HWA-LUNG YUChiang, Chi-TingChi-TingChiangLin, Shu-DeShu-DeLinTSUN-KUO CHANG2018-09-102018-09-102010http://www.scopus.com/inward/record.url?eid=2-s2.0-73249141970&partnerID=MN8TOARShttp://scholars.lib.ntu.edu.tw/handle/123456789/358476Purpose: Incidence rate of oral cancer in Changhua County is the highest among the 23 counties of Taiwan during 2001. However, in health data analysis, crude or adjusted incidence rates of a rare event (e.g., cancer) for small populations often exhibit high variances and are, thus, less reliable. Methods: We proposed a generalized Bayesian Maximum Entropy (GBME) analysis of spatiotemporal disease mapping under conditions of considerable data uncertainty. GBME was used to study the oral cancer population incidence in Changhua County (Taiwan). Methodologically, GBME is based on an epistematics principles framework and generates spatiotemporal estimates of oral cancer incidence rates. In a way, it accounts for the multi-sourced uncertainty of rates, including small population effects, and the composite space-time dependence of rare events in terms of an extended Poisson-based semivariogram. Results: The results showed that GBME analysis alleviates the noises of oral cancer data from population size effect. Comparing to the raw incidence data, the maps of GBME-estimated results can identify high risk oral cancer regions in Changhua County, where the prevalence of betel quid chewing and cigarette smoking is relatively higher than the rest of the areas. Conclusions: GBME method is a valuable tool for spatiotemporal disease mapping under conditions of uncertainty. ? 2010 Elsevier Inc. All rights reserved.1018825 bytesapplication/pdfBME; GIS; Oral Cancer; Spatial Modeling; Spatiotemporal Mapping[SDGs]SDG3Bayes Theorem; Epidemiologic Methods; Female; Humans; Incidence; Male; Mouth Neoplasms; Risk Factors; Space-Time Clustering; Taiwanarticle; Bayes theorem; betel nut; cancer incidence; cancer risk; case finding; cigarette smoking; controlled study; food intake; geographic distribution; high risk population; human; illness trajectory; mastication; mathematical analysis; morbidity; mouth cancer; population research; priority journal; Taiwan; Bayes Theorem; Epidemiologic Methods; Female; Humans; Incidence; Male; Mouth Neoplasms; Risk Factors; Space-Time Clustering; Taiwanjournal article10.1016/j.annepidem.2009.10.005