Burnout has been defined by Maslach et al1 as “a prolonged response to chronic emotional and interpersonal stressors on the job, and is characterized by the 3 dimensions—emotional exhaustion, depersonalization, and reduced personal accomplishment.” Burnout does not only adversely affect the well-being of physicians and their families, but that of patients and health care organizations, and it results in suboptimal patient care.2,3
The Chinese economy has enjoyed a rapid growth in the past 3 decades. At the same time, the Chinese health care system has also undergone tremendous changes to meet the needs of 19% of the world population. However, the medical resources, including human resources, are unevenly distributed creating the potential for substantial work-related stress. Physician shortage is most conspicuous in the specialties of pediatrics, emergency medicine, and anesthesiology.4 Based on the annual statistics released by the National Health and Family Planning Commission of the People’s Republic of China, the number of inpatient surgeries in China has doubled in the past 10 years, while the Chinese anesthesiologist population has increased much more slowly. According to the American and European standards of 2.4 anesthesia providers per 10,000 people,5,6 China should have about 300,000 anesthesia providers. At present, China only has 53,000 anesthesiologists and anesthesia residents, 6700 anesthesiologist assistants and few nonphysician anesthesia providers, thus making overtime work a common situation.7 Therefore, our study aimed to investigate the current professional status of anesthesiologists and anesthesia residents in the populous Beijing–Tianjin–Hebei region. The first objective was to determine the incidence of burnout in anesthesiologists and anesthesia residents in this region using standardized survey tools. The second objective was to measure the level of job satisfaction and to assess factors associated with burnout.
This article adheres to the applicable Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines. The requirement for written informed consent was waived by the institutional review board of Peking Union Medical College Hospital.
Study Design and Participants
The study focused on all hospitals, all anesthesiologists, and all anesthesia residents in Beijing, Tianjin, and Hebei. We used 2 separate sets of questionnaires. Questionnaire A was designed to be completed by the anesthesiology department chief or a designated person at every hospital surveyed. The 18 items in Questionnaire A focused on hospital characteristics such as hospital category and staff number.
Questionnaire B comprised 59 items divided into 4 parts, and was distributed to every anesthesiologist and every anesthesia resident that the study included. Part 1 focused on the demographic characteristics including gender, age, title, working hours per week, average caseload per day, night shift frequency, income, and marital status. Part 2 was the validated Chinese version of the 20-item short form of Minnesota Satisfaction Questionnaire (MSQ),8 which is a commonly used measurement tool for job satisfaction.9,10 A 5-point Likert-type scale was used, ranging from 1 (very dissatisfied) to 5 (very satisfied). Among the 20 items, 12 items belong to the dimension of intrinsic job satisfaction, which concerns ethics, ability utilization, autonomy, stability, achievement, etc. The other 8 items focus on extrinsic job satisfaction that refers to work environment, organizational policies, income, promotion, etc. The total score of all 20 items yielded overall job satisfaction that reflects the whole aspect of job satisfaction. The total score is divided into several groups: “very dissatisfied” or “dissatisfied” (total score is 40 and below), “dissatisfied to moderately dissatisfied” (from 41 to 59), “moderate” (60), “moderate to not fully satisfied” (from 61 to 79), and “satisfied” or “very satisfied” (80 and above). The higher the total score, the more satisfied people feel with their job. Part 3 was designed to evaluate burnout with the validated Chinese version of the Maslach Burnout Inventory-Human Service Survey (MBI-HSS)11 and a 7-point Likert-type scale was used. This is the “gold standard” measurement tool for burnout, and the 22-item assessment evaluates and scores the 3 dimensions of burnout: emotional exhaustion, depersonalization, and reduced personal accomplishment. Scores are then categorized as low level (emotional exhaustion = 0–16, depersonalization = 0–6, personal accomplishment ≥39), moderate level (emotional exhaustion = 17–26, depersonalization = 7–12, personal accomplishment = 32–38), or high level (emotional exhaustion ≥27, depersonalization ≥13, personal accomplishment = 0–31). In common with other previous burnout studies that focused on the presence of a high level of emotional exhaustion or depersonalization as the foundation of burnout in physicians,12,13 we defined burnout as a high level on the emotional exhaustion or a high level on the depersonalization subscale in our study.2 Part 4 included questions such as sleep hours, sleep quality, the frequency of perceived challenging cases to the participant, durations of preoperative discussion with patients, and whether they give explanations or consolation to conscious patients during anesthesia procedures.
