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

BMC Nursing volume 23, Article number: 602 (2024) Cite this article

Abstract

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.

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Introduction

Adverse events, defined as unintended injuries or complications caused by medical management rather than the patient’s underlying disease process, can lead to prolonged hospital stays, disabilities, or even death [1]. Numerous studies have reported higher rates of adverse events worldwide. For instance, Kakemam conducted a cross-sectional online study among 1,004 Iranian nurses and reported rates of adverse events ranging from 26.1 to 71.7% [2]. In Canada, adverse events occur in an estimated 7.5% of all hospitalizations [1], while Gaita´n-Duarte et al. found a 4.6% incidence of adverse events in their study conducted in Colombia [3]. It is noteworthy that an analysis of 25 studies across 27 countries revealed that 10.0% of all in-patients experienced an adverse event, with over 80.0% of these events being deemed preventable [4]. While adverse events are inevitable due to human imperfection [5], controlling nursing-related factors is essential for safeguarding patient safety [6].

Shift work is a common aspect of hospital nursing, as nurses are required to provide around-the-clock healthcare. However, working overnight shifts can lead to adverse health effects. Past research has shown that night shift work can disrupt circadian rhythms, disturb sleep, and induce various behavioral changes [7]. These effects may elevate the risk of chronic diseases [8,9,10], mental disorders [11,12,13], cognitive impairment [14, 15], and even mortality [14, 16]. These problems not only affect nurses’ work efficiency but may also lead to adverse events such as medication errors, patient falls, and delays in care, seriously threatening patient safety and the quality of nursing. In Abdalkarem F’s study, nurses working in public hospitals across various regions of Saudi Arabia were surveyed. The study included 1,256 participants and revealed that the majority of respondents (85.7%) experienced patient safety and performance issues attributed to night shift rotations [17]. Additionally, a significant number of respondents (93.6%) reported experiencing physiological effects as a result of these night shifts [17]. Although hospitals around the world are striving to reduce the frequency of nurses’ night shifts, in reality, a large number of nurses still maintain a high frequency of night shifts. Therefore, we must pay close attention to the impact of nurses’ night shift frequency on the incidence of adverse events.

Existing studies suggest that the number of night shifts may significantly impact the occurrence of adverse events. Niu et al. (2013) conducted a randomized study involving 62 nurses, which revealed that the error rate on a standardized test for those working night shifts was 44% greater than that of nurses on fixed day shifts [18]. Similarly, Johnson et al. (2014) discovered that among 289 nurses working night shifts, 56% experienced sleep deprivation, and those who were sleep-deprived made a higher average number of patient care errors compared to their non-sleep-deprived counterparts [19]. In Lina’s study, a notable variance in adverse events was observed between night shifts and day shifts [20]. A recent meta-analysis also indicated that the frequency of night shifts contributes to an increased likelihood of errors, with an error rate recorded at 20.5% [21]. However, there is still a lack of evidence to thoroughly examine the relationship between night shifts and adverse events in China. Furthermore, the current research methods primarily focus on linear or logistic regression analysis, which may not fully capture the complex association between the number of night shifts and the incidence of adverse events.

Additionally, night shiftwork may also have physiological effects on nurses. Some studies have reported that adverse effects of night shiftwork on the physiological status of nurses include anxiety, depression, and social support [22,23,24]. Furthermore, a study reported that 36% of health workers indicated that night work had an impact on their fatigue levels [25]. These psychological conditions may influence the occurrence of adverse events [26,27,28]. Therefore, it is essential to control for these psychosocial factors.

To our knowledge, the number of night shifts worked by nurses is significantly correlated with their sleep quality. The frequency of night shifts can greatly affect nurses’ sleep duration, and maintaining an optimal amount of sleep—guided by a balanced schedule of night shifts—is essential for their overall health and performance [29, 30]. Deviations from this ideal sleep duration, whether too excessive or too limited, can disrupt physiological processes and cognitive functions [31, 32], thereby increasing the likelihood of adverse events [33]. Consequently, both an excessive and insufficient number of night shifts may contribute to a higher incidence of adverse events among nurses. This leads us to hypothesize that there exists a non-linear relationship between an appropriate number of night shifts and the occurrence of adverse events. Therefore, this study aimed to uncover both linear and nonlinear associations between night shifts and adverse events. Through this, we can optimize the work arrangement of nurses, reduce the incidence of adverse events, and further improve the quality and safety of nursing services.

