血吸虫病医学地理研究的回顾与展望

doi:10.11820/dlkxjz.2010.01.006

Abstract

Abstract:

Medical geography has a long research history, nonetheless schistosomiasis-medical geography is relatively laggard in the field of Geography, where a lot of study was made by non-geographical professionals. This paper first studied the basic connotation and the principal contents of Schistosomiasis-medical Geography according to the mechanism of schistosomiasis transmission. Monitoring and prediction of snail habitats and schistosomiasis epidemicity were regarded as the principal study contents of Schistosomiasis-medical Geography. Second, connection of geographical factor pairs of meteorology and climatology, soil and vegetation, geomorphology and altitude, hydrology and hydraulics, human and economic factor with the shistosome and its hosts were systematically summarized, which was deemed as the foundation of Schistosomiasis-medical Geography. Third, the key research means for determination and prediction of snail habitats and schistosomiasis epidemicity by remote sensing, GIS and statistics (including geo-statistics) were reviewed worldwide, 7 kinds of prediction methods for snail habitats and 5 kinds of prediction methods for schistosomiasis epidemicity were concluded. 7 kinds of prediction methods for snail habitats were interpretation and cartography of snail habitats, meteorologically based method, images-retrieved information based method, unsupervised classification based method, dynamic monitoring of snail habitats in periods of floods, relation between geographic factors and snail habitats based method, and knowledge driven fuzzy-classification method. 5 kinds of prediction methods for schistosomiasis epidemicity were cartography and spatial auto-relation analysis, schistosomiasis transmission index modeling at large scale, quantitative modeling of various life phases of shistosoma and their hosts, regressive relational analysis and Bayesian modeling between epidemicity and geographic factors, and relation study between epidemicity, snail habitats and contact index of infected waters. Finally this paper analyzed the main challenges and endeavor directions for Schistosomiasis-medical Geography in the future.

Key words: GIS, challenges and prospects, schistosomiasis medical-geography, impacts of geographical factors, application of remote sensing &

ZHAO An, JIANG Meixin, JIAN Minfei, NI Caiying. Progress and Prospects of Schistosomiasis-medical Geography[J].PROGRESS IN GEOGRAPHY, 2010, 29(1): 45-51.

