Abstract
Background
Primary health-care workers (PHWs) managed increased workloads and pressure during the COVID-19 pandemic. This study conducted a national survey examining burnout among PHWs at the end of the COVID-19 pandemic, and identifies related factors. By doing so, it addresses the gap in understanding the burnout situation among PHWs at a national level, taking into account urban-rural disparities.
Methods
We conducted a nationwide cross-sectional survey of PHWs in China from May to October 2022, covering 31 provinces. The MBI-HSS was used to measure overall burnout and emotional exhaustion (EE), depersonalization (DP), and reduced personal accomplishment (PA). We used multivariable logistic regression to identify risk factors, and subgroup analyses to identify differences between rural and urban areas.
Results
3769 PHWs from 44 primary health-care institutions completed the survey. Overall, 16.6% reported overall burnout, and the prevalence of EE, DP, and reduced PA was 29.7%, 28.0%, and 62.9%, respectively. The prevalence of overall burnout (17.6% vs. 13.7%, P = 0.004) and EE (31.5% vs. 24.8%, P < 0.001) was higher in urban than rural areas (AOR = 1.285; 95%CI, 1.021–1.617). Job satisfaction was a protective factor against burnout in both settings. The protective factors of overall burnout, EE and DP vary between urban and rural areas.
Conclusions
The Mental Health Status Questionnaire-Short Form (MSQ-SF) score functioned as a protective factor against burnout across both rural and urban locales, highlighting the intrinsic link between job satisfaction and burnout. Other influencing factors differed between urban and rural areas, so interventions should be tailored to local conditions. Rural married PHWs experienced the lower prevalence of burnout indicates the support structure may play a significant role. In urban settings, it is recommended to strategically pre-emptively stock essential supplies like PPE.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12875-024-02593-0.
Keywords: Burnout, China, Primary health-care workers, Risk factors, Urban-rural disparities
Introduction
Burnout, characterized by EE, DP, and PA [1], is a psychological syndrome garnering significant attention within the medical community. The inaugural large-scale national survey of burnout amongst US physicians was conducted in 2011, revealing that approximately 45% reported experiencing at least one symptom of burnout [2]. Recent studies have underscored the alarming escalation of physician burnout, with prevalence rates reaching or surpassing 50% [3, 4]. This trend coincides with notable changes in medical practice dynamics over the past two decades, marked by diminishing autonomy, declining physician status, and escalating work pressures [5], all of which may contribute to the exacerbation of burnout among medical professionals.
During the COVID-19 pandemic, healthcare workers have played a pivotal role in treating affected patients, yet have faced a considerable surge in their workload. Prior research has surveyed physicians at regional or national levels to explore the impact of COVID-19 on burnout, identifying various risk factors such as work-related psychosocial elements, personal protective equipment (PPE) availability, electronic health records, and performance measures [6–10]. Existing literature consistently indicates a heightened prevalence of burnout among medical staff during the COVID-19 crisis. For instance, a national survey conducted in the USA revealed that burnout rates ranging from 33.6 to 53.1%, varying across different categories of medical personnel [11]. Similarly, a national study in China found that burnout among nurses in secondary and tertiary hospitals reached 34% [12]. A meta-analysis of 45 articles worldwide demonstrated that more than half of healthcare workers experienced burnout during the pandemic [13]. The burnout of healthcare workers was linked to the career disengagement, turnover of physicians, and reduction in the quality of patient care. In the context of pandemic, it would damage the sustainability of healthcare system [14]. However, previous reports have overlooked primary health-care workers (PHWs), despite their significant contributions to public health during this pandemic. A bibliometric study revealed a decline in publications trends regarding burnout among primary healthcare workers after 2021, suggesting a decline in attention to job burnout among PHWs [15].
During the COVID-19 pandemic, primary health-care (PHC) initiatives received limited attention compared to the focus on hospital resources and intensive care units. Reports from various countries, including Singapore, Australia, China, Iceland, highlighted lessons learned regarding PHC preparedness. These lessons encompassed strategies such as remote consultations to minimize crowd gathering and infection risks, the provision of treatment and support services particularly targeting the vulnerable populations [16, 17], and close collaboration with public health clinics for screening and testing [18]. China, for instance, maintained its zero-COVID strategy until the end of 2022, during which PHWs played crucial roles in health checks, medical advice dissemination, patient referrals, contact tracing efforts, and the management of both community and hospitalized patients, including those discharged [19]. Studies in Australia [20], America [21], Malaysia [22] and Turkey [23] have found that more than 30% of PHWs experience burnout. Despite their significant contributions, burnout among PHWs remains inadequately addressed, with PHWs in Australia reportedly being undersupported and undervalued during the initial months of the COVID-19 pandemic [24]. International qualitative research conducted in Ontario has highlighted disparities in burnout levels across genders, job categories, and regions (rural/urban) [25]. Although existing research has begun to explore the phenomenon of burnout among PHWs, there is a notable gap in understanding the nuanced regional differences within China, particularly at the rural-urban level.
