Analyzing the spatial distribution pattern and formation factors of medicinal plant resources can provide a scientific basis for the protection and development of traditional Chinese medicine(TCM) resources. This study is based on the survey data of medicinal plant resources in 104 county-level administrative regions of Anhui province in the Fourth National Survey of TCM Resources. The global spatial autocorrelation analysis, trend surface analysis, local spatial autocorrelation analysis, hotspot analysis, and a geodetector were employed to analyze the spatial distribution pattern of medicinal plant richness, and its relationship with natural factors was explored. The results can provide a basis for the formulation of development strategies such as the protection and utilization of TCM resources, as well as offer a scientific foundation for the establishment of regional planning schemes for TCM resources in Anhui province. The results indicated that the richness of medicinal plant resources in Anhui province had significant spatial heterogeneity, exhibiting highly clustered distribution characteristics. Cold spots and hot spots presented clustered distribution patterns, with cold spots mostly located north of the Huaihe River and hot spots south of the Yangtze River. Overall, the distribution of medicinal plant resources in Anhui province showed an overall trend of high in the south and low in the north, which was consistent with the overall geomorphic trend of this province. In addition, natural factors such as altitude, precipitation, and vegetation type played an important role in the diversity and spatial distribution pattern formation of medicinal plant resources. The extraction and analysis of the spatial distribution characteristics of natural factors in cold and hot spot regions discovered that the heterogeneity of eco-environments constituted a fundamental condition for the formation of species diversity.
Plants, Medicinal/classification* ; China ; Spatial Analysis ; Conservation of Natural Resources ; Biodiversity

Objective To reveal the spatial distribution patterns of key pollutants in the Huaihe River Basin and quantify the risks and burdens of non-gastrointestinal cancers by the grade of pollution,providing targets and data support for enhanced management of water pollution in the Huaihe River Basin. Methods Surface water quality data of the Huaihe River Basin were obtained from the National Surface Water Environmental Quality Monitoring Network(2021).Incidence data of seven cancers were extracted from the 2019 Annual Report of the China Cancer Registry.Random forest and SHapley Additive exPlanations were employed to select key pollutants,and pollution was graded based on the spatial analysis of the Huaihe River Basin.The cancer risks and population attributable fractions were calculated under pollution grades. Results Five key pollutants linked to cancers were identified,including total nitrogen,total phosphorus,chemical oxygen demand,biochemical oxygen demand after 5 days,and arsenic.Pollution was graded into three levels regarding the combined effects of pollutants.Compared with the low pollution areas,high pollution areas showed increased risks of lung cancer(RR=1.26,95%CI:1.06-1.50),breast cancer(female)(RR=1.46,95%CI:1.21-1.77),pancreatic cancer(RR=1.46,95%CI:1.06-2.01),brain cancer(RR=1.44,95%CI:1.05-1.98),and gallbladder cancer(RR=1.60,95%CI:1.03-2.50).The grade of pollution contributed to more than 5% of cases for most cancers above. Conclusions The potential cancer risks and burdens attributed to surface water pollution cannot be overlooked.Addressing this challenge necessitates close collaboration of various stakeholders to strengthen policy development,enhance environmental governance,and implement public health interventions.
Humans ; China/epidemiology* ; Rivers/chemistry* ; Neoplasms/etiology* ; Water Quality ; Environmental Monitoring ; Water Pollutants, Chemical/analysis* ; Phosphorus/analysis* ; Spatial Analysis ; Nitrogen/analysis* ; Arsenic/analysis* ; Water Pollution/adverse effects* ; Female

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.
Humans ; Incidence ; Scarlet Fever/epidemiology* ; China/epidemiology* ; Spatial Analysis ; Cluster Analysis ; Spatio-Temporal Analysis

From 2015 to 2019, the annual average incidence rate of scarlet fever was 7.80/100 000 in Yantai City, which showed an increasing trend since 2017 (χ²_trend=233.59, P<0.001). The peak period of this disease was from April to July and November to January of the next year. The ratio of male to female was 1.49∶1, with a higher prevalence among cases aged 3 to 9 years (2 357/2 552, 92.36%). Children in kindergartens, primary and middle school students, and scattered children were the high risk population, with the incidence rate of 159.86/100 000, 25.57/100 000 and 26.77/100 000, respectively. The global spatial auto-correlation analysis showed that the global Moran's I index of the reported incidence rate of scarlet fever in Yantai from 2015 to 2019 was 0.28, 0.29, 0.44, 0.48, and 0.22, respectively (all P values<0.05), suggesting that the incidence rate of scarlet fever in Yantai from 2015 to 2019 was spatial clustering. The local spatial auto-correlation analysis showed that the "high-high" clustering areas were mainly located in Laizhou City, Zhifu District, Haiyang City, Fushan District and Kaifa District, while the "low-high" clustering areas were mainly located in Haiyang City and Fushan District.
Child ; Humans ; Male ; Female ; Scarlet Fever/epidemiology* ; Spatial Analysis ; Cities/epidemiology* ; Seasons ; Risk Factors ; Incidence ; Cluster Analysis ; China/epidemiology*

