Changes in primary healthcare workforce burnout during and after the termination of COVID-19 emergency response: a one and half year observational study

doi:10.21203/rs.3.rs-4101043/v1

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Changes in primary healthcare workforce burnout during and after the termination of COVID-19 emergency response: a one and half year observational study

https://doi.org/10.21203/rs.3.rs-4101043/v1

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Background Primary healthcare workforce (PHCW) should be suffered from less burnout after the termination of the COVID-19 response. The current study compared the changes in the three dimensions of burnout in PHCW during and after the response.

Methods Two convenience-sampling, online, cross-sectional questionnaire studies were conducted in local PHCW. Studies were administered in April, 2022 and eight months after the termination. Burnout was measured by the Chinese version of 15-item Maslach Burnout Inventory-General Survey (MBI-GS), which assesses three dimensions: emotional exhaustion (EE), depersonalization (DP) and reduced personal accomplishment (reduced PA). The primary outcome was the prevalence of its three dimensions. Data on demographics, work environment, health conditions and outlets for stress reduction were collected. We compared burnout and associated factors between the study periods by using Student’s t-, chi-, or Mann‒Whitney tests. The associations between factors and burnout was identified by a logistic regression model.

Results In total, 162 and 200 participants completed the questionnairesduring and after the response. No significant differences in demographics, including age, gender, education attainment, work experience or seniority level were observed. The prevalence of burnout-free status was similar (9.9% vs. 12.5%, P=0.434) between thetwo periods. Severe burnout decreasedfrom 45.7% to zero%, and moderate burnout nearly doubled after the response. The prevalence of EE decreased the most, by 55.0%, followed by that of DP, which decreased by38.4% (all P<0.001); however, there wasno difference in the prevalence of reduced PA (77.2% vs. 74.5%, P=0.557). Logistic regression showed thatpromotion and alcohol consumption decreased the risk of EE. Considering leaving the job increases the risk of DP. A lowerself-evaluated health score and more distress were associated with EE and DP. Exercise decreased the risk of reduced PA.

Conclusions Inconsistent with hypothesis, we found that severe burnout decreased but moderate burnout increased in PHCWs after the response. EE and DP decreased more, but reduced PA had no change. Incentives, improved self-evaluated health conditions, alcohol consumption and exercise ameliorate burnout. Healthcare policy makers must consider multiple effective ways to mitigate burnout in the post-epidemic era.

Burnout

associated factors

primary healthcare workforce

termination of COVID-19 response

Burnout is a work-related syndrome involving emotional exhaustion (EE), depersonalization (DP) and a sense of reduced personal accomplishment (PA). EE is defined as feeling overworked, overextended and incapable of showing compassion, leading to significant mental distress. DP includes feelings of treating patients as objects rather than as human beings and becoming more callous toward patients. PA encompasses feelings of ineffectiveness in helping patients with their problems and a lack of value of the results of work-related activities such as patient care or professional achievements[1]. Burnout thus has negative consequences for patient care, the physician workforce, and the health care system [1, 2]. Age, gender, marital status, work environment, interpersonal and professional conflicts, emotional distress, low social support, having less education, having an intermediate professional title, having a permanent contract or working more hours were related to burnout[3, 4]. Structural or organizational intervention-workload or schedule-rotation, stress management training programs, group face-to-face delivery, and teamwork/transitions are effective at improving burnout [5].

A strategy priority of China’s primary healthcare workforce (PHCW) is to provide citizens with universal and equitable access to high-quality health care for chronic diseases and infectious diseases [6]. Half of PHCW in China suffered from burnout before the COVID-19 response [7] due to workforce shortages and increased workloads. Guangzhou, for example, the largest port city in southern China, has 18 million permanent citizens, and only 41 thousand (41,301) qualified PHCW were in service by the end of 2021. The prevalence of burnout in PHCW in China is close to that in the US [4] but higher than that in low- and middle-income countries [8].

