Background: Considering the interactions between heavy metals, a comprehensive evaluation of the effects of exposure to various types of co-interacting heavy metals on health is required.This study assessed the association between dyslipidemia markers and blood mercury, lead, cadmium, iron, zinc, and nickel levels in residents of an abandoned refinery plant. Methods: A total of 972 individuals (exposed group: 567, control group: 405) living near the Janghang refinery plant in the Republic of Korea were included. Blood mercury, lead, cadmium, iron, zinc, nickel, cholesterol, and triglyceride levels were measured. The combined effect of the six heavy metals on dyslipidemia markers was evaluated using a Bayesian kernel machine regression (BKMR) model and compared with the results of a linear regression analysis. The BKMR model results were compared using a stratified analysis of the exposed and control groups. Results: In the BKMR model, the combined effect of the six heavy metals was significantly associated with total cholesterol (TC) levels both below the 45th percentile and above the 55th percentile in the total population. The combined effect range between the 25th and 75th percentiles of the six metals on TC levels was larger in the exposed group than that in the total population. In the control group, the combined effects of the changes in concentration of the six heavy metals on the TC concentration were not statistically significant. Conclusion These results suggest that the cholesterol levels of residents around the Janghang refinery plant may be elevated owing to exposure to multiple heavy metals.

Purpose: To compare actual versus expected nursing hours based on patients’ nursing care needs. Methods: The nursing care needs of 898 inpatients in 20 wards at 11 hospitals were measured using the 14 items developed by the National Health Insurance Service (NHIS). Nursing activities from 474 nursing personnel were observed every 10 minutes for 24 hours. Actual hours indicated direct care hours per patient day provided by registered nurses according to 3 types: (1) standard hours based on staffing standards approved by the NHIS, (2) scheduled hours excluding overtime hours, and (3) observed hours including overtime. Expected hours were estimated from the linear mixed effect model including hospital type, nursing care need items and their interaction terms. Results: Standard hours ranged from 0.92 to 2.15; scheduled hours from 0.88 to 1.95; observed hours from 1.00 to 2.40; expected hours from 0.88 to 2.33. Eight hospitals had standard hours not meeting the expected hours and 2 hospitals did observed hours not meeting the expected hours due to nurses’ overtime. In 3 hospitals, all types of actual hours exceeded the expected hours. Conclusion Staffing needs to be determined based on patients’ care needs and to be improved to minimize nurses’ overtime work.