Spatiotemporal heterogeneity of the association between socioeconomic development and health policy attention: a geographically and temporally weighted regression modeling study in China.

Objectives: Health policy attention (HPA) refers to the extent of attention given by governments to health issues in public policy and is generally influenced by socioeconomic development. This study aimed to examine the spatiotemporal heterogeneity and clustering of the associations between socioeconomic factors and HPA. Study design: Longitudinal study. Methods: This study examined the spatiotemporal heterogeneity of the association between public and provincial government attention, economic development, and demographic transition and HPA by using geographically and temporally weighted regression (GTWR). Word2Vec machine learning technology was utilized to calculate HPA data in 323 cities and independent variable data was collected in each city in China over the period of 2018-2021. Results: The results showed that there is a substantial overall rise in HPA levels throughout China following the COVID-19 pandemic. Furthermore, the GTWR results revealed significant spatiotemporal heterogeneity in the associations between HPA and public and provincial government attention, economic development, and demographic transition, particularly in the context of COVID-19. The impact of provincial government attention on HPA decreased from the capital of the political center outward, while the impact of public financial investment decreased in less developed cities during the pandemic. It was only cities with high levels of aging are more likely to receive greater HPA. Conclusion: The finding highlighted the remarkable spatial and temporal variations in the associations between the variables and HPA across different regions in China, emphasizing the need for region-specific policies to strengthen the focus on health by municipal governments.