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Background: Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China. Methods: We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk. Results: Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E–H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China. Conclusions: China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease. [ABSTRACT FROM AUTHOR]

Background: There is a dearth of research combining geographical big data on medical resource allocation and growth with various statistical data. Given the recent achievements of China in economic development and healthcare, this study takes China as an example to investigate the dynamic geographical distribution patterns of medical resources, utilizing data on healthcare resources from 290 cities in China, as well as economic and population-related data. The study aims to examine the correlation between economic growth and spatial distribution of medical resources, with the ultimate goal of providing evidence for promoting global health equity. Methods: The data used in this study was sourced from the China City Statistical Yearbook from 2001 to 2020. Two indicators were employed to measure medical resources: the number of doctors per million population and the number of hospital and clinic beds per million population. We employed dynamic convergence model and fixed-effects model to examine the correlation between economic growth and the spatial distribution of medical resources. Ordinary least squares (OLS) were used to estimate the β values of the samples. Results: The average GDP for all city samples across all years was 36,019.31 ± 32,029.36, with an average of 2016.31 ± 1104.16 doctors per million people, and an average of 5986.2 ± 6801.67 hospital beds per million people. In the eastern cities, the average GDP for all city samples was 47,672.71 ± 37,850.77, with an average of 2264.58 ± 1288.89 doctors per million people, and an average of 3998.92 ± 1896.49 hospital beds per million people. Cities with initially low medical resources experienced faster growth (all β < 0, P < 0.001). The long-term convergence rate of the geographic distribution of medical resources in China was higher than the short-term convergence rate (|βi + 1| > |βi|, i = 1, 2, 3, ..., 9, all β < 0, P < 0.001), and the convergence speed of doctor density exceeded that of bed density (bed: |βi| >doc: |βi|, i = 3, 4, 5, ..., 10, P < 0.001). Economic growth significantly affected the convergence speed of medical resources, and this effect was nonlinear (doc: βi < 0, i = 1, 2, 3, ..., 9, P < 0.05; bed: βi < 0, i = 1, 2, 3, ..., 10, P < 0.01). The heterogeneity between provinces had a notable impact on the convergence of medical resources. Conclusions: The experiences of China have provided significant insights for nations worldwide. Governments and institutions in all countries worldwide, should actively undertake measures to actively reduce health inequalities. This includes enhancing healthcare standards in impoverished regions, addressing issues of unequal distribution, and emphasizing the examination of social determinants of health within the domain of public health research. [ABSTRACT FROM AUTHOR]

Background: Brucellosis poses a significant public health concern. This study explores the spatial and temporal dynamic evolution of human brucellosis in China and analyses the spatial heterogeneity of the influencing factors related to the incidence of human brucellosis at the provincial level. Methods: The Join-point model, centre of gravity migration model and spatial autocorrelation analysis were employed to evaluate potential changes in the spatial and temporal distribution of human brucellosis in mainland China from 2005 to 2021. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multi-scale Geographically Weighted Regression (MGWR) models were constructed to analyze the spatial and temporal correlation between the incidence rate of human brucellosis and meteorological and social factors. Results: From 2005 to 2021, human brucellosis in China showed a consistent upward trend. The incidence rate rose more rapidly in South, Central, and Southwest China, leading to a shift in the center of gravity from the North to the Southwest, as illustrated in the migration trajectory diagram. Strong spatial aggregation was observed. The MGWR model outperformed others. Spatio-temporal plots indicated that lower mean annual temperatures and increased beef, mutton, and milk production significantly correlated with higher brucellosis incidence. Cities like Guangxi and Guangdong were more affected by low temperatures, while Xinjiang and Tibet were influenced more by beef and milk production. Inner Mongolia and Heilongjiang were more affected by mutton production. Importantly, an increase in regional GDP and health expenditure exerted a notable protective effect against human brucellosis incidence. Conclusions: Human brucellosis remains a pervasive challenge. Meteorological and social factors significantly influence its incidence in a spatiotemporally specific manner. Tailored prevention strategies should be region-specific, providing valuable insights for effective brucellosis control measures. [ABSTRACT FROM AUTHOR]