Abstract: Objective To analyze the development,methodology,and bottleneck of research on prediction of chronic disease prevalence trend in China and to provide a theoretical basis by optimizing prevalence prediction model for prevention and control of chronic diseases.Methods We conducted a meta-analysis of relevant literatures,systemically reviewed development status and bottlenecks of chronic disease prevalence prediction in China,and optimized models for chronic disease prevalence prediction.Results There is a lack in concerns to the prediction for chronic disease prevalence trend and there are a few studies on prediction of chronic disease incidence.The application of methodology is still restricted to linear or multiple regression.We screened out influential variants in scopes of population,economy,and society and then constructed a state space model with those variants.The established model demonstrated a higher fitness than other time series autoregressive models.Conclusion The use of state space model in conducting prevalence prediction of chronic disease for a specific region could improve the precision and sensitivity of long term prediction and to provide strong evidences for evidence-based decision-making.