Evidence map›Paper›PMID 34813869›Full record

ArticleJournal of affective disorders2022

Predictors of Intentional Self -Harm Among Medicaid Mental Health Clinic Clients In New York.

Mahfuza Rahman, Emily Leckman-Westin, Barbara Stanley, Jamie Kammer, Deborah Layman, Christa D Labouliere, Anni Cummings, Prabu Vasan, Katrina Vega, Kelly L Green and 3 more

Open access · greenAbstract read
In one paragraph

Article in Journal of affective disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 31% of its field
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. 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

13 authors at 6 institutions in 1 country.

Mahfuza RahmanNew York State Office of Mental Health, NY. Electronic address: mahfuza.rahman@omh.ny.gov.
Emily Leckman-WestinNew York State Office of Mental Health, NY; Department of Epidemiology and Biostatistics, University at Albany-SUNY, School of Public Health.
Barbara StanleyNew York State Psychiatric Institute, NY; Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, NY.
Jamie KammerNew York State Office of Mental Health, NY.
Deborah LaymanNew York State Office of Mental Health, NY.
Christa D LabouliereNew York State Psychiatric Institute, NY; Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, NY.
Anni CummingsNew York State Office of Mental Health, NY.
Prabu VasanNew York State Office of Mental Health, NY.
Katrina VegaNew York State Office of Mental Health, NY.
Kelly L GreenDepartment of Psychiatry Perelman School of Medicine University of Pennsylvania, PA.
Gregory K BrownDepartment of Psychiatry Perelman School of Medicine University of Pennsylvania, PA.
Molly FinnertyNew York State Office of Mental Health, NY; Department of Child and Adolescent Psychiatry, New York University Langone Health, NY.
Hanga GalfalvyDepartment of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, NY; Department of Biostatistics Columbia University Mailman School of Public Health, NY.
New York State Office of Mental Health · USNew York State Psychiatric InstituteUniversity of Pennsylvania · USColumbia University · USNYU Langone Health · USUniversity at Albany, State University of New York · US

Funding

Zero Suicide Implementation and Evaluation in Outpatient Mental Health ClinicsR01MH112139 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI STANLEY, BARBARA · 2016 to 2020
$3.8M
NIMH NIH HHS L30 MH114339NIMH NIH HHS R01 MH112139
6 · The paper itself

Abstract

backgroundBehavioral health outpatients are at risk for self-harm. Identifying individuals or combination of risk factors could discriminate those at elevated risk for self-harm.

methodsThe study population (N = 248,491) included New York State Medicaid-enrolled individuals aged 10 to 64 with mental health clinic services between November 1, 2015 to November 1, 2016. Self-harm episodes were defined using ICD-10 codes from emergency department and inpatient visits. Multi-predictor logistic regression models were fit on a subsample of the data and compared to a testing sample based on discrimination performance (Area Under the Curve or AUC).

resultsOf N = 248,491 patients, 4,224 (1.70%) had an episode of intentional self-harm. Factors associated with increased self-harm risk were age 17-25, being female and having recent diagnoses of depression (AOR=4.3, 95%CI: 3.6-5.0), personality disorder (AOR=4.2, 95%CI: 2.9-6.1), or substance use disorder (AOR=3.4, 95%CI: 2.7-4.3) within the last month. A multi-predictor logistic regression model including demographics and new psychiatric diagnoses within 90 days prior to index date had good discrimination and outperformed competitor models on a testing sample (AUC=0.86, 95%CI:0.85-0.87). LIMITATIONS: New York State Medicaid data may not be generalizable to the entire U.S population. ICD-10 codes do not allow distinction between self-harm with and without intent to die.

conclusionsOur results highlight the usefulness of recency of new psychiatric diagnoses, in predicting the magnitude and timing of intentional self-harm risk. An algorithm based on this finding could enhance clinical assessments support screening, intervention and outreach programs that are at the heart of a Zero Suicide prevention model.

Indexed as

Self-Injurious BehaviorSuicideAdolescentAdultFemaleHumansMedicaidMental HealthNew YorkYoung AdultIntentional self-harmMedicaidPredictive modelingSuicide attempt

Identifiers

PMID34813869
PMCPMC8808564
OpenAlexW3217620903

What OpenQuestion 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 OpenQuestion graph.