Association between the bed-to-nurse ratio and 30-day post-discharge mortality in patients undergoing surgery: a cross-sectional analysis using Korean administrative data

Abstract

Background: The likelihood of inpatient mortality has been found to be reduced by increased nurse staffing in several settings, including general wards, emergency departments, and intensive care units. However, less research has investigated cases where patients die in the community setting due to a health problem that occurred after they were discharged post-surgery, because it is difficult to integrate hospital data and local community data. Therefore, this study investigated the association between the bed-to-nurse ratio and 30-day post-discharge mortality in patients undergoing surgery using national administrative data. Methods: The study analyzed data from 129,923 patients who underwent surgery between January 2014 and December 2015. The bed-to-nurse ratio was categorized as level 1 (less than 2.5), level 2 (2.5-3.4), level 3 (3.5-4.4), and level 4 (4.5 or greater). The chi-square test and GEE logistic regression analyses were used to explore the association between the bed-to-nurse ratio and 30-day post-discharge mortality. Results: 1355 (0.01%) patients died within 30 days post-discharge. The 30-day post-discharge mortality rate in hospitals with a level 4 was 2.5%, representing a statistically significant difference from the rates of 0.8, 2 and 1.8% in hospitals with level 1, level 2, and level 3 staffing, respectively. In addition, the death rate was significantly lower at hospitals with a level 1 (OR = 0.62) or level 2 (OR = 0.63) bed-to-nurse ratio, using level 4 as reference. Conclusion: The results of this study are highly meaningful in that they underscore the necessity of in-hospital discharge nursing and continued post-discharge nursing care as a way to reduce post-discharge mortality risk. Furthermore, the relationship between nurse staffing levels and 30-day post-discharge mortality implies the need for a greater focus on discharge education. Policies are required to achieve proper nurse staffing levels in Korea, and thereby to enhance patient outcomes.

Analysis of Factors Influencing Safety Attitudes of Operating Room Nurses and Their Cognition and Attitudes toward Adverse Event Reporting

“…As nursing care increases, so does the risk, incidence rate, and even death rate of patients. Even if the overall allocation of nursing staff is adequate, the acceleration of patient turnover increases the risk of patient safety and death [ 25 , 26 ]. According to a previous study in Holland [ 27 ], the role of nurses in clinical work, nursing behavior, and nursing autonomy are closely related to manpower allocation.…”

Retention Rates and the Associated Risk Factors of Turnover among Newly Hired Nurses at South Korean Hospitals: A Retrospective Cohort Study

“…Sub-variables for hospital location were Seoul, metropolitan cities, and medium-sized and small cities, and the sub-variables for hospital ownership were public sector, educational foundation, medical or other corporation, and for-profit medical institutions. The bed-to-nurse ratio was the mean number of beds divided by the mean number of full-time equivalent nurses as of the second quarter of 2018 at general wards, and was divided into 4 categories (<2.5, ≥2.5 to <3.5, ≥3.5 to <4.5, or ≥4.5) [ 12 , 23 ]. Standardized monthly income (average monthly compensation) was defined as the standardized income at each medical institution used to determine the health care premiums of nurses.…”

On the Relationships among Nurse Staffing, Inpatient Care Quality, and Hospital Competition under the Global Budget Payment Scheme of Taiwan’s National Health Insurance System: Mixed Frequency VAR Analyses

“…Although the harmful effects of PNR on patient outcomes were confirmed from previous studies in the NHS [2][3][4]7,8,10,[14][15][16][17][18] and SHI systems [5,9,[14][15][16][17][18], most of these studies belonged to the cross-sectional or static-type studies. It follows that these studies failed to identify causality between nurse staffing and patient outcomes, evaluate the propagation mechanism of nurse staffing on patient outcomes, and avoid the potential aggregation biases [2][3][4][5][6][7][8][9][10][14][15][16][17][18]. Therefore, the methodologies (such as the MF-VAR model, Granger causality test, and impulse-response analyses) used in this study not only generated results echoing the evidence obtained from previous studies [2][3][4][5][6][7][8][9][10][14][15][16][17]…”

“…It follows that we observed a common privatization trend in healthcare provision, and, in turn, it created a severer market competition in many publicly financed healthcare systems such as the NHS (e.g., Australia, Belgium, Finland, Iceland, Ireland, Norway, Spain, and United Kingdom) and SHI systems (e.g., Austria, Canada, Korea, and Japan) [26,28]. Although the harmful effects of PNR on patient outcomes were confirmed from previous studies in the NHS [2][3][4]7,8,10,[14][15][16][17][18] and SHI systems [5,9,[14][15][16][17][18], most of these studies belonged to the cross-sectional or static-type studies. It follows that these studies failed to identify causality between nurse staffing and patient outcomes, evaluate the propagation mechanism of nurse staffing on patient outcomes, and avoid the potential aggregation biases [2][3][4][5][6][7][8][9][10][14][15][16][17][18].…”

“…A crucial conclusion of their study is that each additional patient per nurse is associated with higher patient mortality in the US. Continuing this line of research, one strand of studies provided evidence of the positive effect of hospital nurse staffing levels on patient mortality worldwide, such as in Australia [2,3], Belgium [4], Canada [5], Chile [6], England [7], Finland [8], Korea [9], Norway [10], Taiwan [11][12][13], and a group of European and OECD countries [14][15][16][17][18]. Another strand of the literature investigated the relationships between nurse staffing levels and various nursing-sensitive patient outcomes such as fall, pressure ulcer, medication error, various infections, physical restraint, missed observation, failure to respond to patients, length of stay, readmission to hospitals, emergency department attendance, etc.…”