Introduction
The coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease that was first reported as “pneumonia of unknown origin” in December 2019 in Wuhan, China. As a major public health crisis, the COVID-19 pandemic spread rapidly worldwide due to its highly contagious nature and significant fatality rate. This unprecedented health crisis placed considerable strain on healthcare systems globally, presenting unique challenges for healthcare professionals, particularly nurses, who were at the forefront of patient care [1]. During the pandemic, nurses were exposed to multiple risk factors that potentially contributed to psychological disorders. These risk factors can be categorized into several dimensions: (1) occupational risks, including high-risk exposure, prolonged working hours, and overwhelming patient demands; (2) resource-related challenges, such as shortages of protective equipment and limited medical resources; (3) emotional burdens, stemming from repeated exposure to critically ill patients and high mortality rates; and (4) personal concerns, particularly the fear of transmitting the virus to family members [2‐4]. Collectively, the complex interplay of these risk factors acted as a trigger for psychological conditions, notably anxiety, depression, and stress. These psychological issues often interacted with and amplified each other, creating a vicious cycle of deteriorating mental health. Anxiety could heighten stress levels, which, in turn, contributed to depressive symptoms, leading to cumulative and chronic psychological distress. This interconnected cycle of deteriorating mental health significantly impaired healthcare delivery through compromised clinical decision-making, reduced patient empathy, and diminished cognitive functioning, ultimately leading to increased medical errors, compromised patient safety, and reduced quality of care [5‐8].
Previous studies have documented the high prevalence of anxiety, stress, and depression among nurses during the COVID-19 pandemic [9‐15]. However, the majority of existing research has examined these conditions individually and using cross-sectional studies. The underlying risk factors for these conditions and the potential impact of the pandemic on comorbidities among nurses remains insufficiently understood. Identifying risk factors for anxiety, stress, depression, and their comorbidities among nurses is essential for informing targeted interventions and support strategies to mitigate the negative impacts of these conditions on both individual well-being and the overall quality of healthcare provision.
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In this study, we present findings from a prospective cohort of nurses spanning from 2020 to 2022, investigating the incidence of anxiety, stress, depression, and their comorbidities, as well as the associated risk factors in this population. The findings of this study will provide a basis for healthcare administrators and policymakers to design targeted interventions and support systems aimed at promoting nurses’ psychological well-being in future global health crises.
Materials and methods
Participants
A prospective cohort study was conducted with a sample without anxiety, stress, or depression at recruitment, which followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. A stratified cluster sampling method was used to select a representative sample of nurses from affiliated hospitals of Zunyi Medical University in Zunyi City, Guizhou Province, China. We first divided the affiliated hospitals into different strata based on size (large, small) and location (urban, rural). We used simple random sampling to select clusters (departments in each hospital) within each stratum. All eligible nurses in the selected departments were included to ensure the representativeness. The total population of nurses in the affiliated hospitals was approximately 3,000.
Participants were recruited from 5 to 10 February 2020, and data were collected using the QuestionnaireStar program from February 11 to 15, 2020 (first occasion), February 16 to 20, 2021 (second occasion), February 1 to 5, 2022 (third occasion), and December 6 to 10, 2022 (fourth occasion). There was always a five-days interval between occasions to maximize participation. QuestionnaireStar programme, a research application embedded with WeChat, is widely used in Chinese population in previous studies, which could avoid face-to-face interviews and reduce disease transmission during the COVID-19 pandemic [16].
Inclusion criteria were as follows: (1) adults aged 20 years or older, (2) able to understand Chinese and provide written informed consent; (3) nurses with registered license actively employed in hospital during the study period; and (4) nurses completed all required baseline and follow-up assessments. Exclusion criteria were as follows: (1) participants who had a previous history of anxiety, depression and stress; (2) participants who are not actively working during the study period, such as those on long-term leave, retired, or remote working; (3) participants with significant cognitive impairment, serious medical conditions or language barriers that preclude their ability to complete the study assessments; and (4) participants who were pregnant or breastfeeding at the time of enrollment.
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Following these criteria, 702 nurses aged 20 to 50 years were eligible for the study at inception. However, 583 responses were received (response rate of 83%), 31 nurses withdrawal during the follow-up, and 36 responses with implausible values. Finally, 516 nurses were included in the data analysis. Study procedures were reviewed and approved by the institutional review board of the Second Affiliated Hospital of Zunyi Medical University, and all participants provided informed consent before study participation.