The electronic version of the questionnaires was built on the Diaochapai platform (http://www.diaochapai.com) and was distributed by Beijing–Tianjin–Hebei Anesthesia Integration Alliance via WeChat, which is a widely used mobile social platform that attracts a broad range of users in China. As of September 2015, WeChat had more than a billion created accounts and 650 million active users. Throughout the nation, the coverage of WeChat has more than 90% of smartphone users. To ensure getting the maximum amount of data, most questions were designated as required questions. The response rate of each hospital was reported to the department chief every week, and they were reminded to redistribute questionnaire B to their colleagues if the response rate was <50%. It took 3 months to distribute questionnaire B in the Beijing–Tianjin–Hebei region: 1 month per region. Each mobile phone was allowed to submit questionnaire B only once to prevent duplicates.
Standard descriptive statistics were used to summarize the data. Basic characteristics are presented as the percentage and 95% confidence interval (CI) in each subgroup for categorical variables and mean ± standard deviation for continuous variables. The difference between burnout and nonburnout participants was evaluated using t test (for continuous variables) or χ2 test (for categorical variables). All statistical tests were 2 sided, with a type I error level of 0.05. Spearman correlation was performed to assess the relationship between burnout and job satisfaction as this analysis method is a nonparametric measure of rank correlation for monotonic relationships (whether linear or not). Multivariable logistic regression analysis was performed to identify the risk factors for burnout. We selected the potential risk factors for the multiple regression based on the potential variables in previous studies, the actual situation in China and the result of single factor analysis. The variables we selected for the multivariable logistic regression were age, hospital category, title, working hours per week, caseload per day, taking night shifts or not, income per month, frequency of perceived challenging cases, sleep quality, and sleep hours. To establish the logistic model, we used a forward stepwise likelihood ratio elimination method and the criteria for entry and removal from the model at each step were set at 0.01 and 0.05, respectively. Odds ratios and 95% CIs were calculated for the variables in the final equation. Most of the analyses were completed using commercially available statistical software SAS 9.4 (SAS Institute Inc, Cary, NC). For sample size estimation, a pilot study of 198 cases before the survey indicated a burnout rate of 51%. We assumed a 5% error was acceptable and then set the confidence level at 95%. By considering the response rate, the sample size was 764. Fortunately, taking advantage of efficiency and convenience of mobile phones and the rapid dissemination power of mobile-based WeChat, more than 4000 anesthesiologists participated in this survey.
Questionnaires A and B were distributed and collected from June 9, 2015, to August 6, 2015. Of 287 hospitals and 4111 anesthesiologists and anesthesia residents, 211 hospitals and 2873 individuals completed the survey (response rate 74% and 70%, respectively). The descriptive statistics are shown in Table 1.
The Figure presents the results of the 3 burnout dimensions for all the 2873 respondents. The rate of both high levels of emotional exhaustion and depersonalization was 38% (95% CI, 36%–40%), while the rate of both high levels of emotional exhaustion and reduced personal accomplishment was 40% (95% CI, 38%–42%). As many as 29% (95% CI, 27%–31%) of the respondents presented a high level of emotional exhaustion, depersonalization, and reduced personal accomplishment simultaneously. If burnout is defined as a high level on the emotional exhaustion or depersonalization subscales,14,15 the percentage of burnout in the 2873 anesthesiologists was 69% (95% CI, 67%–71%). The percentages of burnout anesthesiologists in the 3 surveyed provinces of Beijing, Tianjin, and Hebei were 69%, 70%, and 68%, respectively, which were not significantly different (P = .674).
Comparison of respondent characteristics among anesthesiologists who met the criteria for burnout compared with nonburnout participants is presented in Table 2. Age, title, hospital category, working hours per week, caseload per day, frequency of perceived challenging cases, taking night shift, income, MSQ score, sleep hours, and sleep quality were significantly different between these 2 groups.
The correlation between job satisfaction assessed by MSQ and burnout dimensions assessed by MBI is presented in Table 3. The Spearman correlation analysis was used to assess the relationship between job satisfaction and burnout dimensions because they do not follow a bivariate normal distribution. The Spearman correlation coefficient of 0.1–0.3 was regarded as weak correlation, 0.3–0.5 as moderate correlation, and >0.5 as strong correlation. P value of <.05 was regarded as statistically significant. Therefore, a strong correlation exists between low job satisfaction and high emotional exhaustion, and moderate correlations exist between low job satisfaction and the other 2 dimensions of burnout. When analyzing the risk factors for burnout, job satisfaction was not considered due to its strong correlation with burnout. Based on the potential variables in previous studies, the actual situation in China and the result of single factor analysis, the variables we selected for the multivariable logistic regression were age, hospital category, title, working hours per week, caseload per day, taking night shifts or not, income per month, frequency of perceived challenging cases, sleep quality, and sleep hours. The final multivariable logistic model of burnout risk factors included 7 independent variables, namely age, hospital category, working hours per week, caseload per day, income, frequency of perceived challenging cases, and sleep quality, as shown in Table 4.
