Discussion: Measuring Job Satisfaction

RESEARCH ARTICLE Open Access

Moderating role of job satisfaction on turnover intention and burnout among workers in primary care institutions: a cross-sectional study Xuyu Chen1†, Li Ran1†, Yuting Zhang1, Jinru Yang2, Hui Yao1, Sirong Zhu1 and Xiaodong Tan1*

Abstract

Background: Global countries are suffering from a shortage of health professionals. Turnover intention is closely related to job satisfaction and burnout, making good use of these relationships could alleviate the crisis. Our research aims to examine the mediating role of job satisfaction in the relationship between burnout and turnover intention.

Methods: This research was conducted in Huangpi, China. The convenience sampling method and self- administereded questionnaires were used. 1370 of valid samples were collected with 97.72% effective rate. Descriptive analyses were conducted to describe social demographic factors. The structural equation model (SEM) was performed to adjust model fitting, and the mediation effect test was carried out by using the bootstrap method. Sobel-Z test was used to verify the significance of mediation effect.

Results: The mean age was 36.98 (SD = 9.84). The fitting indices of hypothetical model are not good. After the adjustments, χ2/df = 5.590, GFI = 0.932, AGFI = 0.901, CFI = 0.977, NFI = 0.973, IFI = 0.977, TLI = 0.970, RESEA = 0.058. The revised model fitted well, and the SEM was put up by using the bootstrap method. The mediating effect is partial, and Soble-Z test indicates that the mediation effect is significant. Burnout is negatively correlated with job satisfaction (p < 0.01) and the standardized path coefficient is − 0.41. Job satisfaction is also negatively correlated with turnover intention (p < 0.01) and the standardized path coefficient is − 0.18. Burnout is positively correlated with turnover intention (p < 0.01) and the standardized path coefficient is 0.83.

Conclusions: Job satisfaction is a mediating variable that affects the relationship between burnout and turnover intention. The mediating effect was a partial mediating effect and has a low impact of 7.4%. Improving treatment and giving more promotion opportunities for workers to improve job satisfaction, conducting career planning course and paying attention to employee psychological health to reduce job burnout. The above measures may be helpful to reduce employee turnover rate and alleviating the current situation of a shortage of health personnel in China.

© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

* Correspondence: [email protected] †Xuyu Chen and Li Ran are co-first author. 1School of Health Sciences, Wuhan University, Donghu Road 115, Wuhcang District, Wuhan 430000, Hubei, China Full list of author information is available at the end of the article

Chen et al. BMC Public Health (2019) 19:1526 https://doi.org/10.1186/s12889-019-7894-7

Background Turnover is generally viewed as the movement of staff out of an organization. It was regarded as a two- dimensional concept, distinguishing between the act of leaving as voluntary or involuntary, and between the leaving and joining of an individual to an organization [1]. A previous study [2] defined turnover intention as the next withdrawal behavior when employees encounter dissatisfaction. Mobley et al. [3] pointed out that turn- over intention was the intention of a worker to leave an organization deliberately after a period of time working in a particular organization, after careful consideration, which belonged to voluntary turnover. It is considered as an outcome of affective variables (such as burnout and job satisfaction) rather than actual turnover [4]. That is, turnover intention can predict actual turnover behavior. An American clinical psychologist first proposed the

term “job burnout [5]”. At present, job burnout was de- fined as the symptoms of practitioners in the service in- dustry who were unable to cope effectively with the continuing pressure at work, included emotional exhaus- tion, depersonalization and reduced personal accom- plishment [6]. Initially, job satisfaction was described as the physical and psychological satisfaction of staff in their work [7]. In later studies, it was defined as the realization of a person’s work values in the work situ- ation, resulting in a pleasurable emotional state [8, 9]. The definition of “the attitude towards one’s work and related emotions, beliefs and behaviors” was used in our study, which not only depended on the nature of the work, but also the personality, attitude and expectation of medical staff [10, 11]. Low job satisfaction is the most common cause of staffs’ turnover and low quality of health care service. There is a plethora of researches on job satisfaction,

burnout and turnover intention. A study found that higher work pressure and lower job satisfaction could easily lead to burnout, that is, job satisfaction was one of the important predictors of burnout [12]. However, an- other study of American surgeons had the opposite re- sult: burnout is the biggest predictor of job satisfaction [13]. In fact, satisfaction and burnout are in no particular order and they need to be determined on a case-by-case basis. Yin et al. [14] found that satisfaction with work it- self, occupational risks and off-duty arrangements were the main factors affecting doctors’ job burnout through a survey on doctors in public hospitals. A study showed that job satisfaction had a direct predictive effect on burnout of middle school teacher, and it also existed as a mediating variable between job stress and job burnout [15]. Arie R and Yoram N had nosed out that negative correlation was existed in job satisfaction and job stress, burnout [16].

