Association between long working hours and suboptimal health status on primary care physicians：a cross-sectional survey from primary care institutions in China

doi:10.21203/rs.3.rs-2993360/v1

Download PDF

Research Article

Association between long working hours and suboptimal health status on primary care physicians：a cross-sectional survey from primary care institutions in China

https://doi.org/10.21203/rs.3.rs-2993360/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Primary care physicians in China generally work overtime and are exposed to higher incidence of suboptimal health status (SHS). However, the quantitative relationship between long working hours and SHS on primary care physicians remains unclear. This study aims to analyze the association of long working hours and SHS in China.

Methods

This nationwide cross-sectional survey was conducted with a randomized stratified sampling strategy. The Sub-Health Measurement Scale V1.0 was applied to measure SHS and logit regression was further adopted to explore association between long working hours and SHS.

Results

A total of 1227 questionnaires from 545 primary care institutions in China returned, of which 687 were valid with 56% effective rate. All the surveyed primary care physicians reported to work over 10 hours in at least one day per week. Compared to 6–7 days/week working over 10h, the risk of global SHS in the physicians who worked over 10 hours 4–5 days/week, 2–3 days/week and 1–2 days/week were increase with OR = 2.11([95%CI [1.11 ~ 4.01],P = 0.023),OR = 2.08 (95%CI [1.13 ~ 3.81], P = 0.018),OR = 1.86 (95%CI[1.03 ~ 3.37], P = 0.039), respectively. Compared to 6–7 days/week working over 10h, the risk of social SHS in the physicians who worked over 10 hours 4–5 days/week, 2–3 days/week and 1–2 days/week were increase with OR = 2.63([95%CI [1.35 ~ 5.14], P = 0.005),OR = 1.9 (95%CI [1.04 ~ 3.49], P = 0.037),OR = 1.92 (95%CI [1.06 ~ 3.48], P = 0.031), respectively.

Conclusion

Different from previous studies, this study surprisingly found that higher frequency of long working hours was related to lower incidence of global SHS and social SHS among primary care physicians in China, which indicated that long working hours not a risk factor for SHS and probably since hard-working increases Chinese physicians’ self-social values further reduces the incidence of SHS.

suboptimal health status

long working hours

primary care physicians

primary care institutions

nationwide survey

Health defined by the World Health Organization (WHO) is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity(1). Originated from Traditional Chinese Medicine, suboptimal health status (SHS) is defined as an inter-mediate status with low-quality of health in physiology, psychology and social adaptation between disease and health(2). SHS is also called “sub-health state”, “third state” or “grey state”(3), which is similar to chronic fatigue syndrome(4–7). Remaining in a SHS state may develop into a sub-clinical state and then into a disease state. A global survey by WHO showed that 75% of people were in a state of transition from health to disease(8). SHS has become an increasingly concerning problem in many countries.

The incidence of SHS among primary care physicians in China remains high. The Chinese Medical Doctor Association survey report in 2016 revealed that 63.6% of primary care physicians were in SHS(9). Study of the SHS and influencing factors of primary care physicians is the premise and foundation for improving health. In fact, it has become a new public health challenge in China(10).

Primary care physicians in China have a heavy workload and must work long time. The number of doctors per thousand people in China is 1.8, which is far lower than other countries in the world (United States 2.6; Germany 4.2; France 3.2)(11). According to the statistical bulletin of the development of health care in China in 2021 issued by the National Health Commission of the People’s Republic of China (NHCPRC)(12), health care workers in primary medical institutions in China accounted for 31.7% of the total health care workers, but the total number of consultations of primary medical institutions reached 4.25 billion, accounting for 50.2% of that in China. Therefore, primary care physicians must undertake a huge workload and need to work overtime. According to the 2021 Survey Report of Doctor(13), the average working hours of doctors in China are 9.24 hours per day, 5.77 days per week, and the average working hours per week exceed 53 hours, which far exceeds that in developed countries such as Germany (38.7h/ week), France (38.7h/ week) and Italy (39.6h/ week)(14).

Currently, many studies have shown that working overtime will damage health, but there remains controversy in studies about the relationship between long working hours and health. At present, studies have shown that long working hours were related to sleep, subjective fatigue symptoms, depression and so on in nurses, employees, workers, programmers and other occupations(15–19). For example, a research on sub-health from China shown that overtime work will raise the incidence of sub-health of programmers and long working hours are a negative factor for health(19). However, contrary to most of the current studies, several studies have shown that long working hours are related to lower sick leave levels(20, 21) and there was no trade-off between long working hours and physical activities, as it would not affect the health as giving up exercise for long working hours(22).

