ArticleCurrent epidemiology reports2019
Identifying and Addressing Confounding Bias in Violence Prevention Research.
Article in Current epidemiology reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- Identifying confounders and estimating the causal effect of antenatal care on age-specific childhood vaccination.Frontiers in public health · 2025Article
- Identification of confounders and estimating the causal effect of place of birth on age-specific childhood vaccination.BMC medical informatics and decision making · 2024Article
- Historical redlining and the epidemiology of present-day firearm violence in the United States: A multi-city analysis.Preventive medicine · 2022Article
- Mental Health Professionals' Perspectives on Group Intervention for Women Survivors of Intimate Partner Violence: A Qualitative Study.Journal of psychiatry spectrumArticle
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Purpose of review: Violence prevention research has enhanced our understanding of individual and community risk and protective factors for aggression and violence. However, our knowledge of risk and protective factors for violence is highly dependent on observational studies, since there are few randomized trials of risk and protective factors for violence. Observational studies are susceptible to systematic errors, specifically confounding, and may lack internal validity. Recent findings: Many violence prevention studies utilize methods that do not correctly identify the set of covariates needed for statistical adjustment. This results in unwarranted matching and restriction leading to further confounding or selection bias. Covariate adjustment based on purely statistical criteria generates inconsistent results and uncertain conclusions. Summary: Conventional methods used to identify confounding in violence prevention research are often inadequate. Causal diagrams have potential to improve the understanding and identification of potential confounding biases in observational violence prevention studies, and methods like sensitivity analysis using quantitative bias analysis can help to address unmeasured confounding. Violence research studies should make more use of these methods.
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Registered trials
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