Department of Zoology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan; Department of Zoology, Faculty of Chemical and Life Sciences, Abdul Wali Khan University, Mardan, Pakistan.
Department of Zoology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan.
Acta Trop. 2021 May;217:105861. doi: 10.1016/j.actatropica.2021.105861. Epub 2021 Feb 12.
While Cutaneous leishmaniasis (CL) is not a life-threatening disease, it leads to devastating effects on local community. CL is widely scattered manifesting a noticeable epidemiological pattern around the globe. The present study was planned to address the role of Geographic Information System (GIS) using CL clinico-epidemiological data to determine the high-risk areas of CL. Recorded data (2014-2018) of 3630 positive individuals was collected from Basic Health Units of the Upper and Lower Dir Districts, Khyber Pakhtunkhwa, Pakistan. Descriptive and statistical analysis was used for clinico-epidemiological characterization. For spatial analysis, ArcGIS V.10.3 was used and the CL average incidence was tagged on the proportional, choropleth, and digital elevation model maps. For focal transmission and high-risk zones, Inverse Density Weight (IDW) spatial interpolation, focal statistics, hot spot, cluster and outlier, and Bayesian geostatistical analysis were used. The trend of CL cases was elevated from 2014 to 2016 except for 2017 and 2018. Individuals of both genders younger than 20 years old were highly susceptible. Single lesions were more prominent (56%) and frequently affected facial parts (51%). The size and pretreatment duration of the CL lesion was significantly associated. Spatially, a choropleth map displayed the maximum CL incidences in Tehsil Balambat, Khal, and Termergara (31%-13%) located within a range of 948-1947m elevation in the central regions with proximal CL transmissions. Hot spot and cluster and outlier analysis affirmed that Tehsil Khal was the high-risk CL foci. The Bayesian geostatistical analysis revealed high temperature, less altitude, and annual precipitation as important risk factors. An increasing trend in CL transmission has become evident, affecting both genders and <20 years old children. GIS resolute the concealed CL hubs in the least elevated central regions which warrant the control strategies to restrict future epidemics.
虽然皮肤利什曼病（CL）不是一种危及生命的疾病，但它会对当地社区造成毁灭性的影响。CL 广泛分布，在全球范围内呈现出明显的流行病学模式。本研究旨在利用 CL 临床流行病学数据，通过地理信息系统（GIS）确定 CL 的高风险区域。从巴基斯坦开伯尔-普赫图赫瓦省上下迪尔地区的基本保健单位收集了 2014 年至 2018 年 3630 名阳性个体的记录数据。对记录数据进行描述性和统计分析，以进行临床流行病学特征描述。为了进行空间分析，使用了 ArcGIS V.10.3，并在比例、专题和数字高程模型地图上标记了 CL 的平均发病率。为了确定焦点传播和高风险区域，使用了反向密度权重（IDW）空间插值、焦点统计、热点、聚类和异常值以及贝叶斯地质统计分析。除 2017 年和 2018 年外，2014 年至 2016 年期间 CL 病例的趋势呈上升趋势。20 岁以下的男女均易感染。单一病变更为突出（56%），常累及面部（51%）。CL 病变的大小和预处理持续时间存在显著相关性。从空间上看，专题地图显示 Tehsil Balambat、Khal 和 Termergara（31%-13%）的 CL 发病率最高，位于海拔 948-1947m 的中心区域，有近端 CL 传播。热点和聚类及异常值分析证实，Tehsil Khal 是 CL 的高风险焦点。贝叶斯地质统计分析显示，高温、低海拔和年降水量是重要的风险因素。CL 传播的上升趋势已经明显，影响到男女和<20 岁的儿童。GIS 确定了海拔较低的中心区域中隐藏的 CL 中心，这需要采取控制策略来限制未来的疫情。