Primary care physicians have a higher incidence of sub-health and long working hours, but the quantitative relationship between long working hours and sub-health is unclear. Thus, the purpose of this study was to verify the relationship between long working hours and sub-health among primary care physicians through a nationwide cross-sectional investigation.

2.1 Study design and participants

Primary care physicians in China were the survey object. The inclusion criteria were as follows: (1) the primary care physicians were at least 18 years of age; (2) working in primary care institutions for 1 year or above. (3) available and willing to participate in the study. Each eligible respondent was provided with informed consent. The Exclusion criteria were as follows: (1) has been diagnosed as serious organic diseases or diseases that have a significant influence on physical condition; (2) unable to cooperate with the final investigation; (3) According to the norms of percentile for SHMS V1. 0 scores of Chinese urban residents, the sub-health score obtained from the survey was classified as the disease status.

A stratified sampling strategy was adopted. All 31 provincial administrative regions (including provinces, autonomous regions, and municipalities directly under the central government) in mainland China were covered in the sampling. Cities in all provinces, autonomous regions and districts were divided into 3 groups on average according to the GDP per capita in 2020, which is associated with the factors influencing the working status of medical staff(23), and 93 groups were generated. In each group, one city or region was selected by the random number method, and 93 cities or regions were selected by so. In each selected city or district, 2 to 4 tertiary hospitals were surveyed by convenience based on the hospital administrators’ permission to conduct the survey, with the hospital level verified by consulting the hospital information tool by the NHCPRC. This ensured that at least 186 hospitals would be selected. In each surveyed hospital, 2 participants were recommended by the hospital administrator(s) or another participant who completed the survey. Overall, 1227 questionnaires were distributed.

2.2 Patient and public involvement

The participants were not involved in the development of the research question or design of this study.

2.3 Survey instrument

5 experts in healthcare administration and health statistics from universities were consulted for the design of the study questionnaire. The questionnaire consists of three parts and was written in Chinese.

Section 1: Consisting of two aspects of information. The former aspect was general demographic characteristics, which included age, gender, marital status, highest education level, professional title, number of children, per capita monthly household income, because these factors are potentially associated with the primary care physicians’ health status in China(24). It has been proved that lifestyle will affect physicians’ health(25) so another aspect was lifestyle, which included smoking, alcohol intake, diet habits, breakfast consumption, physical exercise and night shift.

Section 2: This section was a survey of long working hours. The opinions of five clinical experts from primary medical institutions in China were referred to the design of the way of asking questions of long working hours and the items. Experts agreed that 10 h/ day was the appropriate limit to distinguish between proper and excessive working hours and the frequency of days working over 10h in a week can be taken as the measurement of long working hours. Thus, items were set as 0–1 day/week working over 10h, 1–2 days/week working over 10h, 2–3 days/week working over 10h, 4–5 days/week working over 10h and 6–7 days/week working over 10h.

Section 3: This section was SHS scale. The SHMS V1.0 was developed by Jun Li etc. based on China's cultural background and social values(26, 27)was performed to assess SHS. SHMS V1.0 was proven to have high reliability and validity in a Chinese population(27) and has been widely used in SHS evaluation of urban residents(28), nurses(29), college students(30, 31), migrant workers(32), teachers(33, 34) and other groups of people. The SHMS V1.0 comprises of 39 items, adopting the internationally accepted Likert five-grade scoring method and consisting of three subscales: physiological suboptimal health status (PS), mental suboptimal health status (MS), and social suboptimal health status (SS). The original rough scores of dimensions were calculated as sums of item scores in each subscale, and the sum of the scores of the three subscales is the original rough score of the global SHS. Then the original rough score scores were transformed to a scale ranging from 0 to 100. (transformed score = (original score- lowest theoretical score)/ (highest theoretical score- lowest theoretical score) ×100). Finally, according to norms of percentile for SHMS V1. 0 score of Chinese urban residents, whose health status was divided.

All instruments were proofed by the researchers, followed by a pilot survey in tertiary hospitals in Nanjing, Jiangsu province, China, to test the rationality, understandability, and readability of the questionnaire.

2.4 Data collection

A total of 538 undergraduate students from all over the country with a university education background in pharmacy or pharmaceutical science were recruited for data collection, and all the data collectors had been trained in determination of research object, explanation of research content, research form and process, research etiquette, etc. According to the principle of proximity, data collectors selected the primary care institutions close to the place of their residence to conduct research during July to September of 2021. After obtaining the consent of the managers of primary care institutions, making use of the free time of potential respondents, the data collector asked the basic information of potential respondents to determine whether they meet the inclusion criteria, and then explained the purpose, content, and requirements of this survey to the respondents, and asked them if they are willing to participate. After obtaining the informed consent signed by the respondents, the investigation could be officially carried out.