Most of researches supported that burnout had a posi- tively strong predictive effect on turnover intention [17– 22]. For example, emotional exhaustion, negative stagna- tion and other factors related to burnout have a signifi- cant positive correlation with turnover intention [23]. Beyond that, a study revealed that job satisfaction was negative correlated with turnover intention [24]. In the study of Guo et al. [25], turnover intention decreased with the increase of job satisfaction, this opinion was verified from a study of medical workers in Macao. And Wu et al. [26] also confirmed this result from a research of clinical nurses in Changsha. Therefore, the impact of job satisfaction on turnover intention is important, dir- ect and negative. Satisfaction has a significant predictive effect on turnover intention. At present, the medical working environment in China

is tense, more and more medical personnel have left their jobs and changed their posts. As a result, there is a shortage of medical staff, which destroys the medical en- vironment and disrupts the medical order and affects the health of the people. A study based on the National Health Service Survey in 2013 on China [27] found that the percentage of employees with low, medium and high turnover intentions was 44.5, 43.7 and 11.8% respectively in primary care institutions. And a foreign study [28] has investigated the turnover intention of nurses, and also found that 54% of them have turnover intention and 35% have turnover behavior. In view of the current shortage of medical personnel in various countries [29], job satisfaction and turnover intention may therefore be a crucial and topic concern. Most previous research focused on the relationship be-

tween job satisfaction, turnover intention and burnout, and exploring the factors affecting the three. However, few studies focus on the mediating effect of a certain factor. Such research in China is still in its infancy. The purpose of the research is to examine the mediating role of job satisfaction in the relationship between burnout and turnover intention.

Methods Participants and setting A cross-sectional survey design was used. This quantita- tive research was conducted in Huangpi District, Wu- han, China. The investigation involved a total of 20 public primary care institutions in Huangpi, including 18 township health centers and 2 community health centers. It conducted from March 2019 to June 2019. The Convenience sampling method was used. Taking into account the scale and level of institutions in rural area, 25 copies of each institutions were determined. Sample size was amplified according to the inefficiency of 10%, and the final expected sample size was 550. Spe- cialized investigators were trained to reduce information

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bias, inclusion and exclusion criteria was made to reduce selection bias. The inclusion criteria of participants were as follows, (1) Eligible participants had at least 6 months’ work experiences in their own workplace. (2) An em- ployee who had not suffered from mental illness and had not been stimulated by major adverse life events in the near future. (3) Participants were voluntary. And we excluded staffs with a working time of less than 6 months, and employees who were not in the post during the investigation. A flow diagram of participants was

shown in Fig. 1 in supplementary materials. There were 1402 questionnaires distributed. Questionnaires with un- completed answers or suspected unreal answers were ex- cluded. Finally, a total of 1370 of valid samples was collected with 97.72% effective rate. All responses were anonymous to protect the privacy of participants.

Measures We used self-administered questionnaires, which were classified into three parts, job satisfaction, burnout and

Fig. 1 The hypothetical model of the relationship between job satisfaction, burnout and turnover intention

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turnover intention. Then we adjusted the questionnaire according to the pilot survey, actual situation and local culture. All the measures were followed the translation and back-translation process from English to Chinese [30]. The three scales all use the 5-point Likert scale. Content of each scale was shown in Table 1 in supple- mentary materials. The KMO measure and Bartlett’s spherical test were used to test construct validity, it was acceptable if values of KMO measure were greater than 0.50 and p value of Bartlett’s spherical test less than 0.05. Cronbach’s alpha coefficient was calculated to examine internal consistency reliability, values higher than 0.70 were considered satisfactory.

Job satisfaction scale Job satisfaction was measured with 18 items selected from the Minnesota Satisfaction Questionnaire (MSQ) [31] and the Job Satisfaction Survey (JSS) [32]. The con- tent of job satisfaction included satisfaction with envir- onment, remuneration, management, the work itself [22, 33]. Sample item includes “The comfort level of the working environment (office environment, greening, lighting) will satisfy you.” (KMO measure =0.957, p < 0.01, Cronbach’s α = 0.970).