As a significant public health crisis, the COVID-19 pandemic has amplified burnout among medical workers, yet comprehensive nationwide research, particularly focusing on PHWs, remains scarce. While previous studies underscored the pivotal role of PHWs during the pandemic and highlighted their increased workload [26, 27], the precise extent of burnout among PHWs at a national scale, three years post-pandemic onset, remains undetermined. Therefore, we conducted a nationwide survey to ascertain the prevalence of burnout among PHWs in China following three years of battling COVID-19. Additionally, we aimed to identify associated risk factors, with separate analysis conducted for subgroups working in rural and urban settings.
Methods
Study design
A nationwide cross-sectional online survey was conducted from May to October 2022, encompassing 31 provinces in China. This timeframe coincided with the enforcement of the zero-COVID policy, which remained in effect until December 2022.
Sampling
To ensure nationwide coverage across mainland China, we employed a combination of multistage and cluster sampling methods. First, we randomly selected a community health center (CHC) within the capital city of each province. This selection was based on a meticulously compiled sampling frame, which included all CHC in the provincial capitals. A random number generator was utilized to ensure the selection process adhered to principles of true randomness, with the randomization validated to confirm its integrity. Second, we focused on rural PHC facilities by randomly selecting an administrative city, excluding the provincial capitals, and identifying a township hospital along with its affiliated village clinics within this city. The sampling frame for this phase was similarly comprehensive, encompassing all township hospitals in the selected administrative cities. The random selection process was again facilitated by a random number generator, ensuring that the chosen samples were representative and randomly distributed. Typically, urban PHC facilities comprise CHCs their associated stations, while rural counterparts include township hospitals and affiliated village clinics. However, in some administrative cities, township hospitals have been restructured into CHCs due to urbanization; in such cases, we still sampled CHCs instead of township hospitals. In our sample, 9 township hospitals and affiliated village clinics in non-provincial capital cities have been transformed into CHCs. For the four municipalities directly under central government jurisdiction, —Beijing, Shanghai, Tianjin, and Chongqing—we randomly selected one CHC each from urban and suburban areas, and a random number generator was used to sample from these distinct groups. Ultimately, our study included 44 CHCs representing urban areas and 18 township hospitals representing rural areas from 27 provinces and four municipalities (excluding Hong Kong, Macao, and Taiwan).
Participants and data collection
From May to October 2022, a nationwide cross-sectional online survey was conducted in 31 provinces in China. All PHWs within the sampled PHC facilities were invited to participate in the survey. We define PHWs as those who work at CHC or township hospitals and affiliated village clinics. Individuals lacking a smartphone or facing challenges in completing the electronic questionnaire due to visual or intellectual impairments were excluded. We reached out to the directors of the selected PHC facilities via academic organizations. The questionnaire’s QR code, generated through the Questionnaire Star website (https://www.wjx.cn/), was provided to these facility directors, who then disseminated it via a WeChat working group comprising all the facility staff. The total staff count within each facility was reported to us in order to calculate the response rate. In total, 3,769 individuals participated in the survey, yielding a response rate of 93.7% (3,769 out of 4,021).
Measures and covariates
The primary outcome assessed in this study was job burnout, gauged using the Chinese version of Maslach Burnout Inventory-Human Services Survey (MBI-HSS) [28]. This 22-item instrument evaluates three dimensions: EE (nine items), DP (five items), and reduced PA (eight items). Respondents rated the frequency of experiencing specific feelings on a seven-point Likert scale ranging from 0 (never) to 6 (every day). Scores across items with each dimension were summed to determine overall burnout and levels within each domain [29]. In our analysis, we adopted widely utilized scoring criteria based a systematic review [28], employing the following cut-off points: overall burnout (EE ≥ 27, DP ≥ 10 and PA ≤ 33), high EE (EE ≥ 27), high DP (DP ≥ 10), and low PA (PA ≤ 33). The Chinese version of MBI-HSS has been shown to have good reliability and validity [30]. In our study, Cronbach’s α coefficient for the overall MBI-HSS, EE, DP, and PA were 0.89, 0.71, 0.74 and 0.72, respectively, indicating high internal consistency.
Covariates were categorized into four dimensions. Sociodemographic factors encompassed age, gender, marital status, educational status, living arrangements, and family relationships. Individual health characteristics included smoking, drinking, disability, number of chronic diseases, and self-rated health (SRH). Occupational factors comprised practice location, occupation category, professional title, years of experience in PHC, and monthly income. COVID-19 pandemic-related variables encompassed front-line health worker status, satisfaction with PPE, self-rated work intensity, and job satisfaction. Job satisfaction was assessed using the 20-item Minnesota Satisfaction Questionnaire-Short Form (MSQ-SF) [31], Yielding a Cronbach’s α coefficient of 0.97 in our study. Details of the questionnaires used in the study are shown in the supplementary file 2.