Data collectors used the mobile phones or tablets to open the online survey system, and the respondents completed and submitted it under the guidance of data collectors. In the process of investigation, the data collector could not provide any opinions about the questionnaire, but only the requirements or instructions for filling out the questionnaire. All the research data were collected by team-designed software (for Android: http://117.78.41.192:8088/bistone/framework/app/mianFangMaster.apk) and for iOS: https://itunes.apple.com/us/app/%E9%9D%A2%E8%AE%BF%E5%A4%A7%E5%B8%88/id1063924434?mt=8) and were processed into identifiable electronic data.

If any quality problems in the questionnaire during the investigation fined by the auditor, then the data collector would be asked to reinvestigate a questionnaire until it meets the requirements. If the quality problems were found after the investigation, then the questionnaire would be rejected.

2.5 Data analysis

All data were imported into SPSS 24.0 and checked by two researchers respectively to perform data verification and exclude any questionnaire that were incomplete or obviously erroneous.

Descriptive statistics were used to describe the characteristics of the samples. Logistic regression model was used to assess the association of each independent variable with SHS where the dependent variable was a dichotomous categorical variable (health and sub-health). The total SHS and three dimensional SHS were separately established a logistic regression model while using the same independent variables.

Completely irrelevant variables were added into the regression to check whether the model was stable.

Analyses were conducted using Stata 15.0, and the level of significance was set at p < 0.05.

3.1 Descriptive statistics

Overall, 1227 questionnaires from 545 primary care institutions were uploaded and 687 of those were included (response rate = 56%). Primary reasons for exclusion included: (1) the institution where the respondent belongs to was not a primary healthcare institution; (2) the answers to questions were inconsistent and did not pass the logic test; (3) the questionnaire was incomplete or the datafile of which was damaged and unable to be fixed or rebuilt by return visits; (4) the global and subscales scores obtained from the survey were classified as the disease status. The demographic characteristics of the samples are displayed in Table 1, and the detailed information is displayed in Appendix 1. The average age of the sample was 41 (SD = 8.6) and the ratio of male to female was approximately 1.01:1. Most of the participants were married (90.2%).and most of the participants had a bachelor's degree (63.7%). Among the primary care physicians surveyed, all of them reported working over 10 hours for at least one day per week, and the frequency of most physicians working more than 10h/ day in a week was 1–2 days/week (38.0%) and 2–3 days/week (31.3%). The average score of physicians' global sub-health scale was 76.2(SD = 8.9). According to the norm of urban residents in China, 68.7% were in global sub-health status, 68.6% were in physiological sub-health status, 74.2% were in mental sub-health status and was in social sub-health status.

Table 1

Demographics of the Participants (n = 687)
Items	Frequency	Percentage
Gender
Male	341	49.6%
Female	346	50.4%
Age	41(mean)	8.6(sd)
Marital status
Unmarried	63	9.2%
Married	620	90.2%
Other status (divorced or widowed)	4	0.6%
Education
Senior high school and below	52	7.6%
Universities and colleges	197	28.7%
Bachelor’s degree	355	51.7%
Master’s degree	76	11.1%
Doctoral degree	7	1.0%
Professional title
Junior	279	40.6%
Intermediate	284	41.3%
Vice-senior	68	9.9%
Senior	22	3.2%
Working years	15(mean)	8.7(SD)
Frequency of working more than 10h/ day in a week
0-1day/week	0	0.0%
1–2 days/week	261	38.0%
2–3 days/week	215	31.3%
4–5 days/week	132	19.2%
6–7 days/week	79	11.5%
GS
Health	215	31.3%
Sub-health	472	68.7%
PS
Health	216	31.4%
Sub-health	471	68.6%
MS
Health	177	25.8%
Sub-health	510	74.2%
SS
Health	170	24.7%
Sub-health	517	75.3%
Global SHS score	76.2(mean)	8.9(SD)