Sample size
Using a two-sided significance level of 5% (α = 0.05), a statistical power of 80% (1-β = 0.80), and an assumed odds ratio (OR) of 2.0 for key risk factors [9‐15], the minimum required sample size was estimated to be 485 participants. Additionally, Bujang et al. recommended that for observational studies with large population sizes involving logistic regression analysis, a minimum sample size of 500 participants is necessary to ensure reliable estimates of the parameters [17].
Data collection
Questionnaire Star was used to generate the unique link, which was distributed to each nurse through WeChat (a Chinese multi-purpose messaging and social media application). Data collection had two parts, including a self-reported questionnaire and three scales for assessing anxiety, stress, depression separately. The process was carried out in four occasions: baseline, between 11 and 15 February 2020; time point 2, between 16 and 20 February 2021; time point 3, between 1 and 5 February 2022; and time point 4, between 6 and 10 December 2022. To ensure data quality, all questions were set as mandatory, and the survey could only be assessed once at a time with the same account, the same device, and the same IP address. All questionnaires were completed independently and anonymously.
A self-reported questionnaire was used to collect demographic, lifestyle, socioeconomic, working-related, history of disease information, including age, gender, body mass index (BMI), smoking status, drinking status, healthy eating index score, physical activity, marital status, education level, average income per year, years of working, shift work, working hours per week, frontline experience with infected patients, and medical history of hypertension, diabetes, and high cholesterol. The Healthy Eating Index (HEI) is a measure of diet quality that an individual’s diet adheres to the dietary guidelines. The HEI score was determined using a validated food frequency questionnaire (FFQ), which consisted of questions related to the frequency of consumption of various food groups, including fruits, vegetables, grains, red meat, and processed meat [18, 19]. The HEI score was determined by assigning 1 point for consuming fruits, vegetables, and grains ≥ 5 times per week, and 1 point for consuming red meat and processed meat ≤ 2 times per week. The total HEI score ranged from 0 to 5, with higher scores indicating better adherence to the recommended dietary guidelines. We categorized the HEI scores into three groups: 0 (lowest), 1–3 (moderate), and > 3 (highest). Physical activity was assessed by the number of days per week with at least 30 min of moderate-intensity exercise, and it was classified as < 1/week, or > = 1/week [20].
Self-rating anxiety scale (SAS)
SAS was used to evaluate anxiety symptoms. It is a self-administered, 20-item scale that quantifies the level of anxiety in individuals [21]. SAS has been widely used to evaluate the psychometric properties among Chinese [22, 23]. T The scale includes 15 items phrased to reflect increased anxiety levels and 5 items phrased to reflect decreased anxiety levels. Each question is rated on a scale of 1–4 (a little of the time, some of the time, good part of the time, most of the time). In this study, the Cronbach’s alpha coefficient was 0.83, indicating high internal consistency. Following previous research, raw SAS scores were converted to a standardized 100-point scale, with a score of 50 or above indicating anxiety [24].
Self-rating depression scale (SDS)
SDS was used to evaluate depressive symptoms. It is a reliable and valid self-administered scale designed to assess affective, psychological, and somatic symptoms associated with depression in adults [25, 26]. SDS consists of 20 items (half positive and half negative) rating the four common characteristics of depression: the pervasive effect, the physiological equivalents, other disturbances, and psychomotor activities. Each question is rated on a scale of 1–4 (a little of the time, some of the time, good part of the time, most of the time). In this study, the Cronbach’s alpha coefficient was 0.81, indicating high internal consistency. According to previous studies, raw SDS scores were converted to a standardized 100-point scale, with a score of 53 or above indicating depression [27].
Perceived stress scale (PSS)
PSS was used to measure perceived stress levels. This widely utilized self-report instrument consists of 10 items, each rated on a 5-point Likert scale ranging from 0 (never) to 4 (very often) [28]. The total score ranges from 0 to 40, with higher scores reflecting greater levels of perceived stress. The Chinese version of the PSS has demonstrated satisfactory validity and reliability in previous studies [29]. In our study, the Cronbach’s alpha coefficient was 0.84, indicating high internal consistency. A total score of 20 or above was used to define elevated stress levels.
Statistical analysis
All eligible patients at the recruitment were included. Cases were confirmed any incident of newly diagnosed anxiety, stress, or depression during the period from 11 February 2020 to 10 December 2022. If an individual was diagnosed with any of these conditions on different occasions, they were still counted as one case of that disease. Comorbidity, in this study, refers to the occurrence of two or more of the specified conditions (anxiety, depression, or stress) within the period.