Data analysis
Analyses were performed using SPSS v22 (IBM Corp., Armonk, NY, USA). Participant characteristics and outcomes were succinctly summarized using descriptive statistics such as means and standard deviations (SD), medians and quartiles for continuous variables, or frequencies and percentages for categorical variables. To explore the differences in burnout prevalence between rural and urban PHC facilities, a suite of appropriate tests including the t-test, Kruskal-Wallis H test, and chi-squared test were employed as per the nature of the variables. Potential risk factors were identified through univariate analysis, and significant variables from the univariate analysis were included in a multivariate logistic regression model to obtain adjusted odds ratios (AOR) and 95% confidence intervals (95% CI). Subsequent subgroup analyses within rural and urban areas employed the same set of identified risk factors derived from the overall population. These findings were visually represented using a forest plot. All statistical analyses in this study were two-tailed, with a P-value of < 0.05 was considered statistically significant.
Ethical considerations
This study received ethical approval from Zhongshan Hospital, Fudan University (B2021-605). The online questionnaire includes a checkbox for respondents to make sure whether they give their voluntary informed consent before their involvement.
Results
Sociodemographic characteristics
A total of 3769 PHWs completed the survey comprising 1,174 general practitioners (GPs) (31.1%), 1282 nurses (34.0%), 253 public health physicians (6.7%), 221 managerial staff (5.9%), and 839 support staff (22.3%). Among them, 989 (26.2%) worked in rural areas and 2780 (73.8%) worked in urban areas. The majority of participants were female (2971 [78.8%]), married (2926 [77.6%]), living with their families (3,281 [87.1%]), and reported good family relations (3,060 [81.2%]). Furthermore, most participants were non-smokers (3,453 [91.6%]), non-drinkers (3,211 [85.2%]) and not disabled (3,680 [97.6%]), and did not suffer from chronic diseases (2,936 [77.9%]). A large proportion of participants worked as frontline health workers during COVID-19 pandemic (3649 [96.8%]) and reported satisfaction with the PPE [3050 (80.9%)]. The average MSQ-SF score was 69.9 ± 13.4 (Table 1).
Table 1.
Characteristics of the 3769 participants
| Characteristics | No. (%) |
|---|---|
| Sociodemographic | |
| Age (years) | |
| ≤ 30 | 974(25.8) |
| 31–40 | 1521(40.4) |
| 41–50 | 880(23.3) |
| > 50 | 394(10.5) |
| Gender | |
| Male | 798(21.2) |
| Female | 2971(78.8) |
| Marital status | |
| Single | 649(17.2) |
| Married | 2926(77.6) |
| Divorced/Widowed | 194(5.2) |
| Educational status | |
| High School or below | 407(10.8) |
| Junior college | 1132(30.0) |
| Undergraduate or above | 2230(59.2) |
| Living arrangement | |
| Living alone | 310(8.2) |
| Living with family | 3281(87.1) |
| Living with others | 178(4.7) |
| Family relations | |
| Poor | 132(3.5) |
| General | 577(15.3) |
| Good | 3060(81.2) |
| Individual health | |
| Smoking | |
| Non-smoker | 3453(91.6) |
| Once, now quit | 107(2.8) |
| Current smoking | 209(5.6) |
| Drinking | |
| Non-drinker | 3211(85.2) |
| Once, now quit | 181(4.8) |
| Current drinking | 377(10.0) |
| Disability | |
| Yes | 89(2.4) |
| No | 3680(97.6) |
| Number of chronic diseases | |
| 0 | 2936(77.9) |
| 1 | 598(15.9) |
| 2 and above | 235(6.2) |
| SRH | |
| Bad | 397(10.5) |
| General | 2082(55.3) |
| Good | 1290(34.2) |
| Occupational status | |
| Practice location | |
| Rural area | 989(26.2%) |
| Urban area | 2780(73.8%) |
| Occupation category | |
| GP | 1174(31.1) |
| Nurse | 1282(34.0) |
| Public health physician | 253(6.7) |
| Managerial staff | 221(5.9) |
| Support staff | 839(22.3) |
| Professional title | |
| Not rated | 548(14.6) |
| Junior | 1686(44.7) |
| Intermediate grade | 1225(32.5) |
| Senior | 310(8.2) |
| Length of career in primary care (years) | |
| ≤ 10 | 1945(51.6) |
| 11–20 | 986(26.2) |
| > 20 | 838(22.2) |
| Monthly income (CNY) | |
| < 3000 | 916(24.3) |
| 3000–5000 | 1620(43.0) |
| > 5000 | 1233(32.7) |
| COVID-19 pandemic | |
| Front-line health workers | |
| Yes | 3649(96.8) |
| No | 120(3.2) |
| Satisfaction with institutional provision of PPE | |
| Low | 127(3.4) |
| Medium | 592(15.7) |
| High | 3050(80.9) |
| Self-rated work intensity during COVID-19 | |
| Low | 19(0.5) |
| Medium | 1145(30.4) |
| High | 2605(69.1) |
| MSQ-SF (Mean ± SD) | 69.9 ± 13.4 |
SRH Self-rated health, GP general practitioner, PPE personal prevention equipment, MSQ-SF Minnesota Satisfaction Questionnaire-Short Form
Prevalence of burnout
Among the total sample, 625 (16.6%) participants experienced overall burnout, with 1,120 (29.7%) scoring EE ≥ 27, 1057 (28.0%) scoring DP ≥ 10, and 2371 (62.9%) scoring PA ≤ 33 (Table 2). The prevalence of overall burnout (490 [17.6%] vs. 135 [13.7%], P = 0.004) and EE (875 [31.5%] vs. 245 [24.8%], P < 0.001) was higher among urban than rural PHWs. There was no difference in the prevalence of DP (P = 0.093) and PA (P = 0.532) between rural and urban areas.
