No abstract available.
Spatial Analysis

Epidemiologic Methods ; Spatial Analysis

Tuberculosis (TB) is known as a disease that prone to spatial clustering. Recent development has seen a sharp rise in the number of epidemiologic studies employing Geographical Information System (GIS), particularly in identifying TB clusters and evidences of etiologic factors. The aim of this systematic review is to determine evidence of TB clustering, type of spatial analysis commonly used and the application of GIS in TB surveillance and control. A literature search of articles published in English language between 2000 and November 2015 was performed using MEDLINE and Science Direct using relevant search terms related to spatial analysis in studies of TB cluster. The search strategy was adapted and developed for each database using appropriate subject headings and keywords. The literature reviewed showed strong evidence of TB clustering occurred in high risk areas in both developed and developing countries. Spatial scan statistics were the most commonly used analysis and proved useful in TB surveillance through detection of outbreak, early warning and identifying area of increased TB transmission. Among others are targeted screening and assessment of TB program using GIS technology. However there were limitations on suitability of utilizing aggregated data such as national cencus that were pre-collected in explaining the present spatial distribution among population at risk. Spatial boundaries determined by zip code may be too large for metropolitan area or too small for country. Nevertheless, GIS is a powerful tool in aiding TB control and prevention in developing countries and should be used for real-time surveillance and decision making.
Tuberculosis cluster ; geographical information system ; spatial analysis

Breast Neoplasms ; Spatial Analysis ; Women ; Malaysia

Based on the results of the fourth national survey of traditional Chinese medicine resources in Turpan city, Xinjiang, this study counted the types of traditional Chinese medicine resources in Turpan Basin. The spatial distribution differences of traditional Chinese medicine resources in Turpan Basin of Xinjiang were analyzed by using grid technology, trend surface analysis, global spatial autocorrelation analysis, and local spatial autocorrelation analysis, so as to clarify the overall change trend and aggregation degree of traditional Chinese medicine resources in Turpan Basin in horizontal and vertical directions. The results showed the following: in the horizontal direction, the species richness of traditional Chinese medicine resources in the central part of Turpan Basin was high, and there were great differences in the species richness of traditional Chinese medicine resources in Turpan Basin under different grid sizes. The spatial scale effect of the richness of traditional Chinese medicine resources in Turpan Basin is obvious. Among them, under the 30 km×30 km scale, the richness of the types of Chinese medicine resources shows a high spatial correlation, and the richness of the types of Chinese medicine resources at 5 km×5 km scale presents a near random distribution state, and the richness of the types of Chinese medicine resources at 80, 90, and 100 km scale sits negatively related. Vertical direction, Chinese medicine resources appear rich at the range of-154-150 m and 900-1 050 m following by range of 1 050-1 200 m.
China ; Medicine, Chinese Traditional ; Spatial Analysis ; Technology

Recently PTW developed a MicroLion liquid ionization chamber which is water_equivalent and has a small sensitive volume of 0.002 cm3. The aim of this work is to investigate such dosimetric characteristics as dose linearity, dose rate dependency, spatial resolution, and output factors of the chamber for the external radiotherapy photon beam. The results were compared to those of Semiflex chamber, Pinpoint chamber and Diode chamber with the sensitive volumes of 0.125 cm3, 0.03 cm3 and 0.0025 cm3, respectively and evaluated to be suitable for small fields. This study was performed in the 6MV photon energy from a Varian 2300 C/D linac accelerator and the MP3 water phantom (PTW, Freiburg) was used. Penumbras in the varios field sizes ranged from 0.5x0.5 cm2 to 10x10 cm2 were used to evaluate the spatial resolution. Output factors were measured in the field sizes of 0.5x0.5 to 40x40 cm2. Readings of the chamber was linearly proportional to dose. Dose rate dependency was measured from 100 MU/min to 600 MU/min, showed a maximum difference of 5.0%, and outputs decreased with dose rates. The spatial resolutions determined with comparing profiles for the field sizes of 0.5x0.5 cm2 to 10x10 cm2 agreed between every detector except the Semiflex chamber to within 2%. Outputs of detectors were compared to that of Semiflex chamber and showed good agreements within 2% for every chamber. This study shows that MicroLion chamber characterized by a high signal-to-noise ratio and water equivalence could be suitable for the small field dosimetry.
Dependency (Psychology) ; Reading ; Signal-To-Noise Ratio ; Spatial Analysis ; Water

Traditional Chinese medicinal resource survey method is time-consuming, strenuous, and having great human influence, the precision is not high enough. This paper, by using spatial information technology, carries on spatial sampling survey for wild medicinal plants resource for generous species to arrange the quadrat scientifically and estimate the suitable area, reserve precisely of medicinal plants. It not only improves the survey precision, but reduces the workload of field survey and provides scientific basis for survey method of pilot work on the fourth national traditional Chinese medicinal resource census.
Biodiversity ; Conservation of Natural Resources ; Ecosystem ; Humans ; Plants, Medicinal ; Spatial Analysis

OBJECTIVETo detect the spatial distribution characteristics of iodine in drinking water of residents in Shandong province with spatial autocorrelation analysis.

METHODSThe county-based study set Shandong province as a research site. A total of 108 164 water samples from 140 counties were collected. The drinking water iodine data in county-level city between 2008 to 2010 were obtained from Shandong Institute of Prevention and Control for Endemic Disease and was merged with an electronic map to build a spatial database. Global and local Moran's I index were calculated, respectively, and spatial autocorrelation and cluster range of iodine distribution in drinking water in Shandong province were studied by SaTScan software.