Methods

Participants and procedures

A comprehensive cross-sectional survey was conducted among nurses employed in 18 public hospitals in Dehong City, Yunnan Province. Participants were selected using a convenient sampling method and completed the questionnaires via Wenjuanxing, an online survey platform widely used in China. The nursing departments of each government hospital played a crucial role in distributing the questionnaire link while ensuring participant anonymity and independence during completion. To be included in the study, participants had to meet specific criteria, including current employment at one of the 18 local government hospitals, comprehension of the questionnaire content, willingness to participate with informed consent, and no history of diagnosed mental illness or student nurse status. Participants were informed of their right to withdraw at any point, and it typically took around seven minutes to complete the questionnaire.

In total, 1,965 caregivers were invited, and we achieved a response rate of 90.3% (n = 1774). This research was conducted in accordance with ethical principles and was approved by the Ethics Committee of Dehong people’s hospital (Approval No. DYLL-KY032). Prior to participation, all caregivers were provided with detailed information about the study, including its purpose, procedures, potential risks and benefits, and their rights as participants. They were also informed that their participation was voluntary and that they had the right to withdraw from the study at any time without any negative consequences. Informed consent was obtained from all participants before they completed the questionnaire.

Measures

Socio-demographic variables

The participants’ basic socio-demographic characteristics included age, gender, ethnicity, marital status, place of residence, level of education, whether they were an only child, and monthly income.

Night shift

In this study, we implemented a specific item to assess the frequency of night shifts by asking participants, “In the past years, how many night shifts do you work per month?” The response options include various ranges of night shift frequencies: 0 shifts, 1–2 shifts, 3–4 shifts, 5–6 shifts, and 7 shifts or more [8]. Respondents are required to choose the option that most accurately reflects their individual circumstances.

Depressive symptoms

Depressive symptoms among the participants were assessed using the 9-item Patient Health Questionnaire (PHQ-9) [34], which utilizes a 4-point Likert scale with responses ranging from “not at all” to “nearly every day,” corresponding to scores from 0 to 3. The total score on the PHQ-9 can range from 0 to 27, with higher scores indicating more severe depressive symptoms. A commonly utilized threshold of 10 is typically applied to differentiate between depressive symptoms and the absence of such symptoms [35]. The Chinese version of the PHQ-9 has been shown to have good validity and reliability in the Chinese context [36, 37]. In this study, the Cronbach’s alpha coefficient for the PHQ-9 was calculated as 0.91. The correlation coefficient between the total score and each item ranged from 0.352 to 0.820, with a significance level of P < 0.001. The KMO score was 0.928, and the Bartlett’s test of sphericity produced an approximate χ2 value of 8817.595, with P < 0.001, showing strong reliability and validity.

Anxiety symptoms

Anxiety symptoms among the participants were evaluated using the Generalized Anxiety Disorder-7 (GAD-7) scale [38]. This assessment tool comprises 7 items and utilizes a 4-point response scale, with responses ranging from 0 (representing “not at all”) to 3 (indicating “nearly every day”). The total score on the GAD-7 can vary from 0 to 21, with higher scores indicating more pronounced anxiety symptoms. A threshold score of 10 is generally employed to differentiate between anxiety symptoms and the absence of anxiety [39]. The Chinese version of the GAD-7 has demonstrated strong validity and reliability in the Chinese context [36, 40]. In the present study, the Cronbach’s alpha coefficient for the GAD-7 was calculated as 0.93. The correlation coefficient between the total score and each item ranged from 0.540 to 0.871, with a significance level of P < 0.001. The KMO score was 0.927, and the Bartlett’s test of sphericity produced an approximate χ2 value of 8581.425, with P < 0.001, showing strong reliability and validity.

