Summarizing social disparities in health.
Summarizing social disparities in health.
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DOI：
10.1111/milq.12001
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发表时间：
2013-03
期刊：
影响因子：
--
通讯作者：
Whipp AM
中科院分区：
文献类型：
--
作者：
Asada Y;Yoshida Y;Whipp AM
报告健康差距对于实现减少健康差距的目标至关重要。一个经常被忽视的挑战是确定报告与收入、教育、性别和种族/民族等多重属性相关的差异的最佳方式。本文提出了一种分析方法，总结健康的社会差距，我们展示了它的实证应用，通过比较的程度和模式的健康差距在所有50个州和哥伦比亚特区（DC）。我们使用了2009年美国社区调查，我们的健康指标是功能限制。对于每个州和DC，我们计算了收入，教育，性别和种族/民族在功能限制方面的总体差异和特定属性差异。沿着这些健康差异的州排名，我们根据每个州对整体差异贡献最大的属性开发了健康差异概况。我们的研究结果显示，在各州的功能限制的整体和特定属性的差异排名普遍缺乏一致性。怀俄明州的总体差距最小，西弗吉尼亚州最大。然而，在四个特定属性的健康差距排名中，大多数在整体健康差距方面表现最好和最差的州并不总是好或坏。我们的分析表明各州之间存在以下三种差异：（1）种族/民族的贡献最大（三十四个州），（2）种族/民族和社会经济因素的贡献大致相等（十个州），（3）社会经济因素的贡献最大（七个州）。我们提出的方法提供了政策相关的健康差距信息在一个可比的和可解释的方式，目前公开的数据支持其应用。我们希望这种方法将引发关于如何最好地系统地跟踪社区之间或社区内的健康差距的讨论，以实现“健康人2020”的健康差距目标。
Reporting on health disparities is fundamental for meeting the goal of reducing health disparities. One often overlooked challenge is determining the best way to report those disparities associated with multiple attributes such as income, education, sex, and race/ethnicity. This article proposes an analytical approach to summarizing social disparities in health, and we demonstrate its empirical application by comparing the degrees and patterns of health disparities in all fifty states and the District of Columbia (DC). We used the 2009 American Community Survey, and our measure of health was functional limitation. For each state and DC, we calculated the overall disparity and attribute-specific disparities for income, education, sex, and race/ethnicity in functional limitation. Along with the state rankings of these health disparities, we developed health disparity profiles according to the attribute making the largest contribution to overall disparity in each state. Our results show a general lack of consistency in the rankings of overall and attribute-specific disparities in functional limitation across the states. Wyoming has the smallest overall disparity and West Virginia the largest. In each of the four attribute-specific health disparity rankings, however, most of the best- and worst-performing states in regard to overall health disparity are not consistently good or bad. Our analysis suggests the following three disparity profiles across states: (1) the largest contribution from race/ethnicity (thirty-four states), (2) roughly equal contributions of race/ethnicity and socioeconomic factor(s) (ten states), and (3) the largest contribution from socioeconomic factor(s) (seven states). Our proposed approach offers policy-relevant health disparity information in a comparable and interpretable manner, and currently publicly available data support its application. We hope this approach will spark discussion regarding how best to systematically track health disparities across communities or within a community over time in relation to the health disparity goal of Healthy People 2020.
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