3.2 Association analysis

Four analytic models were constructed and the dependent variables were global suboptimal health status (GS), physiological suboptimal health status (PS), mental suboptimal health status (MS), and social suboptimal health status (SS) with the same independent variables. The regression results are shown in Table 2. Pseudo-R² of the four models were 0.1213、0.0853、0.1332 and 0.0924. The GS model results suggested that compared with 6–7 days/week working over 10h, the GS odds ratio (OR) of 4–5 days/week working over 10h, 2–3 days/week working over 10h and 1–2 days/week working over 10h were 2.11 ([95% CI [1.11–4.01], P = 0.023), 2.08(95%CI[1.13,3.81], P = 0.018) and 1.86(95%CI[1.03,3.37], P = 0.039) respectively. The PS model results suggested that there was no significant relationship between longer working hours and PS. The GS model results suggested that compared with 6–7 days/week working over 10h, the MS odds ratio (OR) of 1–2 days/week working over 10h was 1.9([95%CI [1.01,3.54],P = 0.045]. The SS model results suggested that compared with 6–7 days/week working over 10h, the SS OR of 4–5 days/week working over 10h, 2–3 days/week working over 10h and 1–2 days/week working over 10h were 2.63 ([95%CI [1.35,5.14], P = 0.005],1.9 (95%CI [1.04,3.49], P = 0.037), 1.92(95%CI [1.06,3.48], P = 0.031) respectively.

Table 2

Regression results of models
models	GS health		PS health		MS health		SS health
P	< 0.001		< 0.001		< 0.001		< 0.001
Pseudo R2	0.1213		0.0853		0.1332		0.0924
Items	OR [95%CI]	P	OR [95%CI]	P	OR [95%CI]	P	OR [95%CI]	P
Constant	1.18 [0.25,5.6]	0.837	1.33 [0.3,5.95]	0.712	2.62 [0.5,13.61]	0.252	4.4 [0.87,22.23]	0.073
Frequency of working more than 10h/ day in a week
0-1day/week (control group)
1–2 days/week	2.11 [1.11,4.01]	0.023^*	1.42 [0.75,2.68]	0.282	1.16 [0.6,2.26]	0.662	2.63 [1.35,5.14]	0.005^*
2–3 days/week	2.08 [1.13,3.81]	0.018^*	1.62 [0.89,2.95]	0.114	1.67 [0.88,3.17]	0.115	1.9 [1.04,3.49]	0.037^*
4–5 days/week	1.86 [1.03,3.37]	0.039^*	1.31 [0.73,2.34]	0.362	1.9 [1.01,3.54]	0.045^*	1.92 [1.06,3.48]	0.031^*
Gender
Male (control group)
Female	1.08 [0.71,1.64]	0.729	0.78 [0.52,1.18]	0.24	0.72 [0.46,1.13]	0.148	1.21 [0.78,1.89]	0.396
Age	0.98 [0.94,1.02]	0.255	0.98 [0.94,1.02]	0.4	0.98 [0.93,1.02]	0.286	0.97 [0.93,1.01]	0.131
Number of children
No children (control group)
One child	0.89 [0.29,2.75]	0.845	0.98 [0.34,2.81]	0.966	1.35 [0.43,4.17]	0.606	0.7 [0.19,2.66]	0.604
Two children	1.02 [0.32,3.31]	0.971	1 [0.33,3.02]	1	1.18 [0.36,3.85]	0.783	0.55 [0.14,2.16]	0.389
Three children or above	1.33 [0.27,6.49]	0.728	2.39[0.45,12.66]	0.307	1.34 [0.27,6.63]	0.719	0.46 [0.08,2.53]	0.374
Marital status
Unmarried (control group)
Married	0.96 [0.28,3.31]	0.948	0.98 [0.31,3.11]	0.97	0.74 [0.21,2.62]	0.638	1.21 [0.28,5.23]	0.795
Other status (divorced or widowed)	1.23 [0.07,20.3]	0.886	1.39 [0.09,21.09]	0.814	0.87 [0.05,14.29]	0.923	1.05 [0.06,18.71]	0.975
Annual personal income	0.97 [0.94,1.01]	0.095	0.99 [0.95,1.02]	0.441	0.97 [0.93,1]	0.079	0.96 [0.93,1]	0.031^*
Working years	0.98 [0.95,1.02]	0.427	1 [0.96,1.03]	0.809	0.98 [0.94,1.02]	0.292	1 [0.96,1.04]	0.964
Smoking
None (control group)
Occasionally	1.56 [0.87,2.81]	0.137	1.31 [0.74,2.32]	0.351	0.84 [0.46,1.56]	0.588	0.91 [0.51,1.61]	0.739
Frequently	2.41 [0.71,8.26]	0.16	2.31 [0.69,7.67]	0.172	1.95 [0.46,8.2]	0.361	1.71 [0.52,5.68]	0.38
Very frequently	1.31 [0.11,16.42]	0.832	0.94[0.07,11.95]	0.965	0.78 [0.06,10.32]	0.852	1.36 [0.11,17.09]	0.814
Drinking
None (control group)
Occasionally	2.71 [1.74,4.24]	0.000^**	1.82 [1.19,2.8]	0.006	3.14 [1.92,5.14]	0.000^**	2.31 [1.45,3.7]	0.000^**
General	1.32 [0.63,2.74]	0.462	0.81 [0.4,1.62]	0.549	1.5 [0.69,3.29]	0.307	1.49 [0.69,3.24]	0.31
Frequently	2.18 [0.36,13.06]	0.393	1.68 [0.29,9.74]	0.562	1.57 [0.26,9.63]	0.624	0.84 [0.17,4.03]	0.823
Very frequently	3.79 [0.28,51.74]	0.318	0.61 [0.07,5.62]	0.661	2.44 [0.16,36.15]	0.516	1.85 [0.17,20.53]	0.617
Exercises
Very frequently (control group)
Frequently	1.79 [0.93,3.47]	0.083	1.48 [0.79,2.8]	0.222	1.64 [0.85,3.16]	0.143	0.92 [0.48,1.77]	0.812
General	4.42 [2.35,8.3]	0.000^**	4.15 [2.26,7.64]	0.000^**	4.14 [2.19,7.82]	0.000^**	2.15 [1.15,4.02]	0.016^*
Occasionally	4.38 [2.27,8.47]	0.000^**	3.35 [1.78,6.28]	0.000^**	4.03 [2.07,7.84]	0.000^**	3.31 [1.68,6.54]	0.001^*
Hardly	5.08 [2.07,12.49]	0.000^**	3.86 [1.63,9.14]	0.002^*	8.69 [3.06,24.7]	0.000^**	2.45 [0.98,6.1]	0.054
Night shift needed
Yes (control group)
No	0.97 [0.66,1.43]	0.893	0.99 [0.68,1.44]	0.967	1.14 [0.76,1.71]	0.524	0.87 [0.58,1.31]	0.512
P < 0.05, *P < 0.0