Burnout scale We used 5 items from the Maslach Burnout Inventory (MBI) [34] to measure individual burnout, and aggre- gated it to measure a positive effect on the burnout. Par- ticipants respond to the following items: “I feel that my daily [35] work is meaningless”, “I can’t find a sense of accomplishment at work”, “I feel exhausted when I get off work every day”, “this job has made me indifferent” and “this job makes me feel restless”. (KMO measure = 0.857, p < 0.01, Cronbach’s α = 0.925).

Turnover intention scale The turnover intention questionnaire was designed with reference to turnover intention scale explored by Grif- feth [36]. We measured turnover intention using 5 items. Participants respond to the following items: “I had the idea of leaving this organization”, “within a year, I will go to find a new job”, “If there is an opportunity, I will definitely accept a better job”, “I think the employ- ment situation in this organization is very good” and “Currently, I agree to find a good job in the market”. (KMO measure =0.800, p < 0.01, Cronbach’s α = 0.721).

Statistical analysis Data entry and conversion was completed with Epi- Data 3.0. Double machine imputing method was used to enter the collected data into the computer. Descriptive analyses were conducted to describe social demographic factors. The structural equation model (SEM) was

performed to adjust model fitting, the mediation effect test was carried out by using the bootstrap method. Sobel-Z test was used to verify the significance of medi- ation effect. Using SPSS 20.0 (IBM Corp, Armonk, NY, USA) and AMOS 24.0 (IBM Corp, Armonk, NY, USA) to analyze data, and p < 0.05 was determined to signifi- cant in statistics.

Results Descriptive statistics Table 1 showed the sociodemographic characteristics of the respondents. The mean age was 36.98 ± 9.84 years (minimum: 18 years, maximum: 73 years). 426 (31.09%) medical staffs were male and 944 (68.91%) were female. 42.77% of participants were physicians and 41.02% were nurses. The largest number of participants in the 40–49 age group, accounting for 34.45%, while the group over the age of 49 accounts for 11.31%. Most participants (77.30%) were married, 724 (52.58%) had a junior title and 63 % had no night shift in their work.

Structural equation model constructing and fitting The Hypothetical model was established, as shown in Fig. 1. We have constructed four paths: (1) Path a: Path from independent variable to potential mediator vari- able, the path coefficient of path a represents the indir- ect effect of burnout to job satisfaction (Job satisfaction ← Burnout). (2) Path b: The path from potential medi- ator variable to dependent variable, the path coefficient of path b represents the indirect effect of job satisfaction to turnover intention (Turnover intention ← Job satis- faction). (3) Path c: the path from independent variable to dependent variable, the path coefficient of path c rep- resents the total effect of burnout to turnover intention (Turnover intention ← Burnout). (4) Path c’: Under the influence of potential mediator variables, the path from the independent variable to the dependent variable, the path coefficient of path c’ represents the direct effect of burnout to turnover intention (Turnover intention’ ← Burnout’). As shown in Table 2. From the results of the hypo-

thetical model operation, we found that all fitting indices did not meet the fitting criteria, indicated that the hypo- thetical model was not ideal, so we revised the model. The model path was modified according to the amend- ment advice given by AMOS. We removed some items (A18, B1 and C4) and added lots of bidirectional arrows to make the model fitting better. The final fitting indices results were also shown in Table 2, and the revised stan- dardized path coefficient map was displayed on Fig. 2. After the adjustments, validity and reliability of the three scales remained acceptable. KMO measure, p value for batrtlett’s spherical test and Cronbach’s α for job satis- faction is 0.957, < 0.01 and 0.976, respectively; For

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Table 1 Demographic characteristics of the respondents (n = 1370) Variables Group n % Means (SD a)

Gender Male 426 31.09 –

Female 944 68.91

Age(year) b < 30 438 31.97 36.98 (9.84)

30–39 305 22.26

40–49 472 34.45

> 49 155 11.31

Occupation Physician 586 42.77 –

Nurse 562 41.02

Medical technician 92 6.72

Public health personnel 66 4.82

Pharmacist 34 2.48

Others 30 2.19

Educational background University and above 423 30.88 –

College 636 46.42

High school/Technical school 286 20.88

Junior high school and below 25 1.82

Marital status Married 1059 77.30 –

Unmarried 270 19.71

Divorced/widowed 41 2.99

Professional title Senior title 44 3.21 –

Middle title 207 15.11

Junior title 724 52.85

No title 395 28.83

Monthly income (RMB) > 5000 55 4.01 –

4001~5000 274 20.00

3001~4000 519 37.88

2001~3000 413 30.15

< 2000 109 7.96

Hire form Personnel agent staff 205 14.96 –

permanent staff 221 16.13

Contract staff 185 13.50

Temporary staff 759 55.40

Working time (hours a week) b < 30 18 1.31 42.92 (8.48)