Table 2.
Prevalence of burnout among PHWs in China
| Category | Total | Practice location | |||
|---|---|---|---|---|---|
| Rural | Urban | Statistics | P | ||
| Overall burnout | |||||
| EE ≥ 27 and DP ≥ 10 and PA ≤ 33 [n(%)] | 625(16.6) | 135(13.7) | 490(17.6) | 8.336c | 0.004 |
| EE | |||||
| Mean ± SD | 19.0 ± 12.4 | 17.5 ± 11.9 | 19.5 ± 12.6 | 4.241a | < 0.001 |
| Media (P25, P75) | 18(9,27) | 15(8,26) | 18(9.27) | 17.706b | < 0.001 |
| EE ≥ 27 [n(%)] | 1120(29.7) | 245(24.8) | 875(31.5) | 15.690c | < 0.001 |
| DP | |||||
| Mean ± SD | 6.3 ± 6.7 | 5.8 ± 6.6 | 6.4 ± 6.8 | 2.480a | 0.013 |
| Media (P25, P75) | 4(1,10) | 3(1,10) | 4(1,11) | 8.870b | 0.003 |
| DP ≥ 10 [n(%)] | 1057(28.0) | 257(26.0) | 800(28.8) | 2.816c | 0.093 |
| PA | |||||
| Mean ± SD | 28.5 ± 12.0 | 28.3 ± 12.3 | 28.5 ± 11.8 | 0.483a | 0.629 |
| Media (P25, P75) | 29(21,39) | 29(20,39) | 29(21,39) | 0.048b | 0.827 |
| PA ≤ 33 [n(%)] | 2371(62.9) | 614(62.1) | 1757(63.2) | 0.391c | 0.532 |
PHWs primary health care workers, EE emotional exhaustion, DP depersonalization, PA personal accomplishment; a: t-test; b: Kruskal-Wallis H test; c: Chi-square test
Figure 1 illustrates the prevalence of burnout among PHWs across various provinces in China; darker shades represent higher prevalence of burnout in respective provinces. Our analysis revealed that provinces in north-western and south-eastern China exhibited the highest incidences of overall burnout, EE, and DP, whereas provinces in the north-eastern and south-western regions showed elevated incidences of reduced PA.
Fig. 1.
Geographic prevalence of burnout among PHWs in China. Note: All Chinese maps in Figure 1 were downloaded from the website: http://bzdt.ch.mnr.gov.cn/. The Audit Number is GS（2024）No.0650
Burnout related factors and regional differences
Following significant risk factor identification through univariable analysis (Supplementary Table 1), we conducted multivariate logistic regression (Table 3). Results indicated that PHWs living with families were less likely to suffer from EE (AOR, 0.727; 95%CI, 0.542–0.974) and DP (AOR, 0.592; 95%CI, 0.446–0.787), while those reporting good SRH were less likely to experience overall burnout (AOR,0.610; 95%CI, 0.440–0.846), EE (AOR,0.377; 95%CI, 0.283–0.502), and DP (AOR, 0.643; 95%CI, 0.488–0.848). Conversely, urban PHWs were more likely to suffer from overall burnout (AOR, 1.285; 95%CI, 1.021–1.617) and EE (AOR, 1.289; 95%CI, 1.069–1.554). Compared with GPs, nurses (AOR, 1.552; 95%CI, 1.296–1.858), managerial staff (AOR, 1.430; 95%CI, 1.042–1.962), and support staff (AOR, 1.386; 95%CI, 1.139–1.687) were more prone to low PA, whereas PHWs in intermediate (AOR, 0.709; 95%CI, 0.547–0.920) or senior grades (AOR, 0.665; 95%CI, 0.470–0.942), or with a salary of 3000 to 5000 CNY (AOR, 0.806; 95%CI, 0.665–0.977), were not prone to low PA. PPE was a protective factor against overall burnout (AOR, 0.581; 95%CI, 0.373–0.907), EE (AOR, 0.483; 95%CI, 0.319–0.731), and DP (AOR, 0.536; 95%CI, 0.358–0.801). Furthermore, higher MSQ-SF scores were associated with reduced susceptibility to overall burnout and each domain of burnout (P < 0.001).