Social support

Social support was assessed by the multidimensional scale of perceived social support (MSPSS) [41], initially developed by Zimet and subsequently translated into Chinese by Jiang Qianjin, serves as a tool for assessing an individual’s multidimensional social support, encompassing family support (items 3, 4, 8, 11), friend support (items 6, 7, 9, 12), and other forms of support (items 1, 2, 5, 10). The Chinese version of this scale have been used in Chinese population [42, 43]. And the Cronbach’s α = 0.96 in this study. The correlation coefficient between the total score and each item ranged from 0.582 to 0.907, with a significance level of P < 0.001. The KMO score was 0.941, and the Bartlett’s test of sphericity produced an approximate χ2 value of 23322.441, with P < 0.001, showing strong reliability and validity.

Level of fatigue

In this study, we employed a single-item question: “What number best represents your current level of fatigue?” [44] Participants were requested to choose a number from 0 to 10 that best reflected their current feelings regarding their level of fatigue. This scale encompasses a range from “No Fatigue” to “Extreme Fatigue,” with a total of 11 levels. For example, selecting “0” signifies that the participant experiences no fatigue, whereas choosing “10” indicates an extreme level of fatigue.

Adverse events

In this study, we employed a single-item question: “Have you experienced any nursing adverse events in the past year?“ [2]. Additionally, we provided a specific definition for “nursing adverse events,” which includes situations such as ‘Pressure ulcers’, ‘Patient falls’, ‘Medication errors’, ‘Surgical wound infections’, ‘Infusion or transfusion reactions’, ‘Patient and family verbal abuse’, and ‘Patient or family complaints.’ These events encompass various circumstances resulting from nurses’ lack of accountability, failure to adhere to operational protocols, or lapses in implementing essential systems. Participants were required to select either “Yes” or “No” based on this defined criteria to indicate if they had encountered such events within the past year.

Statistical analysis

In our study, we conducted a descriptive analysis to gain insights into the data. Qualitative variables were presented using frequencies and percentages (N/%), while quantitative data were described using means ± standard deviations (SDs).

To further explore the relationship between night shifts and adverse events, logistic regression models were utilized. Three logistic models were constructed to investigate this association: Model 1 examined adverse events in relation to night shifts; Model 2 included basic socio-demographic covariates along with night shifts; and Model 3 incorporated basic socio-demographic factors, psychosocial variables, and night shifts.

Furthermore, a dose-response analysis was carried out to investigate the non-linear correlation between night shifts and adverse events. This analysis employed restricted cubic splines (RCS), a flexible modeling technique that captures intricate non-linear relationships. RCS can be viewed as a piecewise polynomial regression method, fitting data with cubic polynomials between specified knots while preserving linearity or constancy outside of these knots. This approach effectively mitigates the risk of overfitting. The data were adjusted for multiple covariates such as age, sex, ethnicity, marital status, residence, education level, only-child status [45], income [46, 47], depressive symptoms, anxiety symptoms, social support, and level of fatigue. The RCS was fitted to the data with knots positioned at the 5th, 35th, 65th, and 95th percentiles of night shift duration, with the 5th percentile serving as the reference point. Wald tests were conducted to evaluate the statistical significance of the non-linear trends identified by the RCS coefficients.

It is important to highlight that the RCS analysis was performed using R version 3.6.2, along with the “rms” and “ggplot2” packages, while all other statistical analyses were conducted using SPSS version 22.0. For all statistical tests in this study, a significance level of 0.05 was set, and a two-tailed approach was adopted for hypothesis testing.

Results

Out of the total participant pool of 1,774 nurses in the statistical analysis, 325 cases (18.3%) were identified as adverse events. Table 1 outlines the baseline socio-demographic characteristics and psychological outcomes of the participants. The majority of the participants were female, comprising 1,666 individuals (93.9%), while 498 participants (28.1%) were classified as ethnic minorities. The average age of the participants was 32.00 ± 7.99, with a notable portion (674 or 38.0%) falling within the 25–29 age range. Marital status data indicated that 1,200 individuals (67.6%) were married, with 1,071 participants (60.4%) living in rural areas. Educational background revealed that around one-third of nurses (614 or 34.6%) had completed education up to high school level or lower. In terms of income, the majority reported earnings between 3001 and 7000, with 1,107 participants (62.4%) falling into this category. The mean values for various psychological parameters and occupational factors were as follows (Table 2): depressive symptoms (7.42 ± 5.13), anxiety symptoms (6.29 ± 4.32), social support (62.60 ± 14.02), fatigue levels (5.55 ± 2.50), and night shifts (2.95 ± 1.46).