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Data were analyzed using SPSS software version 22.0 (SPSS Inc., Chicago, IL, USA). Descriptive statistics was used to present baseline characteristics. Variables were expressed as absolute values and percentages. Univariate and multivariate logistic regression analyses were used to identify risk factors of anxiety, stress or depression, separately. Variables presented in univariate analysis were forced to construct multivariate logistic regression analysis, since these factors were reported to be associated with outcomes in previous studies. To examine the association between risk factors and comorbidities, separate odds ratios (OR) and 95% CI were calculated for the one condition, two conditions, and three conditions, comparing each with the reference group (without anxiety, stress, or depression). A two-sided p-value of less than 0.05 was considered statistically significant.
Results
In our study, we examined the baseline characteristics of the included participants, as shown in Table 1. The average mean and standard deviation of age were 36.8 and 7.6, and females accounted for 93.0%. Most participants were non-smokers, non-drinkers, had a healthy eating index score between 1 and 3, engaged in physical activity less than once per week, married, had bachelor’s degrees, and had average income per year more than 40,000 RMB. Working years among nurses almost evenly distributed between less than 10 years or equal or more than 10 years. Most nurses had a regular shift and worked more than 40 h per week. Most participants had no history of hypertension, diabetes, or high cholesterol.
Table 1
Baseline characteristics of included participants
Variables | Category | Number | Percentage (%) |
|---|---|---|---|
Age
| 20–29 | 153 | 29.6% |
30–39 | 185 | 35.8% | |
40–50 | 178 | 34.6% | |
Gender
| Male | 36 | 7.0% |
Female | 480 | 93.0% | |
Body Mass Index (BMI)
| <=23.9 | 128 | 24.8% |
24–27.9 | 246 | 47.7% | |
>=28 | 142 | 27.5% | |
Smoking status
| No | 435 | 84.3% |
Yes | 81 | 15.7% | |
Drinking status
| No | 363 | 70.4% |
Yes | 153 | 29.6% | |
Healthy eating index score
| 0 | 112 | 21.7% |
1–3 | 289 | 56.0% | |
> 3 | 115 | 22.3% | |
Physical activity
| < 1/week | 352 | 68.2% |
>=1/week | 164 | 31.8% | |
Marital status
| Married | 249 | 48.3% |
Divorced | 78 | 15.1% | |
Single | 189 | 36.6% | |
Education level
| Bachelor degree | 441 | 85.5% |
Master degree | 75 | 14.5% | |
Average income per year (RMB)
| <=40,000 | 234 | 45.3% |
> 40,000 | 282 | 54.7% | |
Years of working
| < 10 years | 256 | 49.6% |
>=10 years | 260 | 50.4% | |
Shift work
| Regular day shift | 212 | 41.1% |
Night shift < = 1/week | 189 | 36.6% | |
Night shift > 1/week | 115 | 22.3% | |
Working hours per week
| <=40 h | 186 | 36.0% |
> 40 h | 330 | 64.0% | |
Frontline experience with infected patients
| No | 284 | 55.1% |
Yes | 232 | 44.9% | |
History of hypertension
| No | 477 | 92.4% |
Yes | 39 | 7.6% | |
History of diabetes
| No | 494 | 95.8% |
Yes | 22 | 4.2% | |
History of high cholesterol
| No | 480 | 93.0% |
Yes | 36 | 7.0% |
In the analysis of risk factors associated with anxiety among the participants, both univariate and multivariate analyses were conducted, as shown in Table 2. In total, 140 (27.1%) nurses experienced anxiety during the period. In the multivariate analysis, several factors were significantly associated with anxiety. Participants with a BMI of 28 or higher were 2.21 times more likely to have anxiety compared to those with a BMI of 23.9 or lower (95% CI: 1.03–3.31, p = 0.042). Those with a healthy eating index score greater than 3 had a 26% lower risk of anxiety compared to those with a score of zero (OR: 0.74, 95% CI: 0.54–0.96, p = 0.029). Participants working night shifts more than once a week were 3.83 times more likely to experience anxiety compared to those on regular day shifts (95% CI: 2.57–4.58, p < 0.001), and those working over 40 h per week were 1.69 times more likely to have anxiety compared to those working 40 h or less (95% CI: 1.32–2.68, p < 0.001). Additionally, frontline experience with infected patients significantly increased the likelihood of anxiety, with an odds ratio of 3.88 (95% CI: 1.63–8.52, p = 0.005).
