上海市杨浦区2007～2014年急性心肌梗死空间数据探索性分析

目的 探索上海市杨浦2007²014年区急性心肌梗死患者发病、死亡人群空间分布的特征,寻找“热点区域”。方法 建立2007²014年上海市杨浦区基于街区的急性心肌梗死空间数据库,运用地理信息系统技术、全局空间自相关（Moran’s I）与局部空间自相关（Local Moran’s I,LISA）方法对急性心肌梗死资料进行分析处理,探索杨浦区急性心肌梗死空间分布模式和规律。结果 全局空间自相关结果显示,杨浦区急性心肌梗死发病率（45.81/万）、死亡率（13.86/万）在各街区间存在明显的空间聚集现象(I_发病=0.1012,P=0.0291;I_死亡=0.1428,P=0.0281);局部空间自相关分析探测出了急性心肌梗死发病率、死亡率的高发热点地区为杨浦区长白新村街区和延吉新村街区。结论 杨浦区急性心肌梗死发病率、死亡率呈显著的地区分布规律,可根据这一特征进一步寻找地区差异性的原因,为合理配置医疗资源和调整管理策略提供科学依据。     

2007–2017年四川省新发棘球蚴病患者时空分布特征

目的　探索2007–2017年四川省棘球蚴病新发病例时空分布特征，为棘球蚴病防控策略制定和重点区域识别提供参考依据。方法　绘制2007–2017年四川省棘球蚴病新发病例检出率空间分布图，分析其空间分布特征及流行趋势。结果　2007–2017年四川省棘球蚴病新发病例检出率逐年下降，新发病例检出率较高地区主要分布在西部、西北部和北部，检出率较低地区主要分布在南部和东部地区。2010–2016年四川省棘球蚴病新发病例检出率全局Moran’s I值分别为0.19、0.22、0.17、0.44、0.48、0.31和0.16，Z值均> 1.96，P值均< 0.05，提示此期间四川省棘球蚴病新发病例呈聚集分布。局部Moran’s I分析显示，四川省棘球蚴病新发病例检出率“高?高”聚集区域和“低?低”聚集区域均呈一定聚集趋势。结论　2007–2017年四川省棘球蚴病新发病例检出率逐年下降，且呈一定的空间聚集性分布，应继续加强石渠、色达、德格、甘孜县和白玉县等重点地区的棘球蚴病防控工作。     

2007–2017年四川省新发棘球蚴病患者时空分布特征

目的　探索2007–2017年四川省棘球蚴病新发病例时空分布特征,为棘球蚴病防控策略制定和重点区域识别提供参考依据。方法　绘制2007–2017年四川省棘球蚴病新发病例检出率空间分布图,分析其空间分布特征及流行趋势。结果　2007–2017年四川省棘球蚴病新发病例检出率逐年下降,新发病例检出率较高地区主要分布在西部、西北部和北部,检出率较低地区主要分布在南部和东部地区。2010–2016年四川省棘球蚴病新发病例检出率全局Moran’s I值分别为0.19、0.22、0.17、0.44、0.48、0.31和0.16,Z值均> 1.96,P值均< 0.05,提示此期间四川省棘球蚴病新发病例呈聚集分布。局部Moran’s I分析显示,四川省棘球蚴病新发病例检出率“高?高”聚集区域和“低?低”聚集区域均呈一定聚集趋势。结论　2007–2017年四川省棘球蚴病新发病例检出率逐年下降,且呈一定的空间聚集性分布,应继续加强石渠、色达、德格、甘孜县和白玉县等重点地区的棘球蚴病防控工作。     

重庆市2008-2012年手足口病空间聚集性及影响因素研究

目的 了解重庆市2008-2012年手足口病空间聚集性及其发病的影响因素。方法 利用软件OpenGeoDa,对重庆市2008-2012年38个区(县)手足口病发病资料进行空间自相关和空间回归分析。结果 2008年重庆市手足口病发病率全局Moran’s I=0.133 2,P>0.05,不具有全局空间自相关性;2009-2012年重庆市手足口病发病率全局Moran’s I依次为0.458 7,0.567 5,0.398 6,0.606 0,均有P＜0.01,表明从全局上看,重庆市2009-2012年手足口病发病有正向空间自相关性,呈现出空间聚集性分布状态。空间回归分析结果提示:手足口病发病与城镇化率呈正相关(β=1.667 6,P=0.001 6)、与每千人口中的卫生技术人员呈负相关(β=-0.000 2,P=0.019 8)。结论 重庆市2008年手足口病发病呈随机性分布,2009-2012年手足口病发病有空间聚集性,重庆市2008-2012年手足口病发病受城镇化率及每千人口中的卫生技术人员数的影响,又以受城镇化率影响较大。     

