Utilizing Exploratory Spatial Data Analysis to Examine Health and Environmental Disparities in Disadvantaged Neighborhoods.
Utilizing Exploratory Spatial Data Analysis to Examine Health and Environmental Disparities in Disadvantaged Neighborhoods.
复制标题
利用探索性空间数据分析来检查弱势社区的健康和环境差异。
DOI：
10.1089/env.2013.0010
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Calhoun,ElizabethA
中科院分区：
文献类型：
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作者：
Osiecki,KristinM;Kim,Seijeoung;Chukwudozie,IfeanyiB;Calhoun,ElizabethA
健康差距研究主要集中在健康结果的种族和社会经济差异上。尽管社区特征和建成环境的概念已被证明会影响个人健康，但衡量环境风险对健康的影响一直是差异研究中一个不太发达的领域。为了检验社会人口学特征、环境癌症风险和癌症发病率的空间关联性和地理分布模式，我们利用了来自多个来源的现有数据。我们初步分析的结果与以前的研究结果一致，后者往往报告健康差距指标和环境负担之间的关系喜忧参半。然而，用改进的模型进一步分析表明，癌症风险测量的几个关键人口统计学和子域显示出具有空间成分。通过探索性空间数据分析的应用，我们能够识别贫困率和少数民族都很高的地区，以进一步检查可能与环境癌症风险有关的因素。全球空间自相关发现，黑人百分比、贫困百分比、点癌症和非点癌症的空间聚集风险需要进一步的空间分析，以确定基于地理的显著关系。这一方法基于与数据和应用程序相关的特定假设，需要满足这些假设。我们的结论是，需要对数据和应用程序进行仔细的评估，才能正确解释研究结果，以了解脆弱人口与环境负担之间的关系。
Health disparities research has focused primarily on racial and socioeconomic differences in health outcomes. Although neighborhood characteristics and the concept of built environment have been shown to affect individual health, measuring the effects of environmental risks on health has been a less developed area of disparities research. To examine spatial associations and the distribution of geographic patterns of sociodemographic characteristics, environmental cancer risk, and cancer rates, we utilized existing data from multiple sources. The findings from our initial analysis, which concerned with proximity to environmental hazards and at-risk communities, were consistent with results of previous studies, which often reported mixed relationships between health disparity indicators and environmental burden. However, further analysis with refined models showed that several key demographic and subdomains of cancer risk measures were shown to have spatial components. With the application of exploratory spatial data analysis, we were able to identify areas with both high rates of poverty and racial minorities to further examine for possible associations to environmental cancer risk. Global spatial autocorrelation found spatial clustering with percent black, percent poverty, point and non-point cancer risks requiring further spatial analysis to determine relationship of significance based on geography. This methodology was based upon particular assumptions associated with data and applications, which needed to be met. We conclude that careful assessment of the data and applications were required to properly interpret the findings in understanding the relationship between vulnerable populations and environmental burden.