The US Census Bureau will implement a new 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. 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 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.

翻译：美国人口普查局将在公开的2020年人口普查数据中实施一套新的隐私保护披露规避系统（DAS），其中包括应用差分隐私。有人担忧DAS可能使小区域和按人口统计分层的人口计数产生偏差，而这类人口计数在公共卫生研究中扮演关键角色，作为疾病/死亡率估计中的分母。利用三种DAS演示产品，我们在标准的小区域疾病制图模型中量化了依赖DAS保护的分母对描述健康不平等特征所造成的误差。我们针对马萨诸塞州和佐治亚州人口普查区层面的早死不平等进行了模拟研究和真实数据分析。结果表明，按种族群体和经济贫困程度划分的不平等整体模式并未受DAS影响。尽管在马萨诸塞州，早期版本的DAS导致黑人群体死亡率估计误差大于非西班牙裔白人群体，但这一情况在较新的DAS版本中得到了改善。