The US Census Bureau will implement a new privacy-preserving disclosure avoidance system (DAS), which includes application of differential privacy, on the public-release 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 and policy, serving as denominators in estimation of disease/mortality rates. Employing 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. 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 for non-Hispanic white populations, this issue is ameliorated in newer DAS versions.

翻译：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套新的隐私保护披露避免系统（DAS），该系统包含差分隐私的应用。有担忧认为，DAS可能会使小区域和按人口统计特征分层的人口计数产生偏差，而这些数据在公共卫生研究和政策中扮演关键角色，作为疾病/死亡率估计中的分母。利用三种DAS示范产品，我们量化了在标准小区域疾病制图模型中依赖DAS保护的分母以表征健康不平等所导致的误差。我们开展了模拟研究和对马萨诸塞州人口普查区层面过早死亡率不平等的真实数据分析。结果表明，按种族群体和经济剥夺程度划分的不平等总体模式并未因DAS而受损。尽管早期版本的DAS在死亡率估计中引入了误差，且黑人群体比非西班牙裔白人群体误差更大，但这一问题在较新版本的DAS中已得到改善。