The COVID-19 pandemic has resulted in high levels of stress on healthcare workers and has caused great burnout globally [9]. The workload from providing vaccination services, conducting epidemiological investigations and massive nucleic acid screenings among the whole population, imposing strict quarantine measures on both local close contacts and international arrivals[10] was so heavy that most PHCW had to work for more than 60 hours per week during the response[11]. As the first HCW on the frontline to fight for the first COVID-19 Omicron wave in April, 2022, Guangzhou’s HCW must endure tremendous stress and face more burnout. After 7 months of consecutive implementation of the dynamic zero policy, on November 30, 2022, the local government declared that it would implement the accommodated control policy. Thirty-nine days later, on January 8th, 2023, the national response was terminated. All PHCW returned their routine work on providing generalist care services and implementing the National Basic Public Health Services Program (e.g., chronic disease management and vaccination services). Given that normal primary healthcare work was usually suspended because of the response, the stress related to accumulative workload and working in primary healthcare over the course of a 3-year pandemic may have residual, long-term effects. To better understand the effect of the COVID-19 response on burnout, the work environment, health conditions and behaviors in PHCW, we conducted two cross-sectional studies during the COVID-19 epidemic response and after the termination of the response. It is hypothesized that the prevalence of burnout decreases after the termination of response.

An anonymous e-questionnaire, including social demographic variables, self-evaluated health conditions, and 15 items from the Maslach Burnout Inventory scale (MBI-GS, Chinese version) [7], was administered to PHCW from 60 primary healthcare centers. The e-questionnaire link was to the working group of WeChat during the third year of COVID-19 response (April 2022) and eight months after the termination of the response (September 2023). The recruitment strategy was to distribute the study widely to maximize the number and diversity of respondents. This involved convenience sampling by invitation to complete the anonymous survey. PHCW who were willing to participate in the research completed the e-questionnaires without payment. Informed consent was obtained during data collection. This research was approved by the Guangzhou Centers for Disease Control and Prevention Ethics Committee. The survey consisted of three parts as follows.

Part 1 investigated demographic information, including age, gender, education attainment, seniority level, practicing years and profession.

Part 2 investigated the PHCW’s work status and health status (physical, health behaviors and psychological conditions). This included changes in income, promotion, and consideration of leaving the job, with each question asking respondents to indicate ‘Yes’ or ‘No’ response and working hours. A question on physical health was presented as ‘How do you feel about your health, including your physical and mental health?’, which was tailored on a Likert scale (0–10) from ‘0 is the worst, 10 is the finest’. A question on distress is ‘Does work pressure bring you distress during the last week (yes/no)?’ A question on sleep deprivation is ‘How about your sleep quality during the past week (mild, moderate, severe)?

In part 3, burnout was measured using the Chinese version of the MBI-GS, which consists of 15 questions measuring three distinct dimensions of burnout: emotional exhaustion (5 questions), depersonalization (4 questions) and reduced personal accomplishment (6 questions). Each dimension is scored individually on a Likert scale (0–6), with each question asking respondents to describe the frequency (from ‘never’ to ‘every day’) with which they experience the statement. A reduced PA was reversely scored. The burnout scores for each category were calculated using the MBI tool and are reported as the mean and standard deviation as recommended[7]. Higher EE and DP scores and lower PA scores represent greater burnout. The following equation was adopted to calculate the weighted sum score of burnout:

Burnout= (0.4 * EE + 0.3 * DP + 0.3 * reduced PA)/5

The cutoff points were used to classify the participants into the three following groups based on their score: group 1, no burnout (0–1.49); group 2, moderate burnout (1.50–3.49); and group 3, severe burnout (3.5–6.0). The criteria used to indicate the presence of EE, DP and reduced PA were the average scores of the items for subcomponents ≥ 3.2, > 2.2 and ≤ 4.0, respectively.

Data analysis

The results were exported and analyzed using IBM SPSS (version 26). Descriptive statistical analyses were reported using the mean (SD) for normally distributed continuous variables or using the median (1st, 3rd quartile) when a normal distribution was not met. Categorical variables are presented as frequencies (relative frequencies of our valid data). Binary logistic regression with multiple variables was used to estimate the associations between different demographic characteristics of participants and various risk factors and burnout. All tests were two-sided, and P was considered significant if it was ≤ 0.05.

A total of 162 and 200 PHCW from primary healthcare centers participated in this survey (Table 1).

The respondents were more likely to be female (71.3%), younger (mean age = 35.0 ± 6.9), and highly educated with a bachelor’s degree (82.9%), to have middle seniority (49.7%) and to have nearly 12.5 years of working experience. No significant difference in demographics was observed between the two groups during and after the response. The rate of salary increase and promotion increased, but considering leaving the job, self-reported distress and severe sleep deprivation decreased significantly after the response (all P < 0.001). The number of working hours per week decreased from 45 to 40, and self-evaluations of health increased from 6 to 7 (both P < 0.001).

