Breast cancer is the most common malignant tumor among Chinese females. We should focus on the research of risk assessment models of gene-environmental factors to guide primary and secondary prevention, and this public health strategy is expected to maximize the health benefits of the population. This paper introduces previous studies of risk factors and predictive models for Chinese breast cancer and provides three points for future research. Firstly, we should explore the specific risk factors related to breast cancer risk in Chinese population, such as overweight or reproductive control measures. Secondly, we should use evidence-based and machine learning methods to select environmental-genetic risk factors. Finally, we should set up an information collective platform for breast cancer risk factors to test the validity of prediction models based on a long-term follow-up cohort of Chinese females.
ObjectivePublic health information collection is critical in improving the capacity of basic public health services. Our study took the "Wei Jian E Tong" APP as an example to evaluate the willingness and influencing factors of rural public health service personnel to continue using such APPs.MethodsWe applied exploratory sequential design in mixed-method research and chose Renshou county in Sichuan province as the representative region. Firstly, we used the personal in-depth interview to initially explore the status quo, applicability, continued willingness to use APP and other issues. Secondly, we used unified theory of acceptance and use of technology (UTAUT) and expectation confirmation theory (ECT) to construct a hypothetical model of influencing factors of user satisfaction. We then designed a structured questionnaire covering 7 measurement dimensions to survey all users of the APP at the survey site. Finally, we used structural equation model to verify the research hypothesis.ResultsA total of 21 individuals were interviewed in this survey, including leaders of township health centers, public health doctors, and rural doctors. Qualitative results showed the major defects were insufficient funds and policy support in the promotion and application, additionally lack of software functionalities and system incompatibility. A total of 593 valid questionnaires were collected from the quantitative survey on the satisfaction of township doctors and village doctors. Structural equation model results showed that seven direct hypotheses were established, of which compatibility had the largest effect value user satisfaction with a total effect value of 0.617, followed by facilitating condition (r=0.211), performance expectancy (r=0.137), effort expectancy (r=0.091) and social influence (r=0.068).ConclusionsTo promote the application of information collection apps in primary public health services and improve user satisfaction, the focus should be on solving software incompatibility and create interconnection among all levels of medical systems. At the same time, it is necessary to solve funding problems as a whole, optimize software functions, improve the performance evaluation system, and improve software training and promotion.
ObjectivesTo explore the construction method of prediction model of absolute risk for breast cancer and provide personalized breast cancer management strategies based on the results.MethodsA case-control design was conducted with 2 747 individuals diagnosed as primary breast cancer by pathology in West China Hospital of Sichuan University from 2000 to 2017 and 6 307 healthy controls from Breast Cancer Screening Cohort in Sichuan Women and Children Center and Chengdu Shuangliu District Maternal and Child Health Hospital. Standardized questionnaires and information management systems in hospital were used to collect information. Decision trees, logistic regression, the formula in Gail model and registration data in China were used to estimate the probability of 5-year risk of breast cancer. Eventually a ROC (receiver operating characteristics) curve was drawn to identify optimal cut-off value, and the power was evaluated.ResultsThe decision tree exported 4 variables, which were urban or rural sources, number of live birth, age and age at menarche. The median 5-year risk and interquartile range of the controls was 0.027% and 0.137%, while the median 5-year risk and interquartile range of the cases was 0.219% and 0.256%. The ROC curve showed the cut-off value was 0.100%. Through verification, the sensitivity was 0.79, the specificity was 0.73, the accuracy was 0.75, and the AUC (area under the curve) was 0.79.ConclusionsThe methods used in our study based on 9 054 female individuals in Sichuan province could be used to predict the 5-year risk for breast cancer. Predictor variables include urban or rural sources, number of live birth, age, and age at menarche. If the 5-year risk is more than 0.100%, the person will be judged as a high risk individual.
ObjectiveTo establish a hypertension prediction model for middle-aged and elderly people in China and to use the basic public health service database for performance validation. MethodsThe literature related to hypertension was retrieved from the internet. Using meta-analysis to assess the effect value of influencing factors. Statistically significant factors, which were also combined in the database, were extracted as the predictors of the models. The predictors’ effect values were logarithmarithm-transformed as the parameters of the Logit function model and the risk score model. Participants who were never diagnosed with hypertension at the physical examination of health service project of Hongguang Town Health Center in Pidu District of Chengdu from January 1, 2017, to January 1, 2022, were considered as the external validation group. ResultsA total of 15 original studies were involved in the meta-analysis and 11 statistically significant influencing factors for hypertension were identified, including age, female, systolic blood pressure, diastolic blood pressure, BMI, central obesity, triglyceride, smoking, drinking, history of diabetes and family history of hypertension. Of 4997 qualified participants, 684 individuals were identified with hypertension during the five-years follow-up. External validation indicated an AUC of 0.571 for the Logit function model and an AUC of 0.657 for the risk score model. ConclusionIn this study, we developed two different prediction models based on the results of meta-analysis. National basic public health service database is used to verify the models. The risk score model has a better prediction performance, which may help quickly stratify the risk class of the community crowd and strengthen the primary-level assistance system.