Table 3.
Multivariate logistic regression of burnout risk factors
| Variables | Overall | EE | DP | PA | ||||
|---|---|---|---|---|---|---|---|---|
| AOR(95%CI) | P | AOR(95%CI) | P | AOR(95%CI) | P | AOR(95%CI) | P | |
| Sociodemographic | ||||||||
| Age (years) | ||||||||
| ≤ 30 | Ref. | Ref. | Ref. | Ref. | ||||
| 31–40 | 0.806(0.607–1.070) | 0.135 | 0.750(0.588–0.957) | 0.021 | 0.806(0.634–1.025) | 0.079 | 0.970(0.767–1.226) | 0.798 |
| 41–50 | 0.584(0.381–0.896) | 0.014 | 0.499(0.353–0.773) | < 0.001 | 0.519(0.363–0.740) | < 0.001 | 0.776(0.566–1.063) | 0.114 |
| > 50 | 0.720(0.417–1.243) | 0.238 | 0.621(0.402–0.958) | 0.031 | 0.514(0.327–0.809) | 0.004 | 0.684(0.466–1.004) | 0.052 |
| Marital status | ||||||||
| Single | Ref. | Ref. | Ref. | Ref. | ||||
| Married | 0.893(0.650–1.222) | 0.467 | 0.793(0.609–1.032) | 0.085 | 0.913(0.705–1.182) | 0.489 | 1.010(0.800-1.276) | 0.934 |
| Divorced/Widowed | 0.650(0.359–1.106) | 0.115 | 0.691(0.450–1.063) | 0.093 | 0.631(0.407–0.979) | 0.040 | 1.261(0.853–1.862) | 0.245 |
| Education status | NA | |||||||
| High School or below | Ref. | Ref. | Ref. | |||||
| College | 1.286(0.870–1.899) | 0.207 | 1.181(0.869–1.606) | 0.288 | 0.999(0.739–1.349) | 0.992 | ||
| Undergraduate or above | 1.150(0.772–1.713) | 0.491 | 1.206(0.896–1.625) | 0.217 | 0.925(0.677–1.266) | 0.628 | ||
| Living arrangement | NA | |||||||
| Living alone | Ref. | Ref. | Ref. | |||||
| Living with family | 0.735(0.525–1.029) | 0.073 | 0.727(0.542–0.974) | 0.033 | 0.592(0.446–0.787) | < 0.001 | ||
| Living with others | 0.835(0.517–1.350) | 0.463 | 0.824(0.539–1.259) | 0.370 | 1.067(0.714–1.594) | 0.751 | ||
| Family relations | ||||||||
| Poor | Ref. | Ref. | Ref. | Ref. | ||||
| General | 1.364(0.798–2.331) | 0.257 | 1.224(0.771–1.943) | 0.392 | 1.091(0.695–1.711) | 0.706 | 0.854(0.549–1.329) | 0.485 |
| Good | 1.198(0.718–1.999) | 0.490 | 0.999(0.647–1.544) | 0.998 | 0.928(0.607–1.418) | 0.728 | 0.944(0.627–1.422) | 0.783 |
| Individual health | ||||||||
| Disability | NA | NA | ||||||
| No | Ref. | Ref. | ||||||
| Yes | 1.422(0.880–2.298) | 0.151 | 1.359(0.844–2.189) | 0.206 | ||||
| Number of chronic diseases | NA | NA | ||||||
| 0 | Ref. | Ref. | ||||||
| 1 | 1.034(0.826–1.293) | 0.773 | 0.844(0.687–1.036) | 0.104 | ||||
| 2 and above | 0.991(0.709–1.387) | 0.959 | 1.010(0.738–1.381) | 0.951 | ||||
| SRH | ||||||||
| Bad | Ref. | Ref. | Ref. | Ref. | ||||
| General | 0.954(0.726–1.253) | 0.734 | 0.556(0.434–0.712) | < 0.001 | 0.957(0.751–1.220) | 0.722 | 1.030(0.795–1.334) | 0.825 |
| Good | 0.610(0.440–0.846) | 0.003 | 0.377(0.283–0.502) | < 0.001 | 0.643(0.488–0.848) | 0.002 | 0.831(0.626–1.104) | 0.201 |
| Occupational status | ||||||||
| Practice location | NA | NA | ||||||
| Rural area | Ref. | Ref. | ||||||
| Urban area | 1.285(1.021–1.617) | 0.032 | 1.289(1069 − 1.554) | 0.008 | ||||
| Occupation category | NA | |||||||
| GP | Ref. | Ref. | Ref. | |||||
| Nurse | 1.164(0.912–1.485) | 0.223 | 1.071(0.877–1.309) | 0.502 | 1.552(1.296–1.858) | < 0.001 | ||
| Public health physician | 0.851(0.566–1.280) | 0.439 | 0.8901(0.648–1.253) | 0.5436 | 1.312(0.973–1.769) | 0.075 | ||
| Managerial staff | 1.008(0.641–1.583) | 0.974 | 1.029(0.713–1.486) | 0.878 | 1.430(1.042–1.962) | 0.027 | ||
