基于人口和气候因素的明古鲁市登革热易感性模型

Q4 Medicine
Dessy Triana, M. Martini, A. Suwondo, Muchlis Achsan Udji Sofro Achsan Udji Sofro, S. Hadisaputro, S. Suhartono
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引用次数: 0

摘要

目的:登革热病例数量增加的原因是复杂和多因素的。所采取的方法必须结合影响因素,需要综合预防策略,包括影响登革热的所有因素,以预测疾病的发病率。本研究旨在分析人口密度、15岁以下人口密度、卫生条件、温度、湿度和降雨量等人口和气候因素对登革热发病率的影响。材料和方法:本研究采用横断面设计,研究样本为明古鲁市各街道,印度尼西亚(67个分区)。通过SmartPLS应用程序,使用结构方程建模进行数据分析,以创建基于人口和气候因素的登革热建模。结果:人口和气候因素与登革热发病率有显著关系,p值分别为0.018和0.000。人口和气候因素对登革热发病率有一定的百分比影响(36.9%)。影响登革热发病率的因素很多,因此需要对各种影响因素进行更全面的建模。登革热建模作为早期预防登革热爆发的预警系统至关重要,这样实施的控制策略才能更加有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dengue Hemorrhagic Fever (DHF): Vulnerability Model Based on Population and Climate Factors in Bengkulu City
Objective: The causes for the increasing number of dengue cases are complex and multifactorial. The approach taken must combine influencing factors, and  comprehensive prevention strategy is needed that includes all the components of factors that influence dengue disease to predict the incidence of the disease. This research aimed to analyze the relationship between population and climate components including population density, population density <15 years old, sanitation, temperature, humidity and rainfall, on the incidence rate of Dengue Hemorrhagic Fever (DHF).Material and Methods: This study used a cross-sectional design, with the research sample being all sub-districts in Bengkulu City, Indonesia (67 sub-districts). Data analysis was conducted using structural equation modeling to create a dengue modeling based on population and climate factors, through the SmartPLS application.Results: Population and climate factors had a significant relationship with the incidence rate of dengue, with p-values of 0.018 and 0.000, respectively. Population and climate factors had a percentage effect on the incidence rate of dengue (36.9%).Conclusion: Population and climate factors had an influence of 36.9% on the incidence of dengue. There were many factors affecting the incidence of dengue, so a more comprehensive modeling of the various influencing factors is needed. Dengue modeling is crucial as an early warning system for the early prevention of dengue outbreaks, so that the control strategies implemented can be more effective.
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