OBJECTIVE: To identify the spatial distribution pattern of Oncomelania hupensis spread in Hubei Province, so as to provide insights into precision O. hupensis snail control in the province. METHODS: Data pertaining to emerging and reemerging snails were collected from Hubei Province from 2020 to 2022 to build a spatial database of O. hupensis snail spread. The spatial clustering of O. hupensis snail spread was identified using global and local spatial autocorrelation analyses, and the hot spots of snail spread were identified using kernel density estimation. In addition, the correlation between environments with snail spread and the distance from the Yangtze River was evaluated using nearest-neighbor analysis and Spearman correlation analysis. RESULTS: O. hupensis snail spread mainly occurred along the Yangtze River and Jianghan Plain in Hubei Province from 2020 to 2022, with a total spread area of 4 320.63 hm², including 1 230.77 hm² emerging snail habitats and 3 089.87 hm² reemerging snail habitats. Global spatial autocorrelation analysis showed spatial autocorrelation in the O. hupensis snail spread in Hubei Province in 2020 and 2021, appearing a spatial clustering pattern (Moran's I = 0.003 593 and 0.060 973, both P values < 0.05), and the mean density of spread snails showed spatial aggregation in Hubei Province in 2020 (Moran's I = 0.512 856, P < 0.05). Local spatial autocorrelation analysis showed that the high-high clustering areas of spread snails were mainly distributed in 50 settings of 10 counties (districts) in Hubei Province from 2020 to 2022, and the high-high clustering areas of the mean density of spread snails were predominantly found in 219 snail habitats in four counties of Jiangling, Honghu, Yangxin and Gong'an. Kernel density estimation showed that there were high-, secondary high- and medium-density hot spots in snail spread areas in Hubei Province from 2020 to 2022, which were distributed in Jingzhou District, Wuxue District, Honghu County and Huangzhou District, respectively. There were high- and medium-density hot spots in the mean density of spread snails, which were located in Jiangling County, Honghu County and Yangxin County, respectively. In addition, the snail spread areas negatively correlated with the distance from the Yangtze River (r = -0.108 9, P < 0.05). CONCLUSIONS There was spatial clustering of O. hupensis snail spread in Hubei Province from 2020 to 2022. The monitoring and control of O. hupensis snails require to be reinforced in the clustering areas, notably in inner embankments to prevent reemerging schistosomiasis.
Animals ; Schistosomiasis/prevention & control* ; Spatial Analysis ; Ecosystem ; Gastropoda ; Rivers ; China/epidemiology*

OBJECTIVE: To investigate the spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody, and to examine the correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, so as to provide insights into advanced schistosomiais control in the province. METHODS: The epidemiological data of schistosomiasis in Hunan Province in 2020 were collected, including number of permanent residents in survey villages, number of advanced schistosomiasis patients, number of residents receiving serological tests and number of residents seropositive for anti-Schistosoma antibody, and the prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were descriptively analyzed. Village-based spatial distribution characteristics of prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were identified in Hunan Province in 2020, and the correlation between the revalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody was examined using Spearman correlation analysis. RESULTS: The prevalence of advanced schistosomiasis was 0 to 2.72% and the seroprevalence of anti-Schistosoma antibody was 0 to 20.25% in 1 153 schistosomiasis-endemic villages in Hunan Province in 2020. Spatial clusters were identified in both the prevalence of advanced schistosomiasis (global Moran's I = 0.416, P < 0.01) and the seroprevalence of anti-Schistosoma antibody (global Moran's I = 0.711, P < 0.01) in Hunan Province. Local spatial autocorrelation analysis identified 98 schistosomiasis-endemic villages with high-high clusters of the prevalence of advanced schistosomiasis, 134 endemic villages with high-high clusters of the seroprevalence of anti-Schistosoma antibody and 36 endemic villages with high-high clusters of both the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province. In addition, spearman correlation analysis showed a positive correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody (r_s = 0.235, P < 0.05). CONCLUSIONS There were spatial clusters of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, which were predominantly located in areas neighboring the Dongting Lake. These clusters should be given a high priority in the schistosomiasis control programs.
Animals ; Humans ; Prevalence ; Seroepidemiologic Studies ; Schistosomiasis/epidemiology* ; Schistosoma ; Spatial Analysis ; Antibodies, Helminth ; China/epidemiology*