References

[1]   Engels D, Chitsulo L, Montresor A, et al. The global epidemiological situation of schistosomiasis and new approaches to control and research. Acta Tropica，2002, 82:139-146.
[2]   郝阳, 吴晓华, 郑浩, 等. 2006年全国血吸虫病疫情通报. 中国血吸虫病防治杂志, 2007,(6):326-328.
[3]   Zhao A, Bao S M, Gong P. Susceptibility and infection risk of Schistosomiasis disease.Geographic Information Sciences, 2006, 12(1): 44-50.
[4]   林涛,姜庆五. 遥感数据在钉螺生态学和血吸虫病流行病学研究中的应用. 中国媒介生物学及控制杂志，2003，14(6)：475-476.
[5]   袁鸿昌, 张邵基, 姜庆五. 血吸虫防治理论与实践. 上海：复旦大学出版社，2003: 116.
[6]   中华人民共和国卫生部地方病防治司：血吸虫病防治手册. 上海: 上海科学技术出版社，1990: 37-45，55-79.
[7]   Takahaschi T, Mori K, Kiseichugaku Z, et al. Phototactic, thermotactic and geotactic responses of miracidia of Schistosoma japonicum. Japanese Journal for Parasitology, 1961(10): 686-691.
[8]   周述龙, 林建银, 蒋明森. 血吸虫学(2版). 北京: 科学出版社，2001: 146-148.
[9]   袁鸿昌, 张邵基, 姜庆五. 血吸虫防治理论与实践.上海：复旦大学出版社，2003: 119-120,123.
[10] 林涛：利用遥感图像对湖沼型血吸虫病疫区洲滩植被的聚类分析.中华预防医学杂志，2001,(5)：312-314.
[11] 钟久河,张绍基,刘志德, 等. 鄱阳湖钉螺分布与植被、土壤关系的研究.中国血吸虫病防治杂志，1995，7(4): 206-209.
[12] 张旭东,杨晓春,彭钲华.钉螺分布与滩地环境因子的关系.生态学报，1999，19(2): 265-269.
[13] 钱晓红,杨筠,陈琳，等. 安宁河流域钉螺孳生地微环境理化因素研究. 现代预防医学，2000，27(1): 15-17.
[14] 林丹丹,张邵基.鄱阳湖区地理环境与血吸虫病传播.中华流行病学杂志，2002，23(2): 90-93.
[15] 何尚英,刘惠生,许正元，等.日本血吸虫毛蚴与钉螺感染率关系的计量研究.中国血吸虫病防治杂志，1989，1(1)：28-31.
[16] 罗茂林，黄德强，纪明亮，等.湘江望城段洲滩“迷魂阵”捕鱼网具携带钉螺情况的调查.中国血吸虫病防治杂志，2002，14(1): 58-59.
[17] 孙乐平，周晓农，洪青标, 等.长江下游江滩地区血吸虫病再流行规律的研究: I钉螺的迁入与消长. 中国血吸虫病防治杂志，2001，13(4): 213-215.
[18] 周艺彪，赵根明，顾弋安, 等. 血吸虫病垸内型和洲岛型流行区居民接触疫水方式聚类分析. 疾病控制杂志, 2003, 7(3):194-197.
[19] Cross E R, Perrine R, Sheffield C, et al. Predicting areas endemic for schistosomiasis using weather variables and landsat database. Military Medicine, 1984, 149(3): 342-544.
[20] Chen S, Hu J. Geo-ecological zones and endemic diseases in China: A sample study by remote sensing. Preventive Veterinary Medicine, 1991，11：335-344.
[21] 李钟武，袁佩新，印仁高，等.应用遥感技术确定钉螺生态环境分布区的研究.环境科学学报，1990，10(2)：217-225.
[22] 涂敏，王庆云. 湖沼钉螺孳生环境分布的遥感调查研究.长江流域资源与环境, 1995，4：81-85.
[23] 郑英杰，姜庆五，赵根明, 等. 空间叠加技术分析气象条件在钉螺分布中的作用. 中国公共卫生，1998，14：724-725.
[24] 彭文祥，张志杰，周艺彪, 等.基于空间分析预测钉螺分布的气温指标研究. 中国血吸虫病防治杂志，2006，18(3)：182-184.
[25] Guo J G, Penelope V, Cao C L，et al. A geographic information and remote sensing based model for prediction of Oncomelania hupensis habitats in the Poyang Lake area, China. Acta Tropica, 2005, 96: 213-222.
[26] 张世清，姜庆五，汪天平，等.遥感技术用于洲滩型血吸虫病流行区钉螺孳生地生态的监测.中华预防医学杂志.2003，37(5)：331-334.
[27] 姜庆五，林丹丹，刘建翔, 等.应用卫星图像对江西省蚌湖钉螺孳生草洲植被的分类研究，中华流行病学杂志.2001，22(2)：114-115.
[28] 张治英,徐德忠,周晓农, 等. 应用LANDSAT ETM+ 图像监测江宁县江滩钉螺孳生地. 第四军医大学学报,2003, 24 (2):139-142.
[29] Zhou X N, Lin D D, Yang H M, et al. Use of landsat TM satellite surveillance data to measure the impact of the 1998 flood on snail intermediate host dispersal in the lower Yangtze River Basin. Acta Tropica,2002,82:199-205.
[30] 高扬，张晓波，何日, 等，应用钉螺孳生地分布模型预测1998年特大洪水对扬州市江滩钉螺分布的影响. 中国血吸虫病防治杂志, 2001，13(5): 285-288.
[31] Kristensen T K,Malone J B, McCarroll J C. Use of satellite remote sensing and geographic information systems to model the distribution and abundance of snail intermediate hosts in Africa: A preliminary model for Biomphalaria pfeifferi in Ethiopia. Acta Tropica, 2001, 79: 73-78.
[32] Zhang Z Y, Xu D Z,Zhou X N, et al. Remote sensing and spatial statistical analysis to predict the distribution of Oncomelania hupensis in the marshlands of China. Acta Tropica 2005, 96: 205-212.
[33] Xu B, Gong P, Biging G, et al. Snail Density Prediction for Schistosomiasis Control Using Ikonos and ASTER Images. Photogrammetric Engineering & Remote Sensing, 2004, 70(11):1285-1294.
[34] Zhang Z J, Ong S H, Peng W X, et al. A model for the prediction of Oncomelania hupensis in the lake and marshland regions, China.Parasitology International,2008，57: 121-131.
[35] Zhao A, Bao S M. A preliminary knowledge-driven prediction model of snail distribution in the Poyang Lake region. Chinese Science Bulletin, 2008, 53(1): 115-123.
[36] 杨国静，周晓农，汪天平, 等. 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.中国寄生虫学与寄生虫病杂志,2002,20(1):6-9.
[37] Zhang Zhijie, Tim E C, Yue Chen, et al. Identifying high-risk regions for schistosomiasis in Guichi, China: A spatial analysis. Acta Tropica, 2008,107: 217-223.
[38] 周晓农，胡晓抒，孙宁生，等.地理信息系统应用于血吸虫病的监测(II): 流行程度的预测.中国血吸虫病防治杂志，1999，11(2)：66-70.
[39] Zhao A, Gong P, Bao S M. A readapted Malone Schistosome transmission index model. Acta Tropica,2009,109:98-102.
[40] Maszle D R, Whitehead P G, Johnson R C,et al. Hydrological studies of schistosomiasis transport in Sichuan Province, China. The Science of the Total Environment, 1998, 216:193-203.
[41] Liang S, Maszle D, Spear R C. A quantitative framework for a multi-group model of Schistosomiasis japonicum transmission dynamics and control in Sichuan, China. Acta Tropica, 2002, 82: 263-277.
[42] Clements A C A, Lwambo N J S, Blair L, et al. Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania.Tropical Medicine and International Health，2006，11(4): 490-503.
[43] Malone J B, Yilma J M, McCarroll J C, et al. Satellite climatology and the environmental risk of Schistosoma mansoni in Ethiopia and east Africa. Acta Tropica 2001, 79: 59-72.
[44] Clements A C A, Moyeed R, Brooker S. Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa. Parasitology, 2006, 133: 711-719.
[45] Clements A C A, Brooker S, Nyandindi U, et al. Bayesian spatial analysis of a national urinary schistosomiasis questionnaire to assist geographic targeting of schistosomiasis control in Tanzania, East Africa. International Journal for Parasitology, 2008, 38: 401-415.
[46] Julie A C, Peter L M, Eric M M, et al. Spatial and temporal variations in local transmission of schistosoma haematobium in Msambweni, Kenya. American Journal of Tropical Medicine and Hygiene, 2006, 75(6):1034-1041.
[47] Zhao A, Bao S M, Gong P. Susceptibility and infection risk of schistosomiasis disease. Geographic Information Sciences, 2006(1): 44-50.

Progress and Prospects of Schistosomiasis-medical Geography

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