| Support staff | 1.203(0.927–1.562) | 0.165 | 1.182(0.953–1.465) | 0.128 | 1.386(1.139–1.687) | 0.001 | ||
| Professional title | NA | |||||||
| Not rated | Ref. | Ref. | Ref. | |||||
| Junior | 0.924(0.689–1.240) | 0.599 | 1.082(0.845–1.385) | 0.531 | 0.822(0.654–1.033) | 0.093 | ||
| Intermediate grade | 1.003(0.711–1.415) | 0.985 | 1.068(0.799–1.429) | 0.656 | 0.709(0.547–0.920) | 0.010 | ||
| Senior | 1.007(0.598–1.694) | 0.980 | 0.886(0.574–1.367) | 0.583 | 0.665(0.470–0.942) | 0.022 | ||
| Length of career in primary care (years) | ||||||||
| ≤ 10 | Ref. | Ref. | Ref. | Ref. | ||||
| 11–20 | 0.831(0.641–1.077) | 0.162 | 1.110(0.893–1.379) | 0.346 | 0.928(0.747–1.151) | 0.496 | 0.945(0.774–1.153) | 0.578 |
| > 20 | 0.817(0.541–1.234) | 0.337 | 1.091(0.785–1.517) | 0.604 | 1.047(0.747–1.468) | 0.790 | 0.875(0.659–1.163) | 0.358 |
| Monthly income (CNY) | NA | NA | ||||||
| ≤ 3000 | Ref. | Ref. | ||||||
| 3000–5000 | 0.999(0.815–1.226) | 0.995 | 0.806(0.665–0.977) | 0.028 | ||||
| > 5000 | 1.171(0.921–1.489) | 0.198 | 0.844(0.678–1.050) | 0.128 | ||||
| COVID-19 pandemic | ||||||||
| Satisfaction with institutional provision of PPE | ||||||||
| Low | Ref. | Ref. | Ref. | Ref. | ||||
| Medium | 0.919(0.578–1.460) | 0.719 | 0.780(0.503–1.210) | 0.267 | 0.955(0.626–1.458) | 0.832 | 1.449(0.926–2.268) | 0.105 |
| High | 0.581(0.373–0.907) | 0.017 | 0.483(0.319–0.731) | 0.001 | 0.536(0.358–0.801) | 0.002 | 1.249(0.824–1.893) | 0.294 |
| Self-assessment of work intensity during COVID-19 | ||||||||
| Low | Ref. | Ref. | Ref. | Ref. | ||||
| Medium | 0.751(0.191–2.956) | 0.682 | 0.960(0.266–3.467) | 0.950 | 1.507(0.433–5.249) | 0.520 | 1.395(0.532–3.660) | 0.499 |
| High | 1.008(0.258–3.942) | 0.991 | 1.912(0.532–6.867) | 0.321 | 1.530(0.441–5.316) | 0.503 | 1.331(0.508–3.485) | 0.561 |
| MSQ-SF (Mean ± SD) | 0.953(0.945–0.961) | < 0.001 | 0.956(0.949–0.963) | < 0.001 | 0.962(0.956–0.969) | < 0.001 | 0.956(0.950–0.962) | < 0.001 |
SRH Self-rated health, GP general practitioner, PPE personal prevention equipment, MSQ-SF Minnesota Satisfaction Questionnaire-Short Form
The difference in risk factors between rural and urban areas
According to Fig. 2, marital status and SRH emerged as protective factors against overall burnout, EE, and DP among PHWs in different regions. Specifically, rural married PHWs exhibited the lowest rates of overall burnout (AOR,0.435; 95%CI, 0.243–0.778), EE (AOR,0.566; 95%CI, 0.346–0.927), and DP (AOR, 0.505; 95%CI, 0.309–0.827). Moreover, urban PHWs with good SRH were less likely to experience overall burnout (AOR, 0.04; 95%CI, 0.394–0.837), EE (AOR, 0.315; 95%CI, 0.226–0.439), or DP (AOR, 0.615; 95%CI, 0.446–0.846). Occupation category emerged as an independent risk factor for low PA among urban PHWs, with nurses, managerial staff, and support staff exhibiting a higher likelihood of reporting low PA than GPs. Satisfaction with the institutional provision of PPE was associated with reduced overall burnout, EE, and DP, among urban PHWs, while rural PHWs who were in the intermediate level of satisfaction with PPE provision were more likely to experience DP than those reporting lower satisfaction levels (AOR, 2.334; 95%CI, 1.007–5.409). The MSQ-SF score influenced all domains of burnout and affected both rural and urban PHWs.
Fig. 2.
