Spatial clustering analysis of scarlet fever incidence in China from 2016 to 2020 / 南方医科大学学报

OBJECTIVE@#To investigate the incidence trend and spatial clustering characteristics of scarlet fever in China from 2016 to 2020 to provide evidence for development of regional disease prevention and control strategies.@*METHODS@#The incidence data of scarlet fever in 31 provinces and municipalities in mainland China from 2016 to 2020 were obtained from the Chinese Health Statistics Yearbook and the Public Health Science Data Center led by the Chinese Center for Disease Control and Prevention.The three-dimensional spatial trend map of scarlet fever incidence in China was drawn using ArcGIS to determine the regional trend of scarlet fever incidence.GeoDa spatial autocorrelation analysis was used to explore the spatial aggregation of scarlet fever in China in recent years.@*RESULTS@#From 2016 to 2020, a total of 310 816 cases of scarlet fever were reported in 31 provinces, municipalities directly under the central government and autonomous regions, with an average annual incidence of 4.48/100 000.The reported incidence decreased from 4.32/100 000 in 2016 to 1.18/100 000 in 2020(Z=103.47, P < 0.001).The incidence of scarlet fever in China showed an obvious regional clustering from 2016 to 2019(Moran's I>0, P < 0.05), but was randomly distributed in 2020(Moran's I>0, P=0.16).The incidence of scarlet fever showed a U-shaped distribution in eastern and western regions of China, and increased gradually from the southern to northern regions.Inner Mongolia Autonomous Region and Hebei and Gansu provinces had the High-high (H-H) clusters of scarlet fever in China.@*CONCLUSION@#Scarlet fever still has a high incidence in China with an obvious spatial clustering.For the northern regions of China with H-H clusters of scarlet fever, the allocation of health resources and public health education dynamics should be strengthened, and local scarlet fever prevention and control policies should be made to contain the hotspots of scarlet fever.

Spatial distribution of cognitive dysfunction and its risk factors in Chinese population aged 45 years and above / 南方医科大学学报

OBJECTIVE@#To analyze the spatial distribution of the prevalence of cognitive dysfunction and its risk factors in Chinese population aged 45 years and above to provide evidence for formulating regional prevention and control strategies.@*METHODS@#The study subjects with complete cognitive function data were selected from the follow-up data of the China Health and Retirement Longitudinal Study (CHARLS) Phase IV. ArcGis 10.4 software was used for spatial analysis of the prevalence of cognitive dysfunction in the population aged 45 years and above for each province based on the geographic information system (GIS) technology.@*RESULTS@#In 2018, the overall prevalence of cognitive dysfunction was 33.59% (5951/17716) in individuals aged 45 and above in China. Global spatial autocorrelation analysis indicated a spatial clustering and a positive autocorrelation (P < 0.001) of the prevalence of cognitive dysfunction in the study subjects, with a Moran's I value of 0.333085. The results of local spatial autocorrelation analysis showed that the southwestern region of China was the main aggregation area of patients with cognitive dysfunction. Geographically weighted regression analysis suggested that a male gender, an advanced age, and illiteracy were the major risk factors for cognitive dysfunction (P < 0.05). These 3 risk factors showed a spatial distribution heterogeneity with greater impact in the northern, western, and northwestern regions of China, respectively.@*CONCLUSION@#The prevalence of cognitive dysfunction is relatively high in individuals aged 45 years and above in China. A male gender, an advanced age, and illiteracy are the major risk factors for cognitive dysfunction and show different spatial distribution patterns, with the northern, western and northwestern regions of China as the key areas for prevention and control, where the prevention and control measures should be designed based on local conditions.