Evidence map›Paper›PMID 39372906›Full record

ArticleProceedings. IEEE International Conference on Healthcare Informatics2024

Analyzing Social Factors to Enhance Suicide Prevention Across Population Groups.

Richard Li Xu, Song Wang, Zewei Wang, Yuhan Zhang, Yunyu Xiao, Jyotishman Pathak, David Hodge, Yan Leng, S Craig Watkins, Ying Ding and 1 more

Abstract read
In one paragraph

Article in Proceedings. IEEE International Conference on Healthcare Informatics, 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. Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity.Proceedings. IEEE International Conference on Healthcare Informatics · 2026
    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

11 authors.

Richard Li XuPopulation Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Song WangCockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA.
Zewei WangPopulation Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Yuhan ZhangPopulation Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Yunyu XiaoPopulation Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Jyotishman PathakPopulation Health Sciences, Weill Cornell Medicine, New York, NY, USA.
David HodgeNational Center for Bioethics in Research and Health Care, Tuskegee University, Tuskegee, AL, USA.
Yan LengMcCombs School of Business, The University of Texas at Austin, Austin, TX, USA.
S Craig WatkinsSchool of Journalism and Media, The University of Texas at Austin, Austin, TX, USA.
Ying DingSchool of Information, The University of Texas at Austin, Austin, TX, USA.
Yifan PengPopulation Health Sciences, Weill Cornell Medicine, New York, NY, USA.

Funding

AIM-AHEAD Coordinating Center - All Four CoresOT2OD032581 · OD · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI Paul Avillach, Bettina M. Beech · 2021 to 2026
$168.7M
A framework to enhance radiology structured report by invoking NLP and DL: Models and ApplicationsR00LM013001 · NLM · WEILL MEDICAL COLL OF CORNELL UNIV · PI PENG, YIFAN · 2020 to 2022
$710k
NIH HHS OT2 OD032581NLM NIH HHS R00 LM013001
6 · The paper itself

Abstract

Social factors like family background, education level, financial status, and stress can impact public health outcomes, such as suicidal ideation. However, the analysis of social factors for suicide prevention has been limited by the lack of up-to-date suicide reporting data, variations in reporting practices, and small sample sizes. In this study, we analyzed 172,629 suicide incidents from 2014 to 2020 utilizing the National Violent Death Reporting System Restricted Access Database (NVDRS-RAD). Logistic regression models were developed to examine the relationships between demographics and suicide-related circumstances. Trends over time were assessed, and Latent Dirichlet Allocation (LDA) was used to identify common suicide-related social factors. Mental health, interpersonal relationships, mental health treatment and disclosure, and school/work-related stressors were identified as the main themes of suicide-related social factors. This study also identified systemic disparities across various population groups, particularly concerning Black individuals, young people aged under 24, healthcare practitioners, and those with limited education backgrounds, which shed light on potential directions for demographic-specific suicidal interventions.

Indexed as

Social Determinants of HealthSocial FactorsSuicide

Identifiers

PMID39372906
PMCPMC11450796

What OpenQuestion holds

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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.