Risk factors for burnout stratified by region
Discussion
According to this study, the prevalence of burnout among PHWs was 16.6%, with a higher prevalence observed among those working in urban areas (17.6%) compared to their rural counterparts (13.7%). Wang et al.. reported that lower percentages of rural PHWs experiencing high levels of EE, DP, and reduced PA at 27.66%, 6.06%, and 38.74%, respectively [32], compared to their urban counterparts. Higher burnout among urban PHWs could be attributed to factors such as increased patient visits, higher numbers of contracted residents, and heavier health management workload, owing to the high population densities in urban settings. Further investigation is warranted to explore the difference in burnout between urban and rural PHWs.
Notably, a significant proportion (62.9%) reported low PA, followed by high EE (29.7%) and DP (28%). It is noteworthy that the reduced PA among PHWs in this study was considerably higher than that found in previous studies. Two limited studies published in 2019 in China, one involving 951 participants in Shandong Province and another encompassing 3,236 participants nationwide, have indicated the prevalence rates of high EE, DP, and low PA to be 33.12–43.17%, 8.83–22.93%, and 41.19–41.43%, respectively [33, 34]. A nationwide cross-sectional study in Lebanon, encompassing 960 primary healthcare center healthcare providers, revealed that 22.1%, 10.8%, and 20.8% of the respondents reported high levels of emotional exhaustion, high degrees of depersonalization, and low personal accomplishment, respectively [35]. Our study was conducted at the end of 2022, after nearly three years of the COVID-19 emergency response, which included tasks such as shelter hospital support, airport/crossing support, nucleic acid sampling. The prolonged overload may have eroded PHWs’ of a sense of accomplishment in disease treatment and health management.
Occupational-related factors were found to be significantly associated with reduced PA, rather than EE and DP. While much research has examined individual occupational groups independently, there’s evidence suggesting occupational disparities in burnout among medical staff, which is consistent with our results [36–40]. A study conducted during the COVID-19 pandemic, involving 606 frontline medical staff recruited from 133 cities in China, indicated that nurses experienced the highest incidence of burnout compared to doctors and medical technicians [41]. In this study, nurses, managerial staff, and support staff were more prone to experiencing reduced PA compared to GPs. A review that included 37 studies related to the psychological health of frontline healthcare workers during the pandemic have highlighted increased workload, aspects of the work environment, poor staffing ratios, lack of communication between doctors and nurses, and organizational leadership deficiencies as factors contributing to burnout among nurses and support staff [42]. In the context of this study, GPs primarily led the provision of PHC, while nurses, managerial staff, and support staff often assumed supportive roles to assist physicians in disease treatment and health management.
Interestingly, higher professional title and income levels seemed to offer some protective effect against reduced PA, contradicting the “stress of higher status” hypothesis, which posits that higher-status PHWs are more susceptible to burnout. However, this hypothesis has predominantly been explored in the context of the intensive care unit (ICU) [43], where work intensity and urgency are notably higher than in PHC setting. Surprisingly, work intensity was not a significant predictor variable, as typically observed, potentially due to the sustained high-intensity work experienced by PHWs. In such scenarios, where high-intensity work persists, difference in work intensity among medical staff diminish, with emotional perceptions such as job satisfaction emerging as more direct predictors of burnout.
The COVID-19 pandemic has undeniably posed significant challenges globally, prompting PHWs to engage in extensive efforts such as vaccination promotion, community PCR testing, and management of infected patients [18, 44]. Surprisingly, this study highlights that adequate PPE and job satisfaction played pivotal roles in mitigating burnout during the pandemic, rather than anticipated factor of work intensity. Consistent with existing literature, job satisfaction was inversely correlated with burnout, underscoring its importance in preserving PHWs’ well-being [32, 45, 46]. Conversely, inadequate PPE availability was significantly associated with burnout especially among PHWs in urban area, likely stemming from the heightened anxiety caused by uncertainties surrounding COVID-19 during periods of insufficient protection.
Remarkably, our findings reveal that familial support and higher SRH acted as significant protective factors against burnout among PHWs, especially in mitigating EE and DP. Our research findings demonstrate a lower prevalence of burnout among married rural PHWs. Within the context of China’s emphasis on family institution building and clan systems [47–49], this finding preliminarily suggests the supportive role of traditional Chinese familial culture in mitigating burnout. Recent studies have consistently demonstrated the inverse relationship between perceived family support and burnout among medical professionals. For example, Koutsimani et al. found that high levels of perceived family support were associated with reduced exhaustion and cynicism, emphasizing the protective role of familial support in preventing burnout [50]. Moreover, Taku’s research further elucidated the impact of family support in conjunction with perceived growth, significantly influenced EE and DP [51]. In addition, studies have indicated that physicians, nurses, dentists, and other medical staff with lower SRH were more susceptible to experiencing burnout [52–54]. However, the intricate interaction between SRH and burnout warrants further exploration.
