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ABSTRACT Brazil experienced one of the fastest increasing numbers of coronavirus disease (COVID-19) cases worldwide. The Sao Paulo State (SPS) reported a high incidence, particularly in Sao Paulo municipality. This study aimed to identify clusters of incidence and mortality of hospitalized patients with severe acute respiratory syndrome for COVID-19 in the SPS, in 2020-2021, and describe the origin flow pattern of the cases. Cases and mortality risk area clusters were identified through different analyses (spatial clusters, spatio-temporal clusters, and spatial variation in temporal trends) by weighting areas. Ripley's K12-function verified the spatial dependence between the cases and infrastructure. There were 517,935 reported cases, with 152,128 cases resulting in death. Of the 470,441 patients hospitalized and residing in the SPS, 357,526 remained in the original municipality, while 112,915 did not. Cases and death clusters were identified in the Sao Paulo metropolitan region (SPMR) and Baixada Santista region in the first study period, and in the SPMR and the Campinas, Sao Jose do Rio Preto, Barretos, and Sorocaba municipalities during the second period. We highlight the priority areas for control and surveillance actions for COVID-19, which could lead to better outcomes in future outbreaks.
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Objective:Using a spatio-temporal clustering analysis of varicella in Shanghai from 2006 to 2015 at a subdistrict level， we aim to provide decision support for formulating a reasonable varicella prevention strategy. Methods:Based on the data of varicella cases in Shanghai from 2006 to 2015， SaTScan was employed to detect and analyze the spatial pattern of varicella clusters. Moreover， field investigation was combined to infer and explain the risk factors of varicella clusters. Results:The spread of varicella in Shanghai from 2006 to 2015 had an obvious annual change and spatial differentiation at a subdistrict level. The findings of SaTScan showed that with a confidence level of 99.9%， there were totally 7 spatio-temporal clustering events in Shanghai from 2006 to 2015， in which 3 events were regional events and 4 were independent events. Independent events usually lasted for 2-4 years， while regional events in the "Jiading-Chongming district" and "Songjiang-Minhang district" areas had a longer duration and a larger impact. Conclusions:From 2006 to 2015， there is an obvious temporal and spatial clustering pattern of varicella in Shanghai. Majority of abnormal spatio-temporal clusters occur in rural areas rather than urban areas， which may be related to increasing floating population and migration of susceptible population caused by the implementation of large-scale construction projects.
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Objective To explore the optimal combination of parameters for the maximum spatial cluster size and maximum temporal cluster size of scan statistics. Methods The daily incidence data of hand-foot-and-mouth disease (HFMD) in Jingzhou in 2016 was collected as data source. The maximum spatial cluster sizes were set to 50%, 40%, 30%, 20%, and 10% of the population at risk. The maximum temporal cluster sizes were set to 7d, 14d, 30d, and 60d. A total of 20 parameter setting schemes were formed and spatial-temporal scanning was conducted one by one. The areas where the number of towns covered by the scanning area was less than 25 were selected, and the clustered epidemic of hand-foot-mouth disease can be detected at the same time in Xiejiaping Town of Songzi City and Sanzhou Town of Jianli County. The combination of large LLR and RR values was the optimal parameter setting. Results When the spatial windows were set to 20% of the population at risk, and the temporal windows were set to 30d, a total of 6 aggregation areas were detected. The number of covered townships was less than 25, and the clustered epidemic of Xiejiaping Township and Sanzhou Town were successfully detected. The LLR and RR values of the detected aggregation area were relatively large. This combination was the optimal parameter setting. Conclusion The combination of different parameters has a significant impact on the results of spatial-temporal scan statistics. It is recommended that parameters be optimized before applying this method.