Table 1

Demographics, work status, self-reported health condition of participants during and after the response
	During the response	After the termination of response	P
Characteristic	n (%)	n (%)	P
Total	162(100)	200(100)
Gender
Males	52(32.1)	52(26.0)	0.202
Education
>Bachelors	137(83.6)	163(80.5)	0.708
Level of seniority
Junior	75(46.3)	86(43.0)	0.723
Middle	79(48.8)	101(50.5)
Senior	8(4.9)	13(6.5)
Work status
Raise in salary	13(8.0)	55(27.5)	< 0.001
Promotion	7(4.3)	38(19.0)	< 0.001
Consider leaving the job	83(51.2)	55(27.5)	< 0.001
Outlets for stress reduction
Smoking	6(3.7)	5(2.5)	0.507
Alcohol consumption	10(6.2)	6(3.0)	0.144
Exercise	61(37.7)	92(46.0)	0.110
High energy food consumption	50(30.9)	49(24.5)	0.177
Social media addiction	88(54.3)	118(59.0)	0.371
Confiding in others	104(64.2)	120(60.0)	0.414
Online Shopping	58(35.8)	60(30.0)	0.242
Distress	124(76.5)	97(48.5)	< 0.001
Sleep Deprivation
Mild	17(10.5)	39(19.5)	< 0.001
Moderate	87(53.7)	124(62.0)
Severe	58(35.8)	37(18.5)
	Mean (SD)	Mean (SD)
Age	36.0(5.3)	35.6(6.9)	0.601
Years of cumulative experience	12.6(5.8)	12.4(7.3)	0.806
	Median (P25, P75)	Median (P25, P75)
Working hours weekly	45(40,53)	40(38,44)	< 0.001
Self-evaluation on health	6(5,7)	7(6,8)	< 0.001

The prevalence of burnout in the two groups is shown in Table 2. The overall prevalence of burnout during the survey was 90.1%, with moderate and severe burnout accounting for 44.4% and 45.7%, respectively. No individual experienced severe burnout, but 87.5% experienced moderate burnout after the termination of the study. No significant difference was observed between the two groups in the overall incidence of burnout (χ² = 0.613, P = 0.434). The prevalence of EE was the highest in three dimensions during the COVID-19 response, while reduced PA was the highest after the response. Both EE and DP decreased significantly, but reduced PA had no change.

Table 2

Comparison of three dimensions of burnout during and after the termination of CPVID-19 response
Burnout	During the response	After the termination of response	P
Burnout	n (%)	n (%)	P
No burnout	16(9.9)	25(12.5)	< 0.001
Moderate	72(44.4)	175(87.5)
Severe	74(45.7)	0(0.0)
Burnout domains
EE	145(89.5)	69(34.5)	< 0.001
DP	123(75.9)	75(37.5)	< 0.001
Reduced PA	125(77.2)	149(74.5)	0.557

Table 3 presents the variables associated with the prevalence of the three dimensions of burnout. After adjusting for confounding factors, logistic regression models indicated that individuals who had no job promotion (OR = 6.06, 95% CI: 2.07–17.76) and who felt more distress (OR = 7.72, 95% CI: 3.43–17.38) faced a greater risk of having EE. Individuals who had higher self-evaluated health scores (OR = 0.63, 95% CI: 0.49–0.80) and who consumed alcohol (OR = 0.32, 95% CI: 0.13–0.79) had a lower risk of having EE. Those who felt more distress (OR = 3.53, 95% CI: 1.86–6.70) faced a greater risk of having DP, but those who did not consider leaving their job (OR = 0.25, 95% CI: 0.13–0.50) and had a greater self-evaluated health score (OR = 0.69, 95% CI: 0.56–0.84) faced a lower risk of having DP. Exercise was associated with a lower incidence of reduced PA (OR = 0.48, 95% CI: 0.26–0.90). Compared to during the response, both the risk of having EE and DP decreased significantly after the response (EE, OR: 0.024, 95% CI: 0.007 to 0.092; DP, OR: 0.117, 95% CI: 0.043, 0.317). No differences in reduced PA were detected between during and after the response.

