Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Sci Adv. 2023 Aug 18;9(33):eade8888. doi: 10.1126/sciadv.ade8888.
The U.S. Census Bureau will implement a modernized privacy-preserving disclosure avoidance system (DAS), which includes application of differential privacy, on publicly released 2020 census data. There are concerns that the DAS may bias small-area and demographically stratified population counts, which play a critical role in public health research, serving as denominators in estimation of disease/mortality rates. Using three DAS demonstration products, we quantify errors attributable to reliance on DAS-protected denominators in standard small-area disease mapping models for characterizing health inequities. We conduct simulation studies and real data analyses of inequities in premature mortality at the census tract level in Massachusetts and Georgia. Results show that overall patterns of inequity by racialized group and economic deprivation level are not compromised by the DAS. While early versions of DAS induce errors in mortality rate estimation that are larger for Black than non-Hispanic white populations in Massachusetts, this issue is ameliorated in newer DAS versions.
美国人口普查局将在公开发布的 2020 年人口普查数据中实施一种现代化的隐私保护披露避免系统（DAS），其中包括差分隐私的应用。人们担心，DAS 可能会使小区域和人口分层的人口计数产生偏差，这些计数在公共卫生研究中起着至关重要的作用，是估计疾病/死亡率的分母。使用三种 DAS 演示产品，我们量化了由于依赖 DAS 保护的分母而导致的标准小区域疾病映射模型中用于描述健康不公平性的错误。我们对马萨诸塞州和佐治亚州的普查区一级过早死亡率的不公平性进行了模拟研究和真实数据分析。结果表明，按种族和经济贫困程度划分的不公平总体模式不会因 DAS 而受到影响。虽然早期版本的 DAS 会导致马萨诸塞州黑人群体的死亡率估计误差大于非西班牙裔白人，但在较新版本的 DAS 中，这个问题得到了缓解。
