality data collected by regional cancer registries or other sources. Both these sources of data are likely to be incomplete and to result in underestimation of cancer deaths. On the other hand, the GBD 2000 starts with data on the level of all-cause mortality, and uses all available data on cause of death and cause of death models where such data is not available, to estimate the distribution of major cause groups, including cancers. For regions with insufficient vital registration such as AFRO and SEARO regions, this process significantly increases the mortality envelope for these two broad cause categories. It is possible that these methods result in an overestimate of total cancer deaths in some regions, and continuos efforts are being made to obtain additional data from these regions in order to check the validity of these estimates, and where appropriate, to improve them.
Although cancer is still a fatal disease in many developing countries[
2], there is a growing evidence that cancer survival even in developing countries is continuously improving [
3,
4]. The present study suggests that in all regions mortality was much higher in males while incidence were almost the same due to relatively good prognosis of breast and cervix cancers compared to common cancers among males such as liver and lung cancers. Due to the differences in survival by region, incidence-to-mortality rate ratios were higher in developed in which increasing disability among cancer survivors is warranted [
17-
19]. It is suggested that data on incidence as well as mortality are necessary to understand the magnitude and trends of cancer problems and to evaluate the interventions against cancer in the context of prevention [
1]. When setting priorities, interventions against cancers need to be compared with other health interventions which aims at reducing only morbidity [
5,
20].
GBD 2000 employs a composite measure of disease burden in terms of disability-adjusted life years (DALYs) which consist of years lived with disability (morbidity) and years of life lost (mortality) [
5]. As a part of this exercise, we have estimated both mortality and incidence of cancers by site. We will further estimate the years lived with disability to derive cancer burden in DALYs once weights assigned to disabling conditions are revised based on the estimates from the on-going population-based surveys from more than 70 countries [
21].
The presents study also suggests that there is a significant variation in the distribution of cancer mortality and incidence by region depending on age and population structure, distribution of risk factors, and opportunity of detection and treatment [
2,
22]. However, the frequent cancers such as lung, liver and cervical cancers are potentially preventable [
23]. For example, Parkin has estimated that there would have been 23% fewer cases of cancers in the developing world in 1990, if infections such as hepatitis B and C virus and human papilloma virus had been prevented [
24]. Another estimate suggests that 230,000 deaths (more than 4% of all cancer deaths) from liver cancer could have been avoided with only immunisations against hepatitis B [
13]. Smoking was estimated to be responsible for another 20% of all cancer deaths, all of which are preventable [
13].
Given a high incidence of cancers which are potentially preventable in both developed and developing countries, the role of primary prevention, early detection as well as treatment should be evaluated more carefully [
2]. Cost-effectiveness analysis plays a role for this purpose, which should be generalisable and comparable across various interventions including both currently delivered and potentially feasible ones [
20]. Estimating the magnitude of cancer mortality and incidence is a key input for setting research and intervention priorities. Combined with costs of each intervention, mortality and incidence estimates provide a basis for effectiveness calculation in cost-effectiveness analysis of cancer control programmes.
1 Colin D Mathers,1 Cynthia Boschi-Pinto,2 Alan D Lopez,1 and Christopher JL Murray3
This article has been corrected. See 
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