The irrelevant variable was introduced into the logistic regression model and the results were stable.

The study aims to determine the quantitative relationship between long working hours and SHS in primary care physicians in China. 687 valid questionnaires from nationwide were collected and the sample has a similar distribution of sex, age, and education background to those indicators of the sample reported in a national study of investigation report of doctors in China in 2021 conducted by Tsinghua University(13), indicating acceptable representativeness of the sample. The results of this study show that a higher frequency of long working hours was related to lower quality of global SHS and social SHS among primary care physicians in China.

Long working hours are related to lower incidence of global sub-health, physiological sub-health and social sub-health. The results of this study indicate that compared with primary care physicians who worked 6–7 days/week working over 10h, the total incidence of sub-health is higher when working over 10 hours with lower frequency. The results were inconsistent with most previous studies had shown the adverse effects of long working hours on SHS status and the results could support several previous studies had shown long working hours were inversely related to sick leave(20, 21, 35–37). The differences in study populations and regions may account for some of the contradictory findings. The result of this study that higher frequency of long working hours was associated with lower prevalence of SHS was attributed to the Chinese specialties with demand for primary care physicians in China and the cultural background of hard-working.

Hard-working is considered as a positive spirit and diligence is emphasized that it can make up for the lack of ability and better than innate ability in Chinese society(38). Therefore, grown up in such a cultural environment(39), Chinese workers are proud of being able to persist in their work in a tough working environment. Primary medical institutions in China account for 95% of all medical institutions, which are the first place for most patients. Thus, with a great demand for primary care physicians in society, physicians need to undertake a huge workload and work long hours. On the one hand, physicians can feel that they are needed by society and then realize their personal social value. In addition, because the cultural background, physicians have psychological adaptability. On the other hand, belonging to the frontline workers, primary care physicians strive subjectively to take on more workload and work longer hours to improve their performance and promote upward. In conclusion, the cultural background of long-term survival and the subjective initiative of primary care physicians themselves on long working hours may explain the relationship between a higher frequency of long working hours and lower incidence of global SHS, social SHS and mental sub-health.

However, long working hours are not a positive practice. For individuals, long working hours will cause fatigue, lower sleep quality, etc., and may even bring harm such as an increasing medical error rate and medical obstacles. The policymakers and medical institutions managers should think highly of long working hours and the health status of physicians(40, 41). Some measures should be taken to improve the working conditions of primary doctors, such as increasing the staffing of physicians in primary care institution, optimizing working facilities and equipment, and making full use of the internet healthcare to improve the way of working and enhance working efficiency to reduce the working hours of physicians.