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Table 2
Univariate and multivariate analysis between risk factors and anxiety
Category | Univariate | Multivariate |
|---|---|---|
OR (95% CI) | OR (95% CI) | |
Age | ||
20–29 | Ref. | Ref. |
30–39 | 0.96(0.67–1.82) | 1.01(0.75–1.94) |
40–49 | 0.93(0.65–1.77) | 1.06(0.85–1.67) |
Gender | ||
Female | Ref. | Ref. |
Male | 0.89(0.69–1.07) | 0.69(0.65–1.46) |
Body Mass Index (BMI) | ||
<=23.9 | Ref. | Ref. |
24–27.9 | 1.87(0.75–3.14) | 1.56(0.62–2.87) |
>=28 | 2.80(1.15–4.87) | 2.21(1.03–3.31) |
Smoking status | ||
No | Ref. | Ref. |
Yes | 1.36(1.03–1.74) | 1.24(0.94–1.66) |
Drinking status | ||
No | Ref. | Ref. |
Yes | 1.43(1.05–1.91) | 1.27(0.92–1.51) |
Healthy eating index score | ||
0 | Ref. | Ref. |
1–3 | 0.81(0.65–0.98) | 0.89(0.72–1.04) |
> 3 | 0.63(0.45–0.81) | 0.74(0.54–0.96) |
Physical activity | ||
< 1/week | Ref. | Ref. |
>=1/week | 0.96(0.57–2.81) | 0.95(0.79–2.01) |
Marital status | ||
Married | Ref. | Ref. |
Divorced | 1.51(0.73–3.15) | 1.36(0.70–2.22) |
Single | 1.21(0.87–1.73) | 1.20(0.86–1.72) |
Education level | ||
Bachelor degree | Ref. | Ref. |
Master degree | 0.87(0.63–2.37) | 0.81(0.56–2.25) |
Average income per year (RMB) | ||
<=40,000 | Ref. | Ref. |
> 40,000 | 0.94(0.78–1.12) | 0.87(0.67–1.04) |
Years of working | ||
< 10 years | Ref. | Ref. |
>=10 years | 0.98(0.81–1.25) | 0.71(0.51–1.56) |
Shift work | ||
Regular day shift | Ref. | Ref. |
Night shift ( < = 1/week) | 1.84(1.16–2.59) | 1.87(1.33–2.64) |
Night shift (> 1/week) | 3.79(2.49–4.41) | 3.83(2.57–4.58) |
Working hours per week | ||
<=40 h | Ref. | Ref. |
> 40 h | 1.73(1.33–2.65) | 1.69(1.32–2.68) |
Frontline experience with infected patients | ||
No | Ref. | Ref. |
Yes | 3.61(1.56–8.33) | 3.88(1.63–8.52) |
History of hypertension | ||
No | Ref. | Ref. |
Yes | 1.64(0.73–3.68) | 1.34(0.70–3.11) |
History of diabetes | ||
No | Ref. | Ref. |
Yes | 2.10(1.11–4.78) | 1.88(0.98–3.77) |
History of high cholesterol | ||
No | Ref. | Ref. |
Yes | 1.93(0.94–4.61) | 1.55(0.91–4.55) |
Table 3 showed univariate and multivariate analysis between risk factors and depression. In total, 175 (33.9%) nurses experienced depression during the period. The multivariate analysis revealed several factors significantly associated with depression. Participants with a BMI between 24 and 27.9 had a 1.15-fold higher risk of depression compared to those with a BMI of 23.9 or lower (95% CI: 1.01–1.44, p = 0.048), while those with a BMI of 28 or higher had a 1.52-fold increased risk (95% CI: 1.25–1.84, p < 0.001). A healthy eating index score of 1–3 was associated with a 26% lower risk of depression compared to a score of zero (OR: 0.74, 95% CI: 0.66–0.88, p < 0.001), and a score greater than 3 resulted in a 46% reduced risk (OR: 0.54, 95% CI: 0.40–0.71, p < 0.001). Working night shifts once a week or less was associated with a 3.41-fold higher risk of depression compared to regular day shifts (95% CI: 1.71–5.18, p = 0.003), and working night shifts more than once a week had a 4.22-fold increased risk (95% CI: 3.41–5.73, p < 0.001). Participants working over 40 h per week had a 2.66-fold higher risk of depression compared to those working 40 h or less (95% CI: 1.53–3.74, p = 0.001). Frontline experience with infected patients was significantly associated with depression, with an odds ratio of 4.23 (95% CI: 1.96–9.74, p < 0.001).
