This study investigated the cumulative effects of depressive symptoms on cognitive function over time in community-dwelling older adults. Methods: Data were investigated from 2,533 community-dwelling older adults who participated in the Korean Longitudinal Study of Aging (KLoSA) from the 5th (2014) to the 8th wave (2020). The association between cumulative depressive symptoms and cognitive function was identified through multiple regression analysis. Results: When the multiple regression analysis was conducted from each wave, the current depressive symptoms scores and cognitive function scores were negatively associated, regardless of the waves (B5th = - 0.26, B6th = - 0.26, B7th = - 0.26, and B8th = - 0.27; all p < .001). Further, when all the previous depressive symptoms scores were added as explanatory variables in the 8th wave, the current one (B8th = - 0.09, p < .001) and the previous ones (B5th = - 0.11, B6th = - 0.09, and B7th = - 0.13; all p < .001) were also negatively associated with the cognitive function score. The delta R2 , which indicates the difference between the model’s R2 with and without the depressive symptoms scores, was greater in the model with all the previous and current depressive symptoms scores (6.4%) than in the model with only the current depressive symptoms score (3.6%). Conclusion: Depressive symptoms in older adults have a long-term impact. This results in an accumulated adverse effect on the cognitive function. Therefore, to prevent cognitive decline in older adults, we suggest detecting their depressive symptoms early and providing continuous intervention to reduce exposure to long-term depressive symptoms.

Purpose: To analyze the effects of average length of stay (ALOS) on RN staffing. Methods: Public data of patient surveys collected 8 times between 1996 and 2016 were analyzed. The sample included 2,408,669 discharged patients from 2,266 general hospitals. The ALOS for each hospital was computed by dividing the sum of inpatient days by the number of discharges. RN staffing was defined as the number of RNs per 100 inpatients. ALOS was transformed into base-2 logarithmic values for regression analysis. Results: ALOS decreased from 13.3 to 9.6 days.Large hospitals in the capital region had the greatest reduction, from 15.7 to 7.4 days. RN staffing increased from 32.7 to 54.8 RNs per 100 patients. ALOS had an inverse relationship with RN staffing. Controlling for other factors, a 50% reduction in ALOS was associated with increases in RN staffing by 12.18 and 13.72 RNs per 100 inpatients in large hospitals in the capital region and elsewhere, respectively. Conclusion Hospitals may have to increase staffing to respond to the increased workload resulting from the shortened ALOS. It remains uncertain whether such increases in staffing were sufficient for the increased workload. Changes in ALOS should be taken into account when determining appropriate staffing.

PURPOSE: To determine nurse staffing by classifying patients based on their nursing care needs and to benchmark current staffing against the Safer Nursing Care Tool (SNCT) staffing requirements. METHODS: Cross-sectional data were collected from four general wards at a tertiary hospital. Nursing activities conducted by 86 registered nurses were observed at 10-minute intervals. The nursing care needs of 780 inpatients were measured with two dimensions: acuity (10 nursing activities) and dependency (four activities of daily living). RESULTS: Nurses worked for 9.3 hours per shift on average, reflecting overtime work of 1.3 hours per nurse. Nurses spent 37% of their time on direct care, 54% on indirect care, and 9% on associated work. Nursing hours per patient day increased as nursing care needs became higher. Patients were classified into four groups based on their level of nursing care needs. The staffing ratio of groups 1-4 was 1:9.8, 1:8.0, 1:7.0, and 1:4.6, respectively. The current staffing (i.e., nursing hours) was as low as 53% of the SNCT benchmark, resulting in informal caregiving by patients' family or their privately hired attendants. CONCLUSION Appropriate and safe staffing is required to meet patients' nursing care needs and to improve the quality of nursing care.

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.