Evidence map›Paper›PMID 34401464›Full record

ArticleSSM - population health2021

Matching participant address with public records database in a US national longitudinal cohort study.

Marquita S Brooks, Aleena Bennett, Gina S Lovasi, Philip M Hurvitz, Natalie Colabianchi, Virginia J Howard, Jennifer Manly, Suzanne E Judd

Abstract read
In one paragraph

Article in SSM - population health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Accuracy of LexisNexis-derived retrospective address histories in the Sister Study cohort.Journal of exposure science & environmental epidemiology · 2026
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  2. Evaluating Linkage Approaches for Address-Level Socioenvironmental Exposure Assessment.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
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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

8 authors.

Marquita S BrooksDepartment of Biostatistics, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.
Aleena BennettDepartment of Biostatistics, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.
Gina S LovasiUrban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA.
Philip M HurvitzCenter for Studies in Demography and Ecology, University of Washington, Seattle, WA, USA.
Natalie ColabianchiEnvironment and Policy Lab, University of Michigan School of Kinesiology, Ann Arbor, MI, USA.
Virginia J HowardDepartment of Epidemiology, UAB, Birmingham, AL, USA.
Jennifer ManlyNeurology at Gertrude H. Sergievsky Center and the Taub Institute for Research in Aging and Alzheimer's Disease, Columbia University, New York, NY, USA.
Suzanne E JuddDepartment of Biostatistics, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.

Funding

Scientific/Technical CoreP2CHD042828 · NICHD · UNIVERSITY OF WASHINGTON · PI SARA R. CURRAN · 2017 to 2026
$6.3M
Communities Designed to Support Cardiovascular Health for Older AdultsR01AG049970 · NIA · DREXEL UNIVERSITY · PI LOVASI, GINA S · 2015 to 2018
$2.8M
ConProject-003R56AG049970 · NIA · DREXEL UNIVERSITY · PI LOVASI, GINA S · 2022 to 2023
$1.3M
NIA NIH HHS R01 AG049970NIA NIH HHS R56 AG049970NICHD NIH HHS P2C HD042828
6 · The paper itself

Abstract

backgroundEpidemiological studies utilize residential histories to assess environmental exposure risk. The validity from using commercially-sourced residential histories within national longitudinal studies remains unclear. Our study assessed predictors of non-agreement between baseline addresses from the commercially-sourced LexisNexis database and participants in the national longitudinal study, REasons for Geographic and Racial Differences in Stroke (REGARDS). Additionally, we assessed differences in stroke risk by neighborhood socioeconomic score (nSES) based on participant reported address compared to nSES from LexisNexis/REGARDS matched baseline address.

methodsFrom January 2003-October 2007, REGARDS enrolled 30,239 black and white adults aged 45 and older within the continental United States and collected their baseline address. ArcGIS Desktop 10.5.1 with ESRI 2016 Business Analyst Data was used to geocode baseline addresses from LexisNexis and REGARDS. Logistic regression was used to estimate the likelihood that LexisNexis address matched REGARDS baseline address for each participant. Survival analysis was used to estimate association between nSES and incident stroke.

resultsApproximately 91% of REGARDS participants had a LexisNexis address. Of these geocoded addresses, 93% of REGARDS baseline addresses matched LexisNexis addresses. Odds of agreement between LexisNexis and REGARDS was higher for older-aged participants (OR = 1.02 per year, 95% CI: 1.01, 1.02), blacks compared to whites (OR = 1.16, 95% CI: 1.05, 1.29), females compared to males (OR = 1.15, 95% CI: 1.04, 1.26), participants with an income of $34k-74k compared to an income less than $20k (OR = 1.62, 95% CI: 1.39, 1.89). Odds of agreement were lower for residents in Midwest compared to residents in the south (OR = 0.82, 95% CI: 0.73, 0.94). No significant differences in nSES-stroke associations were observed between REGARDS only and LexisNexis/REGARDS matched addresses; however, differences in interactions were observed.

conclusionAgreement between LexisNexis and REGARDS addresses varied by sociodemographic groups, potentially introducing bias in studies reliant on LexisNexis alone for residential address data.

Indexed as

Environmental exposureResidential addressResidential history

Identifiers

PMID34401464
PMCPMC8358447

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.