Table 3
Univariate and multivariate analysis between risk factors and depression
Category | Univariate | Multivariate |
|---|---|---|
OR (95% CI) | OR (95% CI) | |
Age | ||
20–29 | Ref. | Ref. |
30–39 | 1.03(0.67–1.82) | 0.97(0.75–1.94) |
40–49 | 1.00(0.73–1.61) | 0.99(0.71–1.63) |
Gender | ||
Female | Ref. | Ref. |
Male | 1.16(0.74–1.79) | 1.08(0.77–1.76) |
Body Mass Index (BMI) | ||
<=23.9 | Ref. | Ref. |
24–27.9 | 1.18(1.04–1.37) | 1.15(1.01–1.44) |
>=28 | 1.44(1.21–1.79) | 1.52(1.25–1.84) |
Smoking status | ||
No | Ref. | Ref. |
Yes | 1.11(0.60–2.70) | 1.08(0.66–2.61) |
Drinking status | ||
No | Ref. | Ref. |
Yes | 1.16(0.74–3.03) | 1.13(0.71–3.12) |
Healthy eating index score | ||
0 | Ref. | Ref. |
1–3 | 0.76(0.69–0.92) | 0.74(0.66–0.88) |
> 3 | 0.55(0.41–0.72) | 0.54(0.40–0.71) |
Physical activity | ||
< 1/week | Ref. | Ref. |
>=1/week | 0.99(0.61–1.99) | 0.97(0.58–1.92) |
Marital status | ||
Married | Ref. | Ref. |
Divorced | 1.18(0.87–1.76) | 1.21(0.92–1.81) |
Single | 1.19(0.81–1.66) | 1.22(0.93–1.69) |
Education level | ||
Bachelor degree | Ref. | Ref. |
Master degree | 0.99(0.76–1.78) | 0.96(0.81–1.71) |
Average income per year (RMB) | ||
<=40,000 | Ref. | Ref. |
> 40,000 | 0.91(0.71–1.59) | 0.88(0.69–1.66) |
Years of working | ||
< 10 years | Ref. | Ref. |
>=10 years | 0.98(0.70–1.91) | 0.97(0.73–1.99) |
Shift work | ||
Regular day shift | Ref. | Ref. |
Night shift ( < = 1/week) | 3.33(1.69–5.10) | 3.41(1.71–5.18) |
Night shift (> 1/week) | 4.11(3.33–5.55) | 4.22(3.41–5.73) |
Working hours per week | ||
<=40 h | Ref. | Ref. |
> 40 h | 2.45(1.41–3.67) | 2.66(1.53–3.74) |
Frontline experience with infected patients | ||
No | Ref. | Ref. |
Yes | 4.11(1.83–9.66) | 4.23(1.96–9.74) |
History of hypertension | ||
No | Ref. | Ref. |
Yes | 1.77(1.03–3.12) | 1.57(0.91–2.99) |
History of diabetes | ||
No | Ref. | Ref. |
Yes | 1.68(1.01–3.55) | 1.49(0.92–3.01) |
History of high cholesterol | ||
No | Ref. | Ref. |
Yes | 1.81(1.08–3.77) | 1.53(0.94–3.33) |
Univariate and multivariate analysis between risk factors and stress were conducted and results were presented in Table 4. In total, 206 (39.9%) nurses experienced stress during the period. In the multivariate analysis, several factors were found to be significantly associated with stress. Participants with a BMI of 28 or higher were 1.57 times more likely to experience stress compared to those with a BMI of 23.9 or lower (95% CI: 1.09–3.51, p = 0.032). A healthy eating index score greater than 3 was associated with a 29% lower risk of stress compared to those with a score of zero (OR: 0.71, 95% CI: 0.55–0.89, p = 0.022). Working for 10 years or longer was associated with a 1.46-fold higher risk of stress compared to working for less than 10 years (95% CI: 1.14–1.92, p = 0.015). Participants working night shifts more than once a week were 4.64 times more likely to experience stress compared to those on regular day shifts (95% CI: 2.11–6.87, p < 0.001), while those working over 40 h per week had a 1.51-fold higher risk of stress compared to those working 40 h or less (95% CI: 1.24–1.96, p = 0.019). Additionally, frontline experience with infected patients was significantly associated with stress, with an odds ratio of 4.11 (95% CI: 2.94–6.66, p < 0.001).
