Centre for Interdisciplinary Social Research, Phenikaa University, Hanoi 100803, Vietnam.
A.I. for Social Data Lab (AISDL), Vuong & Associates, Hanoi 100000, Vietnam.
Int J Environ Res Public Health. 2023 Mar 15;20(6):5173. doi: 10.3390/ijerph20065173.
Patients with serious illnesses or injuries may decide to quit their medical treatment if they think paying the fees will put their families into destitution. Without treatment, it is likely that fatal outcomes will soon follow. We call this phenomenon "near-suicide". This study attempted to explore this phenomenon by examining how the seriousness of the patient's illness or injury and the subjective evaluation of the patient's and family's financial situation after paying treatment fees affect the final decision on the treatment process. Bayesian Mindsponge Framework (BMF) analytics were employed to analyze a dataset of 1042 Vietnamese patients. We found that the more serious the illnesses or injuries of patients were, the more likely they were to choose to quit treatment if they perceived that paying the treatment fees heavily affected their families' financial status. Particularly, only one in four patients with the most serious health issues who thought that continuing the treatment would push themselves and their families into destitution would decide to continue the treatment. Considering the information-filtering mechanism using subjective cost-benefit judgments, these patients likely chose the financial well-being and future of their family members over their individual suffering and inevitable death. Our study also demonstrates that mindsponge-based reasoning and BMF analytics can be effective in designing and processing health data for studying extreme psychosocial phenomena. Moreover, we suggest that policymakers implement and adjust their policies (e.g., health insurance) following scientific evidence to mitigate patients' likelihood of making "near-suicide" decisions and improve social equality in the healthcare system.
患有严重疾病或受伤的患者如果认为支付费用会使家庭陷入贫困,可能会决定放弃治疗。如果没有治疗,很可能很快就会导致致命后果。我们称这种现象为“准自杀”。本研究试图通过研究患者疾病或伤害的严重程度以及患者及其家庭在支付治疗费用后的主观财务状况评估如何影响治疗过程的最终决策来探讨这一现象。贝叶斯思维海绵框架(BMF)分析被用于分析来自 1042 名越南患者的数据集。我们发现,如果患者认为支付治疗费用会严重影响其家庭的财务状况,那么他们的疾病或伤害越严重,他们就越有可能选择放弃治疗。特别是,只有四分之一的患有最严重健康问题的患者认为继续治疗会使自己和家人陷入贫困,他们会决定继续治疗。考虑到使用主观成本效益判断的信息过滤机制,这些患者可能会选择家庭成员的财务幸福和未来,而不是自己的个人痛苦和不可避免的死亡。我们的研究还表明,基于思维海绵的推理和 BMF 分析可有效地用于设计和处理健康数据,以研究极端的社会心理现象。此外,我们建议政策制定者根据科学证据实施和调整政策(例如,医疗保险),以降低患者做出“准自杀”决策的可能性,并提高医疗保健系统中的社会公平性。