Sarah N Musy1, Olga Endrich2, Alexander B Leichtle3, Peter Griffiths4, Christos T Nakas5, Michael Simon6. 1. Institute of Nursing Science, Department of Public Health, Faculty of Medicine, University of Basel, Bernoullistrasse 28, 4056 Basel, Switzerland; Nursing & Midwifery Research Unit, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland. Electronic address: sarah.musy@unibas.ch. 2. Medical Directorate, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland; Insel Data Science Center (IDSC), Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland. Electronic address: Olga.Endrich@insel.ch. 3. Insel Data Science Center (IDSC), Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland; University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland. Electronic address: alexander.leichtle@insel.ch. 4. Health Sciences, University of Southampton, Southampton SO17 1BJ, UK; National Institute for Health Research Applied Research Collaboration (Wessex), Southampton SO17 1BJ, UK; LIME Karolinska Institutet, 17177 Stockholm, Sweden. Electronic address: peter.griffiths@soton.ac.uk. 5. University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland; Laboratory of Biometry, University of Thessaly, 38446 Volos, Greece. Electronic address: cnakas@icloud.com. 6. Institute of Nursing Science, Department of Public Health, Faculty of Medicine, University of Basel, Bernoullistrasse 28, 4056 Basel, Switzerland; Nursing & Midwifery Research Unit, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland. Electronic address: m.simon@unibas.ch.
Abstract
BACKGROUND: Worldwide, hospitals face pressure to reduce costs. Some respond by working with a reduced number of nurses or less qualified nursing staff. OBJECTIVE: This study aims at examining the relationship between mortality and patient exposure to shifts with low or high nurse staffing. METHODS: This longitudinal study used routine shift-, unit-, and patient-level data for three years (2015-2017) from one Swiss university hospital. Data from 55 units, 79,893 adult inpatients and 3646 nurses (2670 registered nurses, 438 licensed practical nurses, and 538 unlicensed and administrative personnel) were analyzed. After developing a staffing model to identify high- and low-staffed shifts, we fitted logistic regression models to explore associations between nurse staffing and mortality. RESULTS: Exposure to shifts with high levels of registered nurses had lower odds of mortality by 8.7% [odds ratio 0.91 95% CI 0.89-0.93]. Conversely, low staffing was associated with higher odds of mortality by 10% [odds ratio 1.10 95% CI 1.07-1.13]. The associations between mortality and staffing by other groups was less clear. For example, both high and low staffing of unlicensed and administrative personnel were associated with higher mortality, respectively 1.03 [95% CI 1.01-1.04] and 1.04 [95% CI 1.03-1.06]. DISCUSSION AND IMPLICATIONS: This patient-level longitudinal study suggests a relationship between registered nurses staffing levels and mortality. Higher levels of registered nurses positively impact patient outcome (i.e. lower odds of mortality) and lower levels negatively (i.e. higher odds of mortality). Contributions of the three other groups to patient safety is unclear from these results. Therefore, substitution of either group for registered nurses is not recommended.
BACKGROUND: Worldwide, hospitals face pressure to reduce costs. Some respond by working with a reduced number of nurses or less qualified nursing staff. OBJECTIVE: This study aims at examining the relationship between mortality and patient exposure to shifts with low or high nurse staffing. METHODS: This longitudinal study used routine shift-, unit-, and patient-level data for three years (2015-2017) from one Swiss university hospital. Data from 55 units, 79,893 adult inpatients and 3646 nurses (2670 registered nurses, 438 licensed practical nurses, and 538 unlicensed and administrative personnel) were analyzed. After developing a staffing model to identify high- and low-staffed shifts, we fitted logistic regression models to explore associations between nurse staffing and mortality. RESULTS: Exposure to shifts with high levels of registered nurses had lower odds of mortality by 8.7% [odds ratio 0.91 95% CI 0.89-0.93]. Conversely, low staffing was associated with higher odds of mortality by 10% [odds ratio 1.10 95% CI 1.07-1.13]. The associations between mortality and staffing by other groups was less clear. For example, both high and low staffing of unlicensed and administrative personnel were associated with higher mortality, respectively 1.03 [95% CI 1.01-1.04] and 1.04 [95% CI 1.03-1.06]. DISCUSSION AND IMPLICATIONS: This patient-level longitudinal study suggests a relationship between registered nurses staffing levels and mortality. Higher levels of registered nurses positively impact patient outcome (i.e. lower odds of mortality) and lower levels negatively (i.e. higher odds of mortality). Contributions of the three other groups to patient safety is unclear from these results. Therefore, substitution of either group for registered nurses is not recommended.
Authors: Nompilo Moyo; Martin Jones; Shaun Dennis; Karan Sharma; Richard Gray Journal: Int J Environ Res Public Health Date: 2022-04-05 Impact factor: 3.390