ORIGINAL ARTICLES

Spatial autocorrelation analysis of current schistosomiasis case distribution in Anhui Province

  • Feng-hua GAO ,
  • Jia-chang HE ,
  • Shi-qing ZHANG ,
  • Tian-ping WANG ,
  • Ting-ting LI ,
  • Jing-bo XUE ,
  • Shi-zhu LI
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  • 1 Anhui Provincial Institute of Schistosomiasis Control, Hefei 230061, China
    2 National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology; Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai 200025, China

Received date: 2019-04-01

  Online published: 2019-11-07

Abstract

Objective To analyze the spatial distribution of current schistosomiaisis cases in Anhui Province as reference for facilitating the control of the disease. Methods The epidemiological data of current schistosomiasis cases in Anhui from January 2017 to October 2018, including age, gender, occupation, education, current address, disease stage, economic status, health insurance and treatment, were collected from Epidemiological Dynamics Data Collection Platform (EDDC). A database of Anhui schistosomiasis cases was established using Microsoft Excel 2010. SPSS18.0 was used to analyze the statistical significance of epidemiological characteristics of schistosomiasis cases in Anhui. ArcGIS 10.2 clustering module was used to analyze the spatial autocorrelation of chronic and advanced schistosomiasis cases identified at township level, among them the global spatial autocorrelation analysis was performed using global Moran’s I index and the local spatial autocorrelation analysis using local Getis-Ord G coefficient. Results A total of 20 345 schistosomiasis cases were reported in the province during January 2017-October 2018, including 15 080 chronic clinic cases and 5 265 advanced cases. There was no acute case reported during the period. More male cases (11 319) than female cases (9 026) were reported with male and female ratio of 1.25 : 1. Average age of all cases was 57.8 ± 12.2. Among all chronic cases, the average age was 54.8 ± 11.2 years old. Most of the cases were farmers (96.4%, 14 540/15 080) with low education (primary school/51.9%, middle school/23.0% and illiterate/21.8%). Total 4.9% (736/15 080) of the chronic cases came from families with low income. Among all advanced cases, the average age was 66.4 ± 10.7 years old. The main occupation was farmers (93.8%, 4 936/5 265) with low education level (illiterate/43.6%, primary school/38.1% and middle school/15.2%). More than 10% of the cases (528/5 265) came from the poor families with low income. Chronic cases were distributed in 249 townships within 42 counties (cities or districts) along Yangtze River Basin. There were 38 townships with prevalence of chronic cases higher than 0.5%. The advanced cases were mainly distributed in 340 townships within 45 countries (cities or districts) along Yangtze River Basin and Xin’an River Basin. There were 3 counties with prevalence of advanced schistosomiasis cases higher than 0.5%. The global autocorrelation analysis at the township level showed that both prevalence of chronic schistosomiasis (Moran’s I = 0.202 5, P < 0.01) and the advanced schistosomiasis (Moran’s I = 0.183 4, P < 0.01) possesed spatial autocorrelation in Anhui Province. Analysis of local autocorrelation showed that highly concentrated areas of chronic schistosomiasis were mostly distributed in the townships along the upper and middle reaches of the Yangtze River in Anhui, and highly concentrated areas of advanced schistosomiasis were mainly distributed along the upper and middle reaches of the Yangtze River and the Xin’an River Basin. Conclusion There is spatial accumulation of schistosomiasis case in Anhui Province. The upper and middle reaches of the Yangtze River remain the key areas for schistosomiasis prevention and control in Anhui Province.

Cite this article

Feng-hua GAO , Jia-chang HE , Shi-qing ZHANG , Tian-ping WANG , Ting-ting LI , Jing-bo XUE , Shi-zhu LI . Spatial autocorrelation analysis of current schistosomiasis case distribution in Anhui Province[J]. CHINESE JOURNAL OF PARASITOLOGY AND PARASITIC DISEASES, 2019 , 37(5) : 556 -562 . DOI: 10.12140/j.issn.1000-7423.2019.05.009

References

[1] Zhou XN, Bergquist R, Leonardo L, et al. Schistosomiasis japonica control and research needs[J]. Adv Parasitol, 2010, 72: 145-178.
[2] 李石柱, 许静, 汪天平, 等. 弘扬新时期血防精神推进血吸虫病消除进程[J]. 中国血吸虫病防治杂志, 2019, 31(1): 1-13.
[3] 国家卫生健康委员会.关于开展全国地方病现症病人个案调查工作的通知[Z]. 2018.
[4] Getis A, Ord JK.The analysis of spatial association by use of distance statistics[J]. Perspect Spatial Data Analysis, 2008, 25: 127-145.
[5] 张志杰, 彭文祥, 周艺彪, 等. 安徽省池州市贵池区急性血吸虫病的空间自相关分析[J]. 中国血吸虫病防治杂志, 2007, 19(5): 341-344.
[6] 姜庆五, 赵飞. 空间自相关分析方法在流行病学中的应用[J]. 中华流行病学杂志, 2011, 32(6): 539-546.
[7] 汪天平, 赵飞, 张世清, 等. 2000~2008年安徽省血吸虫病时空聚集性分析[J]. 热带病与寄生虫学, 2011, 9(3): 127-130.
[8] 周晓农, 杨国静, 孙乐平, 等. 地理信息系统在血吸虫病研究中的应用[J]. 中国血吸虫病防治杂志, 1999, 11(6): 378-381.
[9] 杨国静, 周晓农, 汪天平, 等. 长江下游3省血吸虫病GIS数据库的建立与分析[J]. 中国血吸虫病防治杂志, 2002, 14(1): 21-24.
[10] 高风华, 张世清, 汪天平, 等. 2017年安徽省血吸虫病疫情分析[J]. 热带病与寄生虫学, 2018, 16(3): 125-129.
[11] 高风华, 张世清, 汪天平, 等. 2016年安徽省钉螺调查结果分析[J]. 中国血吸虫病防治杂志, 2018, 30(5): 493-499.
[12] 吴观陵. 我国血吸虫病免疫诊断发展的回顾与展望[J]. 中国寄生虫学与寄生虫病杂志, 2005, 23(增刊): 323-327.
[13] 祝红庆, 曹淳力, 王强, 等. 山丘型流行区血吸虫感染者人数低估原因探讨[J]. 中国血吸虫病防治杂志, 2006, 18(3):214-216.
[14] Gao FH, Abe EM, Li SZ, et al. Fine scale Spatial-temporal cluster analysis for the infection risk of schistosomiasis japonica using space-time scan statistics[J]. Parasit vect, 2014, 7(1): 578-588.
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