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Title: Cancer burden in China: a Bayesian approach
Authors: Chen W; Armstrong BK; Zheng R; Zhang S; Yu XQ; Clements M
Categories: Cancer Type - All Cancers combined
Cancer Control, Survivorship, and Outcomes Research - Surveillance
Keywords: cancer; survival; Uncertainty; cancer registry; China; Female; Incidence; Male; methods; mortality; Registries
Year: 2014
Journal Title: BMC Cancer
Volume: 13
Page number start: 458
Abstract: BACKGROUND: Cancer is a serious health issue in China, but accurate national counts for cancer incidence are not currently available. Knowledge of the cancer burden is necessary for national cancer control planning. In this study, national death survey data and cancer registration data were used to calculate the cancer burden in China using a Bayesian approach. METHODS: Cancer mortality and incidence rates for 2004-2005 were obtained from the National Cancer Registration database. The third National Death Survey (NDS), 2004-2005 database provided nationally representative cancer mortality rates. Bayesian modeling methods were used to estimate mortality to incidence (MI) ratios from the registry data and national incidence from the NDS for specific cancer types by age, sex and urban or rural location. RESULTS: The total estimated incident cancer cases in 2005 were 2,956,300 (1,762,000 males, 1,194,300 females). World age standardized incidence rates were 236.2 per 100,000 in males and 168.9 per 100,000 in females in urban areas and 203.7 per 100,000 and 121.8 per 100,000 in rural areas. CONCLUSIONS: MI ratios are useful for estimating national cancer incidence in the absence of representative incidence or survival data. Bayesian methods provide a flexible framework for smoothing rates and representing statistical uncertainty in the MI ratios. Expansion of China's cancer registration network to be more representative of the country would improve the accuracy of cancer burden estimates
Programme: Health Services
Division: Cancer Research Division
DOI: 10.1186/1471-2407-13-458
Appears in Collections:Research Articles

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