Cancer patients often grapple with substantial out-of-pocket (OOP) expenses and productivity loss, with the ramifications being particularly crucial for lower-income households. This study aims to estimate OOP costs incurred by cancer patients, assess their productivity loss, and analyse the financial coping mechanisms employed by individuals within the lower-income bracket. The study employed face-to-face interviews among cancer patients aged 40 years and above, currently undergoing treatment, and belonging to the lower income group. Participants were recruited from six public cancer referral hospitals. OOP expenses, encompassing medical and non-medical costs, along with productivity loss, were measured. A generalized linear model was applied to identify potential OOP determinants. Additionally, the coping mechanisms employed by individuals to finance their cancer OOP expenses were also determined. Among the 430 participants recruited, predominantly female (63.5%), and aged 60 or older (53.9%). The annual mean total cancer costs per patient were US$ 2,398.28 (±2,168.74), including 15% for medical costs US$ 350.95 (±560.24), 34% for non-medical costs US$820.24 (±818.24), and 51% for productivity loss costs US$1,227.09 (±1,809.09). Transportation, nutritional supplements, outpatient treatment, and medical supplies were notable cost contributors to total OOP expenditures. Ethnicity (β = 1.44; 95%CI = 1.15–1.79), household income (β = 1.40; 95%CI = 1.10–1.78), annual outpatient visits (β = 1.00; 95%CI = 1.00–1.01), age (β = 0.74; 95%CI = 0.56–0.98), and employment status (β = 0.54; 95%CI = 0.72–1.34) were identified as significant predictors of OOP costs among cancer patients. Notably, 91% of participants relied on household salaries and savings, while 15% resorted to interest-free borrowing, 11% sold possessions, and 0.5% borrowed with interest to finance their expenses. This study offers crucial insights into the economic impact of cancer on individuals and their families, providing policymakers with valuable information to tackle challenges faced in their journey. Despite substantial public healthcare subsidies, the study revealed that cancer costs can remain a potential barrier to accessing essential treatment. Therefore, there is a need for reinforced system level infrastructure to facilitate targeted financial navigation services.

Background: Emergency Medical Service (EMS) is a very crucial aspect of the healthcare system in providing urgent management and transportation of patients during emergencies. The sustainability of the services is however greatly impacted by the quality and age of ambulances. While this has led to numerous replacement policy recommendations, the implementations are often limited due to a lack of evidence and financial constraints. This study thus aims to develop a cost-effectiveness model and testing the model by evaluating the cost-effectiveness of 10-year and 15-year compulsory ambulance replacement strategies in public healthcare for the Malaysian Ministry of Health (MOH). Methods: A Markov model was developed to estimate the cost and outcomes ambulance replacement strategies over a period of 20 years. The model was tested using two alternative strategies of 10-year and 15-year. Model inputs were derived from published literature and local study. Model development and economic analysis were accomplished using Microsoft Excel 2016. The outcomes generated were costs per year, the number of missed trips and the number of lives saved, in addition to the Incremental Cost-Effectiveness Ratio (ICER). One-Way Deterministic Sensitivity Analysis (DSA) and Probabilistic Sensitivity Analysis (PSA) were conducted to identify the key drivers and to assess the robustness of the model. Results Findings showed that the most expensive strategy, which is implementing 10 years replacement strategy was more cost-effective than 15 years ambulance replacement strategy, with an ICER of MYR 11,276.61 per life saved. While an additional MYR 13.0 million would be incurred by switching from a 15- to 10-year replacement strategy, this would result in 1,157 deaths averted or additional live saved per year. Sensitivity analysis showed that the utilization of ambulances and the mortality rate of cases unattended by ambulances were the key drivers for the cost effectiveness of the replacement strategies. Conclusions: The cost-effectiveness model developed suggests that an ambulance replacement strategy of every 10 years should be considered by the MOH in planning sustainable EMS. While this model may have its own limitation and may require some modifications to suit the local context, it can be used as a guide for future economic evaluations of ambulance replacement strategies and further exploration of alternative solutions.