印度尼西亚泗水市结核病空间模糊风险图

A. Fariza, Mu’arifin, Amailina Puspitasari
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引用次数: 4

摘要

泗水是印度尼西亚的主要城市之一,是结核病传播的流行地区。泗水市卫生办公室在2018年发现了7,007例结核病病例,这是东爪哇省最高的病例。这一数据表明,结核病仍然是一个主要的健康问题。需要绘制结核病风险图来指导公共卫生服务部门进行结核病控制规划,例如,促进清洁和健康的生活行为、免疫接种、家访计划和优化结核病筛查活动。本文提出了一种空间模糊风险映射的方法,该方法基于成为结核病危险因素的几个标准来构建结核病的空间风险映射。这些标准包括结核病患者人数(BTA阳性)、人口密度、不健康的房屋和卫生设施。通过模糊多准则决策确定各准则的权重值,然后进行排序过程,从街道区域中选择最优方案。经过模糊隶属度计算,将街道区域面积根据规则关联直接划分为低、中、高3个指标等级。结核病风险指数的确定涵盖泗水人口稠密的城市地区31个街道。将风险图可视化为空间GIS地图。近3年(2013-2015年)有4个街道减少(12.9%),6个街道增加(19.4%),其余68.7%没有变化。2015年有13.33%的街道被模糊风险定义为低风险,但被公共卫生服务部门定义为高风险。模糊风险指数结果与实际情况吻合,与公共卫生服务报告吻合。
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Spatial Fuzzy Risk Mapping for Tuberculosis in Surabaya, Indonesia
Surabaya, one of the major cities in Indonesia, is an endemic area for spreading tuberculosis. Surabaya City Health Office in 2018 has found 7,007 cases of tuberculosis which is the highest case in East Java province. This data shows that TB is still a major health problem. TB risk mapping is needed to guide the Public Health Service in TB control planning, for example, the promotion of clean and healthy living behaviors, immunizations, and home visit programs and optimization of TB screening activities. This paper proposes the spatial risk mapping of tuberculosis based on several criteria that become tuberculosis risk factors using a fuzzy method called spatial fuzzy risk mapping. These criteria consist of the number of people with tuberculosis (BTA Positive), population density, unhealthy houses, and health facilities. Fuzzy multi-criteria decision making determines the weight value of each criterion, followed by the ranking process to select the best alternative from the sub-district areas. After fuzzy membership calculation, the sub-district areas area directly classified into 3 index level that is low, medium, and high according to the rule association. The determination of the TB disease risk index covers 31 sub-districts in Surabaya as densely populated urban areas. The risk map is visualized into spatial GIS mapping. In the last 3 years (2013-2015), there were 4 sub-districts are decreasing (12.9%), 6 sub-districts are increasing (19.4%) and the remaining 68.7% did not change. There are 13.33% sub-districts in 2015 that are defined as low risk by the fuzzy risk, but it must be high risk by the Public Health Service. The fuzzy risk index results appropriate with the real condition and it is suitable with the Public Health Service report.
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