Long working hours did not associate with the prevalence of physiological sub-health, which may stem from the good living habits. According to the results of the survey, most of the interviewees have a routine lifestyle, no smoking, no drinking, insisting on having breakfast every day and doing physical exercises frequently. There has been a study indicating that there was no trade-off between long working hours and physical activity(22), and previous studies have highlighted that regular physical exercise is a protective factor for health while poor lifestyles are an important risk factors for health(42). A study has suggested that people with bad lifestyle had a 34-fold higher risk of sub-health than those with good lifestyle(43). The results of the current study support this conclusion as primary care physicians who did not drink alcohol had lower risk of sub-health in physiological, mental and social dimensions than those who occasionally drank alcohol. The higher the frequency of exercise was, the lower was the risk of sub-health in physiological, mental and social dimensions. Long working hours did not contribute significantly to the physiological, mental health of primary care physicians.

The present study showed that income growth was associated with a decrease in social sub-health. The traditional health economics framework of Grossman's health production model expressed that health status increased with the increase of income(44). Being in the front line of social work, physicians in primary care institutions improve their social status by earning money. This may explain the increase in income and the decrease in the incidence of SS sub-health among physicians.

This study has several limitations: Firstly, due to the explorative cross-sectional design of this study, the causal relationship between long working hours and sub-health could not be derived and future studies are required to prove this relationship. Secondly, there could be recall bias while filling out the questionnaire in this study. Thirdly, convenience sampling used in the stratified sampling procedure may have led to a biased sample; however, the sociodemographics of the sample was relatively consistent with the sociodemographics of a national study of investigation report of doctors in a previous study.

This study explored the relationship between long working hours and sub-health of physicians in primary care institutions by using a logistic model through national cross-sectional investigation. This study found that physicians surveyed had more than 10h working days in a week and a higher frequency of working over 10h / day a week was related to the lower incidence of global sub-health and social sub-health of physicians. The present result were generally inconsistent with previous findings. One possible explanation was the influence of culture. The possible explanation was that China's unique industrious spiritual culture, the nature of doctors' work based on experience and the social demand for doctors stimulate doctors' subjective initiative in labor, which buffers the negative impact of long working hours. At the same time, among the surveyed physicians, good living habits bear the negative impact of long hours' work on the health.

Long working hours are objectively not a positive working state for individual doctors. The contribution of this research lies in the vigilance of medical institution managers to pay attention to the workload and working hours of primary doctors. Although doctors can subjectively resolve the contradiction between working hours and individuals in short-term, long working hours will directly affect health.

The study included physicians from 545 primary care institutions and was nationally representative. Although we distributed 1227 questionnaires, the efficiency of the questionnaire was low due to the high standard we set for qualifying questionnaires.

Ethics approval and consent to participate

The ethical approval to conduct the pilot survey, pre-test and main survey was granted by the Ethics Committee of China Pharmaceutical University (Project Number: CPU2019015). The informed consent had been obtained from all subjects and/or their legal guardian(s). We confirm that all methods were performed by the relevant guidelines and regulations or the Declaration of Helsinki and informed consent was obtained from all subjects or their legal guardians.

Patient consent

Informed consent was obtained from all subjects or their legal guardians.

Consent for publication

NA.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

NA.

Author Contributions

Jiayuan Liu, and Xiaoyu Xi designed the research; Rong Huang and Yuankai Huang performed the research; Rong Huang and Yuankai Huang analyzed the data; The first draft of the manuscript was written by Rong Huang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgments

The authors would like to express their gratitude to all the respondents for their participation.

