Non-linear associations between night shifts and adverse events in nursing staff: a restricted cubic spline analysis.

INTRODUCTION

Existing studies suggest that the number of night shifts may impact the occurrence of adverse events. However, while this relationship is well-documented, previous research has not thoroughly examined the non-linear associations between night shifts and adverse events among nursing staff, which remains a gap in our understanding.

METHODS

Participants were 1,774 Chinese nurse staff. Psychosocial characteristics were screened by The Chinese version of the multidimensional scale of perceived social support (MSPSS) for social support, the 9-item Patient Health Questionnaire (PHQ-9) for depressive symptoms, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety symptoms. Binary logistic regression and restricted cubic splines were applied to analyze the data. The statistical software used were R version 3.6.2 and SPSS version 22.0.

RESULTS

Over the past year, 325 cases (18.3%) were classified as adverse events. Logistic regression unveiled that social support played a protective role against adverse events, with an OR of 0.991 (95% CI: 0.983, 0.999). Furthermore, night shifts continued to surface as a substantial risk factor for adverse events, with an OR to 1.300 (95% CI: 1.181, 1.431). The restricted cubic spline regression model highlights a nonlinear relationship between night shifts and adverse events (P for non-liner < 0.001). The probability of adverse events increases with the number of night shifts, but compared to individuals working 3-4 night shifts per month, those working 5-6 night shifts per month have a lower probability of adverse events.

CONCLUSION

Our findings indicate a non-linear relationship between the frequency of night shifts and adverse events, suggesting a complex interplay of factors. This highlights the need for nursing practice and policy to consider the intricacies of night shift scheduling and explore more reasonable rostering strategies to mitigate the probability of adverse events.