Objective: We assessed health-related quality of life in cancer survivors treated in designated cancer centers when compared with the general population in Korea. Methods: A multicenter survey was conducted from July through August 2008 using the quotasampling approach. A general population sample was drawn from the Fourth Korean National Health and Nutrition Examination Survey, second year. We compared the multivariate-adjusted least square means of cancer patients with those of the general population to examine relationships between EuroQol five-dimensional questionnaire components and cancer sites, cancer stage and time since diagnosis. The independent variables of responses to the EuroQol five-dimensional questionnaire were evaluated using logistic regression analysis. Results: Cancer patients scored significantly poorer on measures of self-care (means: stomach 1.25; lung 1.40; liver 1.27; colon 1.26; breast 1.27; cervical 1.29 vs. general 1.18), engagement in usual activities (means: stomach 1.47; lung 1.63; liver 1.45; colon 1.44; breast 1.46; cervical 1.47 vs. general 1.33) and anxiety/depression (means: stomach 1.41; lung 1.50; liver 1.41; colon 1.42; breast 1.50; cervical 1.47 vs. general 1.28). Those in the local stage scored significantly better on mobility (mean ¼ 1.35) than the general population (mean ¼ 1.40). Cancer patients, especially those with lung cancer, in the advanced stage and more than 5 years since diagnosis had poorer health-related quality of life than the general population. Some factors such as medical insurance and healthcare services were related to health-related quality of life among cancer patients. Conclusions: Health-related quality of life of cancer survivors with lung cancer at advanced stages, ,1 year earlier and more than 5 years since diagnosis was poorer than that for the noncancer control group, and these differences were statistically significant. Cancer survivors should be continuously observed and offered support.

The aim of this study was to investigate the whole picture regarding pregnancy, prenatal care, obstetrical complications, and delivery among disabled pregnant women in Korea. Using the data of National Health Insurance Corporation, we extracted the data of women who terminated pregnancy including delivery and abortion from January 1, 2010 to December 31, 2010. Pearson's chi-square test and Student-t test were conducted to examine the difference between disabled women and non-disabled women. Also, to define the factors affecting inadequate prenatal care, logistic regression was performed. The total number of pregnancy were 463,847; disabled women was 2,968 (0.6%) and 460,879 (99.4%) were by non-disabled women. Abortion rates (27.6%), Cesarean section rate (54.5%), and the rate of receiving inadequate prenatal care (17.0%), and the rate of being experienced at least one obstetrical complication (11.3%) among disabled women were higher than those among non-disabled women (P < 0.001). Beneficiaries of Medical Aid (OR, 2.21) (P < 0.001) and severe disabled women (OR, 1.46) (P = 0.002) were more likely to receive inadequate prenatal care. In conclusion, disabled women are more vulnerable in pregnancy, prenatal care and delivery. Therefore, the government and society should pay more attention to disabled pregnant women to ensure they have a safe pregnancy period up until the delivery.Graphical Abstract

Objectives:The hospital standardized mortality ratio (HSMR) has been widely used because it allows for robust risk adjustment using administrative data and is important for improving the quality of patient care.Methods:All inpatients discharged from hospitals with more than 700 beds (66 hospitals) in 2008 were eligible for inclusion. Using the claims data, 29 most responsible diagnosis (MRDx), accounting for 80% of all inpatient deaths among these hospitals, were identified, and inpatients with those MRDx were selected. The final study population included 703 571 inpatients including 27 718 (3.9% of all inpatients) in-hospital deaths. Using logistic regression, risk-adjusted models for predicting in-hospital mortality were created for each MRDx. The HSMR of individual hospitals was calculated for each MRDx using the model coefficients. The models included age, gender, income level, urgency of admission, diagnosis codes, disease-specific risk factors, and comorbidities. The Elixhauser comorbidity index was used to adjust for comorbidities.Results:For 26 out of 29 MRDx, the c-statistics of these mortality prediction models were higher than 0.8 indicating excellent discriminative power. The HSMR greatly varied across hospitals and disease groups. The academic status of the hospital was the only factor significantly associated with the HSMR.Conclusions:We found a large variation in HSMR among hospitals; therefore, efforts to reduce these variations including continuous monitoring and regular disclosure of the HSMR are required.