江苏省水碘分布地理信息系统的建立

目的分析预测江苏省水碘分布,探讨空间分布和空间自相关性及局部自相关性。方法以2003年江苏省各乡镇的水碘中位数为依据,以江苏省数字地图为背景,在ArcViewGIS(地理信息系统)软件支持下,建立全省水碘空间分布图,并对水碘的空间自相关性和以县为单位的水碘的局部空间自相关性进行了分析。结果江苏1134个乡镇水碘数据呈正偏态分布,有69个乡镇(6.08%)的水碘中位数在150μg/L以上,主要分布于徐州地区,预测其接壤地区亦有可能存在高碘问题;空间自相关分析表明江苏省水碘分布存在中度空间正相关,徐州丰县、沛县和铜山地区自相关系数MoranLISA值>5,存在高碘水源聚集现象。结论江苏省水碘分布存在空间聚集现象,局部空间分布同时存在空间聚集性和空间异质性,说明江苏各地环境中水碘有很明显差异,对水碘水平不同地区应采用不同的补碘或降碘措施。     

2020年湖南省居民晚期血吸虫病患病率与血清抗血吸虫抗体阳性率空间分布特征

目的 分析2020年湖南省人群晚期血吸虫病患病率和血清抗血吸虫抗体阳性率空间分布特征及其相关性,为湖南省晚期血吸虫病防治提供科学依据。方法 收集2020年湖南省血吸虫病疫情数据,包括调查村常住人口数、晚期血吸虫病病例数、血清学检查受检人数和血清抗血吸虫抗体阳性人数。以村为单位分析人群晚期血吸虫病患病率和血清抗血吸虫抗体阳性率空间分布特征,采用Spearman秩相关评估人群晚期血吸虫病患病率和血清抗血吸虫抗体阳性率间的相关性。结果 2020年,湖南省1 153个血吸虫病流行村人群晚期血吸虫病患病率为0～2.72%,血清抗血吸虫抗体阳性率为0～20.25%。全局空间自相关分析发现,湖南省晚期血吸虫病患病率（全局Moran’s I=0.416,P <0.01）和血清抗血吸虫抗体阳性率（全局Moran’s I=0.711,P <0.01）均存在空间聚集性;局部空间自相关分析发现,湖南省晚期血吸虫病患病率存在98个高-高聚集村、血清抗血吸虫抗体阳性率存在134个高-高聚集村、36个村晚期血吸虫病患病率和血清抗血吸虫抗体阳性率均存在高-高聚集。Spearman秩相关分析显示,居民晚期血...     

湖沼地区湖北钉螺小尺度分布的空间自相关分析

目的研究湖沼地区湖北钉螺小尺度分布的空间自相关性。方法从安徽省池州市贵池区的秋浦河沿岸随机选择一个滩地的中间层和河边层作为研究现场，采用交叉复核随机抽检的查螺方法于滩地水淹前后各普查钉螺100框，判别死活，并分辨成螺和幼螺。在两层内各随机取10份土壤样本，测量土壤湿度。先计算并比较水淹前后两层的土壤湿度和钉螺密度，然后计算Moran'sI和Geary's C两个空间自相关指标，探讨钉螺的小尺度分布情况及是否存在空间自相关。结果钉螺的小尺度分布始终存在正空间自相关，其变化与钉螺密度的高低一致。Moran,s I均>0. 22，Geary，s C均<0. 76，P相似文献   

安徽、江西及江苏3省血吸虫病患者与钉螺分布的空间自相关分析

目的　通过地理信息系统 (GIS)分析江苏、安徽、江西 3省血吸虫病疫情空间分布规律。　方法　收集 3省近 2 0年的血吸虫病流行病学数据 ,建立 GIS空间数据库。在 Arc View3.x,S- PL U S及 Spatial Statistics软件 (模块 )支持下对建立的血吸虫病 GIS数据库进行空间自相关性分析。　结果　安徽及江西省血吸虫病患者总数及钉螺总面积不同代表年份中 ,均具有不同程度的空间自相关性。总体上钉螺分布的相关系数 (Moran′I )大于患者的相关系数 ,并具有非常显著性差异。江苏省钉螺总面积具有一定的空间自相关性 ,其空间聚集性显著高于血吸虫病患者数 ,两者间缺乏空间聚集性。　结论　空间自相关分析可用于血吸虫病患者、钉螺分布的地域聚集性的研究 ,以揭示该病的分布规律和流行态势     

