Background Out-of-pocket (OOP) payments for healthcare services potentially have severe consequences on households, especially among the poor. Under certain circumstances, healthcare payments are financed through selling household assets, or borrowings. This certainly could influence households’ decision, which likely resorts to forgoing healthcare services. Thus, the focal point of this study is aimed to identify the inequalities and determinants of distress financing among households in Malaysia. Methods This study used secondary data from the National Health and Morbidity Survey (NHMS) 2019, a national cross-sectional household survey that used a two-stage stratified random sampling design involving 5,146 households. The concentration curve and concentration index were used to determine the economic inequalities in distress financing. Whereas, the determinants of distress financing were identified using the modified Poisson regression model. Results The prevalence of borrowing without interest was the highest (13.86%), followed by borrowing with interest (1.03%) while selling off assets was the lowest (0.87%). Borrowing without interest was highest among rural (16.21%) and poor economic status (23.34%). The distribution of distress financing was higher among the poor, with a concentration index of -0.245. The modified Poisson regression analysis revealed that the poor, middle, rich, and richest had 0.57, 0.58, 0.40 and 0.36 times the risk to develop distress financing than the poorest socio-economic group. Whereas, the presence of one and two or more elderly were associated with a 1.94 and 1.59 times risk of experiencing distress financing than households with no elderly members. The risk of developing distress financing was also 1.28 and 1.58 times higher among households with one and two members receiving inpatient care in the past 12 months compared to none. Conclusions The findings implied that the improvement of health coverage should be emphasized to curtail the prevalence of distress financing, especially among those caring for the elderly, requiring admission to hospitals, and poor socio-economic groups. This study could be of interest to policymakers to help achieve and sustain health coverage for all.

Background Despite emergency ambulance services playing a pivotal role in accessibility to life-saving treatments in Malaysia, there are still numerous gaps in knowledge in terms of their utilization and cost. This leads to current policies on procurement, maintenance, and allocation being predicated on historical evidence and expert opinions. This study thus aims to analyse the cost and utilization of ambulance services in selected public health facilities in Malaysia. Methods A cross-sectional study was employed involving 239 ambulances from selected hospitals and clinics. Ambulance service utilization was based on the number of trips, distance and duration of travel obtained from travel logbooks. A mixed top-down and activity-based costing approach was used to estimate the monthly cost of ambulance services. This constituted personnel, maintenance, fuel, overhead, consumables, ambulance, and medical equipment costs. The utilization and costs of ambulance services were further compared between settings and geographical locations. Results The average total cost of ambulance services was MYR 11,410.44 (US$ 2,756.14) for hospitals and MYR 9,574.39 (US$ 2,312.65) for clinics, albeit not significantly different. Personnel cost was found to be the main contributor to the total cost, at around 44% and 42% in hospitals and clinics, respectively. There was however a significant difference in the total cost in terms of the type and age of ambulances, in addition to their location. In terms of service utilization, the median number of trips and duration of ambulance usage was significantly higher in clinics (31.88 trips and 58.58 hours) compared to hospitals (16.25 trips and 39.25 hours). Conclusions The total cost of ambulance services was higher in hospitals compared to clinics, while its utilization showed a converse trend. The current findings evidence that despite the ambulance services being all under the MOH, their operating process and utilization reflected an inherent difference by setting.

Background: Healthcare in Malaysia is largely publicly funded, however, cancer could still result in out-of-pocket (OOP) expenses, which may burden the affected patients. This is especially relevant to those in the lower-income group. This pilot study was conducted to estimate the direct and indirect costs of cancer and evaluate the feasibility of obtaining these costs information from the lower-income cancer patients undergoing treatment. Methods: A cross-sectional study of patients with cancer was conducted in Hospital Kuala Lumpur between September and October 2020. Self-reported data from the patients were collected using face-to-face interviews. Detailed information about cancer-related OOP expenses including direct medical, direct non-medical, and productivity loss in addition to financial coping strategies were collected. Costs data were estimated and reported as average annual total costs per patient. Results: The mean total cost of cancer was estimated at MYR 7955.39 (US$ 1893.46) per patient per year. The direct non-medical cost was the largest contributor to the annual cost, accounting for 46.1% of the total cost. This was followed by indirect costs and direct medical costs at 36.0% and 17.9% of the total annual costs, respectively. Supplemental food and transportation costs were the major contributors to the total non-medical costs. The most frequently used financial coping strategies were savings and financial support received from relatives and friends. Conclusion: This study showed that estimation of the total cost of cancer from the patient's perspective is feasible. Considering the significant impact of direct non-medical and indirect costs on the total costs, it is vital to conduct further exploration of its cost drivers and variations using a larger sample size.

IntroductionAmbulance services are pivotal in any country's healthcare system. An efficient ambulance service not only decreases patient mortality rate but also allows resource prioritization for better outputs. This study aims to measure the efficiency of ambulance services provided by health facilities in the Ministry of Health (MOH), Malaysia.MethodsThis cross-sectional study analyzed the efficiency of 76 Decision-Making Units (DMUs) or health facilities, consisting of 62 health clinics and 14 hospitals. Data Envelopment Analysis (DEA) was used for computing efficiency scores while adopting the Variable Return to Scale (VRS) approach. The analysis was based on input orientation. The input was the cost of ambulance services, while the output for this analysis was the distance coverage (in km), the number of patients transferred, and hours of usage (in hours). Subsequent analysis was conducted to test the Overall Technical Efficiency (OTE), the Pure Technical Efficiency (PTE), the Scale Efficiency (SE), and the Return to Scale with the type of health facilities and geographical areas using a Mann-Whitney U-test and a chi-square test.ResultsThe mean scores of OTE, PTE, and SE were 0.508 (±0.207), 0.721 (±0.185), and 0.700 (±0.200), respectively. Approximately, 14.47% of the total health facilities were PTE. The results showed a significant difference in OTE and SE between ambulance services in hospitals and health clinics (p < 0.05), but no significant difference in PTE between hospitals and clinics (p>0.05). There was no significant difference in efficiency scores between urban and rural health facilities in terms of ambulance services except for OTE (p < 0.05).DiscussionThe ambulance services provided in healthcare facilities in the MOH Malaysia operate at 72.1% PTE. The difference in OTE between hospitals and health clinics' ambulance services was mainly due to the operating size rather than PTE. This study will be beneficial in providing a guide to the policymakers in improving ambulance services through the readjustment of health resources and improvement in the outputs.