Infection status of endoparasites in foreigner workers living in Cheonan City, Chungnam Province, Korea.

At present, more than 500,000 foreigner workers, most of them from Asian countries with high parasitic infection rates, are working in Korea. Since investigation into the prevalence of parasitic infections in foreigner workers has not yet been conducted in Korea, the present study was performed to determine the parasitic infection status of foreigner workers living in Cheonan City, Chungcheongnam-do (Chungnam Province) and to plan, on that basis, effective control measures. From October to December 2013, the parasitic infection status of 231 foreigner workers employed at selected Cheonan-si small businesses was investigated by both stool examination and ELISA. A total of 60 individuals (26.0%) were found to be infected with parasites. The stool examination detected 14 positive cases (6.1%), and ELISA revealed 50 positive people (21.6%), for at least a kind of parasitic disease. The most common infection was cysticercosis (8.7%), followed by toxocariasis (7.8%) and clonorchiasis (7.4%). Since it was proved that parasitic infections were prevalent among foreigner workers living in Cheonan City, more comprehensive study is urgently needed in order to understand the nationwide status of parasitic infections in foreigner workers.

Nursing critical patient severity classification system predicts outcomes in patients admitted to surgical intensive care units: use of data from clinical data repository.

To examine the Critical Patient Severity Classification System (CPSCS) recorded by nurses to predict ICU and hospital lengths of stay and mortality, data were drawn from patients admitted to 2 surgical intensive care units (SICUs) at a university hospital in Seoul, South Korea in 2010. This retrospective study used a large data set retrieved from the Clinical Data Repository System. Among 1432 patients, the mean grade of CPSCS was 4.9 out of 6, which indicated that the subjects had generally severe conditions. The CPSCS was a statistically significant predictor of ICU and hospital LOS and mortality when patients' demographic characteristics were adjusted. In the era of emphasis on using big data, analysis of nursing assessment data should be evaluated to show importance of nursing contribution to predict patients' clinical outcomes.