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Objectives To analyze the spatial and temporal aggregation of multidrug resistant pulmonary tuberculosis (MDR-TB) incidence in Nanning at the township / street scale from 2017 to 2021, to explore the spatial and temporal characteristics of the spread of MDR-TB in Nanning, and to provide a scientific reference basis for the health administrative departments to achieve the precise implementation of MDR-TB prevention and control. Methods Based on the data of MDR-TB cases in Nanning from 2017 to 2021, the spatial-temporal scanning analysis software SaTScan v9.7 was used to retrospectively detect and analyze the areas where MDR-TB cases gathered. Results Through simple spatial scanning analysis, it was found that there were three first-class aggregation areas (the aggregation center was Fujiayuan Street, Jiangnan District, 2017, Xinyang Street, Xixiangtang District, 2019, and Zhonghe Town, Yongning District, 2020), and one second-class aggregation area (the aggregation center was Jinchai Town, Mashan County, 2020). Simple time scanning showed that the clustering occurred from May 2019 to December 2020. Temporal and spatial aggregation analysis showed that Xinyang Street in Xixiangtang District was the center of the first-class aggregation area, Zhonghe Town in Yongning District was the center of the second-class aggregation area, and Jinchai Town in Mashan County was the center of the third-class aggregation area. Conclusion The multidrug resistant pulmonary tuberculosis epidemic in Nanning is distributed in an aggregated manner, especially in Xinyang Street, Xixiangtang District, which has the highest spatial and temporal aggregation. It is necessary to focus on and take regional prevention and control measures to control the epidemic.
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Objective:To analyze the epidemiological characteristics and spatiotemporal characteristics of human brucellosis in Henan Province.Methods:Data of human brucellosis in Henan Province from 2005 to 2021 were collected through the China Disease Prevention and Control Information System, and a descriptive epidemiological method was used to analyze the epidemic profile of brucellosis in Henan Province and the three distribution characteristics. Global and local spatial autocorrelation were used to analyze the spatial distribution and the hot spots of brucellosis in Henan Province, respectively, and spatiotemporal scanning was used to analyze the spatiotemporal clustering regions of brucellosis in Henan Province.Results:A total of 39 862 brucellosis cases were reported in Henan Province from 2005 to 2021, with an average annual incidence of 2.44/100 000, and the number of cases showed an overall increasing trend each year (χ 2trend = 11 127.85, P = 0.001). The onset months were mainly concentrated from March to July, accounting for 59.00% (23 517/39 862), with May as the peak (5 478 cases). Cases of brucellosis were reported in 157 counties (cities, districts) of the province. The ratio of male to female was 2.52∶1.00 (28 542/11 320). Farmers were the main occupation, with 32 985 cases (82.75%). The age of onset was mainly 45 to 65 years old, with 20 226 cases (50.74%). The global spatial autocorrelation analysis showed that the global Moran's I was > 0, Z > 1.96, and P < 0.05 in all years except 2006 - 2008, showing spatial clustering. Further local spatial autocorrelation analysis was performed, and high-high and low-low clustering areas existed in 2012 - 2021 ( P < 0.01). Spatiotemporal scanning analysis showed that there was one spatiotemporal cluster in the high incidence area and two spatiotemporal clusters in the low incidence area. The high incidence cluster was centered in Neixiang County, covering 48 counties (cities, districts) including Song County and Ruzhou City, and the aggregation time was from 2014 to 2021. The two low incidence clusters were centered in Yongcheng City and Boai County, covering 58 and 18 counties (cities, districts), respectively, and the aggregation time was 2016 - 2021 and 2005 - 2012, respectively. Conclusion:The overall incidence of brucellosis in Henan Province is on the rise from 2005 to 2021, with middle-aged and elderly male farmers as the main affected population, and there are spatiotemporal clusters of brucellosis in Henan Province.
