Evidence map›Paper›PMID 37595037›Full record

ArticleScience advances2023

Impacts of census differential privacy for small-area disease mapping to monitor health inequities.

Yanran Li, Brent A Coull, Nancy Krieger, Emily Peterson, Lance A Waller, Jarvis T Chen, Rachel C Nethery

Abstract read
In one paragraph

Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yanran LiDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0009-0009-9263-6789
Brent A CoullDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0002-1808-4156
Nancy KriegerDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0002-4815-5947
Emily PetersonDepartment of Biostatistics and Bioinformatics, Emory Rollins School of Public Health, Atlanta, GA, USA.
Lance A WallerDepartment of Biostatistics and Bioinformatics, Emory Rollins School of Public Health, Atlanta, GA, USA.ORCID 0000-0001-5002-8886
Jarvis T ChenDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0002-7412-1783
Rachel C NetheryDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0001-9895-1477

Funding

Translational Research Support CoreP30ES000002 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI JAIME ELIZABETH HART · 1985 to 2026
$44.6M
Improving statistical methods for small area estimates of public health indicators and demographic characteristicsR01HD092580 · NICHD · EMORY UNIVERSITY · PI Lance A Waller · 2018 to 2026
$2.3M
Causal machine learning methods for studying the effects of environmental exposures on childhood cancer using natural experimentsK01ES032458 · NIEHS · HARVARD SCHOOL OF PUBLIC HEALTH · PI NETHERY, RACHEL C · 2021 to 2023
$417k
NICHD NIH HHS R01 HD092580NIEHS NIH HHS K01 ES032458NIEHS NIH HHS P30 ES000002
6 · The paper itself

Abstract

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.

Indexed as

CensusesPrivacyComputer SimulationData AnalysisHealth Inequities

Identifiers

PMID37595037
PMCPMC10438951

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.