Evidence map›Paper›PMID 42417989›Full record

ArticleSocial psychiatry and psychiatric epidemiology2026

Assessing the utility of health access data and social determinants of health in ecological suicide prediction models.

Matthew D Castner, Christopher Kitchen, Christelle Xiong, Mark J Bittle, Paul S Nestadt, Holly C Wilcox, Hadi Kharrazi

Abstract read
PubMed Publisher
In one paragraph

Article in Social psychiatry and psychiatric epidemiology, 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

7 authors.

Matthew D CastnerDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Christopher KitchenDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Christelle XiongSection of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Mark J BittleDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Paul S NestadtDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Holly C WilcoxDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Hadi KharraziDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. kharrazi@jhu.edu.

Funding

Advancing Maryland's Statewide Suicide Data Warehouse to Improve Individual and Population-level Mortality Prediction and PreventionR01MH124724 · NIMH · JOHNS HOPKINS UNIVERSITY · PI KHARRAZI, HADI · 2020 to 2023
$3.2M
Addressing Suicide Research Gaps: Understanding Mortality Outcomes in the Mid-Atlantic RegionR56MH117560 · NIMH · JOHNS HOPKINS UNIVERSITY · PI KHARRAZI, HADI, WILCOX, HOLLY C · 2018 to 2018
$491k
NIMH NIH HHS R01 MH124724NIMH NIH HHS R56 MH117560
6 · The paper itself

Abstract

purposeAssess the utility of access to healthcare, clinical conditions, and social determinants of health (SDoH) variables in population-level suicide prediction models.

methodsNegative binomial regression models were constructed using data from population-level surveys, state death certificates, federal records of behavioral health services, and U.S. Census data. Outcomes of interest were suicidal ideation and suicide attempt (SISA), inpatient psychiatric hospitalization (IPH), and suicide death. The relative changes in pseudo R

resultsClinical data showed a significant impact, with the largest percent increase in pseudo R

conclusionsClinical data proved to have the most effective variables in predicting a continuum of suicidal behaviors. While the impacts of access to healthcare and SDoH factors were comparatively limited, these variables also contributed to additional model improvements. These findings show the utility of population-level healthcare services and SDoH for ecological suicide behavior risk prediction.

Indexed as

Access to healthcarePopulation healthSocial determinants of healthSuicidal ideationSuicide attemptsSuicide prediction

Identifiers

PMID42417989

What Socratic holds

Textmetadata
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.