World Health Organization. What is the WHO definition of health? : World Health Organization.; [Available from: http://www. who. int/ suggestions/ faq/ en/.
Xue YL, Xu J, Liu GH, Huang C, Feng YFg, Xu MY, et al. Evaluation of the subhealth status of urban residents in China based on the Subhealth Rating Scale (SHMS V1.0). China General Medicine. 2021;24(07):834-41.
Cen Y, Ma XQ, Cheng SS. A bibliometric analysis of clinical nurses' subhealth studies. Nursing and Rehabilitation. 2020;19(07):19-23.
Gu M, Lu CJ, Lee TS, Chen M, Liu CK, Chen CL. Prevalence and Risk Factors of Metabolic Syndrome among the Homeless in Taipei City: A Cross-Sectional Study. International journal of environmental research and public health. 2021;18(4).
Zi MJ, Yang JS, Wang B. The similarities and differences between subhealth states and chronic fatigue syndrome. Chinese Journal of Basic Chinese Medicine. 2007(01):81-2.
Pang J, Li YH, Tao MX. Progress of research on the definition of subhealth and chronic fatigue syndrome. China Health Education. 2010;26(02):129-32.
Wang WL, Zhou MJ, Wang L, Zhang JX. The concept, characteristics and relationship of subhealth with chronic fatigue syndrome. Chinese Journal of Behavioral Medicine and Brain Science. 2010(01):91-3.
Chen JY, Yu KQ, Sun XM, Chen ZW, Kuang LY, Ji YZ, et al. Effect of health promotion lifestyle on the transformation of subhealth status. Journal of Southern Medical University. 2017;37(02):184-91.
Fang M, Lai M, Zhang Q, Lantern S. Investigation and analysis of subhealth status of pediatricians. Everybody's Health (Academic Edition). 2013;7(22):22-3.
Xue Y, Liu G, Feng Y, Xu M, Jiang L, Lin Y, et al. Mediating effect of health consciousness in the relationship of lifestyle and suboptimal health status: a cross-sectional study involving Chinese urban residents. BMJ open. 2020;10(10):e039701.
National Bureau of Statistics of China. International Statistical Yearbook 2018: National Bureau of Statistics of China.2018 [cited 2022 2022/9/27]. Available from: https://data.stats.gov.cn/files/lastestpub/gjnj/2018/zk/indexch.htm.
Department of Planning, Development and Informatization of China. China Health and Health Development Statistics Bulletin 2021 China Department of Planning, Development and Informatization: China Department of Planning, Development and Informatization. [2022/9/27]. Available from: www.nhc.gov.cn/guihuaxxs/s3586s/202207/51b55216c2154332a660157abf28b09d.shtml.
Tsinghua University. 2021 China Physician Survey Report [2020-9-27]. Available from: https://www.soc.tsinghua.edu.cn/info/1198/1454.htm.
(database) OEaLMS. OECD (2022), "Hours Worked: Average usual weekly hours worked - averages" [2020-9-27]. Available from: https://www.oecd-ilibrary.org/employment/data/hours-worked/average-usual-weekly-hours-worked-averages_data-00306-en.
Nakata A. Investigating the associations between work hours, sleep status, and self-reported health among full-time employees. International journal of public health. 2012;57(2):403-11.
Ishikawa M. Long working hours, depression and suicidality among OB/GYNs in Japan. Occupational medicine (Oxford, England). 2022;72(3):200-6.
Nakata A. Long working hours, job satisfaction, and depressive symptoms: a community-based cross-sectional study among Japanese employees in small- and medium-scale businesses. Oncotarget. 2017;8(32):53041-52.
Park J, Kim Y, Chung HK, Hisanaga N. Long working hours and subjective fatigue symptoms. Industrial health. 2001;39(3):250-4.
Tian M, Zhang Q, Zhang Z, Meng J, Liu L. Prevalence of Suboptimal Health Status and Its Influencing Factors among Chinese Software Programmers. Iranian journal of public health. 2021;50(7):1361-71.
Bernstrøm VH. Long working hours and sickness absence-a fixed effects design. BMC public health. 2018;18(1):578.
Vedaa Ø, Pallesen S, Erevik EK, Svensen E, Waage S, Bjorvatn B, et al. Long working hours are inversely related to sick leave in the following 3 months: a 4-year registry study. International archives of occupational and environmental health. 2019;92(4):457-66.
Angrave D, Charlwood A, Wooden M. Long working hours and physical activity. Journal of epidemiology and community health. 2015;69(8):738-44.
Huang Y, Yao D, Zhou H, Xi X, Wang Y, Yao W. Association of hospital pharmacy-related knowledge and skills with occupational stress of clinical pharmacists in tertiary hospitals of China. Journal of the American Pharmacists Association : JAPhA. 2021;61(3):266-75.
Chang H, Bao L, Zhou Q, Chu W. [Level and influencing factors of occupational stress and sub-health of the pharmaceutical marketers in Northeast China]. Wei sheng yan jiu = Journal of hygiene research. 2020;49(1):70-4.