Table 3

Effects of each variable on three dimensions of burnout
Variables	EE		DP		Reduced PA
Variables	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)
Age	0.372	0.96(0.87, 1.05)	0.981	1.00(0.92, 1.08)	0.075	0.94(0.87, 1.01)
Gender (male)	0.951	1.03(0.44, 2.42)	0.573	0.83(0.42, 1.61)	0.507	0.82(0.45, 1.49)
Education (< Bachelors)	0.216	0.54(0.2, 1.43)	0.775	1.12(0.51, 2.49)	0.993	1.00(0.47, 2.13)
Level of seniority (Junior)
Middle	0.928	1.04(0.43, 2.54)	0.390	0.74(0.36, 1.48)	0.330	0.72(0.37, 1.39)
Senior	0.117	4.34(0.69, 27.13)	0.912	0.92(0.22, 3.90)	0.349	2.10(0.45, 9.86)
Years of cumulative experience	0.491	1.03(0.94, 1.13)	0.915	1.00(0.93, 1.09)	0.104	1.06(0.99, 1.14)
Working hours/week	0.328	1.03(0.97, 1.09)	0.352	0.98(0.95, 1.02)	0.320	0.98(0.95, 1.02)
Raise in salary (None)	0.960	0.98(0.42, 2.31)	0.569	0.81(0.38, 1.70)	0.930	0.97(0.48, 1.97)
Promotion (Yes)	0.001	6.06(2.07, 17.76)	0.476	1.31(0.62, 2.76)	0.572	0.83(0.43, 1.60)
Consider leaving the job (No)	0.352	1.50(0.64, 3.50)	< 0.001	3.94(1.99, 7.81)	0.432	0.76(0.39, 1.50)
Self-evaluation on health (Score)	< 0.001	0.63(0.49, 0.80)	< 0.001	0.69(0.56, 0.84)	0.703	0.97(0.82, 1.15)
Outlets for stress reduction
Smoking (No)	0.119	4.34(0.68, 27.55)	0.154	2.96(0.67, 13.2)	0.220	2.40(0.59, 9.70)
Alcohol consumption (No)	0.013	0.32(0.13, 0.79)	0.881	0.95(0.46, 1.94)	0.763	1.11(0.56, 2.23)
Exercise (No)	0.597	1.24(0.56, 2.71)	0.180	1.54(0.82, 2.87)	0.022	0.48(0.26, 0.90)
High energy food consumption (No)	0.325	0.68(0.32, 1.47)	0.481	0.8(0.43, 1.49)	0.929	1.03(0.58, 1.81)
Social media addiction (No)	0.345	1.42(0.69, 2.95)	0.576	1.18(0.66, 2.13)	0.132	1.52(0.88, 2.61)
Confiding in others (No)	0.845	1.13(0.32, 3.97)	0.089	2.27(0.88, 5.86)	0.380	1.45(0.63, 3.33)
Online Shopping (No)	0.369	0.63(0.23, 1.73)	0.673	0.85(0.40, 1.82)	0.072	0.49(0.23, 1.07)
Distress (No)	< 0.001	7.72(3.43, 17.38)	< 0.001	3.53(1.86, 6.70)	0.069	1.87(0.95, 3.68)
Sleep Deprivation (Mild)
Moderate	0.221	1.91(0.68, 5.39)	0.923	0.96(0.40, 2.28)	0.187	1.66(0.78, 3.50)
Severe	0.331	1.99(0.50, 7.92)	0.262	0.53(0.17, 1.61)	0.708	1.21(0.45, 3.26)
Study period (During the response)	< 0.001	0.02(0.01, 0.09)	< 0.001	0.12(0.04, 0.32)	0.864	0.93(0.39, 2.21)

Our findings show that the prevalence of severe burnout during the response was 45.7%, which is close to that reported in previous studies that collected data during the pandemic-31.5% in community pharmacists[12], 47.4% in family medicine residents[13] and 50.0% in primary care nurses[14]. Although it decreased to zero%, moderate burnout increased significantly after the response. Healthcare workers were at risk of developing both short- and long-term mental health problems during severe acute respiratory syndrome (SARS) outbreaks[15]. Five to 20% of health care workers still experienced adverse psychological impacts two years after the start of the COVID-19 pandemic[16]. Hence, it is reasonable that burnout is improved since risk factors are reduced, e.g., adequate protective wearing, fewer shifts, sufficient personal rest time, etc. [17, 18]. As the PHCW reported, their working hours per week decreased from 45 to 40, and their feeling of distress decreased from 76.5 to 48.5%; however, they complained that they were “tired” of so much paperwork and more performance examination rather than matched incentives after the termination of the response. It has been found that professional dissonance caused by less “doctor” work and more “office” work and undervalued by local institutions and the health system was a factor in increasing burnout in primary care practitioners[19]. This may be the main reason why a high prevalence of moderate burnout was observed.

