Refining and Validating a Two-stage and Web-based Cancer Risk Assessment Tool for Village Doctors in China

  • Shen, Xing-Rong (School of Health Service Management, Anhui Medical University) ;
  • Chai, Jing (School of Health Service Management, Anhui Medical University) ;
  • Feng, Rui (Department of Literature Review and Analysis, Library of Anhui Medical University) ;
  • Liu, Tong-Zhu (Anhui Provincial Cancer Hospital) ;
  • Tong, Gui-Xian (School of Health Service Management, Anhui Medical University) ;
  • Cheng, Jing (School of Health Service Management, Anhui Medical University) ;
  • Li, Kai-Chun (Luan Center for Disease Prevention and Control, First Affiliated Hospital of Anhui Medical University) ;
  • Xie, Shao-Yu (Luan Center for Disease Prevention and Control, First Affiliated Hospital of Anhui Medical University) ;
  • Shi, Yong (Luan Center for Disease Prevention and Control, First Affiliated Hospital of Anhui Medical University) ;
  • Wang, De-Bin (School of Health Service Management, Anhui Medical University)
  • Published : 2015.01.22


The big gap between efficacy of population level prevention and expectations due to heterogeneity and complexity of cancer etiologic factors calls for selective yet personalized interventions based on effective risk assessment. This paper documents our research protocol aimed at refining and validating a two-stage and web-based cancer risk assessment tool, from a tentative one in use by an ongoing project, capable of identifying individuals at elevated risk for one or more types of the 80% leading cancers in rural China with adequate sensitivity and specificity and featuring low cost, easy application and cultural and technical sensitivity for farmers and village doctors. The protocol adopted a modified population-based case control design using 72, 000 non-patients as controls, 2, 200 cancer patients as cases, and another 600 patients as cases for external validation. Factors taken into account comprised 8 domains including diet and nutrition, risk behaviors, family history, precancerous diseases, related medical procedures, exposure to environment hazards, mood and feelings, physical activities and anthropologic and biologic factors. Modeling stresses explored various methodologies like empirical analysis, logistic regression, neuro-network analysis, decision theory and both internal and external validation using concordance statistics, predictive values, etc..


Supported by : Natural Science Foundation of China


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