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Impacts of census differential privacy for small-area disease mapping to monitor health inequities
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, wh...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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American Association for the Advancement of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10438951/ https://www.ncbi.nlm.nih.gov/pubmed/37595037 http://dx.doi.org/10.1126/sciadv.ade8888 |
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author | Li, Yanran Coull, Brent A. Krieger, Nancy Peterson, Emily Waller, Lance A. Chen, Jarvis T. Nethery, Rachel C. |
author_facet | Li, Yanran Coull, Brent A. Krieger, Nancy Peterson, Emily Waller, Lance A. Chen, Jarvis T. Nethery, Rachel C. |
author_sort | Li, Yanran |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10438951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104389512023-08-19 Impacts of census differential privacy for small-area disease mapping to monitor health inequities Li, Yanran Coull, Brent A. Krieger, Nancy Peterson, Emily Waller, Lance A. Chen, Jarvis T. Nethery, Rachel C. Sci Adv Social and Interdisciplinary Sciences 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. American Association for the Advancement of Science 2023-08-18 /pmc/articles/PMC10438951/ /pubmed/37595037 http://dx.doi.org/10.1126/sciadv.ade8888 Text en Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Social and Interdisciplinary Sciences Li, Yanran Coull, Brent A. Krieger, Nancy Peterson, Emily Waller, Lance A. Chen, Jarvis T. Nethery, Rachel C. Impacts of census differential privacy for small-area disease mapping to monitor health inequities |
title | Impacts of census differential privacy for small-area disease mapping to monitor health inequities |
title_full | Impacts of census differential privacy for small-area disease mapping to monitor health inequities |
title_fullStr | Impacts of census differential privacy for small-area disease mapping to monitor health inequities |
title_full_unstemmed | Impacts of census differential privacy for small-area disease mapping to monitor health inequities |
title_short | Impacts of census differential privacy for small-area disease mapping to monitor health inequities |
title_sort | impacts of census differential privacy for small-area disease mapping to monitor health inequities |
topic | Social and Interdisciplinary Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10438951/ https://www.ncbi.nlm.nih.gov/pubmed/37595037 http://dx.doi.org/10.1126/sciadv.ade8888 |
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