31–40 973 71.02

41–50 235 17.15

> 50 144 10.51

Working years b 1–5 404 29.49 14.65 (10.87)

6–10 230 16.79

11–15 134 9.78

15–20 169 12.34

21–25 149 10.88

> 25 287 20.95

Frequency of night shift (times a week) 0 868 63.36 –

1–3 450 32.85

> 3 52 3.80

Note: a SD, standard deviation, this indicator was calculated only for quantitative data. b represented quantitative data

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turnover intention is 0.857, < 0.01 and 0.910, respectively; For burnout is 0.798, < 0.01 and 0.879, respectively. As shown in Table 3, we estimated the significance of

total effect, direct effect and indirect effect by bias- corrected approach. The results showed that the total ef- fect was significant of independent variable (burnout) to dependent variable (turnover intention) (p < 0.01), that was, the total effect of path c was statistically significant. The direct effects of path a (Job satisfaction Burnout) (p < 0.01), path b (Turnover intention ← Job satisfac- tion) (p < 0.01) and path c’ (Turnover intention’ ← Burn- out’) (p < 0.01) were also significant. The results of indirect effect test again proved that path c’ (Turnover intention’ ← Burnout’) was statistically significant. We concluded that this mediating effect was a partial medi- ating effect. As shown in Table 4, standardized estimates and its

standard errors were calculated by using the bootstrap method. Standardized path coefficient of path a (Job Sat- isfaction ← Burnout) is − 0.410 and its standard error (Sa) is 0.038. Standardized path coefficient of path b (Turnover intention ← Job satisfaction) is − 0.180 and its standard error (Sb) is 0.028. Standardized path coeffi- cient of path c’ (Turnover intention’ ← Burnout’) is 0.824 and its standard error (Sc′) is 0.029. Looking up the related tables, the standardized path coefficient of path c is 0.899 and its standard error (Sc) is 0.020. Soble-Z test is carried out according to the formula z

¼ abffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sa2b

2þsb2a2 p . Finally, z = 4.506. According to MacKin-

non’s critical value table, the result is p < 0.05, indicating that the mediation effect is significant.

Interpretation of the revised model Model fit is acceptable if χ2/df ≤ 4.0 [37], GFI > 0.90, AGFI > 0.90, CFI > 0.90, NFI > 0.90, IFI > 0.90 [38], TLI > 0.90 and RMSEA < 0.05. As shown in Table 2, all fit indexes are up to standard, except for χ2/df and RMSEA. Our sample size is larger than 1000, the value of χ2/df is acceptable. In another study, the author points out that 0.05 < RMSEA < 0.08 is also acceptable [39]. Overall, the model fits well and the model is established.

As shown in Fig. 2, the standardized path coefficient of burnout to job satisfaction is − 0.41, indicates that burnout is negatively correlated with job satisfaction (p < 0.01). It shows that when the other conditions are unchanged, the turnover intention decreases by 0.41 units for each additional unit of burnout. The standard- ized path coefficient of job satisfaction to turnover intention is − 0.18, demonstrates that job satisfaction is also negatively correlated with turnover intention (p < 0.01). Under the same other conditions, the turnover intention decreases by 0.18 units for each additional unit of job satisfaction. The standardized path coefficient of burnout to turnover intention is 0.83, reveals that burn- out is positively correlated with turnover intention (p < 0.01). That is, under the influence of job satisfaction, the turnover intention increases by 0.83 units for each add- itional unit of burnout. The mediation effect is statistically significant (p <

0.01), and the impact of burnout on turnover intention through the intermediary effect of job satisfaction is 0.074 (a*b = (− 0.180)*-(0.410)). It manifests that when other conditions remain unchanged, the turnover intention will be indirectly increased by 0.074 units for each unit of burnout.

