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.
METHODS: An Excel-based budget impact model was constructed to assess dialysis-associated costs when changing dialysis modalities between PD and ICHD. The model incorporates the current modality distribution and accounts for Malaysian government dialysis payments and erythropoiesis-stimulating agent costs. Epidemiological data including dialysis prevalence, incidence, mortality, and transplant rates from the Malaysian renal registry reports were used to estimate the dialysis patient population for the next 5 years. The baseline scenario assumed a stable distribution of PD (8%) and ICHD (92%) over 5 years. Alternative scenarios included the prevalence of PD increasing by 2.5%, 5.0%, and 7.5% or decreasing 1% yearly over 5 years. All four scenarios were accompanied with commensurate changes in ICHD.
RESULTS: Under the current best available cost information, an increase in the prevalent PD population from 8% in 2014 to 18%, 28%, or 38% in 2018 is predicted to result in 5-year cumulative savings of Ringgit Malaysia (RM) 7.98 million, RM15.96 million, and RM23.93 million, respectively, for the Malaysian government. If the prevalent PD population were to decrease from 8% in 2014 to 4.0% by 2018, the total expenditure for dialysis treatments would increase by RM3.19 million over the next 5 years.
CONCLUSIONS: Under the current cost information associated with PD and HD paid by the Malaysian government, increasing the proportion of patients on PD could potentially reduce dialysis-associated costs in Malaysia.
METHODOLOGY: This study comprised of 249 participants (148 overweight/ obese as a case group and 101 lean participants as controls). The PCR-RFLP technique was performed to distinguish the genotype distribution of Leptin gene polymorphisms. The allele and genotype frequencies were assessed for single and haplotype analyses.
RESULT: Single association analysis of G2548A (P=0.74), A19G (P=0.38), and H1328080 (P=0.56) polymorphisms yielded no statistically significant association. However, haplotype association analysis showed a suggestive indication of AAG haplotype (G2548A, H1328080, and A19G sequence) with susceptibility effect towards obesity predisposition [P=0.002, OR=8.897 (1.59-9.78)].
CONCLUSION: This data on single and haplotype might disclose the preliminary exposure and pave the way for the obesity development with an evidence of revealed susceptibility to obesity.
MATERIALS AND METHODS: This cross-sectional study was conducted in three Malaysian public hospitals using a multilevel sampling technique to recruit 630 respondents. A validated self-developed four-domain questionnaire which includes one domain for health insurance was used to collect the relevant data.
RESULTS: Approximately 31.7% of the respondents owned PHI. The PHI usage was significantly higher among male respondents (p=0.035), those aged 18-40 years old (p<0.001), Indian and Chinese ethnicities (p=0.002), with tertiary education level (p<0.001), employed (p<0.001), working in the private sector (p<0.001), high household income (T20) (p<0.001), home near to the hospital (p=0.001) and medium household size (p<0.001). The significant predictive factors were age 18-40 years aOR 3.01 (95% CI: 1.67-5.41), age 41-60 years aOR 2.22 (95% CI 1.41-3.49), medium (M40) income aOR 2.90 (95% CI: 1.92-4.39) and high (T20) income aOR 3.86 (95% CI: 1.68-18.91), home near to the hospital aOR 1.68 (95% CI: 1.10-2.55), medium household size aOR 2.20 (95% CI: 1.30-3.72) and female head of household aOR 1.79 (95% CI: 1.01-3.16). The type of cancer treatment, the location of treatment, prior treatment in private healthcare facilities and existence of financial coping mechanisms also were significant factors in determining PHI usage among cancer patients in this study.
CONCLUSION: Several factors are significantly associated with PHI usage in cancer patients. The outcome of this study can guide policymakers to identify high-risk groups which need supplementary health insurance to bear the cost for their cancer treatment so that a better pre-payment health financing system such as a national health insurance can be formulated to cater for these groups.