RESULTSAll counties were further grouped according to the "criteria of delimitation for IDD endemic areas" and "determination and classification of the areas of high water iodine and the endemic areas of iodine excess goiter", and 90 counties were iodine deficiency (< 10 µg/L), 31 were iodine suitable (10 - 150 µg/L), and 19 (> 150 µg/L) were high iodine. For the overall study area, the iodine distribution in drinking water in Shandong province existed spatial autocorrelation (Moran's I = 0.52, Z = 7.4, P < 0.01). For the local scale, the drinking water iodine in 18 counties of Dezhou, Liaocheng and Heze city in western Shandong province was clustered, the local Moran's I were between 0.22 - 1.00 (P < 0.01), which were all high-high clusters, indicating the positive spatial correlation. Spatial analysis using SaTScan software detected two cluster areas including 20 counties, which the centers located in Xiajin and Dingtao county, the cluster radiuses were 57.47 km and 65.58 km respectively. The analysis results were consistent with the results of local spatial autocorrelation.

CONCLUSIONThere are apparent spatial autocorrelation and strong spatial heterogeneity existed in the iodine distribution in drink water in Shandong province.

Objective: To analyze the epidemiology and spatial-temporal distribution characteristics of hand, foot and mouth disease (HFMD) in Shanxi province. Methods: The data of HFMD in Shanxi province from 2009 to 2020 were collected from notifiable disease management information system of Chinese information system for disease control and prevention and analyzed by descriptive epidemiology, Joinpoint regression, spatial autocorrelation analysis and spatio- temporal scanning analysis. Results: A total of 293 477 HFMD cases were reported in Shanxi province from 2009 to 2020, with an average annual incidence of 67.64/100 000 (293 477/433 867 454), severe disease rate of 5.36/100 000 (2 326/433 867 454), severe disease ratio of 0.79%(2 326/293 477), mortality of 0.015/100 000 (66/433 867 454), and fatality rate of 22.49/100 000 (66/293 477). The reported incidence rate, severe disease rate, mortality rate and fatality rate of HFMD showed decreasing trends. The main high-risk groups were scattered children and kindergarten children aged 0-5. The incidence of HFMD had obvious seasonal variation, with two peaks every year: the main peak was during June-July, the secondary peak was during September-October and the peak period is from April to November. A total of 13 942 laboratory cases were confirmed, with a diagnosis rate of 4.75% (13 942/293 477), including 4 438 (35.11%, 4 438/293 477) Enterovirus A71 (EV-A71) positive cases, 4 609 (33.06%, 4 609/293 477) Coxsackievirus A16 (CV-A16) positive cases, and 4 895 (31.83%, 4 895/293 477) other enterovirus positive cases. There was a spatial positive correlation (Moran's I ranged from 0.12 to 0.58, all P<0.05) and the spatial clustering was obvious. High-risk regions were mainly distributed in Taiyuan in central Shanxi province, Linfen and Yuncheng in southern Shanxi province, and Changzhi in southeastern Shanxi province. Spatial-temporal scanning analysis revealed 1 the most likely cluster and 8 secondary likely clusters, of which the most likely cluster (RR=2.65, LLR=22 387.42, P<0.001) located in Taiyuan and Jinzhong city, Shanxi province, including 12 counties (districts), and accumulated from April 1, 2009 to November 30, 2018. Conclusions: There was obvious spatial-temporal clustering of HFMD in Shanxi province, and the epidemic situation was in decline. The key areas were the districts in urban areas and the counties adjacent to it. Meanwhile, the monitoring and classification of other enterovirus types of HFMD should be strengthened.
Child ; Humans ; Hand, Foot and Mouth Disease/epidemiology* ; Spatial Analysis ; Enterovirus Infections ; Spatio-Temporal Analysis ; Cluster Analysis

Objective: To analyze the spatial distribution and identify the high risk areas of pulmonary tuberculosis at the township level in Beijing during 2005-2015. Methods: Data on pulmonary tuberculosis cases was collected from the tuberculosis information management system. Global autocorrelation analysis, local indicators of spatial association and Kulldorff's Scan Statistics were applied to map the spatial distribution and detect the space-time clusters of the pulmonary tuberculosis cases during 2005-2015. Results: Spatial analysis on the incidence of pulmonary tuberculosis at the township level demonstrated that the spatial autocorrelation was positive during the study period. The values of Moran's I ranged from 0.224 3 to 0.291 8 with all the P values less than 0.05. Hotspots were primarily distributed in 8 towns/streets as follows: Junzhuang, Wangping, Yongding and Tanzhesi in Mentougou district, Yancun in Fangshan district, Wangzuo town in Fengtai district, Tianqiao street in Xicheng district and Tianzhu town in Shunyi district. Spatiotemporal clusters across the entire study period were identified by using Kulldorff's spatiotemporal scan statistic. The primary cluster was located in Chaoyang and Shunyi districts, including 17 towns/streets, as follows: Cuigezhuang, Maizidian, Dongfeng, Taiyanggong, Zuojiazhuang, Hepingjie, Xiaoguan, Xiangheyuan, Dongba, Jiangtai, Wangjing, Jinzhan, Jiuxianqiao, Laiguangying, Sunhe towns/streets in Chaoyang district, Houshayu and Tianzhu town in Shunyi district, during January to December 2005. Conclusion: Incidence rates of pulmonary tuberculosis displayed spatial and temporal clusterings at the township level in Beijing during 2005-2015, with high risk areas relatively concentrated in the central and southern parts of Beijing.
Beijing ; China ; Cluster Analysis ; Humans ; Incidence ; Spatial Analysis ; Spatio-Temporal Analysis ; Tuberculosis ; Tuberculosis, Pulmonary/ethnology*