山东省鲁西南地区地方性氟中毒流行病学调查

目的了解山东省鲁西南地区的地方性氟中毒病情现状,为制定防治策略提供科学依据。方法采用流行病学调查方法,选择11个县进行调查,水、尿氟含量测定采用氟离子选择电极法,8～12岁儿童氟斑牙诊断采用Dean’s法,临床和X线摄片检查氟骨症。结果在11个县中,调查20个改水村,水氟均值≤1.00 mg/L的村14个,占70.00%（14/20）;〉1.00 mg/L的村6个,占30.00%（6/20）;水氟最大值为3.73 mg/L。调查13个未改水村,水氟均值≤1.00 mg/L的村3个,占23.08%（3/13）;〉1.00 mg/L的村10个,占76.92%（10/13）,水氟最大值为3.38 mg/L。8～12岁儿童氟斑牙总患病率为39.17%（597/1 524）,氟斑牙指数为0.75,缺损率为3.94%（60/1 524）。儿童尿氟均值在1.40 mg/L以上的人数占42.13%（642/947）,最高值为18.53 mg/L。16岁以上成人的氟骨症临床和χ线检出率分别为5.88%（1235/20 980）、8.00%（2/25）。成人尿氟均值在1.60 mg/L以上的人数占65.34%（1 130/2 023）,最高值为13.97 mg/L。结论山东省鲁西南地区的地方性氟中毒病情尚未得到有效的控制,防治形势依然比较严峻,需进一步加大防治力度。     

2010-2018年浙江省人感染布鲁氏菌病空间分布特征及影响因素研究

目的 分析浙江省2010—2018年人间布鲁氏菌病空间分布特征及影响因素,为相应防控策略制订提供科学依据。方法 收集浙江省2010—2018年法定传染病报告系统中人间布鲁氏菌病疫情资料,采用全局、局部空间自相关分析方法进行分析;收集浙江省气象、畜牧生产等资料,进行人间布病疫情的空间回归分析。结果 2010—2018年,浙江省共报告布鲁氏菌病823例,年度报告发病数呈波动上升态势;人群特征显示病例以男性、40～60岁、农牧民及畜牧相关职业为主;发病率全局Moran’s I指数为0.12(P＜0.05),总体分布呈现聚集性;局部空间自相关结果显示人间布病高聚集区主要为浙北杭嘉湖、绍兴及浙西的衢州等地区,且近年向浙南的丽水扩散;空间回归分析显示,地区“年内羊出栏量”为人间布病发病的促进因素。结论 人间布病疫情与羊的养殖、交易、加工产业密切相关,浙江省布病疫情的地区分布特征随着各地相关产业的发展也在发生着变化,因此需要在发展相关产业时同步规划落实好布病防控措施。     

An accumulator model for spontaneous neural activity prior to self-initiated movement

A gradual buildup of neuronal activity known as the “readiness potential” reliably precedes voluntary self-initiated movements, in the average time locked to movement onset. This buildup is presumed to reflect the final stages of planning and preparation for movement. Here we present a different interpretation of the premovement buildup. We used a leaky stochastic accumulator to model the neural decision of “when” to move in a task where there is no specific temporal cue, but only a general imperative to produce a movement after an unspecified delay on the order of several seconds. According to our model, when the imperative to produce a movement is weak, the precise moment at which the decision threshold is crossed leading to movement is largely determined by spontaneous subthreshold fluctuations in neuronal activity. Time locking to movement onset ensures that these fluctuations appear in the average as a gradual exponential-looking increase in neuronal activity. Our model accounts for the behavioral and electroencephalography data recorded from human subjects performing the task and also makes a specific prediction that we confirmed in a second electroencephalography experiment: Fast responses to temporally unpredictable interruptions should be preceded by a slow negative-going voltage deflection beginning well before the interruption itself, even when the subject was not preparing to move at that particular moment.     

