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Spatial analysis of binary health indicators with local smoothing techniques The Viadana study

机译:用局部平滑技术对二元健康指标进行空间分析Viadana研究

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摘要

Introduction: When pollution data from a monitoring network is not available, mapping the spatial distribu tion of disease can be useful to identify populations at risk and to suggest a potential role for suspected emis sion sources. We aimed at obtaining a continuous spatial representation of the prevalence of symptoms that are potentially associated with the exposure to the pollutants emitted from the wood factories in the children who live in the district of Viadana (Northern Italy). Methods: In 2006, all the parents of the children aged 3-14 years residing in the Viadana district (n = 3854), filled in a questionnaire on respiratory symptoms, irritation symptoms of the eyes and skin, use of health ser vices. The children's residential addresses were also collected and geocoded. Generalized additive models and local weighted regression (LOWESS) were used to estimate the distribution of the symptoms, to test for spatial trends of the symptoms' prevalence and to control for potential confounders. Permutation tests were used to identify the areas of significantly increased risk ("hot spots"). Results: The prevalence of respiratory symptoms, eye symptoms and the use of health services showed a sta tistically significant spatial variation (p<0.05), but skin symptoms did not. Symptoms' prevalence was lower in the northern part of the district, where no wood factories were present, and it was higher in the southern part, where the two big chipboard industries were located. Hot spots were identified fairly near to one of the two chipboard industries in the district. Conclusions: The north-to-south trend in the prevalence of respiratory and eye symptoms, but not of skin symptoms, as well as the location of hot spots, are consistent with the potential exposure to air pollutants both emitted by the wood factories and related to traffic. In these "high risk areas" monitoring of pollution and preventive actions are clearly needed.
机译:简介:当没有来自监测网络的污染数据时,绘制疾病的空间分布图可有助于识别处于危险中的人群并建议可疑排放源的潜在作用。我们旨在获得症状流行的连续空间表示,这些症状可能与居住在Viadana(意大利北部)地区儿童中的木工工厂所排放的污染物有关。方法:2006年,所有居住在Viadana区(n = 3854)的3-14岁儿童的所有父母填写了有关呼吸道症状,眼睛和皮肤刺激症状,使用健康服务的问卷。还对儿童的住所地址进行了收集和地理编码。使用广义加性模型和局部加权回归(LOWESS)来评估症状的分布,测试症状流行的空间趋势并控制潜在的混杂因素。排列测试用于确定风险显着增加的区域(“热点”)。结果:呼吸系统症状,眼部症状和使用卫生服务的流行率显示出统计学上显着的空间变化(p <0.05),而皮肤症状没有。在该地区的北部,没有木材工厂,症状的患病率较低,而在两个大型刨花板产业所在的南部,症状的患病率较高。在该地区的两个刨花板行业之一附近发现了热点。结论:呼吸和眼部症状的发生率从北向南趋势,而不是皮肤症状的发生率以及热点位置,与木厂和相关工厂可能暴露于空气污染物的趋势一致交通。在这些“高风险地区”,显然需要对污染和预防措施进行监测。

著录项

  • 来源
    《Science of the total environment》 |2012年第1期|p.380-386|共7页
  • 作者单位

    Unit of Epidemiology and Medical Statistics, Department of Public Health and Community Medicine, University of Verona, Italy;

    Unit of Epidemiology and Medical Statistics, Department of Public Health and Community Medicine, University of Verona, Italy;

    Unit of Epidemiology and Medical Statistics, Department of Public Health and Community Medicine, University of Verona, Italy;

    Unit of Epidemiology, NHS Mantua, Mantua, Italy;

    Unit of Epidemiology, NHS Mantua, Mantua, Italy;

    Unit of Epidemiology and Medical Statistics, Department of Public Health and Community Medicine, University of Verona, Italy;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    disease mapping; loess smoothing; air pollution; children; chipboard industries; epidemiology;

    机译:疾病图谱黄土平滑空气污染;孩子们刨花板行业;流行病学;

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