Evidence map›Paper›PMID 39443967›Full record

ArticleInjury epidemiology2024

Using data fusion with multiple imputation to correct for misclassification in self-reported exposure: a case-control study of cannabis use and homicide victimization.

Seonghun Lee, Guohua Li, Stanford Chihuri, Yuanzhi Yu, Qixuan Chen

Abstract read
In one paragraph

Article in Injury epidemiology, 2024. 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

5 authors.

Seonghun LeeDepartment of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, 10032, USA.
Guohua LiDepartment of Anesthesiology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA.
Stanford ChihuriDepartment of Anesthesiology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA.
Yuanzhi YuDepartment of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, 10032, USA.
Qixuan ChenDepartment of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, 10032, USA. qc2138@cumc.columbia.edu.

Funding

CE19-001, The Columbia Center for Injury Science and Prevention (CCISP)R49CE003094 · CE · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BRANAS, CHARLES C. · 2018 to 2023
$4.3M
Improving the analysis and use of contaminated immunoassays: from methods development to implementationR01ES035784 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Qixuan Chen, ANDREW GELMAN · 2024 to 2026
$1.2M
Bayesian exposure-response analysis for immunoassays data with measurement errorsR21ES029668 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI CHEN, QIXUAN · 2019 to 2020
$417k
CDC HHS R49CE003094NCIPC CDC HHS R49 CE003094NIEHS NIH HHS R01 ES035784NIEHS NIH HHS R21 ES029668NIH HHS R21ES029668
6 · The paper itself

Abstract

backgroundCannabis use has been causally linked to violent behaviors in experimental and case studies, but its association with homicide victimization has not been rigorously assessed through epidemiologic research.

methodsWe performed a case-control analysis using two national data systems. Cases were homicide victims from the National Violent Death Reporting System (NVDRS), and controls were participants from the National Survey on Drug Use and Health (NSDUH). While the NVDRS contained toxicological testing data on cannabis use, the NSDUH only collected self-reported data, and thus the potential misclassification in the self-reported data needed to be corrected. We took a data fusion approach by concatenating the NSDUH with a third data system, the National Roadside Survey of Alcohol and Drug Use by Drivers (NRS), which collected toxicological testing and self-reported data on cannabis use for drivers. The data fusion approach provided multiple imputations (MIs) of toxicological testing results on cannabis use for the participants in the NSDUH, which were then used in the case-control analysis. Bootstrap was used to obtain valid statistical inference.

resultsThe analyses revealed that cannabis use was associated with 3.55-fold (95% CI: 2.75-4.35) increased odds of homicide victimization. Alcohol use, being Black, male, aged 21-34 years, and having less than a high school education were also significantly associated with increased odds of homicide victimization.

conclusionsCannabis use is a major risk factor for homicide victimization. The data fusion with MI method is useful in integrative data analysis for harmonizing measures between different data sources.

Indexed as

Alcohol useCannabis useData integrationMissing at randomMultiple imputationNational surveysStratified bootstrapping

Identifiers

PMID39443967
PMCPMC11515600

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Registered trials

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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.