论文标题
人口普查差异隐私对小区域疾病映射的影响以监控健康不平等
Impacts of Census Differential Privacy for Small-Area Disease Mapping to Monitor Health Inequities
论文作者
论文摘要
美国人口普查局将在公共释放2020人口普查数据上实施新的保存隐私避免避免避免披露系统(DAS),其中包括差异隐私的应用。人们担心DAS可能会偏向小区域和人口统计学分层的人口计数,这些人数在公共卫生研究和政策中起着至关重要的作用,在估计疾病/死亡率的情况下是分母。我们使用三种DAS示范产品,我们量化了归因于标准的小面积疾病映射模型中对DAS保护的分母的依赖,以表征健康不平等。我们在马萨诸塞州的人口普查水平上进行过早死亡率的不平等现象进行了模拟研究和实际数据分析。结果表明,DAS并未损害种族化群体和经济剥夺水平的整体不平等模式。尽管DAS的早期版本会导致黑色的死亡率估计错误估计值大于非西班牙裔白人种群,但在较新的DAS版本中,此问题可以改善。
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.