SAFETYLIT WEEKLY UPDATE

We compile citations and summaries of about 400 new articles every week.
RSS Feed

HELP: Tutorials | FAQ
CONTACT US: Contact info

Search Results

Journal Article

Citation

Vuong QH, Le TT, Jin R, Khuc QV, Nguyen HS, Vuong TT, Nguyen MH. Int. J. Environ. Res. Public Health 2023; 20(6).

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph20065173

PMID

36982083

PMCID

PMC10049730

Abstract

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.


Language: en

Keywords

Humans; patients; Family/psychology; Patients; *Suicide; Bayes Theorem; BMF analytics; financial burden; Insurance, Health; mindsponge theory; near-suicide

NEW SEARCH


All SafetyLit records are available for automatic download to Zotero & Mendeley
Print