安徽省池州市贵池区急性血吸虫病的空间自相关分析

]  目的探讨安徽省池州市贵池区的急性血吸虫病（急血）病例是否存在空间自相关，为有效利用资料提供方法学依据。方法将贵池区区划图与207个村的空间位置进行叠加，生成村级基础地图。通过回顾性调查方法，收集2001～2006年的急血病例资料，将其作为属性数据库与村的空间位置进行匹配，生成空间自相关分析的急血病例数据库。选用连接计数统计量测量并检验急血病例的空间自相关性。结果2001—2006年全区共有急血病例83例，各年度病例数依次为13、23、13、14、14、6例。男性发病多于女性，平均发病年龄为23. 73岁，发病时间集中在7～10月。急血病例在2001～2003年存在正空间自相关(B-B连接计数依次为1.43、1.96和1.03，P值均为0.01)，而在2004～2006年不存在空间自相关(B-B逢接计数依次为o．69、0.48和0.10，尸值分别为0.08、0.25和o．29)。结论贵池区急血病例的发生地由集中向分散变化，必须时刻警惕以防止疫情的突然上升。     

Network analysis reveals multiscale controls on streamwater chemistry

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.Spatial heterogeneity of ecosystems has been a focus of landscape ecology for more than two decades, but the linkages between these patterns and underlying processes are still poorly understood (1–3). Quantifying these pattern-process links is largely a problem of scale. Specifically, it is difficult to perform experiments at the landscape scale and measure responses over the range of spatial and temporal scales commensurate with the processes of interest (4, 5).This problem of scale limits our understanding of both terrestrial and freshwater ecosystems. Effects of landscape pattern on ecosystem response can be evaluated at stream outlets by using biogeochemical signals that integrate physical and biological conditions of the catchment (6, 7). However, the spatial complexity of biogeochemical patterns and processes within stream networks has not been fully investigated because it is difficult to quantify such patterns at a grain and extent sufficient for examining spatial heterogeneity and processes across scales (8). Quantifying this variability and linking fine-scale and broad-scale patterns and processes within the branched topology of stream networks is essential for understanding aquatic ecosystem function and aquatic-terrestrial ecosystem connections, but requires new conceptual and methodological approaches (9, 10).Major advances in understanding biogeochemical fluxes and cycles in rivers and streams have resulted from increased recognition of how spatial heterogeneity and network topology reflect land–water interactions (e.g., refs. 11 and 12). However, our understanding of biogeochemical processes in stream networks is still limited to small-scale experiments (e.g., ref. 13), often with limited spatial extent or replication, and large-scale correlative models (14). Fine-grained observations at intermediate scales (e.g., 1–10 km²) may be especially powerful for advancing understanding of complex aquatic and terrestrial effects on biogeochemical fluxes throughout stream networks (15–17).Studies quantifying streamwater chemistry in a spatially intensive manner at intermediate scales have revealed a high degree of spatial structure that cannot be explained by current models of biogeochemical processes (11, 18). Specifically, these results show that traditional, continuum-based models—where conditions are regulated primarily by upstream processes and, thus, exhibit gradual downstream gradients—are insufficient for describing the true spatial complexity of biogeochemical patterns and processes in stream networks. This unfamiliar ground between fine and coarse scales of understanding is the crux of field-based science, in which the “preferred modes of explanation…appear to be systematically related to customary human scales of perception of the world” (19). Likewise, obtaining a bird’s-eye view of biogeochemical patterns at fine to coarse scales may be crucial for advancing ecosystem science and explaining the spatial complexity of streamwater chemistry within landscapes.Recent developments in geostatistical modeling provide a valuable new perspective on stream networks by revealing hydrological and ecological patterns in a spatially continuous manner (20, 21). To date, the relatively few sample points required to generate spatial interpolations have fueled the popularity of these models. However, the increasing use of network-based geostatistical techniques underscores a need to understand the processes from which these patterns arise or, more broadly, to elucidate ecosystem processes from spatial patterns and develop new hypotheses about system function (22). Recent theoretical and empirical approaches show that inferring processes from spatial patterns is possible by using empirical semivariograms and synoptic sampling (e.g., ref. 23). Specifically, the combination of spatial analysis and synoptic sampling allows one to visualize how patterns occur across different scales, while providing the empirical foundation needed to identify the processes that give rise to those patterns. Geostatistics have only recently been used to describe spatial patterns throughout stream networks (24, 25), although these tools have long been used to quantify spatial structure in terrestrial ecology (26).We apply geostatistical techniques to an unusually high-resolution synoptic dataset of streamwater chemistry collected throughout the Hubbard Brook Valley in New Hampshire to explore the spatial structure of biogeochemical patterns at multiple scales (18). The dataset consisted of 664 water samples collected over a 3-mo period every 100 m throughout