Table 1 Socio-demographic of the study participants (N = 1774)
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Table 2 Psychological characteristics of the study participants.
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In Table 3, logistic regression analysis was performed to explore the relationship between night shifts and adverse events. Model 1 includes only the night shift as a variable, Model 2 adds general demographic characteristics alongside the night shift, and Model 3 further incorporates psychological factors in addition to the general demographic characteristics and the night shift. In model 1, night shifts emerged as a notable risk factor for adverse events, exhibiting an odds ratio (OR) of 1.336 (95% CI: 1.223, 1.459). Transitioning to model 2, the analysis revealed being an only child as a risk factor for adverse events, with an OR of 1.426 (95% CI: 1.002, 2.016). Despite a slight reduction, night shifts retained significance as a risk factor for adverse events in this model, with an OR of 1.330 (95% CI: 1.211, 1.461). Notably, no other variables demonstrated statistically significant associations with adverse events within this model. Advancing to model 3, the investigation 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, showcasing a notable shift in the OR to 1.300 (95% CI: 1.181, 1.431). Amidst these findings, no other variables exhibited statistically significant links with adverse events.

Table 3 Logistic regression analysis of the association between night shift and adverse events
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The restricted cubic spline regression model, depicted in Fig. 1, highlights a nonlinear relationship between night shifts and adverse events. When considering various factors such as age, sex, ethnicity, marital status, residence, education level, being an only child, income, depressive symptoms, anxiety symptoms, social support, and level of fatigue, it becomes evident that the likelihood of adverse events escalates with an increase in monthly night shifts. Intriguingly, the probability of adverse events decreases for individuals working 5–6 night shifts per month compared to those working 3–4 night shifts, before exhibiting a rise with further increments in monthly shifts. Subsequent RCS analyses conducted for distinct genders (Fig. 2a) and age groups (Fig. 3b) unveil consistent patterns akin to Fig. 1. Moreover, it was noted that, given an equivalent number of night shifts, women manifested a lower probability of adverse events compared to men. Among diverse age brackets, nurses aged 30–34 exhibited the highest probability of encountering adverse events.

Fig. 1
figure 1

Non-liner association between night shift and adverse events using a Restricted Cubic Spline Regression Model. Graphs show ORs for adverse events according to night shift adjusted for age, sex, ethnic, marital status, residence, education level, only child, income, depressive symptoms, anxiety symptoms, social support, level of fatigue. Data were fitted by a logistic regression model, and the model was conducted with 4 knots at the 5th, 35th, 65th, 95th percentiles of nightshift (reference is the 5th percentile). Solid lines indicate ORs, and shadow shape indicate 95% CIs. OR, odds ratio; CI, confidence interval

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Fig. 2a
figure 2

Non-liner association between night shift and adverse events using a Restricted Cubic Spline Regression Model, separated by sex. Graphs show ORs for adverse events according to night shift adjusted for age, sex, ethnic, marital status, residence, education level, only child, income, depressive symptoms, anxiety symptoms, social support, level of fatigue. Data were fitted by a logistic regression model, and the model was conducted with 4 knots at the 5th, 35th, 65th, 95th percentiles of nightshift (reference is the 5th percentile). Solid lines indicate ORs, and shadow shape indicate 95% CIs. OR, odds ratio; CI, confidence interval

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Fig. 2b
figure 3

Non-liner association between night shift and adverse events using a Restricted Cubic Spline Regression Model, separated by age. Graphs show ORs for adverse events according to night shift adjusted for age, sex, ethnic, marital status, residence, education level, only child, income, depressive symptoms, anxiety symptoms, social support, level of fatigue. Data were fitted by a logistic regression model, and the model was conducted with 4 knots at the 5th, 35th, 65th, 95th percentiles of nightshift (reference is the 5th percentile). Solid lines indicate ORs, and shadow shape indicate 95% CIs. OR, odds ratio; CI, confidence interval

