Suicidal behaviors among high school graduates with preexisting mental health problems: A machine learning and GIS-based study.

IF 2.5 4区医学 Q2 PSYCHIATRY International Journal of Social Psychiatry Pub Date : 2025-02-01 Epub Date: 2024-09-05 DOI:10.1177/00207640241279004

Firoj Al-Mamun, Md Emran Hasan, Nitai Roy, Moneerah Mohammad ALmerab, Mohammed A Mamun

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Abstract

Background: Suicidal behavior among adolescents with mental health disorders, such as depression and anxiety, is a critical issue. This study explores the prevalence and predictors of past-year suicidal behaviors among Bangladeshi high school graduates, employing both traditional statistical and machine learning methods.

Aims: To investigate the prevalence and predictors of past-year suicidal behaviors among high school graduates with mental health disorders, evaluate the effectiveness of various machine learning models in predicting these behaviors, and identify geographical disparities.

Methods: A cross-sectional survey was conducted with 1,242 high school graduates (54.1% female) in June 2023, collecting data on sociodemographic characteristics, mental health status, sleep patterns, and digital addiction. Statistical analyses were performed using SPSS, while machine learning and GIS analyses were conducted with Python and ArcMap 10.8, respectively.

Results: Among the participants, 29.9% reported suicidal ideation, 15.3% had suicide plans, and 5.4% attempted suicide in the past year. Significant predictors included rural residence, sleep duration, comorbid depression and anxiety, and digital addiction. Machine learning analyses revealed that permanent residence was the most significant predictor of suicidal behavior, while digital addiction had the least impact. Among the models used, the CatBoost model achieved the highest accuracy (69.42% for ideation, 87.05% for planning, and 94.77% for attempts) and demonstrated superior predictive performance. Geographical analysis showed higher rates of suicidal behaviors in specific districts, though overall disparities were not statistically significant.

Conclusion: Enhancing mental health services in rural areas, addressing sleep issues, and implementing digital health and community awareness programs are crucial for reducing suicidal behavior. Future longitudinal research is needed to better understand these factors and develop more effective prevention strategies.

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存在心理健康问题的高中毕业生的自杀行为：基于机器学习和地理信息系统的研究。

背景：患有抑郁症和焦虑症等精神疾病的青少年的自杀行为是一个关键问题。本研究采用传统统计和机器学习方法，探讨了孟加拉国高中毕业生中上一年自杀行为的发生率和预测因素。研究目的：调查患有心理健康障碍的高中毕业生中上一年自杀行为的发生率和预测因素，评估各种机器学习模型在预测这些行为方面的有效性，并确定地域差异：我们于 2023 年 6 月对 1242 名高中毕业生（54.1% 为女性）进行了横断面调查，收集了有关社会人口学特征、心理健康状况、睡眠模式和数字成瘾的数据。统计分析使用 SPSS，机器学习和地理信息系统分析分别使用 Python 和 ArcMap 10.8：在参与者中，29.9%的人有自杀倾向，15.3%的人有自杀计划，5.4%的人在过去一年中自杀未遂。重要的预测因素包括农村居住地、睡眠时间、合并抑郁和焦虑以及数字成瘾。机器学习分析表明，永久居住地是预测自杀行为的最重要因素，而数字成瘾的影响最小。在所使用的模型中，CatBoost 模型的准确率最高（意念为 69.42%，计划为 87.05%，企图为 94.77%），并显示出卓越的预测性能。地域分析表明，特定地区的自杀行为发生率较高，但总体差异并无统计学意义：结论：加强农村地区的心理健康服务、解决睡眠问题、实施数字健康和社区宣传计划对于减少自杀行为至关重要。未来需要开展纵向研究，以更好地了解这些因素，并制定更有效的预防策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Social Psychiatry PSYCHIATRY-

CiteScore

12.30

自引率

1.30%

发文量

120

期刊介绍： The International Journal of Social Psychiatry, established in 1954, is a leading publication dedicated to the field of social psychiatry. It serves as a platform for the exchange of research findings and discussions on the influence of social, environmental, and cultural factors on mental health and well-being. The journal is particularly relevant to psychiatrists and multidisciplinary professionals globally who are interested in understanding the broader context of psychiatric disorders and their impact on individuals and communities. Social psychiatry, as a discipline, focuses on the origins and outcomes of mental health issues within a social framework, recognizing the interplay between societal structures and individual mental health. The journal draws connections with related fields such as social anthropology, cultural psychiatry, and sociology, and is influenced by the latest developments in these areas. The journal also places a special emphasis on fast-track publication for brief communications, ensuring that timely and significant research can be disseminated quickly. Additionally, it strives to reflect its international readership by publishing state-of-the-art reviews from various regions around the world, showcasing the diverse practices and perspectives within the psychiatric disciplines. This approach not only contributes to the scientific understanding of social psychiatry but also supports the global exchange of knowledge and best practices in mental health care.