Determinants of hospital fall rate trajectory groups

Abstract

Background Patient falls in acute care hospitals represent a significant patient safety concern. Although cross-sectional studies have shown that fall rates vary widely between acute care hospitals, it is not clear whether hospital fall rates remain consistent over time. Purpose The aim of this study was to determine whether hospitals can be categorized into fall rate trajectory groups over time and to identify nurse staffing and hospital characteristics associated with hospital fall rate trajectory groups. Methodology/Approach We conducted a 54-month (July 2006–December 2010) longitudinal study of U.S. acute care general hospitals participating in the National Database for Nursing Quality Indicators® (2007). We used latent class growth modeling to categorize hospitals into groups based on their long-term fall rates. Nurse staffing and hospital characteristics associated with membership in the highest hospital fall rate group were identified using logistic regression. Findings A sample of 1,529 hospitals (mean fall rate of 3.65 per 1,000 patient days) contributed data to the analysis. Latent class growth modeling findings classified hospital into three groups based on fall rate trajectories: consistently high (mean fall rate of 4.96 per 1,000 patient days), consistently medium (mean fall rate of 3.63 per 1,000 patient days), and consistently low (mean fall rate of 2.50 per 1,000 patient days). Hospitals with higher total nurse staffing (odds ratio [OR] = 0.92, 95% confidence interval [CI] [0.85, 0.99]), Magnet status (OR = 0.49, 95% CI [0.35, 0.70]), and bed size greater than 300 beds (OR = 0.70, 95% CI [0.51, 0.94]) were significantly less likely to be categorized in the “consistently high” fall rate group. Practice Implications Over this 54-month period, hospitals were categorized into three groups based on long-term fall rates. Hospital-level factors differed among these three groups. This suggests that there may be hospitals in which “best practices” for fall prevention might be identified. In addition, administrators may be able to reduce fall rates by maintaining greater nurse staffing ratios as well as fostering an environment consistent with that of Magnet hospitals.

Multilevel factors influencing falls of patients in hospital: The impact of nurse staffing

“…Among organizational factors, hours per patient day by RNs was a factor influencing the fall rate in this study, corroborating the findings of other studies (Everhart et al, 2014;Kouatly et al, 2018;Lake et al, 2010;Staggs & Dunton, 2014). When nursing hours were increased TA B L E 4 Mixed-effect model of fall rates with individual and organizational factors by 1 hr, the risk of falls was reduced by 67 percent.…”

“…This study was based on patient data from all hospitals that oper- The fall rate was estimated to be 0.92 per 1,000 patient days in this study, which was lower than the result reported in previous studies (Everhart et al, 2014;Lake et al, 2010). Because this study included only patients who were admitted to integrated care units, the fall rate in this study cannot be generalized to all hospitals in Korea.…”

“…Recently, nursing hours per patient day (NHPPD), as well as total nurse staffing have been investigated for their association with patient falls. Previous studies have reported that NHPPD was negatively associated with patient falls (Everhart et al, 2014;Kouatly, Nassar, Nizam, & Badr, 2018;Lake, Shang, Klaus, & Dunton, 2010;Staggs & Dunton, 2014). A recent study revealed a significant association between RN NHPPD and patient falls (Kouatly et al, 2018;Lake et al, 2010).…”

Nurse staffing and hospital‐acquired conditions: A systematic review

“…Seven studies used a longitudinal study design (Everhart et al, 2014;Kim & Han, 2018;Mark & Harless, 2010;Mark, Harless, McCue, & Xu, 2004;Patrician et al, 2017;Staggs, Knight, & Dunton, 2012;Unruh, 2003) while other used a cross-sectional study design.…”

“…Three studies (Frith et al, 2010;Mark & Harless, 2010;Razmus, 2018) examined the relationship between RN hours per patient day and three hospital-acquired conditions, and nonsignificant relationships were found. Two other studies (Frith et al, 2010;Mark & Harless, 2010) Eight studies (Blegen et al, 2011;Dunton, Gajewski, Taunton, & Moore, 2004;Everhart et al, 2014;Kouatly, Nassar, Nizam, & Badr, 2018;Patrician et al, 2017;Shuldham, Parkin, Firouzi, Roughton, & Lau-Walker, 2009;Staggs et al, 2012;Van den Heede et al, 2009) examined the relationship between nursing hours per patient day and seven hospital-acquired conditions. Including hospital-acquired pressure injury (HAPI), falls/unassisted falls, injury falls, central line-associated blood stream infection (CLABSI) and TA B L E 1 Summary of quality assessment of the studies included…”