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Discussion

In this study, we identified social support as a protective factor against adverse events, with an odds ratio (OR) of 0.991 (95% CI: 0.983, 0.999). Additionally, night shifts emerged as a significant risk factor for adverse events, with an OR of 1.300 (95% CI: 1.181, 1.431), highlighting the pronounced impact of shift work on adverse event occurrence. Moreover, our investigation unveiled a non-linear relationship between night shifts and adverse events. The probability of adverse events escalated with an increase in monthly night shifts. Interestingly, individuals working 5–6 night shifts per month exhibited a decreased probability of adverse events compared to those working 3–4 night shifts, followed by a subsequent rise with higher monthly shift frequencies. Furthermore, our analysis revealed that women demonstrated a lower probability of adverse events compared to men when considering an equivalent number of night shifts. Among different age groups, nurses aged 30–34 displayed the highest likelihood of experiencing adverse events.

It was observed that 325 cases (18.3%) were classified as adverse events in the past year in this study. The observed adverse event rate is higher than that reported in a recent study conducted in China, where the rate was 13.9% among operating room nurses [28]. However, it’s important to note that this difference may stem from variances in our target populations and measurement methodologies. Our study encompassed all nursing staff in the hospital, whereas the Chinese study specifically focused on operating room personnel. Furthermore, their definition of adverse events was narrower, limited to instances where physical damage was caused to the patient, whereas our definition of adverse events encompassed a broader range of scenarios.

Based on our findings, it appears that the frequency of night shifts for nurses is a risk factor for adverse events. This aligns with Muzio and his colleagues’ comprehensive systematic review examining the relationship between nursing shift work and clinical risk [48]. Their research revealed that, on average, night shift nurses slept for over an hour less during rest periods compared to their day shift counterparts. Additionally, workload and inadequate sleep were identified as the primary reasons for medical errors [48]. The results of this study also showed that the likelihood of adverse events increased with the number of night shifts per month, but those working 3–4 night shifts per month demonstrated a reduced likelihood of adverse events compared to those working 5–6 night shifts per month, which subsequently increased with the frequency of monthly shifts. This suggests that nurse managers should arrange for sufficient human resources to reduce the number of night shifts for nurses when arranging the frequency of night shifts as much as possible, but in clinical practice, due to human resource constraints, 5–6 night shifts per month is ideal and favourable to reduce the incidence of clinical adverse events.

In this study, we identified social support as a protective factor against adverse events, with an odds ratio (OR) of 0.991 (95% CI: 0.983, 0.999). This aligns with Khatatbeh’s research conducted in Jordan, which also found a noteworthy inverse relationship between adverse events and both familial and managerial support [49]. Our study further reinforces this idea and suggests that by enhancing overall support for healthcare professionals—particularly by fostering a healthier work environment for nurses—patient safety can be significantly improved [50, 51]. This highlights the importance of social support in mitigating adverse events and emphasizes its potential to enhance patient safety and the quality of medical services.

Under the same number of night shifts, women demonstrate a lower probability of experiencing adverse events compared to men. This finding is not in alignment with the previous study conducted by Song and his colleagues among operating room nurses, which found no significant difference in the rate of adverse events between male and female nurses [26]. This discrepancy may be attributed to variations in sample design and research methodology. Thus, we must approach this finding with caution. Firstly, the uneven gender distribution in the nursing profession is an undeniable fact, with women comprising the majority. Consequently, this difference may largely reflect occupational characteristics, working environments, and job pressures, rather than gender itself. Although our study indicates that women exhibit a lower probability of experiencing adverse events under the same number of night shifts—potentially due to their more meticulous attitude towards their work, which may reduce the occurrence of such events—we cannot solely attribute this difference to inherent gender tendencies.

It is worth noting that among nurses of different age groups, those aged 30–34 face the highest probability of adverse events. Consistent with Saifuddin’s study, age is an important factor in the occurrence of adverse events [52]. We speculate that this may be due to specific circumstances faced by nurses in this age bracket. On one hand, they have accumulated a certain amount of work experience, which could potentially lead to complacency or carelessness. On the other hand, they may also be in a critical phase of their family life, such as having young children to care for, which could potentially distract them at work. In light of these findings, hospital policymakers should pay extra attention and provide additional support to nurses in this age group. The aim should be to reduce the likelihood of adverse events, ensuring high-quality service and patient safety within the hospital.

