Search Results (3)

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. Full article

The dataset contains 1042 records obtained from inpatients at hospitals in the northern region of Vietnam. The survey process lasted 20 months from August 2014 to March 2016, and yielded a comprehensive set of records of inpatients’ financial situations, healthcare, and health insurance information, as well as their perspectives on treatment service in the hospitals. Five articles were published based on the smaller subsets. This data article introduces the full dataset for the first time and suggests a new Bayesian statistics approach for data analysis. The full dataset is expected to contribute new data for health economic researchers and new grounded scientific results for policymakers. Full article

In the last three decades many developing and middle-income nations’ health care systems have been financed via out-of-pocket payments by individuals. User fees charges, however, may not be the best approach or thenmost equitable approach to finance and/or reform health services in developing nations. This study investigates the status of Vietnam’s current health system as a result of implementing user fees policies. A recent mandate by the government to increase the universal cover to 100% attempts to tackle inadequate insurance cover, one of the four major factors contributing to the high and increasing probability of destitution for Vietnamese patients (the other three being: non-residency, long stay in hospital, and high cost of treatment). Empirical results however suggest that this may be catastrophic for low-income earners: if insurance cover reimbursement decreases below 50% of actual health expenditures, the probability of Vietnamese falling into destitution will rise further. Our findings provide policy implications and directions to improve Vietnam’s health care system, in particular by ensuring the utilization of health services and financial protection for the people. Full article