Logistic regression revealed that the prevalence of both EE and DP decreased (EE: 55.0%, DP: 38.4%) due to the termination of the response. Consistent with previous studies[20, 21], we found that participants who had a job promotion and high self-evaluated health score face a lower risk of having EE, who had no consideration of leaving the job and who have a high self-evaluated health condition face a lower risk of having DP. Since 2021, China’s National Health Commission has launched a series of policies for improving the working conditions, wages, and benefits of medical workers[22]. In the present study, PHCW reported a significant increase in salary, promotion and less consideration in leaving the job after the termination of the response. However, the prevalence of reduced PA did not differ during or after the response, which was greater than that in a previous review of 60% of primary health-care professionals in low- and middle-income countries[8]. This result may indicate that opportunities for further education, professional development and career progression for PHCW are extremely limited[6].

Traditionally, health-related behaviors such as exercise, alcohol and tobacco abuse, and overeating are outlets for stress reduction. Alcohol consumption was negatively associated with EE, and exercise was negatively associated with reduced PA in the present study. Increased alcohol use is associated with work-related stress caused by COVID-19 among healthcare workers (HCW) [23] who most commonly use or abuse alcohol, prescription drugs and nonprescription drugs (opiates, illicit drugs) to reduce stress [24]. Exercise is protective against burnout and its three dimensions[25, 26]. Alcohol consumption decreased but exercise increased slightly, indicating that unhealthy behavior for stress reduction was not maintained for a long time. In fact, healthcare professionals’ health-related behaviors are better than those of the general population[27].

Our study highlights the effectiveness of post-epidemic healthcare policies on burnout in PHCW and shows that job promotion, incentives, improved self-evaluated health conditions, alcohol consumption and exercise play crucial roles in reducing the risk of burnout among PHCW. Furthermore, the findings emphasize that a healthy work-life balance can help mitigate the negative effects of stress and burnout. Managers and healthcare centers should be proactive in identifying and addressing the risk factors associated with emotional exhaustion and depression, such as high distress and alcohol consumption. However, it is important to note that our study has some limitations. The cross-sectional design prevents us from drawing conclusions about the causal relationship between the factors studied and the outcomes. Self-selection bias is possible if respondents are motivated to complete the survey due to their high levels of mental and physical stress and burnout. Moreover, the sample size was limited. Future studies should aim to replicate and expand upon these findings by considering longitudinal designs to better understand the complex relationships between PHCW well-being and the factors that contribute to burnout.

PHCW

Primary healthcare workforce

MBI-GS

Maslach Burnout Inventory-General Survey

emotional exhaustion

depersonalization

reduced PA

reduced personal accomplishment

HCW

healthcare workers

Odd Ratio.

Ethics approval and consent to participate

Informed consent was obtained from the PHCW during data collection in accordance with the Declaration of Helsinki. This research was approved by the Guangzhou Centers for Disease Control and Prevention

Ethics Committee.

Consent for publication

All the authors provided their consent for publication.

Availability of data and materials

The data will be available upon reasonable request.

Competing interests

All the authors declare that they have no financial or personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Funding

This research received funding from the Medical Science and Technology Foundation of Guangdong Province (CN) (A2023215) and the Guangzhou Municipal Science and Technology Project (2023A03J0462).

Authors' contributions

Conceptualization, WXJ and ZZB; methodology, RHH, XCJ and FXY; organization, data collection and quality control, RHH; formal analysis, WXJ; writing—original draft preparation, WXJ; writing—review and editing,ZZB and R O'D. All authors have read and agreed to the published version of the manuscript. WXJ and RHH contributed equally to the article and share the first authorship.

Acknowledgements

Not applicable.

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Changes in primary healthcare workforce burnout during and after the termination of COVID-19 emergency response: a one and half year observational study

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