Occupational stress contributes to a wide range of health problems, including acute traumatic injuries, psychological issues, musculoskeletal disorders, and cardiovascular diseases. As a group, these disorders are responsible for much morbidity, mortality, and disability, as well as healthcare utilization (1). According to the National Institute for Occupational Safety and Health, occupational stress is defined as the stress that occurs when the needs of the job poorly align with the abilities of the employee, available resources, and expectations of the employer and this stress is thought to cause harmful physical and emotional responses (2). The electronic manufacturing services (EMS) industry refers to the companies that engaged in testing, distribution, and providing return and repair services for electronic components and assembling for original equipment manufacturers. With further industrialization and modernization, EMS workers are exposed to increasing occupational psychological problems have gained attention from all social sectors (3). Occupational Stress Surveillance Program (OSSP) is an ongoing, nationwide, surveillance program that collects self-reported information on psychosocial conditions and well-being at workplace, in order to explore occupational stress level of key population from typical industry and to improve mental health and well-being among occupational populations. It is administered by National Institutes of Occupational Health and Poison Control, China CDC, in collaborating with participating local occupational disease prevention institutions.
In the present report, we analyzed combined OSSP data from years of 2015, 2016, and 2017 that focusing on EMS industry of the survey. Response rates for the 3 years of surveys ranged from 91.0% to 94.4% and it produced a 3-year sample of 21,362 participants from 20 electronic manufacturing companies. Among those, as the typical areas of EMS industry in China, five distributed in the central and western regions (Sichuan and Hunan), seven around Bohai Rim (Beijing and Tianjin), two around Yangtze River Delta (Jiangsu), and six around Pearl River Delta (Guangdong) regions.
All questionnaires and group interviews were completed during July to September of each year. Participants were considered to be working at current position for at least more than half a year and without any history of mental disorders or relative medications. Specifically, they were asked to complete a self-report questionnaire consisting of socio-demographic characteristics (examples including age, gender, and education level), job characteristics ( such as hours of work, number of shifts, etc.) and occupational stress, as well as symptoms of other mental disorders (not involved in this report).
Occupational stress was assessed using the Chinese short version of occupational stress inventory, which is developed based on two extensive used theoretical frameworks, Job-Demand-Control and Effort-Reward-Imbalance models, and demonstrated with good reliability and validity (4-5). It consists of a set of 38 items that assess 6 dimensions of psychosocial factors: work demands, job control, social support, effort, reward, and over-commitment. Each item is measured on a five-point Likert scale ranging from one (never) to five (almost all the time). Each variable was then added up separately. Each sum of demand and control was dichotomized into high and low using the median of the distribution as cut-off. Then high strain was labelled according to dichotomized variables (high job demand with low control). Another two summed variables, effort and reward, were divided (Σeffort/Σreward) and then multiplied with a correction factor (0.4545), thus creating the effort-reward ratio. A larger ratio indicates a greater imbalance between effort and reward. The effort-reward imbalance was characterized when the ratio is greater than one.
Prevalence for high strain and effort-reward imbalance were calculated overall and by socio-demographic characteristics and job characteristics. The chi-square test was used to determine statistically significant difference between groups. All statistical assessments were considered significant at p<0.05. Statistical analyses were performed using SPSS statistical software (version 22.0, SPSS Inc, Chicago, IL, USA).
Among all respondents, the prevalence of high strain and effort-reward imbalance were 19.5% and 15.8%, respectively (Table 1). Characterized by socio-demographic factors, the prevalence of high strain were significantly differed by age, education and income level, while the prevalence of effort-reward imbalance were significantly differed by gender and income level. Compared to those working at other job positions, assembly-line workers present higher level of occupational stress. Specifically, 20.9% of assembly-line workers were exposed to high strain due to high demand with low control job and 17.4% of them were exposed to effort-reward imbalance. Migrant workers among those respondents reported significantly higher prevalence of high strain and effort-reward imbalance (21.1% and 16.5%) than non-migrant workers. For those working in a shift position, the prevalence of high strain were significantly higher (20.0%) compared to non-shift workers. Exposed to long working hours may increase the risk of occupational health, for those who has been working over 50 hours per week averagely had higher level of high strain (20.8%) and effort-reward imbalance (18.3%).
Characteristics Number High strain (high demand – low control) Chi-square test p value Effort reward imbalance (E/R ratio >1) Chi-square test p value Number % Number % Overall 21,362 4,159 19.5 3,376 15.8 Gender Male 10,039 2,023 20.2 1,948 19.4 Female 11,323 2,136 18.9 5.623 0.018 1,428 12.6 184.526 <0.001 Age (years) 18–25 2,391 436 18.2 402 16.8 26–30 6,999 1,365 19.5 1,101 15.7 31–35 3,411 1,346 21.0 1,023 16.0 36–40 3,215 633 19.7 484 15.1 ≥41 2,346 379 16.2 29.328 <0.001 366 15.6 3.401 0.493 Education <high school 5,811 1,066 18.3 896 15.4 ≥high school 15,551 3,093 19.9 6.439 0.011 2,480 15.9 0.888 0.346 Marital status Married 13,721 2,659 19.4 212 15.5 Unmarried 7,641 1,500 19.6 0.199 0.656 1,255 16.4 3.445 0.063 Income (¥) <3,000 9,380 1,752 18.7 1,423 15.2 3,000–5,000 9,762 1,933 19.8 1,545 15.8 >5,000 2,220 474 21.4 9.447 0.009 408 18.4 13.889 0.001 Migrant worker Yes 16,819 3,541 21.1 2,777 16.5 No 4,543 618 13.6 126.628 <0.001 599 13.2 29.736 <0.001 Position Assembly-line 10,222 2,135 20.9 1,778 17.4 Others 11,140 2,024 18.2 25.109 <0.001 1,598 14.3 37.246 <0.001 Shift Yes 12,224 2,448 20.0 1,964 16.1 No 9,138 1,711 18.7 5.655 0.017 1,412 15.5 1.485 0.223 Work hours ≤50 h 10,528 1,907 18.1 1,398 13.3 >50 h 10,834 2,252 20.8 24.329 <0.001 1,978 18.3 99.456 <0.001
Table 1. Prevalence of occupational stress among EMS workers based on JDC and ERI models by selected demographic and job characteristics (n=21,362).