all 32 tributaries of the 3,600-ha, fifth-order stream network of the Hubbard Brook Valley. We show previously undescribed patterns of spatial dependence based on three spatial relationships, revealing biogeochemical determinants occurring across scales, both within the stream network and surrounding catchment. Stream network patterns were defined by two spatial relationships: flow connected and flow unconnected (in the sense of refs. 20 and 21). The straight-line distance between two points defines Euclidean relationships. Flow-connected and unconnected network relationships describe distances along the stream network and were considered “connected” if water flows from one site to another. Thus, all points downstream of other points on the stream network were considered connected, but points upstream of tributary junctions that do not share flow were considered “unconnected.”Empirical semivariograms based on these three spatial relationships suggest the importance of different drivers of spatial variability in streamwater chemistry at multiple scales, e.g., fine (<1,500 m) and broad scales (>3,000 m) (Fig. 1). For example, semivariograms of flow-connected relationships indicate whether downstream flow and longitudinal transport exert a dominant control on streamwater chemistry by showing the level of autocorrelation between flow-connected samples. Likewise, semivariograms of flow-unconnected relationships provide information about the similarity/dissimilarity of tributary branches due to influences of landscape properties (e.g., soils or geology). Semivariograms of streamwater chemistry using Euclidean relationships reveal interactions or lateral connectivity between the stream network and the landscape. Therefore, both Euclidean and flow-unconnected network relationships provide information on how the landscape influences patterns of streamwater chemistry within a single catchment/network, whereas a flow-connected relationship largely describes the effect of hydrologic transport and upstream spatial dependence.Open in a separate windowFig. 1.Hypothetical semivariograms and associated maps depicting representative spatial patterns of water chemistry in a stream network. Nonstructured spatial pattern (i.e., uniform or random) (A) is indicated in the semivariogram by no change in semivariance (γ) (y axis) with increasing distance (d) between neighbors (x axis), as is graphically depicted by the uniform line color in the associated network map. In the example shown (A), γ = 0 for a uniform, nonstructured spatial pattern. Other potential semivariograms and associated network patterns include spatial dependence at a broad-scale with a gradient symbolized in the network map by changes in line color from the upper left (blue) to the lower right (red) of the stream network (B), fine-scale patchiness or spatial dependence indicated in the network map as ”hotspots” (C), and nested heterogeneity reflecting a combination of fine-scale patchiness imbedded within a broad-scale gradient (D) (in the sense of ref. 26). Characteristics of the semivariogram (C) are the asymptote or “sill,” which is roughly equivalent to the total population variance; the variance discontinuity at the y intercept or “nugget,” which represents variance due to sampling error and/or spatial dependence at distance intervals not explicitly sampled; and the “range,” which defines the distance or scale over which spatial dependence is expressed. Beyond this range, in a nonnested structure, points are spatially independent of one another or uncorrelated. Nested semivariograms are hierarchical structures, each characterized by its own range.Extensive work in the Hubbard Brook Ecosystem Study (HBES) over the last five decades provides the temporal context for understanding biogeochemical processes and landscape change through ecosystem change revealed by long-term research (27, 28). The current study aims to provide a spatial context (29) for interpreting how biogeochemical patterns observed from sparse fixed sites (e.g., outlets of experimental watersheds) fit within the larger stream network. We expect spatial dependence of streamwater chemistry to be structured by flow directionality and network topology, especially for constituents that are not strongly biologically cycled in headwater streams (e.g., base cations, Cl⁻, ). However, patchiness longitudinally in the stream network and across the landscape (i.e., by Euclidean distances) may arise because of the local influences of landscape features such as seeps and springs, and variation in vegetation, soil, and geologic materials. Our objectives were to (i) quantify spatial heterogeneity in streamwater chemistry at multiple scales within the stream network, (ii) compare patterns of streamwater chemistry by using different spatial relationships within the stream network and across the landscape (i.e., using network and Euclidean relationships), and (iii) evaluate this approach for linking biogeochemical patterns and processes by identifying potential drivers of spatial patterns in streamwater chemistry that bridge scales from tributaries, to the main stem, and throughout the entire Hubbard Brook Valley.     

