运行状况报告不佳?使用小插曲恢复健康梯度的财富

Laura Rossouw, Teresa Bago d'Uva, E. van Doorslaer
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引用次数: 5

摘要

尽管南非等国在财富和死亡率等客观健康指标方面存在众所周知的巨大差距,但在自我报告的健康指标中,财富造成的健康不平等似乎几乎不存在。我们使用锚定小插图测试并纠正了南非老年人按财富和种族自我评估健康的16个领域的报告异质性。我们发现,高财富群体和低财富群体之间的显著报告差异导致了对健康财富差距的严重低估:穷人对同样健康状况的评价相对高于富人。使用分层有序概率(HOPIT)模型,我们表明,在校正后,穷人出现了显著和实质性的健康劣势。我们还解决了健康不平等和报告异质性是否以及如何因种族而混淆的问题。我们发现,在种族群体中——尤其是在黑人中,但也在白人中——报告异质性导致低估了最富裕和最贫穷之间的健康不平等。最后,我们表明,在最富有的五分之一人群中,一旦我们纠正了报告趋势,明显的黑人(vs .白人)健康劣势就会消失。总而言之,我们的研究结果表明,报告倾向是衡量健康差异的一个重要偏差来源,锚定小插曲和HOPIT模型可以在纠正这些偏差方面发挥作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Poor Health Reporting? Using Vignettes to Recover the Health Gradient by Wealth
In spite of the well-known wide disparities in wealth and in objective measures of health like mortality in countries like South Africa, health inequality by wealth in self-reported health measures appears to be nearly non-existent. We test and correct for reporting heterogeneity in sixteen domains of self-assessed health by wealth and race among elderly South Africans using anchoring vignettes. We find that significant reporting differences between high and low wealth groups lead to severe underestimation of the health-wealth gap: poorer individuals rate the same health relatively higher than richer. Using hierarchical ordered probit (HOPIT) modeling, we show that a significant and substantial health disadvantage of the poor emerges after correction. We also address the question whether and how health inequality and reporting heterogeneity are confounded by race. We find that within race groups - especially among Blacks but also among Whites - reporting heterogeneity leads to the underestimation of the health inequalities between richest and poorest. Finally, we show that the apparent Black (vs White) health disadvantage within the top wealth quintile disappears once we correct for reporting tendencies. All in all, our findings suggest that reporting tendencies are an important source of bias in the measurement of health disparities and that anchoring vignettes and HOPIT models can play a role in correcting for these biases.
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