Spatiotemporal Analysis and Mapping of Oral Cancer Risk in Changhua County (Taiwan): An Application of Generalized Bayesian Maximum Entropy Method

Yu, Hwa‐Lung; Chiang, Chi-Ting; Lin, Shu-De; Chang, Tsun-Kuo

doi:10.1016/j.annepidem.2009.10.005

Cited by 16 publications

(9 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Few studies have examined geographic disparities in breast cancer mortality across the entire U.S, but new statistical approaches are now available that can take into account spatial autocorrelations, and can identify where disparities in the risk of disease are most pronounced even when data are based on few cases or small population size (Berke, 2004; Goovaerts, 2005; Haining et al, 1994; Yu et al, 2010). Along with the maturing of techniques for handling spatiotemporal data and with the improvement of software and hardware for complex calculations, there is still a tendency to analyze data which contains risk factors and outcome variables but lacks geographical details (Copeland, 2010).…”

Section: Introductionmentioning

confidence: 99%

Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.

Chien

Schootman

2013

Spatial and Spatio-temporal Epidemiology

Self Cite

View full text Add to dashboard Cite

This study identified geographic disparities in breast cancer mortality across the U.S. using kriging to overcome unavailability of data because of confidentiality and reliability concerns. A structured additive regression model was used to detect where breast cancer mortality rates were elevated across nine divisions with 3109 U.S. counties during 1982-2004. Our analysis identified at least 25.8% of counties where breast cancer mortality rates were elevated. High-risk counties compared to lower-risk counties had higher relative risks for African American women than for White women. Greater geographic disparities more likely present in African American women and younger women. To sum up, our statistical approach reduced the impact of unavailable data, and identified the number and location of counties with high breast cancer mortality risk by race and age across the United States.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.

Chien

Schootman

2013

Spatial and Spatio-temporal Epidemiology

Self Cite

View full text Add to dashboard Cite

show abstract

“…SOM content predictions are usually sought at unsampled locations across space. Different kinds of zsoft include, e.g., intervals of SOM content estimated from old soil maps based on polygon's color and legends, and probabilistic functions of secondary information originated from historical attribute data or from fuzzy data obtained by means of other methods (Gesink Law et al, 2006;Heywood et al, 2006;Jiang and Woodbury, 2006;Yu et al, 2007bYu et al, , 2010.…”

Section: Bme Methodologymentioning

confidence: 99%

Improving Environmental Prediction by Assimilating Auxiliary Information

Yang¹,

Zhang²,

Zhang³

et al. 2015

J ENV INFORM

View full text Add to dashboard Cite

The concern of this work is the systematic synthesis of site-specific samples and auxiliary information (including continuous and categorical variables) aiming at improving spatial prediction of natural attributes (soil properties, contaminant processes etc.). Bayesian Maximum Entropy (BME) is the theoretical support of the proposed synthesis. The significance of the synthesis is that it can increase the prediction accuracy of natural attributes in a physically meaningful and technically efficient manner. The spatial prediction approach is applied in a real world case study that combines soil organic matter (SOM) content samples with auxiliary information (terrain indices, soil types, and soil texture) to generate predictive maps. Prediction was affected by soil type and soil texture (prediction accuracy increased when categorical variables were included). In the same case study, the BME-based approach was compared with mainstream spatial statistics techniques, like Regression Kriging (RK) with auxiliary information, and hard data-driven Ordinary Kriging (OK). The numerical results demonstrated the superiority of the BME-based approach over the Kriging-based techniques, whereas it was found that some key BME parameters (counts of soft data, predicted variables categories, and continuous auxiliary variable categories) can have different effect on SOM prediction accuracy. The success of BME-based prediction relied heavily on finding adequate auxiliary information about the study situation.

show abstract

“… generally represents normalized constraints, expectation (first-order moment) constraints, variance (second-order moment) constraints or covariance (second-order mixed moment) constraints. In this paper, indicates covariance of morbidity in and in Anhui Province [ 31 – 33 ]: where and are sill coefficients; and represent spatial and temporal range of spatio-temporal covariance, respectively. According to Format (3) and Constraint (4), the maximum of priori PDF is where and is lagrange multiplier, is given by (2) Calculating posterior PDF.…”

Section: Appendixmentioning

confidence: 99%

Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China

Wang

Liu

Bergquist³

et al. 2021

BMC Infect Dis

View full text Add to dashboard Cite

Background “Schistosomiasis” is a highly recurrent parasitic disease that affects a wide range of areas and a large number of people worldwide. In China, schistosomiasis has seriously affected the life and safety of the people and restricted the economic development. Schistosomiasis is mainly distributed along the Yangtze River and in southern China. Anhui Province is located in the Yangtze River Basin of China, with dense water system, frequent floods and widespread distribution of Oncomelania hupensis that is the only intermediate host of schistosomiasis, a large number of cattle, sheep and other livestock, which makes it difficult to control schistosomiasis. It is of great significance to monitor and analyze spatiotemporal risk of schistosomiasis in Anhui Province, China. We compared and analyzed the optimal spatiotemporal interpolation model based on the data of schistosomiasis in Anhui Province, China and the spatiotemporal pattern of schistosomiasis risk was analyzed. Methods In this study, the root-mean-square-error (RMSE) and absolute residual (AR) indicators were used to compare the accuracy of Bayesian maximum entropy (BME), spatiotemporal Kriging (STKriging) and geographical and temporal weighted regression (GTWR) models for predicting the spatiotemporal risk of schistosomiasis in Anhui Province, China. Results The results showed that (1) daytime land surface temperature, mean minimum temperature, normalized difference vegetation index, soil moisture, soil bulk density and urbanization were significant factors affecting the risk of schistosomiasis; (2) the spatiotemporal distribution trends of schistosomiasis predicted by the three methods were basically consistent with the actual trends, but the prediction accuracy of BME was higher than that of STKriging and GTWR, indicating that BME predicted the prevalence of schistosomiasis more accurately; and (3) schistosomiasis in Anhui Province had a spatial autocorrelation within 20 km and a temporal correlation within 10 years when applying the optimal model BME. Conclusions This study suggests that BME exhibited the highest interpolation accuracy among the three spatiotemporal interpolation methods, which could enhance the risk prediction model of infectious diseases thereby providing scientific support for government decision making.

show abstract

Spatiotemporal Analysis and Mapping of Oral Cancer Risk in Changhua County (Taiwan): An Application of Generalized Bayesian Maximum Entropy Method

Cited by 16 publications

References 45 publications

Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.

Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.

Improving Environmental Prediction by Assimilating Auxiliary Information

Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China

Contact Info

Product

Resources

About