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Magnitude of social determinants in high risk areas of leprosy in a hyperendemic city of northeastern Brazil: An ecological study

机译:巴西东北腹期峡谷城市风险高危地区的社会决定因素幅度:生态学研究

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Objective: To investigate the association of certain social determinants with areas at high-risk for leprosy in a hyperendemic city in northeastern Brazil. Methods: An ecological study was carried out in Imperatriz, a hyperendemic municipality of northeastern Brazil. The units of analysis were urban census tracts, in which variables related to the number of dwellers per domicile, literacy and per capita monthly income were selected for the construction of the social determinants dimensions, by means of the Principal Components Analysis (PCA) technique. Spatial scan statistics were applied to identify areas of elevated leprosy risk and binary logistic regression was performed, considering the high-risk areas as the dependent variable and the social determinants dimensions as the independent variable. The Odds Ratios (ORs) were calculated with its 95% Confidence Intervals (CIs). The Receiver Operating Characteristic (ROC) Curve was applied to verify the discriminative power of the model. Type I error was set at 5% (p < 0.05). Results: 2,552 leprosy cases were georeferenced and the scan statistic identified high-risk areas. The logistic model showed that social determinants were associated with high-risk areas (OR = 1.25, 95% CI = 1.07-1.49, p = 0.006). The area under the ROC curve value confirmed the discriminatory capacity for the model at 65%, which was considered sufficient. Conclusions: The high-risk areas for leprosy are associated with social determinants of low household per capita income, low educational levels and high numbers of residents per household.
机译:目的:探讨巴西东北大型峡谷城市麻风病的高风险区域的某些社会决定因素的关联。方法:在巴西东北大型峡谷的Imperatriz进行了生态学研究。分析单位是城市人口普查,其中根据主要成分分析(PCA)技术,选择了与每个住所,识字和人均月收入的居民,识字和人均月收入相关的变量。将空间扫描统计数据应用于识别Leposy风险的升高的区域,并且考虑到依赖变量和社会决定因子的高风险区域以及作为独立变量的高危区域进行了二进制逻辑回归。使用其95%置信区间(CIS)计算差距量值(或s)。应用了接收器操作特征(ROC)曲线以验证模型的辨别力。 I型错误设置为5%(P <0.05)。结果:2,552个麻风病例是地理学,扫描统计识别的高风险区域。物流模型表明,社会决定因素与高风险区域相关(或= 1.25,95%CI = 1.07-1.49,P = 0.006)。 ROC曲线下的该地区确认了65%的模型的歧视能力,被认为是足够的。结论:麻风病的高风险地区与人均收入低,教育水平低,每户居民较少的社会决定因素有关。

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