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Objective @#To investigate the spatiotemporal clustering characteristics of hand, foot, and mouth disease in Shangcheng District, Hangzhou City in 2021, so as to provide insights into prevention and control of hand, foot, and mouth disease. @*Methods@#Data pertaining to the incidence of hand, foot and mouth disease in Shangcheng District in 2021 were collected from Chinese Disease Prevention and Control Information System, spatial and temporal distribution of hand, foot and mouth disease were described, and the spatiotemporal clusters were identified.@*Results@#A total of 2 473 cases with hand, foot and mouth disease were reported in Shangcheng District in 2021, with a reported incidence rate of 186.86/105. The incidence of hand, foot and mouth disease appeared a bimodal distribution, with the first peak seen during the period between May and July (1 164 cases, 47.07%) and the second peak during the period between October and December (659 cases, 26.65%). Hand, foot and mouth disease was reported across all streets in Shangcheng District, with the highest incidence seen in Dinglan Street (398.84/105). There were one primary cluster and ten secondary clusters of hand, foot and mouth disease, and the primary cluster was centered in Jianqiao Street with 1.81 km in radius, which covered 9 communities in Jianqiao Street and was clustered during the period from March 19 and April 13. Of the 10 secondary clusters, there were three clusters in Dinglan Street, one in each of Jiubao, Sijiqing, Wangjiang and Kaixuan streets, and three clusters crossing streets. In addition, no spatiotemporal clusters of hand, foot and mouth disease were identified in Nanxing Street, Qingbo Street or Ziyang Street.@*Conclusion@#There was a remarkable spatiotemporal cluster in the incidence of hand, foot and mouth disease in Shangcheng District in 2021, with the peaks in incidence from May to July and from October to December, and the incidence of hand, foot and mouth disease was mainly clustered in the urban-rural junction and the central urban regions.
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Mining activities can threaten residents' health even lives. Integrating spatial empirical Bayesian smoothing, joinpoint regression and spatiotemporal scanning methods, we analyzed aggregations and possible factors of four tumor mortality rates at township and village scales from 2012 to 2016 in Suxian district of Hunan Province, China. Results indicate: (1) Mortality rates were ranked: lung cancer > liver cancer > gastric cancer > colorectal cancer. (2) Lung cancer had a higher five-year mortality rate in the middle; relative risk (RR) of death from lung cancer from 2012 to 2015 in Xujiadong Village was 7.48. Liver cancer had a higher five-year mortality rate in the Middle West; RR in areas centered on Nanta Street with a radius of 9.87 km from 2015 to 2016, was 1.83. Gastric cancer had a higher five-year mortality rate in the east; RR in Xujiadong Village from 2012 to 2014 was 6.9. Five-year mortality rate of colorectal cancer was higher in the northwest; RR in regions centered on Huangcao Village with a radius of 12.11 km in 2016, was 2.88. (3) Pollution from ore mining and smelting, heavy metal and non-metallic, and mine transportation were the main possible factors. This research provides a method reference for studying spatiotemporal patterns of disease in China even the world.
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Neoplasias Colorretais , Neoplasias Hepáticas , Neoplasias Pulmonares , Poluentes do Solo , Neoplasias Gástricas , Humanos , Poluentes do Solo/análise , Monitoramento Ambiental/métodos , Teorema de Bayes , Análise FatorialRESUMO
BACKGROUND: As a common infectious disease, hand, foot and mouth disease (HFMD) is affected by multiple environmental and socioeconomic factors, and its pathogenesis is complex. Furthermore, the transmission of HFMD is characterized by strong spatial clustering and autocorrelation, and the classical statistical approach may be biased without consideration of spatial autocorrelation. In this paper, we propose to embed spatial characteristics into a spatiotemporal additive model to improve HFMD incidence assessment. METHODS: Using incidence data (6439 samples from 137 monitoring district) for Shandong Province, China, along with meteorological, environmental and socioeconomic spatial and spatiotemporal covariate data, we proposed a spatiotemporal mixed model to estimate HFMD incidence. Geo-additive regression was used to model the non-linear effects of the covariates on the incidence risk of HFMD in univariate and multivariate models. Furthermore, the spatial effect was constructed to capture spatial autocorrelation at the sub-regional scale, and clusters (hotspots of high risk) were generated using spatiotemporal scanning statistics as a predictor. Linear and non-linear effects were compared to illustrate the usefulness of non-linear associations. Patterns of spatial effects and clusters were explored to illustrate the variation of the HFMD incidence across geographical sub-regions. To validate our approach, 10-fold cross-validation was conducted. RESULTS: The results showed that there were significant non-linear associations of the temporal index, spatiotemporal meteorological factors and spatial environmental and socioeconomic factors with HFMD incidence. Furthermore, there were strong spatial autocorrelation and clusters for the HFMD incidence. Spatiotemporal meteorological parameters, the normalized difference vegetation index (NDVI), the temporal index, spatiotemporal clustering and spatial effects played important roles as predictors in the multivariate models. Efron's cross-validation R2 of 0.83 was acquired using our approach. The spatial effect accounted for 23% of the R2, and notable patterns of the posterior spatial effect were captured. CONCLUSIONS: We developed a geo-additive mixed spatiotemporal model to assess the influence of meteorological, environmental and socioeconomic factors on HFMD incidence and explored spatiotemporal patterns of such incidence. Our approach achieved a competitive performance in cross-validation and revealed strong spatial patterns for the HFMD incidence rate, illustrating important implications for the epidemiology of HFMD.
