Experiences of discrimination are an important aspect of women’s life in Brazil, especially Black women. The Experiences of Discrimination scale (EOD) is often used for assessing discrimination in epidemiological studies, although divergent cutoff points have been used to characterize the exposure. We used latent class analysis (LCA) and logistic regression to identify and characterize subgroups of women exposed to discrimination and compared with a cutoff-based assignment of subgroups. One thousand two-hundred and four women living in Salvador, Brazil, responded to the EOD. We selected models with two latent classes, highly and lowly exposed. The classes differed in self-reported skin color and education level, revealing that darker skinned (odds ratio [OR] = 11.3, 95% confidence interval [CI: 1.54, 82.7]) and more educated (OR = 2.09, 95% CI [1.17, 3.72]) women were more likely to be classified into the highly exposed class. Comparing with LCA, the use of cutoff points overestimated the reporting of discrimination. Researchers should consider the use of more accurate measures of discrimination in order to identify the most vulnerable individuals so that prevention efforts could be better targeted.

中文翻译：

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巴西城市女性的歧视经历和肤色：潜在类别分析

歧视经历是巴西妇女生活的一个重要方面，尤其是黑人妇女。歧视经验量表 (EOD) 通常用于评估流行病学研究中的歧视，尽管已使用不同的截止点来表征暴露。我们使用潜在类别分析 (LCA) 和逻辑回归来识别和表征遭受歧视的女性亚组，并与基于临界值的亚组分配进行比较。生活在巴西萨尔瓦多的一千二百零四名妇女对排爆事件做出了回应。我们选择了具有两个潜在类别的模型，高暴露和低暴露。这些班级在自我报告的肤色和教育水平方面存在差异，表明肤色较深（比值比 [OR] = 11.3，95% 置信区间 [CI：1.54, 82.7]）和受过更多教育（OR = 2.09，95% CI [比值比 [OR] = 11.3，95% 置信区间 [CI: 1.54, 82.7]] 1. 17, 3.72]) 女性更有可能被归类为高度暴露的类别。与 LCA 相比，使用截止点高估了歧视的报告。研究人员应考虑使用更准确的歧视措施，以识别最脆弱的个体，从而使预防工作更有针对性。