Evidence map›Paper›PMID 40834909›Full record

ArticlePsychiatry investigation2025

Contribution of Chronic Disease in Predicting Depression and Suicidal Ideation Among the Older Adult Population.

Youngbin Seo, Hae-Young Kim, KiBong Choi, Sunmi Song, Junesun Kim

Abstract read
In one paragraph

Article in Psychiatry investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Youngbin SeoDepartment of Health Care Sciences, Graduate School, Transdisciplinary Major in Learning Health Systems, Korea University, Seoul, Republic of Korea.
Hae-Young KimDepartment of Health Care Sciences, Graduate School, Transdisciplinary Major in Learning Health Systems, Korea University, Seoul, Republic of Korea.
KiBong ChoiDepartment of Health Policy and Management, College of Health Science, Korea University, Seoul, Republic of Korea.
Sunmi SongDepartment of Public Health Sciences, Graduate School, Transdisciplinary Major in Learning Health Systems, Korea University, Seoul, Republic of Korea.
Junesun KimDepartment of Public Health Sciences, Graduate School, Transdisciplinary Major in Learning Health Systems, Korea University, Seoul, Republic of Korea.

Funding

Korea Health Industry Development Institute H21C0572Ministry of Health and Welfare
6 · The paper itself

Abstract

objectiveThis study aimed to clarify how chronic diseases (CDs) contribute to depression and suicidal ideation (SI) prediction using machine learning (ML) techniques among the older adult population.

methodsNational representative data of 5,419 older adults from the Korea National Health and Nutrition Examination Survey conducted in 2013, 2015, 2017, and 2019 were used in this study. The number and type of CDs were incorporated into Models 1 and 2, respectively, using five ML methods.

resultsThe average age of the participants was 72.7 years, with 43.2% males, 15.2% reporting depression, and 7.3% reporting SI. The number of CDs was correlated with increased depression and SI. The ML models showed moderate-to-good performance in the prediction of depression and SI. The area under the curve (AUC) values for Model 1 ranged from 0.729 to 0.772 for depression, and from 0.754 to 0.793 for SI. In Model 2, the AUC ranged from 0.704 to 0.768 for depression and from 0.750 to 0.785 for SI. More depression and SI were expected when the number of CDs was one or more and two or more, respectively. The top predictors of depression were osteoarthritis, myocardial infarction, diabetes, asthma, and stroke, whereas those predicting SI were stroke, hypertension, asthma, myocardial infarction, and rheumatoid arthritis.

conclusionThe number and specific types of CDs predicted depression and SI among Korean older adults. These results may help enhance cooperation with clinicians treating CDs and promote the early detection and prevention of further SI and behaviors.

Indexed as

AgedChronic diseaseDepressionMachine learningSuicidal ideation

Identifiers

PMID40834909
PMCPMC12444204

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