Objective: The aim of this study was to conduct a cost-effectiveness analysis of spending on healthcare R&D to address the needs of developing innovative therapeutic products in Indonesia.
Methods: A decision tree model was developed by taking into account four stages of R&D: stage 1 from raw concept to feasibility, stage 2 from feasibility to development, stage 3 from development to early commercialization, and stage 4 from early to full commercialization. Considering a 3-year time horizon, a stage-dependent success rate was applied and analyses were conducted from a business perspective. Two scenarios were compared by assuming the government of Indonesia would increase GERD in health and medical sciences up to 2- and 3-times higher than the baseline (current situation) for the first and second scenario, respectively. Cost per number of innovative products in health and medical sciences was considered as the incremental cost-effectiveness ratio (ICER). Univariate sensitivity analysis was conducted to investigate the effects of different input parameters on the ICER.
Results: There was a statistically significant association (P-value<0.05) between countries' GERD in medical and health sciences with the number of innovative products. We estimated the ICER would be $8.50 million and $2.04 million per innovative product for the first and second scenario, respectively. The sensitivity analysis showed that the success rates in all stages and total GERD were the most influential parameters impacting the ICER.
Conclusion: The result showed that there was an association between GERD in medical and health sciences with the number of innovative products. In addition, the second scenario would be more cost-effective than the first scenario.
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.