Department of Vector Biology and Control, Rajendra Memorial Research Institute of Medical Sciences (ICMR), Agamkuan, Patna, Bihar, 800 007, India.
Parasit Vectors. 2018 Apr 2;11(1):220. doi: 10.1186/s13071-018-2707-x.
Visceral leishmaniasis (VL) in Bihar State (India) continues to be endemic, despite the existence of effective treatment and a vector control program to control disease morbidity. A clear understanding of spatio-temporal distribution of VL may improve surveillance and control implementation. This study explored the trends in spatio-temporal dynamics of VL endemicity at a meso-scale level in Vaishali District, based on geographical information systems (GIS) tools and spatial statistical analysis.
A GIS database was used to integrate the VL case data from the study area between 2009 and 2014. All cases were spatially linked at a meso-scale level. Geospatial techniques, such as GIS-layer overlaying and mapping, were employed to visualize and detect the spatio-temporal patterns of a VL endemic outbreak across the district. The spatial statistic Moran's I Index (Moran's I) was used to simultaneously evaluate spatial-correlation between endemic villages and the spatial distribution patterns based on both the village location and the case incidence rate (CIR). Descriptive statistics such as mean, standard error, confidence intervals and percentages were used to summarize the VL case data.
There were 624 endemic villages with 2719 (average 906 cases/year) VL cases during 2012-2014. The Moran's I revealed a cluster pattern (P < 0.05) of CIR distribution at the meso-scale level. On average, 68 villages were newly-endemic each year. Of which 93.1% of villages' endemicity were found to have occurred on the peripheries of the previous year endemic villages. The mean CIR of the endemic villages that were peripheral to the following year newly-endemic villages, compared to all endemic villages of the same year, was higher (P < 0.05).
The results show that the VL endemicity of new villages tends to occur on the periphery of villages endemic in the previous year. High-CIR plays a major role in the spatial dispersion of the VL cases between non-endemic and endemic villages. This information can help achieve VL elimination throughout the Indian subcontinent by improving vector control design and implementation in highly-endemic district.
尽管存在有效的治疗方法和控制疾病发病率的病媒控制计划,但印度比哈尔邦的内脏利什曼病(VL)仍持续流行。对 VL 时空分布的清晰了解可能会改善监测和控制的实施。本研究基于地理信息系统(GIS)工具和空间统计分析,探讨了在 Vaishali 区中尺度水平上 VL 流行的时空动态趋势。
使用 GIS 数据库整合了 2009 年至 2014 年研究区域的 VL 病例数据。将所有病例在中尺度水平上进行空间链接。采用地理空间技术,如 GIS 图层叠加和映射,可视化和检测全区 VL 流行爆发的时空模式。空间统计莫兰指数(Moran's I)用于同时评估流行村庄之间的空间相关性以及基于村庄位置和病例发生率(CIR)的空间分布模式。使用均值、标准误差、置信区间和百分比等描述性统计来总结 VL 病例数据。
2012-2014 年期间,共有 624 个流行村庄,2719 例(平均每年 906 例)VL 病例。Moran's I 揭示了中尺度水平上 CIR 分布的聚类模式(P<0.05)。平均每年有 68 个新村庄出现流行。其中,93.1%的村庄流行是在前一年流行村庄的外围发生的。与同年所有流行村庄相比,下一年新流行村庄周边村庄的平均 CIR 更高(P<0.05)。
结果表明,新村庄的 VL 流行倾向于在前一年流行村庄的外围发生。高 CIR 在非流行和流行村庄之间 VL 病例的空间扩散中起着重要作用。这些信息可以通过改进高度流行地区的病媒控制设计和实施,帮助在整个印度次大陆消除 VL。