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The “Regional Development Plan of the Yangtze River Delta (YRD)”, or the “YRD Development Policy”, is a national policy of China aimed at promoting industrial structure upgrading and the high-quality development of the regional economy in the YRD. To test the implementation effect of this policy, this work applied the synthetic control method (SCM) to explore the impact of the YRD Development Policy on industrial structure upgrading in the YRD based on 15-year panel data of 30 provinces in mainland China. The results showed that, as a whole, the implementation of the YRD Development Policy has contributed to industrial structure upgrading in the YRD. The trend of industrial structure upgrading is more rapid in the original YRD than in the new YRD. From a local perspective, the YRD Development Policy has hindered the upgrading of the industrial structure in Anhui Province but promoted upgrading in the rest of the YRD. From a long-term perspective, the effectiveness of the YRD Development Policy is limited, presenting a clear N-shaped development trend. In terms of industrial structure changes, the impact of the YRD Development Policy on the three types of industries in the YRD shows obvious regional differences. Furthermore, economic development, urbanization, and technology innovation have a significant and positive impact on the industrial structure upgrading of the YRD. These findings have policy-making implications, enrich the research on the impact of the YRD Development Policy on industrial structure upgrading, and provide empirical reference for subsequent policy improvements.

BACKGROUND: The health risk assessment aims to describe and evaluate the possibility of a certain disease, hospitalization, or death. With the in-depth research of big data and machine learning technology, the health risk of individuals can be assessed by using the technology, and intervention measures can be taken in advance to reduce the risk. OBJECTIVE: This study aims to accurately predict and evaluate the possible risks of the population and individuals caused by environmental factors, and constantly improve the medical implementation process. METHODS: The relationship between air pollutants and health risk is analyzed from three dimensions of the respiratory system, circulatory system, and digestive system, the prediction method of health quantity related to environmental factors is explored, and a hybrid time series model HTSM (Heuristic Test Strategy Model) based on nonparametric regression and residual fitting is proposed. RESULTS: Respiratory and circulatory diseases are pollutant-sensitive diseases, while the elderly (>  65 years old) are the high-risk population. The improved model can effectively predict the unplanned readmission data in the actual medical scene, and the accuracy of the improved model is 11.11%higher than that of the traditional prediction model. In contrast to the single prediction model, HTSM’s error index for different systems is much lower. The mixed model HTSM is better than the single model in fitting the original data. CONCLUSION: HTSM model based on time series can effectively predict pollutant-sensitive diseases, which can provide an effective theoretical basis for assessing and predicting the population and individual health risks.