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评估美国种族、不平等、犯罪与淋病和衣原体之间的空间关系。

Assessing Spatial Relationships between Race, Inequality, Crime, and Gonorrhea and Chlamydia in the United States.

机构信息

Columbia University, New York, NY, USA.

出版信息

J Urban Health. 2017 Oct;94(5):683-698. doi: 10.1007/s11524-017-0179-5.

Abstract

Incidence rates of chlamydia and gonorrhea reached unprecedented levels in 2015 and are concentrated in southern counties of the USA. Using incidence data from the Center for Disease Control, Moran's I analyses assessed the data for statistically significant clusters of chlamydia and gonorrhea at the county level in 46 states of the USA. Lagrange multiplier diagnostics justified selection of the spatial Durbin regression model for chlamydia and the spatial error model for gonorrhea. Rates of chlamydia (Moran's I = .37, p < .001) and gonorrhea (Moran's I = .38, p < .001) were highly clustered particularly in the southern region of the USA. Logged percent in poverty (B = .49, p < .001 and B = .48, p < .001) and racial composition of African-Americans (B = .16, p < .001 and B = .40, p < .001); Native Americans (B = .12, p < .001 and B = .20, p < .001); and Asians (B = .14, p < .001 and B = .09, p < .001) were significantly associated with greater rates of chlamydia and gonorrhea, respectively, after accounting for spatial dependence in the data. Logged rates of rates violent crimes were associated with chlamydia (B = .053, p < .001) and gonorrhea (B = .10, p < .001). Logged rates of drug crimes (.052, p < .001) were only associated with chlamydia. Metropolitan census designation was associated with logged rates of chlamydia (B = .12, p < .001) and gonorrhea (B = .24, p < .001). Spatial heterogeneity in the distribution of rates of chlamydia and gonorrhea provide important insights for strategic public health interventions in the USA and inform the allocation of limited resources for the prevention of chlamydia and gonorrhea.

摘要

2015 年,衣原体和淋病的发病率达到了前所未有的水平,且主要集中在美国南部各州。利用美国疾病控制中心的数据,莫兰指数分析评估了美国 46 个州县级别的衣原体和淋病的统计学显著聚类数据。拉格朗日乘数诊断证明,选择空间杜宾回归模型来分析衣原体数据,选择空间误差模型来分析淋病数据是合理的。衣原体(莫兰指数 I=0.37,p<0.001)和淋病(莫兰指数 I=0.38,p<0.001)的发病率高度聚集,尤其是在美国南部地区。贫困率的自然对数(B=0.49,p<0.001 和 B=0.48,p<0.001)和非裔美国人的种族构成(B=0.16,p<0.001 和 B=0.40,p<0.001);美国原住民(B=0.12,p<0.001 和 B=0.20,p<0.001);和亚洲人(B=0.14,p<0.001 和 B=0.09,p<0.001)与衣原体和淋病的高发病率显著相关,在数据中考虑到空间相关性后。暴力犯罪的发生率与衣原体(B=0.053,p<0.001)和淋病(B=0.10,p<0.001)显著相关。毒品犯罪率(0.052,p<0.001)仅与衣原体相关。大都市人口普查指定与衣原体(B=0.12,p<0.001)和淋病(B=0.24,p<0.001)的发生率自然对数显著相关。衣原体和淋病发病率分布的空间异质性为美国的战略公共卫生干预提供了重要的见解,并为预防衣原体和淋病的有限资源分配提供了信息。

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