Purpose/Significance To investigate the status quo and influencing factors of self-management level of elderly diabetic mellitus patients in Dongcheng District of Beijing,and to provide references for further development of community diabetes health educa-tion and health management.Method/Process The survey is conducted on 1 962 cases of elderly patients with type 2 diabetes mellitus who are randomly selected from community-level health service institutions in Dongcheng District of Beijing and are investigated by self-made questionnaires and the summary of diabetes self-care activities.Multiple stepwise regression and logistic regression are used to analyze the influencing factors of patients'self-management level.Result/Conclusion The self-management level of elderly diabetic mellitus patients is affected by many factors,among which the awareness of disease-related knowledge,the form of medical security and the use of health-related software to manage chronic diseases are the main influencing factors(P＜0.05).Local management depart-ments can take targeted health interventions to further improve the self-management ability of elderly diabetic mellitus patients.

Purpose/Significance The paper systematically reviews relevant research on infectious disease prediction models based on internet data,helps to realize the advancement of infectious disease surveillance,and provides references for the construction of intelli-gent three-dimensional prevention and treatment system of infectious diseases.Method/Process The development history and research direction of infectious disease surveillance and early warning based on internet data collected in the core database of Web of Science and CNKI in the past 20 years are reviewed,major existing problems and challenges are analyzed,and common prediction models and their optimization directions are summarized.Result/Conclusion The study on internet infectious disease surveillance shows the trend of diver-sification of monitoring diseases,refinement and specialization of data sources.Due to the complexity and uncertainty of internet data,most of the existing models are only suitable for short-term or real-time prediction.By constructing a combination model,strengthening multi-source data fusion,improving the selection of keywords and influencing factors,the model can be further optimized and the fitting effect and prediction capacity can be strengthened.

Purpose/Significance To explore the construction pathway of an intelligent automation model tailored for primary health-care institutions,aiming to address the issue of repetitive reporting.Method/Process Through methods such as on-site investigations and expert consultations,a field study is conducted in primary healthcare institutions in Dongcheng District,Beijing.Utilizing information resource planning methods and data integration and mapping technologies,the business interactions and information flow within these in-stitutions are analyzed to investigate the construction pathway of an automated reporting model.Result/Conclusion The business flow and data flow are mapped out by the modeling process,a repository of relevant reporting indicators is organized,and a multi-source data au-tomatic mapping model and rules are developed.The study provides a feasible reference pathway for realizing intelligent reporting in pri-mary healthcare institutions.