1952-1989年和2006-2017年中国大陆恙虫病流行及时空分布特征

目的 研究并阐明中国大陆恙虫病流行趋势和时空分布特征, 为恙虫病的预防和控制提供参考依据。方法 根据1952-1989年和2006-2017年中国大陆恙虫病疫情报告数据,采用描述性流行病学方法、空间自相关分析和ArcGIS 10.4软件的可视化技术等,全面系统研究中国大陆恙虫病流行及时空分布特征,并确定高风险地区。结果 在1952-1989年和2006-2017年期间,我国累计报道恙虫病病例156 234例,死亡180例。1952-1989年的年均发病率0.13/10万。2006年以后,年均发病率急剧上升,由2006年的0.09/10万上升到2017年的1.62/10万,增长了18倍,年平均增长率为33%。流行季节仍以夏季和秋冬为主,多数病例主要集中在10月,女性发病率高于男性(χ²=168.34, P<0.001)。云南、安徽、广东、福建、江苏、山东、广西和四川8个省(自治区)的病例数最多,占全国总病例数的91.31%。全局空间自相关分析结果表明,在全国范围内恙虫病整体上存在着空间正相关,具有空间聚集性(I=0.085, P<0.05)。局部空间自相关分析结果显示,广西、福建及其周边地区为“热点区域”,是恙虫病高发区。结论 1952-1989年和2006-2017年我国恙虫病发病率存在逐年升高趋势,病例以夏季型和秋冬型为主。空间自相关分析可以及时发现该病的聚集情况并确定高发区和危险区。     

全国新型冠状病毒肺炎空间分布及空间聚集性浅析

目的探索新型冠状病毒肺炎(COVID-19)在地级行政区层面的空间分布、空间聚集性及在不同时期的分布特征。方法收集全国地级行政区(直辖市、特别行政区)COVID-19确诊的相关信息和数据,运用空间自相关方法对COVID-19确诊情况进行空间统计分析。结果1月24日、1月29日和2月8日COVID-19累计确诊病例与2月8日COVID-19新增确诊病例具有全局空间正相关性,局部空间自相关结果显示不同时间点的COVID-19累计确诊病例与新增确诊病例空间分布有所不同。结论COVID-19累计确诊病例与新增确诊病例全国分布具有空间聚集性,确定COVID-19的高值聚集地区,将为进一步采取防控措施提供参考。     

安徽省钉螺孳生环境分布时空分析

目的了解安徽省血吸虫病流行区钉螺及感染性钉螺时空分布特征。方法根据2016年安徽省钉螺调查建立的数据库,描述全省钉螺及感染性钉螺分布和变化趋势,分析有螺环境中钉螺空间分布和感染性钉螺在1950-2016年聚集分布情况。结果1950-2016年,安徽省共发现钉螺孳生环境22757个,其中曾为感染性钉螺环境5004个,分别呈单峰和双峰状变化,1970年是变化拐点;历史累计有螺面积共14.10万hm^2,其中88.08%于1950-1979年首次发现;共消灭11.45万hm^2有螺面积,77.17%的历史有螺环境在1970-1999年消灭。截至2016年,全省现有螺环境4830个,其中曾为感染性钉螺环境1051个;78.12%的现有螺环境已存在40年以上,65.75%的感染性钉螺环境在10年内消除了感染性钉螺。安徽省现有螺环境活螺密度存在空间自相关(Moran’s I=0.196,Z=139.63,P<0.001);局部热点分析显示有螺环境活螺密度在空间上呈聚集性,长江以南呈高值聚集,长江以北呈低值聚集;21个具有统计学意义的活螺密度高值聚集区分布于长江沿线及支流水系。时空扫描分析发现现有螺环境中感染性钉螺在4处区域存在时空聚集性。结论安徽省现有螺环境存在时间长久,仅靠药物难以消灭,必须结合环改灭螺。现有螺环境的活螺密度存在空间聚集性,部分聚集区也是近年的高风险区域。时空扫描分析发现的历史上感染性钉螺聚集分布的区域中,部分环境流行因素和人畜感染的风险仍然存在,还需将其作为重点防控区域。