Zheng XL, Shi X, Deng YF, Xu XL, Wen AP, Zhu ZQ. Analysis of subhealth status and influencing factors among pediatric health care workers. Preventive Medicine. 2019;31(09):959-62.
Miao J, Liu J, Wang Y, Zhang Y, Yuan H. Reliability and validity of SHMS v1.0 for suboptimal health status assessment of Tianjin residents and factors affecting sub-health: A cross-sectional study. Medicine. 2021;100(17):e25401.
Xu J, Xue YL, Liu GH, Feng YF, Xu MY, Xie J, et al. Construction of a normative subhealth scale for urban residents in China. Journal of Southern Medical University.2019;39(03):271-8.
Liu L, Wang DP, Li YH, Xia YJ. Logistic regression analysis of factors influencing subhealth status in community populations. Inner Mongolia Medical Journal. 2017;49(06):648-51.
Zhang XX, Cai WW, Xin HJ, Yang QY, He XY, Bai Y, et al. Survey on the subhealth status of night shift nurses and analysis of risk factors. China Medical Herald.2022;19(06):43-7.
Chen YX, Xu QZ, Liu KX, Ni Q, Feng W, Ren XL, et al. Current status and influencing factors of subhealth among college students in Luzhou, Sichuan Province. Journal of Southwest Medical University. 2022;45(04):322-7.
Yang R, Li D, Wang J, Wan Y, Tao F, Zhang S. [Association of health literacy and physical sub-health among Chinese middle school students]. Wei sheng yan jiu = Journal of hygiene research. 2019;48(5):711-6.
Zhou J, Wu Q, Wang Z. Effect of self-employment on the sub-health status and chronic disease of rural migrants in China. BMC public health. 2021;21(1):2250.
Lai QY, Pan HS, Yang L, Jiao RY, Chen DR. Analysis of subhealth survey results among female teachers in Guangzhou universities. Applied Preventive Medicine. 2020;26(01):53-5.
Chang WJ, Shao HM, Zhi XY, Xu J, Xie J. [Sub-health status of middle school teachers and its correlation analysis with occupational stress]. Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases. 2017;35(8):585-8.
Bannai A, Tamakoshi A. The association between long working hours and health: a systematic review of epidemiological evidence. Scandinavian journal of work, environment & health. 2014;40(1):5-18.
Song JT, Lee G, Kwon J, Park JW, Choi H, Lim S. The Association between Long Working Hours and Self-Rated Health. Annals of occupational and environmental medicine. 2014;26(1):2.
Cho SS, Ki M, Kim KH, Ju YS, Paek D, Lee W. Working hours and self-rated health over 7 years: gender differences in a Korean longitudinal study. BMC public health. 2015;15:1287.
Hau K-T, Salili F. Prediction of Academic Performance among Chinese Students: Effort Can Compensate for Lack of Ability. Organizational Behavior and Human Decision Processes. 1996;65(2):83-94.
Dai Z, Zhang Y, Zhang XH. A study on the social practice collaborative education mechanism in colleges and universities to sharpen the spirit of hardship and hard work. University. 2020(11):147-8.
Yang LH., Tang CM., Zhang WM., Feng M, Wei PL, Guo F, et al. Study on the correlation between physician work hours and medical errors. Chinese Health Care Management. 2020;37(05):349-52.
Fell DB, Kephart G, Curtis LJ, Bower K, Muhajarine N, Reid R, et al. The relationship between work hours and utilization of general practitioners in four Canadian provinces. Health services research. 2007;42(4):1483-98.
Wu S, Lin S, Zhang Q, Wu Y, You J, Li H. [A study of sub-healthy status of community residents and its relevant factors in Fuzhou City]. Wei sheng yan jiu = Journal of hygiene research. 2015;44(5):738-42, 66.
Chen JY, Yang LB, Jiang PP, Sun XM, Yu KQ, Li F, et al. [Associations between health-promoting lifestyle and suboptimal health status in Guangdong: a cross sectional study]. Nan fang yi ke da xue xue bao = Journal of Southern Medical University. 2016;36(4):538-43.
M. G. Concept of Health Capital and Demand for Health. J Polit Econ. 1972;80(2):223–5.

No competing interests reported.

Appendix1.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Association between long working hours and suboptimal health status on primary care physicians：a cross-sectional survey from primary care institutions in China

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

1 Background

2 Methods

2.1 Study design and participants

2.2 Patient and public involvement

2.3 Survey instrument

2.4 Data collection

2.5 Data analysis

3 Results

3.1 Descriptive statistics

3.2 Association analysis

4 Discussion

5 Conclusion

Strengths and limitations of this study

Declarations

Ethics approval and consent to participate

Patient consent

Consent for publication

Data Availability Statement

Conflict of Interest

Funding

Author Contributions

Acknowledgments

References

Additional Declarations

Supplementary Files

Status:

Version 1