We also analyzed differences in physician–patient communication among the different burnout levels. The depersonalization dimension showed the greatest discrepancy, as presented in Table 5. Anesthesiologists with a high level of depersonalization tended to have shorter preoperative conversations with patients, speak less about pain with them, provide less anesthesia procedure-related information, and to have less empathy with them. A high level of emotional exhaustion or reduced personal accomplishment affected the length of preoperative conversation and the empathy given to nervous patients, while the pain and the procedure information given by anesthesiologists was not influenced.
We obtained burnout and job satisfaction survey responses from 2873 anesthesiologists working at 211 hospitals in the Beijing–Tianjin–Hebei region of China. Sixty-nine percent met the burnout criteria. Burnout was associated with an age range of 30–39 years, working at a tertiary hospital, long working hours per week, greater caseload per day, low income, high patient acuity, and poor sleep quality. To our knowledge, this is so far the largest study of job satisfaction and burnout among Chinese anesthesiologists. In March 2014, the New Youth Anesthesia Forum conducted a survey entitled “Large-Scale Online Survey of Professional Status Among Chinese Anesthesiologists,” and enrolled 12,788 participants.16 About half of the participants were residents or interns, and the other half were attendings or professors. The study found that 78% of the participants claimed to be “very tired” or “extremely tired,” which was quite alarming. However, no validated questionnaires were used.
The medical literature is inconsistent in the reported incidence of burnout due to the use of different threshold values and whether a single subscale or particular combination is used as the criterion.11 Shanafelt et al17 considered physicians with high scores on either emotional exhaustion or depersonalization subscales as having at least 1 manifestation of professional burnout, and using this approach, they obtained a 46% burnout rate among US physicians. Such an approach in our study results in 69% (95% CI, 67%–71%) of Beijing–Tianjin–Hebei anesthesiologists meeting this definition of burnout. A large survey of family doctors in 12 European countries found that 43% of respondents scored high level for emotional exhaustion, 35% for depersonalization, 32% for reduced personal accomplishment, and 12% having high level scores in all 3 dimensions.18 The corresponding percentages in Beijing–Tianjin–Hebei anesthesiologists were 57% (95% CI, 55%–59%), 49% (95% CI, 47%–51%), 57% (95% CI, 55%–58%), and 29% (95% CI, 27%–31%), respectively. There is a common perception that intensivists are particularly exposed to high stress and are therefore prone to burnout.19 In a systematic review article, the reported prevalence rate of burnout among intensive care unit doctors and nurses, measured with MBI, varied from 14%, after a preventive intervention, to 70% when burnout was defined as a high score on only 1 subscale.20 When we adjusted the criteria for burnout according to this study, the burnout rate among Beijing–Tianjin–Hebei anesthesiologists would reach 79% (95% CI, 78%–81%), which is incredibly high.
Based on the multivariable logistic analysis, 7 independent variables were identified as risk factors for burnout, namely age, hospital category, working hours per week, caseload per day, frequency of perceived challenging cases, income, and sleep quality. The group most vulnerable to burnout was those 30–39 years of age and 60% of them are attending doctors. (In China, “attending doctor” only refers to junior doctors who have completed residency and have final responsibility for patient care. Young attending doctors are all required to get more clinical experience and to accomplish some research study for the promotion to associate professor or full professor.) The burnout rate of Chinese anesthesia residents was 69% while the rate of Chinese anesthesia attending doctors was 73%. This finding is in contrast to high-income countries where the highest rate of burnout has been found in residents.12,21 In China, the attending doctors are stressed by long working hours, medical responsibility, research work required for promotion, and being the main source of family income. The burnout rate of Chinese anesthesia residents was also higher than the West.21,22 Working at a tertiary hospital in China significantly increased burnout risk as expected, as good medical resources are concentrated in tertiary hospitals and appropriately tiered medical services have not yet been successfully established. Patients therefore flock to tertiary hospitals and physicians do not have the right of refusal.
Long working hours, heavy workload, and fast work pace are all factors associated with burnout.23 Reported working hours per week may not seem extraordinarily high. The RAND Corporation study of US anesthesia workforce trends stated that anesthesiologists worked an average of 63 hours per week, of which 49 hours are clinical.24 In our study, only clinical work was counted so the actual working hours were likely underestimated. It is interesting that although working hours per week appear comparable in Chinese and US anesthesiologists, the burnout rate in US anesthesiologists is much less (69% vs 46%),17 indicating that feasible approaches to alleviate the burnout problem in Chinese anesthesiologists may lie in other aspects. Half of our respondents claimed to have perceived challenging cases more than once per week, which results in high patient acuity and burnout.