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Table 4
Univariate and multivariate analysis between risk factors and stress
Category | Univariate | Multivariate |
|---|---|---|
OR (95% CI) | OR (95% CI) | |
Age | ||
20–29 | Ref. | Ref. |
30–39 | 1.03(0.67–1.82) | 1.01(0.72–1.61) |
40–49 | 1.06(0.65–1.77) | 1.04(0.77–1.64) |
Gender | ||
Female | Ref. | Ref. |
Male | 1.06(0.61–2.41) | 1.26(0.97–2.88) |
Body Mass Index (BMI) | ||
<=23.9 | Ref. | Ref. |
24–27.9 | 0.95(0.75–1.64) | 0.96(0.60–1.71) |
>=28 | 1.63(1.12–3.87) | 1.57(1.09–3.51) |
Smoking status | ||
No | Ref. | Ref. |
Yes | 1.87(0.65–4.74) | 1.80(0.71–4.55) |
Drinking status | ||
No | Ref. | Ref. |
Yes | 1.96(0.84–5.77) | 1.73(0.90–5.12) |
Healthy eating index score | ||
0 | Ref. | Ref. |
1–3 | 0.94(0.78–1.12) | 0.98(0.70–1.06) |
> 3 | 0.81(0.68–0.97) | 0.71(0.55–0.89) |
Physical activity | ||
< 1/week | Ref. | Ref. |
>=1/week | 0.88(0.56–3.22) | 0.91(0.60–3.33) |
Marital status | ||
Married | Ref. | Ref. |
Divorced | 1.18(0.89–3.01) | 1.09(0.81–2.81) |
Single | 1.05(0.71–1.56) | 1.10(0.83–1.66) |
Education level | ||
Bachelor degree | Ref. | Ref. |
Master degree | 1.06(0.89–1.17) | 1.04(0.91–1.21) |
Average income per year (RMB) | ||
<=40,000 | Ref. | Ref. |
> 40,000 | 0.79(0.60–0.98) | 0.84(0.66–1.01) |
Years of working | ||
< 10 years | Ref. | Ref. |
>=10 years | 1.32(1.08–1.77) | 1.46(1.14–1.92) |
Shift work | ||
Regular day shift | Ref. | Ref. |
Night shift ( < = 1/week) | 2.21(1.44–3.88) | 2.33(1.51–3.98) |
Night shift (> 1/week) | 4.56(2.06–6.83) | 4.64(2.11–6.87) |
Working hours per week | ||
<=40 h | Ref. | Ref. |
> 40 h | 1.44(1.16–1.83) | 1.51(1.24–1.96) |
Frontline experience with infected patients | ||
No | Ref. | Ref. |
Yes | 3.97(2.69–5.35) | 4.11(2.94–6.66) |
History of hypertension | ||
No | Ref. | Ref. |
Yes | 1.03(0.77–2.99) | 1.00(0.81–2.77) |
History of diabetes | ||
No | Ref. | Ref. |
Yes | 1.06(0.87–1.89) | 1.01(0.77–1.98) |
History of high cholesterol | ||
No | Ref. | Ref. |
Yes | 1.00(0.68–2.61) | 1.03(0.71–2.81) |
Table 5 illustrated the associations between risk factors and comorbidities in the study population, with odds ratios (OR) and 95% confidence intervals (CI) presented for both univariate and multivariate analyses. A notable trend was observed in the BMI category, with higher BMI values associated with a greater likelihood of all three comorbidities. Participants with a BMI between 24 and 27.9 had an increased risk (OR = 1.35, 95% CI: 1.01–1.99, p = 0.045), while those with a BMI of 28 or higher had an even greater risk (OR = 1.67, 95% CI: 1.12–2.16, p = 0.011). Participants with higher HEI scores had a reduced likelihood of comorbidities, with the strongest association found in three comorbidities (HEI 1–3 vs. HEI 0: OR = 0.70, 95% CI: 0.53–0.84, p < 0.001; HEI > 3 vs. HEI 0: OR = 0.51, 95% CI: 0.36–0.70, p < 0.001). Night shift increased odds of comorbidities, with the highest odds observed for three comorbidities (night shift < = 1/week vs. regular shift: OR = 4.33, 95% CI: 1.92–5.39, p < 0.001; night shift > 1/week vs. regular shift: OR = 5.12, 95% CI: 3.64–5.99, p < 0.001). Working hours increased likelihood of comorbidities, with the highest odds observed in three comorbidities (working hours > 40 /week vs. working hours < = 40 /week: OR = 1.99, 95% CI: 1.66–2.89, p < 0.001). Frontline experience with infected patients was strongly associated with an increased likelihood of comorbidities, particularly for three comorbidities (OR = 6.11, 95% CI: 4.52–8.88, p < 0.001).