Discussion Our results demonstrated that for medical workers in primary care institutions, a mediator variable was existed in burnout and turnover intention: job satisfaction. Job satisfaction was usually regarded as a dependent variable [40–42] or an independent variable [43, 44] in most of the current studies. And work to family conflict [42], work engagement [40], burnout and workload [45] were viewed as mediator variables. However, an American study suggested that burnout was the biggest predictor of job satisfaction [13]. There were few researches focus on job satisfaction as a mediator variable, which provides new ideas for future research. That’s why we try to study how job satisfaction as a mediating variable affects the correlation between burnout and turnover intention. The results of the study provided further support for the

importance of job satisfaction in engaging the workforce and retaining staff to settle the demands and challenges

Table 2 Comparison of different fitting indices on hypothetical model and adjusted model

Fit index χ2 a χ2/df b GFI c AGF d CFI e NFI f IFI g TLI h RMSEA i AIC j

Optimum model – 2–5 > 0.90 > 0.90 > 0.90 > 0.90 > 0.90 > 0.90 < 0.05 –

Hypothetical model 9695.143 27.940 0.600 0.533 0.801 0.796 0.802 0.784 0.140 9813.143

Results – unfit unfit unfit unfit unfit unfit unfit unfit –

Adjusted model 1157.059 5.590 0.932 0.901 0.977 0.973 0.977 0.970 0.058 1343.059

Results – acceptable fit fit fit fit fit fit acceptable –

Note: a χ2, Chi-square. b χ2/df, Chi-square divided by degree of freedom. c GFI Goodness of fit index, d AGFI Adjusted goodness of fit index, e CFI Comparative fit index, f NFI Normed fit index, g IFI Incremental fit index. h TLI Tucker-Lewis index. I RMSEA Root mean square error for approximation. j AIC Akaike information criterion

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facing health care setting in primary care institutions. Turnover intention was negatively related to job satisfac- tion and positively related to burnout. Negative correl- ation was found between job satisfaction and burnout. Some studies conducted in China have also obtained simi- lar results [14, 15, 22–24, 26, 27, 46], but the correlation coefficient of their results is greater than ours. In our study, the correlation coefficient of path b (Turnover

intention ← Job satisfaction) is relatively small, only − 0.18. We speculated that traditional studies which usually used methods such as multivariate linear regression, logis- tic regression, and ANOVA may neglect the measurement error, so its negative correlation is stronger. However, the error is taken into account in the SEM. Our finding showed that the mediating effect was a

partial mediating effect. First of all, we need to realize

Fig. 2 The revised structural equation modeling of the relationship between job satisfaction, burnout and turnover intention

Table 3 The p-value of significance test of total effect, direct effect and indirect effect by bias-corrected approach

Burnout Job satisfaction Turnover intention

Total effect Job satisfaction 0.001 –

Turnover intention 0.001 0.001 –

Direct effect Job satisfaction 0.001 – –

Turnover intention 0.001 0.001 –

Indirect effect Job satisfaction – – –

Turnover intention 0.001 – –

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what the difference between partial mediation and complete mediation is. In the process of partial medi- ation, any variable in the causal chain, when it controls the variable before it (including the independent vari- able), it will affect its subsequent variables significantly. And in the process of complete mediation, after control- ling the mediation variables, the influence of the inde- pendent variables on the dependent variables is not significant. Job burnout is closely related to turnover intention, and job burnout affects turnover intention dir- ectly [17–22], thus causing a partial mediating effect. Although the mediation effect test confirms that the ex-

istence of job satisfaction as a mediating variable of job burnout affects turnover intention, the mediation effect has a low impact of 7.4%. That is to say, the mediation effect accounts for 7.4% of the variation of dependent variables. Despite the low mediation effect, we still believe that the re- search is valuable and meaningful. We speculated that the low mediation effect was caused by the following reasons. Firstly, it is related to the choice of independent variables closely. Job burnout includes three dimensions: emotional exhaustion, depersonalization and reduced personal accomplishment [6]. For example, family conflict and doctor-patient relationship are more intuitive than job burnout as important factors that directly affect emotions. If we take them as independent variables directly, the re- spondent would understand the meaning of the question more clearly, therefore, the path coefficients of path a (mediator variable ← independent variables) would be lar- ger than those of the study (job satisfaction ← burnout), which will lead to a higher mediation effect. Secondly, turnover intention, burnout and job satisfaction are diffi- cult to measure directly. The items and measuring methods may be somewhat various in different studies [47]. Therefore, the difference of instrument selection is also one of the important reasons for the inconsist- ency of results. Thirdly, the influence of job satisfac- tion on turnover intention is limited. There are many factors affecting turnover intention. Turnover intention is influenced not only by job satisfaction, but also by social demographic factors such as educa- tion [43], years in work [43], family’s relationship [43], monthly income, social support [48], mentoring [49], etc. and other unobserved factors. Therefore, these demographic factors and unobserved factors should be taken into account in the future study.

