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It is important to forecast incidence rates of infectious disease for the development of a better program on its prevention and control. Since the incidence rate of infectious disease is influenced by multiple factors, and the action mechanisms of these factors are usually unable to be described with accurate mathematical linguistic forms, the radial basis function (RBF) neural network is introduced to solve the nonlinear approximation issues and to predict incidence rates of infectious disease. The forecasting model is constructed under data from hepatitis B monthly incidence rate reports from 1991-2002. After learning and training on the basic concepts of the network, simulation experiments are completed, and then the incidence rates from Jan. 2003-Jun. 2003 forecasted by the established model. Through comparing with the actual incidence rate, the reliability of the model is evaluated. When comparing with ARIMA model, RBF network model seems to be more effective and feasible for predicting the incidence rates of infectious disease, observed in the short term.

Objective: To measure and assess the quality of life (QOL) and to explore the influencing factors on patients with malignant lymphoma., Methods: QOL of 110 patients with malignant lymphoma were marked using EORTC QLQ-C30 short form, and multiple linear regression models were used to study the main factors influencing the QOL of patients with malignant lymphoma on five functional scales (physical, role, cognitive, emotional, and social) and the total scores., Results: The influencing factors of quality of life on patients with malignant lymphoma appeared to be: history of relapse, refraining from smoking, older age, educational level, space for living, exercises, medical care system, and available health care programs. Relapse (beta = 5.997, P= 0.020) and refraining from smoking (beta = -6.526, P= 0.006) were associated with total QOL scores, educational level (beta = -2.144, P= 0.057), History of relapse (beta = 5.857, P = 0.003) was associated with total functional scales while exercises (beta= -0.771, P = 0.097) and refraining from smoking (beta= -4.106, P = 0.005) were with physical scales, refraining from smoking (beta = -4.644,P = 0.008) and older age (beta = 0.989, P= 0.029) were with role scales, relapse (beta = 14.035, P= 0.001) and older age (beta = 2.230, P= 0.023) were with cognitive scales, relapse (beta = 8.500, P= 0.031) and living space (beta = - 3.054, P= 0.0901) were with emotional scales and medical care system and available health care programs (beta = -6.577, P= 0.018) were with social scales respectively., Conclusion: Factors as prevention of relapse, correct cognition on malignant lymphoma, reasonable exercise, refrain from bad habits, improving medical care system could all increase the functions of malignant lymphoma patient, and to improve their quality of life.

Objective: To evaluate the relationship between circulating levels of insulin-like growth factor-1 (IGF-1), IGF-binding protein-3 (IGFBP-3) and colorectal cancer., Methods: A meta-analysis of 6 epidemiological studies on insulin-like growth factors and risk of colorectal cancer were performed., Results: The pooled odds ratio (OR) of IGF-1 and IGFBP-3 were 1.56 (95% CI: 1.14-2.13) and 0.78 (95% CI: 0.43-1.44) respectively. According to the results from different measurements (enzyme-linked immunoabsorbent assay and immunoradiometric assay), the pooled OR were 1.92 and 1.23 for IGF-1, 0.46 and 1.44 for IGFBP-3 respectively., Conclusion: High serum levels of IGF-1 were independent risk factors of colorectal cancer but the OR of IGFBP-3 was not statistically significant. The heterogeneity between studies on IGFBP-3 and colorectal cancer was caused by different measurements used, but there was still a need to conduct simultaneous large size study under 2 different measurements for further conclusion.

Objective: To discuss the potential application of artificial neural network (ANN) on the epidemiological classification of disease., Methods: Learning vector quantification neural network (LVQNN) and discriminate analysis were applied to data from epidemiological survey in a mine in 1996., Results: The structure of LVQNN was 25-->13-->3. The total veracity rates was 96.98%, and 92.45% among the abnormal blood glucose individuals. Through stepwise discriminate analysis, the discriminate equations were established including 11 variables with a total veracity rate of 87.34%, but was 85.53% in the abnormal blood glucose individuals. Further analysis on 30 cases with missing values showed that the disagreement ratio of LVQ was 1/30, lower than that of discriminate analysis of 7/30., Conclusions: Compared to the conventional statistics method, LVQ not only showed better prediction precision, but could treat data with missing values satisfactorily plus it had no limit to the type or distribution of relevant data, thus provided a new powerful method to epidemiologic prediction.