Evidence map›Paper›PMID 41224015›Full record

ArticleJournal of affective disorders2026

Association between PTSD and health-related social needs in US Veterans: an NLP analysis using Veterans Health Administration Data.

Feiyun Ouyang, Wen Hu, Joel Reisman, Terri K Pogoda, Kathleen F Carlson, Weisong Liu, Yousef Moradi, Yifan Zhang, Shuo Han, Sharmin Sultana and 4 more

Abstract read
In one paragraph

Article in Journal of affective disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Feiyun OuyangMiner School of Computer and Information Sciences, University of Massachusetts Lowell, MA, United States. Electronic address: feiyun_ouyang@uml.edu.
Wen HuMiner School of Computer and Information Sciences, University of Massachusetts Lowell, MA, United States. Electronic address: wen_hu@uml.edu.
Joel ReismanCenter for Health Optimization & Implementation Research, Veterans Affairs Bedford Healthcare System, Bedford, MA, United States. Electronic address: Joel.Reisman@va.gov.
Terri K PogodaCenter for Health Optimization & Implementation Research, VA Boston Healthcare System, Boston, MA, United States; Boston University School of Public Health, Boston, MA, United States. Electronic address: Terri.Pogoda@va.gov.
Kathleen F CarlsonCenter to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, OR, United States; Oregon Health & Science University, Portland State University School of Public Health, Portland, OR, United States. Electronic address: Kathleen.Carlson@va.gov.
Weisong LiuMiner School of Computer and Information Sciences, University of Massachusetts Lowell, MA, United States. Electronic address: weisong_liu@uml.edu.
Yousef MoradiDepartment of Epidemiology and Biostatistics, Faculty of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran. Electronic address: yousefmoradi211@yahoo.com.
Yifan ZhangMiner School of Computer and Information Sciences, University of Massachusetts Lowell, MA, United States. Electronic address: yifan_zhang1@student.uml.edu.
Shuo HanDepartment of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, United States. Electronic address: shuo_han@uml.edu.
Sharmin SultanaMiner School of Computer and Information Sciences, University of Massachusetts Lowell, MA, United States. Electronic address: sharmin_sultana@student.uml.edu.
Won Seok JangMiner School of Computer and Information Sciences, University of Massachusetts Lowell, MA, United States. Electronic address: wonseok_jang@student.uml.edu.
Zonghai YaoManning College of Information and Computer Sciences, University of Massachusetts Amherst, United States. Electronic address: zonghaiyao@umass.edu.
Avijit MitraManning College of Information and Computer Sciences, University of Massachusetts Amherst, United States. Electronic address: avijitmitra@umass.edu.
Hong YuMiner School of Computer and Information Sciences, University of Massachusetts Lowell, MA, United States; Center for Health Optimization & Implementation Research, Veterans Affairs Bedford Healthcare System, Bedford, MA, United States; Manning College of Information and Computer Sciences, University of Massachusetts Amherst, United States; Center for Biomedical and Health Research in Data Sciences, University of Massachusetts Lowell, Lowell, MA, United States. Electronic address: hong_yu@uml.edu.

Funding

Social and behavioral determinants of MOUD utilization and opioid overdoseR01DA056470 · NIDA · UNIVERSITY OF MASSACHUSETTS LOWELL · PI Wenjun Li, DAVID A SMELSON · 2023 to 2026
$2.9M
HSRD VA I01 HX003711NIDA NIH HHS R01 DA056470
6 · The paper itself

Abstract

backgroundPost-traumatic stress disorder (PTSD) significantly impacts US Veterans' well-being by potentially exacerbating health-related social needs (HRSN). This study investigated short- and long-term associations between PTSD diagnosis and nine HRSN indicators.

methodsUtilizing national Veterans Health Administration (VHA) electronic health record (EHR) data, we employed dual designs. A cross-sectional analysis included 62,298 Veterans (PTSD diagnosed in fiscal year [FY] 2012 vs. non-PTSD), matched on key demographic and comorbidity factors. A retrospective cohort followed 11,758 propensity-matched Veterans (no baseline HRSN at FY2012) from FY2013-FY2023. Outcomes were nine HRSN indicators identified via codes and natural language processing in EHRs.

resultsCross-sectionally (N = 62,298), PTSD was linked to higher prevalent HRSN odds at baseline, including violence (adjusted odds ratio [aOR] = 3.98; 95% CI: 3.77-4.20), social problems (aOR = 2.87; 95% CI: 2.73-3.01), and legal issues (aOR = 1.75; 95% CI: 1.64-1.87). In the cohort study (N = 11,758), baseline PTSD strongly predicted incident HRSN across all nine indicators in the first year (e.g., violence: adjusted hazard ratio [aHR] = 3.05; 95% CI: 2.68-3.47). Though strongest initially, these associations attenuated but remained significant up to 10 years post-diagnosis.

conclusionsUS Veterans diagnosed with PTSD face substantially elevated short- and long-term risks for diverse HRSN, including critical social, financial, housing, and legal problems. These persistent vulnerabilities demand integrated healthcare with routine screening, monitoring, and targeted interventions to address complex needs and improve Veteran well-being.

Indexed as

Stress Disorders, Post-TraumaticVeteransAdultAgedCross-Sectional StudiesElectronic Health RecordsFemaleHumansMaleMiddle AgedNatural Language ProcessingRetrospective StudiesUnited StatesUnited States Department of Veterans AffairsViolenceHealth-related social needsNatural language processingPost-traumatic stress disorder

Identifiers

PMID41224015
PMCPMC13242300

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.