Open AccessDifferential Privacy in the 2020 Census Will Distort COVID-19 Rates
Author(s) -
Mathew Hauer,
Alexis R. SantosLozada
Publication year - 2021
Publication title -
socius
Language(s) - English
Resource type - Journals
ISSN - 2378-0231
DOI - 10.1177/2378023121994014
Subject(s) - census , pandemic , pooling , population , demography , covid-19 , mortality rate , geography , actuarial science , medicine , economics , computer science , disease , sociology , infectious disease (medical specialty) , artificial intelligence , pathology
Scholars rely on accurate population and mortality data to inform efforts regarding the coronavirus disease 2019 (COVID-19) pandemic, with age-specific mortality rates of high importance because of the concentration of COVID-19 deaths at older ages. Population counts, the principal denominators for calculating age-specific mortality rates, will be subject to noise infusion in the United States with the 2020 census through a disclosure avoidance system based on differential privacy. Using empirical COVID-19 mortality curves, the authors show that differential privacy will introduce substantial distortion in COVID-19 mortality rates, sometimes causing mortality rates to exceed 100 percent, hindering our ability to understand the pandemic. This distortion is particularly large for population groupings with fewer than 1,000 persons: 40 percent of all county-level age-sex groupings and 60 percent of race groupings. The U.S. Census Bureau should consider a larger privacy budget, and data users should consider pooling data to minimize differential privacy’s distortion.