Evidence map›Paper›PMID 37547048›Full record

ArticleThe Lancet regional health. Western Pacific2023

A network approach to understand co-occurrence and relative importance of different reasons for suicide: a territory-wide study using 2002-2019 Hong Kong Coroner's Court reports.

Yu Cheng Hsu, Alvin Junus, Qingpeng Zhang, Clifford Wong, Tsz Mei Lam, Florence Cheung, Joyce Liu, Ingrid D Lui, Paul S F Yip

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Suicide prevention in Hong Kong: pushing boundaries while building bridges.The Lancet regional health. Western Pacific · 2024
    Review
  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

9 authors.

Yu Cheng HsuThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
Alvin JunusDepartment of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
Qingpeng ZhangSchool of Data Science, City University of Hong Kong, Hong Kong.
Clifford WongThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
Tsz Mei LamThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
Florence CheungThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
Joyce LiuThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
Ingrid D LuiThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
Paul S F YipThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Suicide is a complex and multifaceted issue, and suicidal behaviors are often driven by multiple, interacting factors. It has been challenging to identify reasons for suicide using existing scientific methodologies. This study aims to identify critical reasons for suicide and suicidal behaviors through the application of novel network science methods. Methods: Based on cases investigated by the Hong Kong Coroner's Court from 2002 to 2019, we modelled identified reasons for 13,001 suicide cases as a co-occurrence network, and calculated each reason's eigencentrality to determine their respective relative importance. We then analyzed the temporal and demographic changes in the structure and eigencentrality of the network. We further conducted simulation studies based on the United Nations population projection to assess potential burden of different reasons for suicide on the population in the coming years. Findings: School-related issues had the highest eigencentrality (eigencentrality = 0.49) for individuals younger than 20 years of age. Financial issues were crucial for adults aged 20-59 years, but their importance differed between males (eigencentrality = 0.51) and females (eigencentrality = 0.14). Physical illness (eigencentrality = 0.80) was the core concern for adults over 60 years. Across the Hong Kong population, the reasons for suicide appear to have shifted from financial issues in the early 2000s (eigencentrality = 0.46) to issues related to physical illnesses since 2011 (eigencentrality = 0.58). Simulation findings indicate that, by 2050, most suicides in Hong Kong will be due to physical illness-related issues (eigencentrality = 0.69) due to the rapidly aging population. Interpretation: There have been important sex and age differences over time, in reasons for suicide. Given the projected increasing age of the Hong Kong population over the next decades, older adults with physical illnesses appear to be the highest contributors to suicide cases in the overall population. This novel network analysis approach provides important data-driven information upon which to base effective proactive public health suicide prevention strategies and interventions. Funding: Hong Kong Jockey Club Charities Trust, Collaborative Research Fund (C7151-20G), and General Research Fund (17606521).

Indexed as

ForecastingNetwork scienceSuicide

Identifiers

PMID37547048
PMCPMC10398608

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.