Abstract
AIM: To identify the role of human development in the incidence and mortality rates of gastrointestinal cancers worldwide. METHODS: The age-standardized incidence and mortality rates for gastrointestinal cancers, including cancers of the esophagus, stomach, pancreas, liver, gallbladder, and colorectum, were obtained from the GLOBOCAN 2008 database and United States Cancer Statistics (USCS) report. The human development index (HDI) data were calculated according to the 2011 Human Development Report. We estimated the mortality-to-incidence ratios (MIRs) at the regional and national levels, and explored the association of the MIR with development levels as measured by the HDI using a modified "drug dose to inhibition response" model. Furthermore, countries were divided into four groups according to the HDI distribution, and the MIRs of the four HDI groups were compared by one-way ANOVA followed by the Tukey-Kramer post-hoc test. State-specific MIRs in the United States were predicted from the estimated HDI using the fitted non-linear model, and were compared with the actual MIRs calculated from data in the USCS report. RESULTS: The worldwide incidence and mortality rates of gastrointestinal cancers were as high as 39.4 and 54.9 cases per 100000 individuals, respectively. Linear and non-linear regression analyses revealed an inverse correlation between the MIR of gastrointestinal cancers and the HDI at the regional and national levels (β < 0; P = 0.0028 for regional level and < 0.0001 for national level, ANOVA). The MIR differed significantly among the four HDI areas (very high HDI, 0.620 ± 0.033; high HDI, 0.807 ± 0.018; medium HDI, 0.857 ± 0.021; low HDI, 0.953 ± 0.011; P < 0.001, one-way ANOVA). Prediction of the MIRs for individual United States states using best-fitted non-linear models showed little deviation from the actual MIRs in the United States. Except for 28 data points (9.93% of 282), the actual MIRs of all gastrointestinal cancers were mostly located in the prediction intervals via the best-fit non-linear regression models. CONCLUSION: The inverse correlation between HDI and MIR demonstrates that more developed areas have a relatively efficacious healthcare system, resulting in low MIRs, and HDI can be used to estimate the MIR.