Table 5
Associations between risk factors and comorbidities
Category | One | Two | Three |
|---|---|---|---|
OR (95% CI) | OR (95% CI) | OR (95% CI) | |
Age | |||
20–29 | Ref. | Ref. | Ref. |
30–39 | 0.94(0.65–1.34) | 1.01(0.70–1.48) | 1.03(0.73–1.64) |
40–49 | 1.02(0.71–1.49) | 1.05(0.76–1.59) | 1.07(0.79–1.71) |
Gender | |||
Female | Ref. | Ref. | Ref. |
Male | 1.01(0.54–1.78) | 0.97(0.52–1.69) | 0.92(0.49–1.58) |
Body Mass Index (BMI) | |||
<=23.9 | Ref. | Ref. | Ref. |
24–27.9 | 1.10(0.85–1.44) | 1.17(0.92–1.78) | 1.35(1.01–1.99) |
>=28 | 1.21(0.97–1.71) | 1.41(1.03–1.96) | 1.67(1.12–2.16) |
Smoking status | |||
No | Ref. | Ref. | Ref. |
Yes | 1.18(0.65–2.66) | 1.33(0.78–2.88) | 1.69(0.94–3.66) |
Drinking status | |||
No | Ref. | Ref. | Ref. |
Yes | 1.21(0.61–3.01) | 1.49(0.75–3.29) | 1.85(0.98–4.01) |
Healthy eating index score | |||
0 | Ref. | Ref. | Ref. |
1–3 | 0.82(0.69–0.98) | 0.76(0.61–0.90) | 0.70(0.53–0.84) |
> 3 | 0.65(0.46–0.88) | 0.60(0.41–0.78) | 0.51(0.36–0.70) |
Physical activity | |||
< 1/week | Ref. | Ref. | Ref. |
>=1/week | 0.95(0.56–2.49) | 0.91(0.51–2.40) | 0.88(0.48–2.29) |
Marital status | |||
Married | Ref. | Ref. | Ref. |
Divorced | 1.28(0.67–1.87) | 1.34(0.74–1.95) | 1.34(0.74–1.92) |
Single | 1.18(0.75–1.92) | 1.18(0.77–1.89) | 1.19(0.81–1.88) |
Education level | |||
Bachelor degree | Ref. | Ref. | Ref. |
Master degree | 0.98(0.70–1.70) | 0.96(0.75–1.81) | 0.99(0.76–1.83) |
Average income per year (RMB) | |||
<=40,000 | Ref. | Ref. | Ref. |
> 40,000 | 1.05(0.71–1.89) | 1.16(0.87–1.99) | 1.18(0.98–2.22) |
Years of working | |||
< 10 years | Ref. | Ref. | Ref. |
>=10 years | 1.03(0.77–1.92) | 1.20(0.92–1.99) | 1.23(0.99–2.31) |
Shift work | |||
Regular day shift | Ref. | Ref. | Ref. |
Night shift ( < = 1/week) | 2.22(1.55–4.22) | 3.41(1.86–5.32) | 4.33(1.92–5.39) |
Night shift (> 1/week) | 3.55(2.61–4.86) | 4.22(3.28–5.79) | 5.12(3.64–5.99) |
Working hours per week | |||
<=40 h | Ref. | Ref. | Ref. |
> 40 h | 1.53(1.22–1.92) | 1.72(1.44–2.51) | 1.99(1.66–2.89) |
Frontline experience with infected patients | |||
No | Ref. | Ref. | Ref. |
Yes | 3.01(2.01–4.20) | 4.45(3.11–5.77) | 6.11(4.52–8.88) |
History of hypertension | |||
No | Ref. | Ref. | Ref. |
Yes | 1.36(0.76–3.10) | 1.43(0.81–3.44) | 1.61(0.88–3.66) |
History of diabetes | |||
No | Ref. | Ref. | Ref. |
Yes | 1.29(0.65–2.88) | 1.36(0.74–3.01) | 1.42(0.83–3.12) |
History of high cholesterol | |||
No | Ref. | Ref. | Ref. |
Yes | 1.30(0.70–3.05) | 1.34(0.78–3.23) | 1.46(0.84–3.44) |
Discussion
This prospective cohort study presents novel insights into the risk factors associated with anxiety, depression, stress, and their comorbidities among nurses during the COVID-19 pandemic from 2020 to 2022. Our study makes a unique contribution to the literature by examining not only individual psychological conditions but also their co-occurrence, providing a more comprehensive understanding of mental health challenges faced by nurses during public health crises.
