METHODS AND FINDINGS: Key electronic databases including Medline, Embase, Scopus, Global Health, CinAHL, EconLit and Business Source Premier were searched. We also searched the grey literature, specifically websites of leading organizations supporting health care in LMICs. Only studies using benefit incidence analysis (BIA) and/or financing incidence analysis (FIA) as explicit methodology were included. A total of 512 records were obtained from the various sources. The full texts of 87 references were assessed against the selection criteria and 24 were judged appropriate for inclusion. Twelve of the 24 studies originated from sub-Saharan Africa, nine from the Asia-Pacific region, two from Latin America and one from the Middle East. The evidence points to a pro-rich distribution of total health care benefits and progressive financing in both sub-Saharan Africa and Asia-Pacific. In the majority of cases, the distribution of benefits at the primary health care level favoured the poor while hospital level services benefit the better-off. A few Asian countries, namely Thailand, Malaysia and Sri Lanka, maintained a pro-poor distribution of health care benefits and progressive financing.
CONCLUSION: Studies evaluated in this systematic review indicate that health care financing in LMICs benefits the rich more than the poor but the burden of financing also falls more on the rich. There is some evidence that primary health care is pro-poor suggesting a greater investment in such services and removal of barriers to care can enhance equity. The results overall suggest that there are impediments to making health care more accessible to the poor and this must be addressed if universal health coverage is to be a reality.
OBJECTIVES: The objective of the present study was to assess the ability to pay among Malaysian households as preparation for a future national health financing scheme.
METHODS: This was a cross-sectional study involving representative samples of 774 households in Peninsular Malaysia.
FINDINGS: A majority of households were found to have the ability to pay for their health care. Household expenditure on health care per month was between MYR1 and MYR2000 with a mean (standard deviation [SD]) of 73.54 (142.66), or in a percentage of per-month income between 0.05% and 50% with mean (SD) 2.74 (5.20). The final analysis indicated that ability to pay was significantly higher among younger and higher-income households.
CONCLUSIONS: Sociodemographic and socioeconomic statuses are important eligibility factors to be considered in planning the proposed national health care financing scheme to shield the needed group from catastrophic health expenditures.
STUDY DESIGN: This is a cross-sectional study.
METHODS: In total, 774 households from four states in Malaysia completed face-to-face interviews. A validated structured questionnaire was used, which was composed of a combination of open-ended questions, bidding games and contingent valuation methods regarding the participants' willingness to pay.
RESULTS: The study found that the majority of households supported the establishment of the National Health Financing Scheme, and half proposed that a government body should manage the scheme. Most (87.5%) of the households were willing to contribute 0.5-1% of their salaries to the scheme through monthly deductions. Over three-quarters (76.6%) were willing to contribute to a higher level scheme (1-2%) to gain access to both public and private healthcare basic services. Willingness to pay for the National Health Financing Scheme was significantly higher among younger persons, females, those located in rural areas, those with a higher income and those with an illness.
CONCLUSION: There is a high level of acceptance for the National Health Financing Scheme in the Malaysian community, and they are willing to pay for a scheme organised by a government body. However, acceptance and willingness to pay are strongly linked to household socio-economic status. Policymakers should initiate plans to establish the National Health Financing Scheme to provide the necessary financing for a sustainable health system.
METHODS: A cross-sectional study was conducted at the National Heart Institute of Malaysia involving 503 patients who were hospitalized during the year prior to the survey.
RESULTS: The mean annual out-of-pocket health spending for IHD was MYR3045 (at the time US$761). Almost 16% (79/503) suffered from catastrophic health spending (out-of-pocket health spending ≥40% of household non-food expenditures), 29.2% (147/503) were unable to pay for medical bills, 25.0% (126/503) withdrew savings to help meet living expenses, 16.5% (83/503) reduced their monthly food consumption, 12.5% (63/503) were unable to pay utility bills and 9.0% (45/503) borrowed money to help meet living expenses.
CONCLUSIONS: Overall, the economic impact of IHD on patients in Malaysia was considerable and the prospect of economic hardship likely to persist over the years due to the long-standing nature of IHD. The findings highlight the need to evaluate the present health financing system in Malaysia and to expand its safety net coverage for vulnerable patients.
METHODS: In this cross-sectional study, we include 196 homeless people aged above 18 years, Malaysian who were able to communicate with interviewers, and respondents who were not aggressive. These respondents were transits at Pusat Transit Gelandangan Kuala Lumpur and Anjung Singgah Kuala Lumpur and were available during interview sessions. They were selected via simple random sampling and were interviewed via face to face guided interviews using a validated structured questionnaire. Data were analysed descriptively, as well as using bivariate and multivariate analysis to explore the associated factors.
RESULTS: The study showed that 57.7% homeless utilized the health services with only 37.8% assessed government health services. Only 42.5% of the respondents use their own money and 46.9% received aids to finance their health. Major influencing factors that influence homeless people to use their own money for health services were education level, income and disability, with adjusted OR (95% CI) of 3.15 (1.07-9.25), 0.08 (0.029-3.07) and 0.05 (0.003-0.88) while p value was 0.037,