Assessing and Mapping Spatial Associations among Oral Cancer Mortality Rates, Concentrations of Heavy Metals in Soil, and Land Use Types Based on Multiple Scale Data

Abstract

In this study, a deconvolution procedure was used to create a variogram of oral cancer (OC) rates. Based on the variogram, area-to-point (ATP) Poisson kriging and p-field simulation were used to downscale and simulate, respectively, the OC rate data for Taiwan from the district scale to a 1 km × 1 km grid scale. Local cluster analysis (LCA) of OC mortality rates was then performed to identify OC mortality rate hot spots based on the downscaled and the p-field-simulated OC mortality maps. The relationship between OC mortality and land use was studied by overlapping the maps of the downscaled OC mortality, the LCA results, and the land uses. One thousand simulations were performed to quantify local and spatial uncertainties in the LCA to identify OC mortality hot spots. The scatter plots and Spearman’s rank correlation yielded the relationship between OC mortality and concentrations of the seven metals in the 1 km cell grid. The correlation analysis results for the 1 km scale revealed a weak correlation between OC mortality rate and concentrations of the seven studied heavy metals in soil. Accordingly, the heavy metal concentrations in soil are not major determinants of OC mortality rates at the 1 km scale at which soils were sampled. The LCA statistical results for local indicator of spatial association (LISA) revealed that the sites with high probability of high-high (high value surrounded by high values) OC mortality at the 1 km grid scale were clustered in southern, eastern, and mid-western Taiwan. The number of such sites was also significantly higher on agricultural land and in urban regions than on land with other uses. The proposed approach can be used to downscale and evaluate uncertainty in mortality data from a coarse scale to a fine scale at which useful additional information can be obtained for assessing and managing land use and risk.

Geographical Variation in Oral and Oropharynx Cancer Mortality in Brazil: A Bayesian Approach

“…Recently, it has been reported that environmental exposure to heavy metals is an important risk factor for developing oral cancer, with studies on animals showing that chronic intake of chromium (Cr) could induce oral cancer [27]. Another spatial association between soil heavy metal content and oral cancer incidence and mortality has also been assessed in Taiwan [19,28]. Moreover, a study conducted in Brazil by Moi et al (2018), showed the consumption of pesticides, and HPV contamination, positively correlated with mortality rates from oral cancer in adults [26].…”

Metropolitan age-specific mortality trends at borough and neighborhood level: The case of Mexico City

“…For instance, Population-Weighted Average, local and global Empirical Bayes and Poisson Kriging have been used to estimate disease-specific mortality rates from age-adjusted data. Accounting for spatial correlation patterns for low and high frequency rates, Poisson Kriging have shown better results [ 59 , 60 ]. Kriging methods allow to estimate spatial risk considering heterogeneity among small areas from poorly reported databases [ 61 ].…”

Expansion of Protected Areas under Climate Change: An Example of Mountainous Tree Species in Taiwan

“…where 1000 is the number of realizations, and ( ) is the number from the 1000 realizations in which the simulated species were present at location k. The joint probability (Pj, global uncertainty) in m occurrence locations ( , , , ⋯ ) was written as follows [63,64]:…”