Objective To estimate healthy life expectancy of residents aged 30 and above in Jianyang, Sichuan province, and comprehensively evaluate the health status of the population, so as to provide reference and basis for the formulation of health-related public policies. Methods The population data, death data and health status data of Jianyang city in 2018 were collected, and the self-rated health life expectancy and life expectancy without chronic diseases of residents aged 30 and above were calculated by Sullivan method, and their proportion in life expectancy was calculated. Results A total of 4 482 residents aged 30 years and above were included in Jinyang city, of whom 3 989 were self-rated healthy, accounting for 89.00% of self-rated health, and 1 988 were suffering from chronic diseases, with a chronic disease prevalence rate of 44.36%. In the age group of 30-64, the self-rated health life expectancy of women was significantly higher than that of men (P<0.05), and in the age group of 40-44, the life expectancy without chronic diseases of women was significantly lower than that of men (P<0.05), and the differences were not statistically significant when comparing different genders in other age groups (P>0.05). With the increase of age, the proportion of residents’ self-rated health and the proportion of life expectancy without chronic diseases in healthy life expectancy gradually declined. Conclusions The chronic disease has become the main factor affecting the health of middle-aged and elderly population, especially female population. It is suggested that active measures should be taken to deal with chronic diseases and the elderly health support system should be strengthened to improve healthy life expectancy of residents in Jianyang city.
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.