Objective: To analyze the epidemiological characteristics and spatiotemporal clustering of hepatitis A in Zhejiang Province from 2010 to 2019. Methods: The data of hepatitis A incidence in Zhejiang Province from 2010 to 2019 were collected from the infectious disease surveillance system of China Information System for Disease Control and Prevention. ArcGIS 10.7 software was used for spatial autocorrelation analysis. SaTScan 9.6 software was used for spatiotemporal scanning analysis. SPSS 25.0 software was used for additional analysis. Results: Zhejiang Province has reported 5 465 cases of hepatitis A in 2010-2019 years, with an average annual incidence rate of 1.00/100 000, and periodicity and seasonality are not obvious. The incidence of male was higher than that of female (P=0.023), and the highest incidence rate was 50-59 years old. Spatial autocorrelation analysis showed that there was a positive spatial correlation between the incidence of hepatitis A in Zhejiang Province from 2010 to 2017, with the weakest correlation in 2010 (Moran's I =0.103, Z=1.769, P=0.049), and the strongest correlation in 2016 (Moran's I=0.328, Z=4.979, P=0.001). Spatiotemporal scanning analysis showed that there was spatial aggregation of hepatitis A in Zhejiang Province from 2010 to 2019, with a total of three aggregation areas identified. Among them, the mostly aggregation area was concentrated in Xiangshan county of Ningbo city, which covered 10 counties (cities and districts), including Ninghai county and Yinzhou district, and appeared from January 1 to June 30, 2012. Conclusion: The incidence level of hepatitis A in Zhejiang Province shows a stable fluctuation trend from 2010 to 2019, and the seasonal regularity is not obvious. The population group aged 50-59 years old is the key population. There is spatial aggregation in the epidemic situation of hepatitis A. Targeted prevention and control measures of hepatitis A should be done based on the law of spatiotemporal aggregation and local incidence.
China/epidemiology* ; Cluster Analysis ; Female ; Hepatitis A/epidemiology* ; Humans ; Incidence ; Male ; Middle Aged ; Spatial Analysis

The mortality of female breast cancer in Shandong Province has increased since the 1970. The differential decomposition analysis found that the slight decline in the crude mortality of breast cancer among women was entirely due to non-demographic factors during the 1970-1990, and the significant increase in the crude mortality was due to a combination of demographic and non-demographic factors since the 1990. The contribution rate of demographic factor has gradually increased from 53.5% in 2004-2005 to 59.5% in 2011-2013, while that of non-demographic factor has decreased from 46.5% to 40.5%. The women aged 45-64 years old were the major population of female breast cancer deaths, accounting for 40%-60% of total breast cancer deaths in different times, and then the mortality in female aged 55-64 years old increased rapidly, with increases of 52.12%, 115.19% and 29.01% in 2011-2013 over the 1970-1974, 1990-1992 and 2004-2005, respectively (Z=-7.342,P<0.001). Compared with 1970-1974, the age-standardized mortality rate of rural women increased by 41.86% in 2011-2013 (Z=-17.933, P<0.001), and that of urban women increased by 18.62% in 2011-2013 (Z=-25.642, P<0.001). The age-standardized mortality rate of breast cancer in urban women was higher than that in rural women in different times (all P<0.05). The spatial scan analysis found that eastern Shandong Province was found to be a sustained high-risk area for death, and other high-risk areas were transferred from north to southwest of Shandong between 1970 and 2013.
Breast Neoplasms/epidemiology* ; Female ; Humans ; Middle Aged ; Rural Population ; Spatial Analysis

Spatial epidemiology focuses on the use of geographic information systems and spatial analysis to study spatial distribution and change tendency of diseases and explore the health status of specific populations. In recent years, spatial epidemiology has been applied in the field of HIV/AIDS prevention and control. This review summarizes the progress in the application of spatial epidemiology in the analysis of spatiotemporal distribution, non-monitoring area data estimation, influencing factors of AIDS and health resource allocation and utilization to provide reference for its application in the prevention and control of AIDS in the future.
Acquired Immunodeficiency Syndrome/prevention & control* ; Geographic Information Systems ; HIV Infections/prevention & control* ; Humans ; Spatial Analysis

Objective: To understand the spatial characteristics of echinococcosis and associated factors in the pastoral area of Qinghai province, and provide evidence for the effective prevention and control of echinococcosis. Methods: The number of echinococcosis cases in the pastoral areas of Qinghai in 2019 was collected to perform spatial epidemiological analysis. The thematic map of the distribution of echinococcosis cases was generated with software ArcGIS 10.8 for visual analysis and spatial autocorrelation analysis. The spatial autocorrelation and spatial scanning analysis were performed to estimate the clustering of echinococcosis with software SaTScan 9.5. Software GeoDa 1.14 and ArcGIS 10.8 were used to establish spatial lag model and geographical weighted regression model to analyze the related factors of echinococcosis epidemic. Results: In 2019, the echinococcosis surveillance covered 64 741 people in the pastoral area of Qinghai, and 829 echinococcosis cases were found, with a prevalence rate of 1.28%. The distribution of the cases had spatial correlation (Moran's I=0.41, P<0.001). The most possible clustering areas indicated by spatial scanning analysis included Banma, Jiuzhi, Dari and Gande counties of Guoluo Tibetan Autonomous Prefecture (LLR=460.77, RR=9.20, P<0.001). The prevalence of echinococcosis in the pastoral areas was positively associated with the total annual precipitation (β=0.13, P=0.036), and negatively associated with population density (β=-1.36, P=0.019) and doctors/nurse ratio (β=-25.60, P=0.026). Conclusions: The distribution of echinococcosis cases in the pastoral areas of Qinghai in 2019 had spatial correlation, and the prevalence was affected by total annual precipitation, population density, and doctors/nurse ratio.
China/epidemiology* ; Cluster Analysis ; Echinococcosis/epidemiology* ; Epidemics ; Humans ; Prevalence ; Spatial Analysis