Disparities in the risk factors for burnout were evident between urban and rural areas. Job satisfaction emerged as a protective factor against burnout across all PHWs, irrespective of their workplace setting. Conversely, the impact of PPE on burnout varied, affecting all burnout domains except PA in urban PHWs. This discrepancy in PPE’s protective effect may be attributed to the densely populated nature and unique epidemiological dynamics of urban environments, where PPE sensitivity could be higher due to increased exposure risk. In contrast, in rural areas characterized by vast expanses of land and sparser populations, the sensitivity to PPE may be comparatively lower [55].
Regarding EE and DP, living arrangements and SRH emerged as significant predictors for urban PHWs, whereas marital status was a notable factor for rural practitioners. In traditional rural settings, marriage serves as a protective factor against burnout, aligning with cultural norms that prioritize the supportive role a spouse in alleviating work-related stress [56]. For PA, pronounced disparities were observed among PHWs in different practice locations. In urban areas, occupational differences among physicians, nurses, managerial staff, and support staff influenced PA with higher professional titles associated with increased PA. However, such variations were less discernible in rural areas, where income emerged as a significant predictor. In these contexts, economic factors outweighed professional status and practice type, reflecting the relatively lower level of economic development in rural regions. The distinct patterns of burnout disparities and related factors between urban and rural areas underscore the importance of further investigation into these dynamics.
Our findings underscored the importance of adequate protective measures and health maintenance initiatives for PHWs in urban centers, alongside the traditional focus on ICU resources and care for critically ill patients. Rural-urban burnout gap among PHWs should be taken into account for the next emergency preparedness. Urban areas should strategically pre-emptively stock essential supplies like PPE to mitigate the anxiety among frontline healthcare workers. To alleviate burnout among rural primary healthcare workers, it is essential to foster a robust family support network that provides comprehensive backing.
Conclusion
To our knowledge, our study represents the first nationwide investigation into burnout among PHWs conducted at the conclusion of the COVID-19 pandemic. Notably, a striking 62.9% reported reduced personal accomplishment, potentially attributable to the prolonged and repetitive nature of COVID-related duties, such as extensive testing campaigns, without commensurate recognition or rewards. Furthermore, our study identified disparities in burnout prevalence between urban and rural areas, highlighting distinct impact mechanisms and the need for tailored intervention strategies. While metropolises faced significant strain on healthcare systems due to high population densities, burnout among PHWs in these settings received insufficient attention.
Limitations
This study represents the first nationwide investigation into burnout among PHWs in China during the COVID-19 pandemic. However, several limitations need to be acknowledged. Firstly, the study was cross-sectional, precluding the capture of burnout trajectories over time. Secondly, while variables such as work intensity, self-rated PPE, and occupational satisfaction during COVID-19 were included, additional COVID-related factors, such as the scale of infections could have been considered to provide a more comprehensive analysis. Thirdly, the focus was primarily on PHWs working in PHC facilities, potentially overlooking burnout disparities among specialists working in larger hospitals, warranting further investigation. Fourthly, our study has not extensively investigated the cultural factors underpinning this association. Future research is warranted to explore these cultural determinants in greater depth.
Supplementary Information
Acknowledgements
Our grateful thanks are due to the Society of General Practice of Chinese Medical Association and the Society of General Practice of Cross-straits Medicine Exchange Association for their help in the data gathering. We thank the healthcare workers who participated in this research. We also thank Yaoyao Xu and Yang Zhou of the Society of General Practice of Cross-straits Medicine Exchange Association, Zhigang Pan of Zhongshan Hospital, Fudan University, Mei Feng of Shanxi Medical University, Jingjing Ren of The Second Affiliated Hospital of Zhejiang University and Zhaohui Du of Shanghai Shanggang CHC for their help with the collection.
Authors' contributions
Jie Gu: Design, Conceptualization, Writing–review & editing. Jiaoling Huang: Methodology, Writing– original draft, Writing– review & editing. Jiayi Ji: Writing– review & editing. Ping Zhu: Interpretation of data. Yuge Yan: Visualization, Data analysis, Statistical analysis. Biao Xi: Investigation. Shanzhu Zhu: Investigation.
Funding
This study was supported by the National Natural Science foundation of China [72274122], Science and Technology Commission of Shanghai Municipality [23692113400], Shanghai Municipal Education Commission [C2021027] and Shanghai Municipal Three Year Action Plan for Strengthening the Construction of Public Health System - Excellent Youth Project (GWVI-11.2-YQ54).
Availability of data and materials
The authors declare that the data supporting the findings of this study are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
This study received ethical approval from Zhongshan Hospital, Fudan University (B2021-605). The online questionnaire includes a checkbox for respondents to make sure whether they give their voluntary informed consent before their involvement. The study was conducted in accordance with the Declaration of Helsinki.
Consent for publication
All authors gave their consent for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jiaoling Huang and Ping Zhu these authors contributed equally to this work and should be considered co-first authors.
Contributor Information
Jiayi Ji, Email: jjy2876@gmail.com.
Jie Gu, Email: gu.jie@zs-hospital.sh.cn.
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Associated Data
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Supplementary Materials
Data Availability Statement
The authors declare that the data supporting the findings of this study are available from the corresponding author upon request.