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Objective To analyze the spatial clustering of human schistosomiasis at the village level in key counties in Hubei Province, to provide scientific evidence for formulating strategies for human schistosomiasis prevention and control in the next stage. Methods Gong'an County and Jiangling County in Hubei Province were selected as representative counties for this study. A town or village was set up as a research unit. Schistosomiasis cases of a positive fecal examination in 2015 and cases with positive detection for schistosomiasis serological antibody titer equal or above 80 in 2016-2018 were selected as research subjects in these two counties. The Kulldorff circular scan statistic was used for the spatial clustering analysis of human schistosomiasis infection status in the population. Results There was spatial clustering of positive schistosomiasis cases of fecal examination, at the level of a town or village in both counties in 2015. There was spatial clustering of positive human serological antibody detection at the level of town or village from 2016-2018. Eighty-six endemic villages in five towns in the northeast of Gong'an County, along the Yangtze river,including Mahaokou Town, Zhakou Town, Yangjiachang Town, Jiazhuyuan Town and Douhuti Town, were the most prominent. There was no spatial clustering of positive results of human serological antibody detection at the town and village level in Jiangling County, in2017, while there was spatial clustering of human serological antibody detection in 2016 and 2018,respectively. Fifty-seven endemic villages in two towns (Puji Town and Xionghe Town) in the southwest of Jiangling County, along the Yangtze river,were the most prominent. Conclusion There were spatial clustering of human schistosomiasis epidemic at village level both in Gong'an County and Jiangling County, Hubei Province. Compared with the previous studies, there was a trend of shrink and decline of clustering areas. Therefore, the current situation of the epidemic has put forward higher requirements for the implementation of precise prevention and control in the progress of schistosomiasis elimination work in various epidemic areas.
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Objective To detect the spatial distribution characteristics of water fluoride in Shandong Province.Methods The county-based study set Shandong Province as a research site.The county level fluoride database was matched with electronic maps to build geographic information system (GIS) spatial data platform.Global Moran's I and Local Moran's I index were calculated,respectively,and the cluster range of water fluoride distribution in Shandong Province was studied through SaTScan software.Results The water fluoride was normal (≤ 1.0 mg/L) in 54 counties in Shandong Province,mainly located in Weihai and Yantai in the eastern,Dongying in northern,and vast region in middle and southeastern of Shandong Province.Eighty-six counties were high water fluoride counties (＞ 1.0 mg/L),mainly distributed in the southwest,northwest and north-central of Shandong Province,showing a significant geographical feature.Global spatial autocorrelation analysis showed that the distribution of water fluoride content in Shandong Province showed significant positive spatial autocorrelation (Moran's I =0.44,Z =6.83,P ＜ 0.01).Local Moran's I analysis showed water fluoride in 13 counties had local spatial autocorrelation,being all high-high clusters.And these results were statistically significant (P ＜ 0.05).A cluster area was detected through SaTScan spatial analysis software,including 15 counties.The center was located in Dongming County of Heze City,with radius of 130.08 km.The results and the local spatial autocorrelation results were basically consistent.Conclusions There are apparent spatial autocorrelation and spatial cluster in water fluoridation in Shandong Province.Spatial autocorrelation and SaTScan software can be combined in exploring the spatial distribution of water fluoride.
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OBJECTIVES: Numerous studies have revealed the adverse health effects of acute and chronic exposure to particulate matter less than 10 mum in aerodynamic diameter (PM10). The aim of the present study was to examine the spatial distribution of PM10 concentrations and cardiovascular mortality and to investigate the spatial correlation between PM10 and cardiovascular mortality using spatial scan statistic (SaTScan) and a regression model. METHODS: From 2008 to 2010, the spatial distribution of PM10 in the Seoul metropolitan area was examined via kriging. In addition, a group of cardiovascular mortality cases was analyzed using SaTScan-based cluster exploration. Geographically weighted regression (GWR) was applied to investigate the correlation between PM10 concentrations and cardiovascular mortality. RESULTS: An examination of the regional distribution of the cardiovascular mortality was higher in provincial districts (gu) belonging to Incheon and the northern part of Gyeonggido than in other regions. In a comparison of PM10 concentrations and mortality cluster (MC) regions, all those belonging to MC 1 and MC 2 were found to belong to particulate matter (PM) 1 and PM 2 with high concentrations of air pollutants. In addition, the GWR showed that PM10 has a statistically significant relation to cardiovascular mortality. CONCLUSIONS: To investigate the relation between air pollution and health impact, spatial analyses can be utilized based on kriging, cluster exploration, and GWR for a more systematic and quantitative analysis. It has been proven that cardiovascular mortality is spatially related to the concentration of PM10.