RESUMO
Although there are lots of studies about the risk for the hepatitis B virus infection such as the residual risk for donated blood, there is no research on the risk of HBV infection, from the viewpoint of recipients in Korea. Using the data about HBsAg status of donated blood in 2008 and 2009, the distribution of blood components from the claim data of health insurance in 2009, the distribution of HBsAg and HBsAb of recipients, and some assumptions, we quantified the number of recipients in Korea that might be expected to receive HBV-contaminated blood components, as a proxy index for HBV infection by transfusion in 2009. Of the 376,211 recipients, the number who might be expected to receive blood components with HBV in 2009 was 23.2 (95 % CI 13.6, 36.8) in the basic model, 43.2 (95 % CI 25.4, 68.7) in extended model I, 55.2 (95 % CI 32.5, 87.7) in extended model II and 101.6 (95 % CI 59.8, 161.4) in extended model III. The number of HBV-positive samples per 100,000 transfused units was 0.6 in the basic model (95 % CI 0.3, 0.9), 1.1 in extended model I (95 % CI 0.6, 1.8), 1.4 in extended model II (95 % CI 0.8, 2.2), and 2.6 in extended model III (95 % CI 1.5, 4.1). This study showed that a few recipients might receive HBV-contaminated blood component by transfusion. These results could be used as a scientific evidence for health policy on HBV transfusion infection.
RESUMO
OBJECTIVES: This study establishes the South Korean population-based preference weights for EQ-5D based on values elicited from a representative national sample using the time trade-off (TTO) method. METHODS: The data for this paper came from a South Korean EQ-5D valuation study where 1307 representative respondents were invited to participate and a total of 101 health states defined by the EQ-5D descriptive system were directly valued. Both aggregate and individual level modeling were conducted to generate values for all 243 health states defined by EQ-5D. Various regression techniques and model specifications were also examined in order to produce the best fit model. Final model selection was based on minimizing the difference between the observed and estimated value for each health state. RESULTS: The N3 model yielded the best fit for the observed TTO value at the aggregate level. It had a mean absolute error of 0.029 and only 15 predictions out of 101 had errors exceeding 0.05 in absolute magnitude. CONCLUSIONS: The study successfully establishes South Korean population-based preference weights for the EQ-5D. The value set derived here is based on a representative population sample, limiting the interpolation space and possessing better model performance. Thus, this EQ-5D value set should be given preference for use with the South Korean population.