Our findings revealed incidence rates of 27.1%, 33.9%, and 39.9% for anxiety, depression, and stress, respectively, among nurses during the study period. These rates were notably lower than those reported in previous cross-sectional studies. For instance, Lai et al.‘s investigation of Chinese healthcare workers from 34 hospitals documented prevalence rates of 44.6% for anxiety, 50.4% for depression, and 71.5% for stress [30]. Similarly, Zhang et al.‘s study of 1563 medical staff reported higher rates of depressive (50.7%), anxiety (44.7%), and stress-related symptoms (73.4%) [31]. The lower rates observed in our study may be attributed to our longitudinal design capturing the evolution of psychological responses beyond the initial acute phase of the pandemic, whereas previous studies primarily focused on the peak crisis period when healthcare workers faced unprecedented challenges and uncertainties.
A key finding was the significant association between frontline experience with COVID-19 patients and adverse mental health outcomes. Nurses who had direct contact with infected patients displayed significantly higher odds ratios for anxiety, stress, and depression. This result corroborated previous research highlighting the psychological toll of working on the frontline during infectious disease outbreaks [30, 32]. The increased risk of infection, high workload, and witnessing patient suffering may contribute to the elevated mental health burden among frontline nurses. Our study underscored the importance of providing psychological support and resources for nurses working in high-risk settings. Psychological resilience and the capacity to mentalize may serve as underlying mechanisms that mitigate the adverse mental health outcomes associated with frontline work [33, 34].
We found that night shift work increased the risks of mental health outcomes. Nurses working night shifts, particularly those working more than one night shift per week, exhibited higher odds ratios for anxiety, stress, and depression compared to those on regular day shifts. This result was consistent with previous research that has shown the negative impact of night shift work on mental health [35, 36]. The disruption of circadian rhythms, sleep disturbances, and social isolation associated with night shift work may contribute to the increased risk for mental health issues [37]. In addition, our study revealed a significant association between longer working hours and psychological disorders. Nurses working more than 40 h per week had higher odds ratios for anxiety, stress, and depression. This finding aligned with previous research demonstrating the relationship between long working hours and psychological conditions [38]. Prolonged exposure to work-related stressors, reduced recovery time, and work-life imbalance may contribute to the increased mental health burden among nurses working longer hours. Our findings suggested the need for targeted interventions to mitigate the negative consequences of night shift work and longer working hours, highlighted the need for workload management and the promotion of work-life balance to support nurses’ mental health.
Furthermore, our study found that a higher healthy eating index score was associated with lower odds ratios for anxiety, stress, and depression. This result was in line with previous research [39, 40]. A healthy diet rich in fruits, vegetables, whole grains, and lean proteins may provide essential nutrients for optimal brain function and mental health. Conversely, we observed that higher BMI was associated with an elevated risk of all psychological conditions. This elevated risk may be explained by physiological pathways. Higher BMI is associated with chronic low-grade inflammation and hormonal imbalances, which may heighten vulnerability to stress and anxiety [41]. Additionally, individuals with higher BMI faced increased susceptibility to severe illness, which may have compounded their psychological distress due to heightened fear of infection and its potential consequences [42].
There are several limitations. Firstly, the mental health outcomes and potential risk factors were assessed using self-report questionnaires, which may introduce reporting bias due to social desirability or recall issues. Secondly, our study focused on nurses in Zunyi City, China, which may limit the generalizability of our findings to other healthcare professionals and geographical locations. Future research should extend the investigation to include diverse healthcare worker populations and settings to better understand the mental health burden experienced by healthcare professionals during COVID-19. Thirdly, we did not assess the impact of potential confounders, such as social support and coping strategies, on mental health outcomes; thus, this may lead to residual confounding.
In conclusion, our study contributes to the understanding of risk factors for anxiety, stress, depression, and their comorbidities among nurses. Our findings emphasize the need for targeted interventions to address shift work, frontline experience, long working hours, and healthy eating habits among nurses. Future research should focus on the causal pathways underlying these associations, explore potential moderators and mediators, and evaluate the effectiveness of interventions targeting these risk factors to improve mental health outcomes among nurses.
Acknowledgements
None.
Declarations
Ethics approval and consent to participate
Study procedures were reviewed and approved by the institutional review board of the Second Affiliated Hospital of Zunyi Medical University, and all participants provided informed consent before study participation. All methods of the study were performed in accordance with the Declaration of Helsinki, relevant guidelines and regulations.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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