Observational studyMedical care2019
Measuring Exposure to Incarceration Using the Electronic Health Record.
Observational study in Medical care, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Capturing incarceration in the medical record: we cannot manage what we cannot measure.Health affairs scholar · 2026Article
- A randomized clinical trial testing a health literacy intervention to reduce disparities in access to care among Justice-Impacted Adults (JIA).Health & justice · 2024Article
- Scoping review of military veterans involved in the criminal legal system and their health and healthcare: 5-year update and map to the Veterans-Sequential Intercept Model.Health & justice · 2024Article
- Substance Use Disorders, Mental Illness, and Health Care Utilization Among Adults With Recent Criminal Legal Involvement.Psychiatric services (Washington, D.C.) · 2024Article
- Identifying incarceration status in the electronic health record using large language models in emergency department settings.Journal of clinical and translational science · 2024Article
- Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.Journal of clinical and translational science · 2024Article
- How legal problems are conceptualized and measured in healthcare settings: a systematic review.Health & justice · 2023Article
- Co-occurring homelessness, justice involvement, opioid dependence and psychosis: a cross-sectoral data linkage study.European journal of public health · 2023Article
- Locating Youth Exposed to Parental Justice Involvement in the Electronic Health Record: Development of a Natural Language Processing Model.JMIR pediatrics and parenting · 2022Article
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
- Health Status and Health Care Utilization of US Adults Under Probation: 2015-2018.American journal of public health · 2020Article
- Frequency and Duration of Incarceration and Mortality Among US Veterans With and Without HIV.Journal of acquired immune deficiency syndromes (1999) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundElectronic health records (EHRs) are a rich source of health information; however social determinants of health, including incarceration, and how they impact health and health care disparities can be hard to extract.
objectiveThe main objective of this study was to compare sensitivity and specificity of patient self-report with various methods of identifying incarceration exposure using the EHR. RESEARCH
designValidation study using multiple data sources and types. SUBJECTS: Participants of the Veterans Aging Cohort Study (VACS), a national observational cohort based on data from the Veterans Health Administration (VHA) EHR that includes all human immunodeficiency virus-infected patients in care (47,805) and uninfected patients (99,060) matched on region, age, race/ethnicity, and sex. MEASURES AND DATA SOURCES: Self-reported incarceration history compared with: (1) linked VHA EHR data to administrative data from a state Department of Correction (DOC), (2) linked VHA EHR data to administrative data on incarceration from Centers for Medicare and Medicaid Services (CMS), (3) VHA EHR-specific identifier codes indicative of receipt of VHA incarceration reentry services, and (4) natural language processing (NLP) in unstructured text in VHA EHR.
resultsLinking the EHR to DOC data: sensitivity 2.5%, specificity 100%; linking the EHR to CMS data: sensitivity 7.9%, specificity 99.3%; VHA EHR-specific identifier for receipt of reentry services: sensitivity 7.3%, specificity 98.9%; and NLP, sensitivity 63.5%, specificity 95.9%.
conclusionsNLP tools hold promise as a feasible and valid method to identify individuals with exposure to incarceration in EHR. Future work should expand this approach using a larger body of documents and refinement of the methods, which may further improve operating characteristics of this method.
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Registered trials
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.