Our study also has some limitations. Firstly, Because of the diversity of the participants (including doctors, nurses, technicians.), we don’t use the international scale completely. According to the research purpose, the ap- propriate items have been chosen from these inter- national scales, and the reliability and validity of the questionnaire are still guaranteed. Secondly, sample rep- resentativeness needs to be improved. This research can not be generalized to all china as our study place limited. In the future, we will continue to cooperate with other local governments in central China and to conduct simi- lar surveys to solve the problem.

Conclusions Our study provides a clear understanding of how job satisfaction can mediate the relationship between burn- out and turnover intention. In the process of burnout af- fecting turnover intention, job satisfaction can be regarded as a mediating variable to influence its effect, and the mediating effect was a partial mediating effect. And the mediation effect has a low impact of 7.4%. Turnover intention was negatively related to job satisfac- tion and positively related to burnout, job satisfaction was negative related to burnout. We can make full use of this relationship to adjust the impact of job burnout on turnover intention by improving job satisfaction. Some operable and useful measures were taken to re- duce employee turnover rate and alleviating the current situation of shortage of health personnel in China, such as improving treatment and giving more promotion op- portunities for workers to improve job satisfaction, con- ducting career planning courses and paying attention to employee psychological health to reduce job burnout.

Supplementary information Supplementary information accompanies this paper at https://doi.org/10. 1186/s12889-019-7894-7.

Additional file 1: Figure S1. A flow diagram of participants.

Additional file 2: Table S1. Contents of three scales in the questionnaire.

Abbreviations AGFI: Adjusted goodness of fit index; AIC: Akaike information criterion; ANOVA: Analysis of Variance; CFI: Comparative fit index; GFI: Goodness of fit index; IFI: Incremental fit index; JSS: Job Satisfaction Survey; MBI: Maslach Burnout Inventory; MSQ: Minnesota Satisfaction Questionnaire; NFI: Normed fit index; RMSEA: Root mean square error for approximation; Sa: The standard

Table 4 Standardized path coefficient and standard error of three main paths by bootstrap

Path SE SE-SE Mean Bias SE-Bias

Job Satisfaction ← Burnout 0.038 0.001 −0.410 0.000 0.001

Turnover ← Job Satisfaction 0.028 0.000 −0.180 −0.002 0.001

Turnover’ ← Burnout’ 0.029 0.000 0.824 −0.002 0.001

Note: SE standard error. SE-SE standard error caused by using bootstrap to estimate standard errors. SE-Bias the standard error of bias

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error of standardized path coefficient of path a; Sb: The standard error of standardized path coefficient of path b; Sc: The standard error of standardized path coefficient of path c; Sc′: The standard error of standardized path coefficient of path c’; SD: Standard Deviation; SE: Standard Error; SE-Bias: The standard error of bias; SEM: Structural Equation Model; SE- SE: The standard error caused by using bootstrap to estimate standard errors; TLI: Tucker-Lewis index; χ2: Chi-square; χ2/df: Chi-square divided by degree of freedom

Acknowledgements We would like to thank Wuhan University and Huangpi District Center for Disease Control and Prevention for their support of this project, as well as the efforts of team partners in the project.

Authors’ contributions XyC participated in the survey, the data analysis and the writing of the article. LR took part in the design of the study and the writing of the article. YtZ and JrY contributed to the data collection and screening. HY and SrZ were involved in the data analysis and participated in the literature research. XdT made a second revision of the manuscript. All authors have read and approved the final version.

Funding Not applicable.

Availability of data and materials The data that support the findings of this study are available from the CDC of Huangpi district but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the CDC of Huangpi district.

Ethics approval and consent to participate The ethics committee of Wuhan University School of Medicine (WUSM) reviewed it, and verified it to comply with the Declaration of Helsinki and its revised version, as well as the relevant regulations of biomedical journals, and approved the research (No.2018YF0080). All participants were provided with information about the investigation and gave written informed consent to participate.

Consent for publication Not applicable.

Competing interests The authors declare that they have no competing interests.

Author details 1School of Health Sciences, Wuhan University, Donghu Road 115, Wuhcang District, Wuhan 430000, Hubei, China. 2School of Clinical Medicine, Wuhan University of Science and Technology, 947 Heping Avenue, Qingshan District, Wuhan 430000, Hubei, China.

Received: 21 July 2019 Accepted: 1 November 2019

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