Relatively low income is another important factor. According to Medscape Physician Compensation Report 2015, the average compensation for American specialists was $284,000 per year, and for anesthesiologists, it was $358,000. Physicians are among the best-paid professionals in many high-income countries. However, this is very different in China. Most anesthesiologists in the Beijing–Tianjin–Hebei region earned <10,000 CNY per month, which is approximately an annual income of no more than $18,000, just the same as the average local income of all wage earners in this region. Although the Chinese gross domestic product soared in the past 2 decades, the price of medical services is strictly controlled by the government, and has not been raised for more than 15 years. For instance, in Beijing, the charge for a general anesthesia procedure <6 hours is a flat rate of 150 CNY ($22) per case. Hopefully in the future, the tariff for anesthesia will rise and so will the income of anesthesiologists.
The average MSQ total score of surveyed anesthesiologists was 65.3 ± 11.5, indicating a moderate to not fully satisfied level of job satisfaction. This total score is lower than Turkish doctors (total score of 70.1),25 New England nurses (total score of 77.6),26 and community health workers in Liaoning, China (total score of 68.2).10 In another Chinese study performed in 6 large general hospitals in Liaoning province, the total job satisfaction score of doctors was 65.9 ± 12.8.27 Our study confirmed that low level of job satisfaction is strongly related to burnout. The factors strongly associated with physician satisfaction are all extrinsic factors such as work demands, work control, support of colleagues, and income,28 which are all potentially modifiable. Although the causal relationship between low job satisfaction and high burnout have not been confirmed, several studies indicated that for the same degree of stress, high levels of job satisfaction could offer some protection against burnout.29
The impact of a low level of physician satisfaction and a high level of burnout includes, but is not limited to, impairment of judgment, late and inadequate responses to changes in the clinical situation, less patient satisfaction, and less patient trust in their physicians.28,30–32 In a previous study, more than 50% of anesthesia providers admitted having committed an error in medical judgment that they attributed to fatigue.33 Another concern with burnout and low job satisfaction is that they may adversely affect physician–patient communications.34 In an Italian exploratory study, emotional and interpersonal factors such as “kindness,” “information given by anesthesiologists,” and “feeling safe” were found to be most important for patient satisfaction with anesthesia.35 Those emotional and interpersonal tasks are not possible for a physician with depersonalization who, by definition, experiences “hardened feelings” and “insensitivity.” Orton et al36 conducted a study among general practitioners in the United Kingdom that showed the professional practice and patient-centeredness of consultations were not affected by burnout. However, in our study, all 3 burnout dimensions, and especially the depersonalization dimension, were associated with some impairment of physician–patient communications. Therefore, efforts made to reduce burnout may not only help anesthesiologists but also benefit patient satisfaction with anesthesia.
There are limitations to our study. First, the cross-sectional nature of this study may not be applicable for observing causal relationship between the variables. Second, as the electronic questionnaires were read and filled in on mobile phones, the total number of questions had to be well controlled. Some potential factors were not included such as stress and coping strategy. Third, we only included anesthesiologists in this survey because the population of anesthesia nurses and anesthesiologist assistants is very small. The job satisfaction and burnout level of Chinese anesthesia providers other than anesthesiologists is still unknown. Finally, we did not directly study the relationship between burnout and medical error, or the relationship between burnout and patient satisfaction.
In conclusion, this large survey of Chinese anesthesiologists in the Beijing–Tianjin–Hebei region found that they have a moderate to not fully satisfied level of job satisfaction as assessed by the MSQ and a very high level of burnout as assessed by the Maslach Burnout Inventory-Human Service Survey. Many of the associated factors are reflective of systemic issues in the health care system currently in place in China, and are potentially amenable to corrective measures.
We would like to thank Yuyan Wang, PhD (Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Science & School of Basic Medicine, Peking Union Medical College, Beijing, China), for her assistance with statistics, and Wenping Peng, MD (Department of Anesthesiology, Beijing Hospital, National Center of Gerontology, Beijing, China), for his outstanding coordination work.
Name: Hange Li, MD.
Contribution: This author helped collect, analyze, and interpret the data, and write the manuscript.
Name: Mingzhang Zuo, MD.
Contribution: This author helped design the study, collect the data, and revise the manuscript.
Name: Adrian W. Gelb, MD.
Contribution: This author helped design the study, interpret the data, and revise the manuscript.
Name: Biao Zhang, MD.
Contribution: This author helped analyze and write the manuscript.
Name: Xiaohui Zhao, MD.
Contribution: This author helped design the study, interpret the data, and revise the manuscript.
Name: Dongdong Yao, MD, PhD.
Contribution: This author helped interpret the data and revise the manuscript.
Name: Di Xia, MD.
Contribution: This author helped collect the data and write the manuscript.
Name: Yuguang Huang, MD.
Contribution: This author helped design the study, interpret the data, and revise the manuscript.
This manuscript was handled by: Angela Enright, MB, FRCPC.
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