There are several limitations that need to be considered in this study. Firstly, the cross-sectional design limits the ability to extend results and infer causality. Secondly, participants were recruited using convenient sampling from only one distinct in China, which may limit the generalizability of the findings to a representative national sample. Thirdly, we recognize that reporting and recall biases may still be present in this study, such as adverse events. Despite our efforts to minimize these biases through standardized data collection tools and clear definitions of ‘adverse events’, some limitations remain. We suggest that future research could further investigate this matter using methods such as cohort studies to mitigate these biases. Fourthly, when discussing our research findings, we must acknowledge an important limitation: there is a discrepancy in the time frame of data collection between individual experiences of adverse events and other variables, such as depression, anxiety, and social support. This inconsistency in timing may have impacted our analysis, as data collected at different time points can reflect varying situations and individual states. To better understand the influence of this time difference on the results, we recommend that future research strictly control the time frame of data collection during the design phase, ensuring that all variables are assessed at the same or similar time points. This approach would not only improve data accuracy but also enhance the comparability of the study, leading to more reliable and effective conclusions. Despite this limitation in our research, we believe our findings provide valuable insights into the relationship between adverse events and mental health, offering important references for future studies.

Conclusion

Our study found that 325 cases (18.3%) were classified as adverse events over the past year, highlighting their prevalence and critical nature in nursing practice. The findings indicate a non-linear relationship between the frequency of night shifts and adverse events, suggesting a complex interplay of factors. Significant gender and age disparities were observed in the occurrence of adverse events, with women demonstrating lower susceptibility compared to men when exposed to equivalent numbers of night shifts. Nurses aged 30–34 exhibited the highest likelihood of experiencing adverse events. These insights have profound implications for clinical practice, nursing administration, and future research aimed at reducing adverse events in nursing. Future studies should further explore non-linear relationships between variables such as night shifts and adverse events in different contexts or populations. It is important to investigate these complex interplays of factors more comprehensively to develop targeted interventions and policies that safeguard nurse well-being and enhance patient care outcomes. Understanding these nuanced associations is crucial for advancing knowledge in this area and improving nursing practice.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

MSPSS:

the multidimensional scale of perceived social support

GAD-7:

The generalized anxiety disorder-7

PHQ-9:

the 9-item Patient Health Questionnaire

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Acknowledgements

We would like to thank all nurses who generously shared their time to participate in this survey.

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Author notes

  1. Mao xiaolan and Zhizhou Duan contributed equally.

Authors and Affiliations

  1. The Nursing department, The central hospital of enshi tujia and miao atunomous prefecture, Enshi, Hubei, China

    Mao Xiaolan, Jianmei Jiang & Xiang Wei

  2. Preventive health service, Jiangxi provincial people’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China

    Zhizhou Duan

  3. Department of Environmental Health, School of Public Health, Fudan University, 130 Dong’an Road, Shanghai, 200032, China

    Zhiping Niu

  4. Department of Biobank, Nantong First People’s Hospital, Nantong city, Jiangsu Province, China

    Xiangfan Chen

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Contributions

MX L, ZZ D analyzed the data and wrote manuscript; ZZ D and XF C revised the manuscript; ZP N, JM J, X W, and ZZ D edited the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zhizhou Duan or Xiangfan Chen.

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Ethics approval and consent to participate

Participants provided written informed consent, and the Ethics Committee of Dehong people’s hospital in China (Number: DYLL-KY032) approved this study. And all methods were performed in accordance with t Declaration of Helsinki.

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The authors declare no competing interests.

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Xiaolan, M., Duan, Z., Niu, Z. et al. Non-linear associations between night shifts and adverse events in nursing staff: a restricted cubic spline analysis. BMC Nurs 23, 602 (2024). https://doi.org/10.1186/s12912-024-02259-3

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Keywords

BMC Nursing

ISSN: 1472-6955

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