Methods: Cost and workload data were obtained from hospital records for 2015. Time allocation of staff between laboratory testing and other activities was determined using assumptions from published workload studies.
Results: The laboratory received 20,093 cases for testing in 2015, and total expenditures were US $1.20 million, ie, $61.97 per case. The anatomic pathology laboratory accounted for 5.2% of the laboratory budget at the hospital, compared to 64.3% for the clinical laboratory and 30.5% for the microbiology laboratory. We provide comparisons to a similar laboratory in the United States.
Conclusions: Anatomic pathology is more costly than other hospital laboratories due to the labor-intensive work, but is essential, particularly for cancer diagnoses and treatment.
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.
METHODS: This study was designed in the form of cross-sectional analysis, in which, cancer survivors were recruited from the Sarawak General Hospital, the largest tertiary and referral public hospital in Sarawak. To capture the financial toxicity of the cancer survivors, the Comprehensive Score for Financial Toxicity (COST) instrument in its validated form was adopted. Multivariable logistic regression analysis was applied to determine the relationship between financial toxicity (FT) and its predictors.
RESULTS: The median age of the 461 cancer survivors was 56 while the median score of COST was 22.0. Besides, finding from multivariable logistic regression revealed that low income households (OR: 6.893, 95% CI, 3.109-15.281) were susceptible to higher risk of financial toxicity, while elderly survivors above 50 years old reported a lower risk in financial toxicity. Also, survivors with secondary schooling (OR:0.240; 95%CI, 0.110-0.519) and above [College or university (OR: 0.242; 95% CI, 0.090-0.646)] suffer a lower risk of FT.
CONCLUSION: Financial toxicity was found to be associated with survivors age, household income and educational level. In the context of cancer treatment within public health facility, younger survivors, households from B40 group and individual with educational attainment below the first level schooling in the Malaysian system of education are prone to greater financial toxicity. Therefore, it is crucial for healthcare policymakers and clinicians to deliberate the plausible risk of financial toxicity borne by the patient amidst the treatment process.
METHODS: This cross-sectional study was conducted in three Malaysian public hospitals namely Hospital Kuala Lumpur, Hospital Canselor Tuanku Muhriz and the National Cancer Institute using a multi-level sampling technique to recruit 630 respondents from February 2020 to February 2021. CHE was defined as incurring a monthly health expenditure of more than 10% of the total monthly household expenditure. A validated questionnaire was used to collect the relevant data.
RESULTS: The CHE level was 54.4%. CHE was higher among patients of Indian ethnicity (P = 0.015), lower level education (P = 0.001), those unemployed (P < 0.001), lower income (P < 0.001), those in poverty (P < 0.001), those staying far from the hospital (P < 0.001), living in rural areas (P = 0.003), small household size (P = 0.029), moderate cancer duration (P = 0.030), received radiotherapy treatment (P < 0.001), had very frequent treatment (P < 0.001), and without a Guarantee Letter (GL) (P < 0.001). The regression analysis identified significant predictors of CHE as lower income aOR 18.63 (CI 5.71-60.78), middle income aOR 4.67 (CI 1.52-14.41), poverty income aOR 4.66 (CI 2.60-8.33), staying far from hospital aOR 2.62 (CI 1.58-4.34), chemotherapy aOR 3.70 (CI 2.01-6.82), radiotherapy aOR 2.99 (CI 1.37-6.57), combination chemo-radiotherapy aOR 4.99 (CI 1.48-16.87), health insurance aOR 3.99 (CI 2.31-6.90), without GL aOR 3.38 (CI 2.06-5.40), and without health financial aids aOR 2.94 (CI 1.24-6.96).
CONCLUSIONS: CHE is related to various sociodemographic, economic, disease, treatment and presence of health insurance, GL and health financial aids variables in Malaysia.
METHODS: This study employs a quantitative method by way of a cross-sectional approach. The 2018 JKN claims data, drawn from a 1% sample that JKN annually produces, were analyzed. Nine hundred forty-five HIV patients out of 1,971,744 members were identified in the data sample and their claims record data at primary care and hospital levels were analyzed. Using ICD (International Statistical Classification of Diseases and Related Health Problems), 10 codes (i.e., B20, B21, B22, B23, and B24) that fall within the categories of HIV-related disease. For each level, patterns of service utilization by patient-health status, discharge status, severity level, and total cost per claim were analyzed.
RESULTS: Most HIV patients (81%) who first seek care at the primary-care level are referred to hospitals. 72.5% of the HIV patients receive antiretroviral treatment (ART) through JKN; 22% at the primary care level; and 78% at hospitals. The referral rate from public primary-care facilities was almost double (45%) that of private providers (24%). The most common referral destination was higher-level hospitals: Class B 48%, and Class C 25%, followed by the lowest Class A at 3%. Because JKN pays hospitals for each inpatient admission, it was possible to estimate the cost of hospital care. Extrapolating the sample of hospital cases to the national level using the available weight score, it was estimated that JKN paid IDR 444 billion a year for HIV hospital services and a portion of capitation payment.
CONCLUSION: There was an underrepresentation of PLHIV (People Living with HIV) who had been covered by JKN as 25% of the total PLHIV on ART were able to attain access through other schemes. This study finding is principally aligned with other local research findings regarding a portion of PLHIV access and the preferred delivery channel. Moreover, the issue behind the underutilization of National Health Insurance services in Indonesia among PLHIV is similar to what was experienced in Vietnam in 2015. The 2015 Vietnam study showed that negative perception, the experience of using social health insurance as well as inaccurate information, may lead to the underutilization problem (Vietnam-Administration-HIV/AIDSControl, Social health insurance and people living with HIV in Vietnam: an assessment of enrollment in and use of social health insurance for the care and treatment of people living with HIV, 2015). Furthermore, the current research finding shows that 99% of the total estimated HIV expenditure occurred at the hospital. This indicates a potential inefficiency in the service delivery scheme that needs to be decentralized to a primary-care facility.
Patients and methods: An observational study was conducted at four different intensive care units of an academic medical institution. Demographic characteristics, disease-management casemix information, cost and outcome of the high costing decile, and the rest of the cases were compared.
Results: A total of 3,220 discharges were included in the study. The high-cost group contributed 35.4% of the ICU stays and 38.8% of the total ICU expenditure. Diseases of the central nervous system had higher odds to be in the top decile of costly patients whereas the cardiovascular system was more likely to be in the non-high cost category. The high-cost patients were more likely to have death as an outcome (19.2% vs 9.3%; p<0.001). The most common conditions that were in the high-cost groups were craniotomy, other ear, nose, mouth, and throat operations, simple respiratory system operations, complex intestinal operations, and septicemia. These five diagnostic groups made up 43% of the high-cost decile.
Conclusion: High-cost patients utilized almost 40% of the ICU cost although they were only 10% of the ICU patients. The chances of admission to the ICU increased with older age and severity level of the disease. Central nervous system diseases were the major problem of patients aged 46-69 years old. In addition to cost reduction strategies at the treatment level, detailed analysis of these cases was needed to explore and identify pre-event stage prevention strategies.