Abstract
A cross-sectional survey was carried out among 275 and 760 randomly selected senior officers (SOs) and managerial assistants (MAs) aged between 30 and 60 years. Sum of scores of efforts, rewards, and overcommitment and effort–reward ratio assessed job stress. Blood pressure was measured and classified using JNC-7 guidelines. The response rates of SOs and MAs were 98.9% and 97.2%, respectively. The prevalence of job stress based on high effort–rewards imbalance among SOs and MAs was 74.6% and 80.5%, respectively. The prevalence of overcommitment among SOs and MAs was 35.3% and 29%, respectively. Statistically significant differences (P = .05) were observed between the prevalence of effort–reward imbalance and overcommitment among SOs and MAs. Multivariate analysis indicated effort–reward imbalance (odds ratio [OR] = 2.8; 95% confidence interval [CI] = 1.1–7.4), high efforts (OR = 2.5; 95% CI = 1.2–5.3), and overcommitment (OR = 2.5; 95% CI = 1.1–5.6) were significantly associated with hypertension among SOs. Similarly, effort–reward imbalance and high efforts increased the risk of hypertension by 2-fold (OR = 2.2; 95% CI = 1.1–4.2) and 3-fold (OR = 3.02; 95% CI = 1.9–4.8), respectively, among the MAs. A significant number of administrators are afflicted by job stress, and job stress was significantly associated with hypertension.
Keywords: ERI questionnaire, job stress, hypertension, effort–reward imbalance, administrators
Introduction
Work life has undergone major structural and technological changes since the beginning of the 1990s. Alongside the advances, workplaces with physically taxing and hazardous working conditions have reduced in number; however, the psychosocial conditions that affect the central nervous system have increased.1,2 Studies have repeatedly proven the inverse relationship between unfavorable psychosocial work environments and employee health and well-being.3–6 Many other studies have reported that job stress is related to the incidence and prevalence of cardiovascular disease.5–8 It is postulated that one of the underlying mechanisms through which job stress increases cardiovascular disease is mediated through high blood pressure.7 Job stress has been proven to be associated with high blood pressure. Several analytical studies have proven significant positive associations of job stress and hypertension.7,9,10
Numerous conceptualizations of job stress models have been developed to date.11 However, 2 theoretical models have gained prominence to predict health risks in the exposed populations: the demand control model12 and the effort–reward -imbalance (ERI) model.13 The latter model focuses on nonreciprocity of social exchange, which contributes to stress, and defines 3 psychosocial dimensions at work—effort, reward, and overcommitment—and assumes that effort at work is spent as a part of the work contract, and rewards are provided in terms of money, esteem, and career opportunities including job security.2 In addition to these 2 work-related dimensions, overcommitment at work acts as a personal risk factor and is defined as a set of attitudes, behaviors, and emotions that reflects excessive commitment combined with strong need for approval and esteem.14 Thus, this model considers both the extrinsic factors, efforts and rewards, and the intrinsic characteristic, overcommitment. According to the ERI model, chronic job stress is caused by an imbalance between high efforts spent and low rewards received and is aggravated by overcommitment.13 Both cross-sectional and longitudinal studies have proven that the ERI model has a more predictive ability of job stress compared with the demand control model.15,16
The Ministry of Public Administration and Home Affairs, which is situated in Colombo district, is the Centre of Civil Administration and facilitates and coordinates services related to public administration, district administration, divisional administration, village administration, civil registration, and employees’ welfare. It deals with a number of areas such as public policy making on organizational excellence, human resource management, institutional development, electronic government, and good governance targeting social and economic development along with the national priorities of the government. Senior officers and managerial assistants attached to government public administrative offices are authorized officers to conducted administrative tasks in the country. They are entrusted with coordination and conduct of the aforementioned administrative tasks. The immense weight of responsibility in carrying out administrative work, handling people, and seeking solutions exposes them to a psychologically demanding work environment, leading to occupational stress, which in turn can adversely affect their health. Senior officers and managerial assistants are 2 different study populations although they work under the same roof. These 2 populations have different job roles and responsibilities, exposing them to different determinants of ill health, especially hypertension. Although administrative officers are burdened with high workload, no study has specifically examined the association of occupational status and work-related factors such as job stress and health effects. Although risk factors like age, unhealthy diet, alcohol consumption, and smoking are well-known determinants, these factors explain only a part of the risk of development of hypertension.9
Colombo district was chosen for the present study for several reasons. Recent urbanization and rapid social, demographic, and economic transition have affected employees’ physical and psychosocial health in many ways. This problem is most significant in Colombo district, which consists of a diverse socioeconomic composition and which has the highest population density of all Sri Lankan districts. An adequate sample size of the 2 populations, especially the senior officers, could be recruited from Colombo district, as a majority of offices that operates under the Ministry of Public Administration and Home Affairs are situated here.
Although job stress serves as a risk factor for psychological and physical ill-health, Sri Lanka has a paucity of evidence on the prevalence of job stress and its impact on cardiovascular diseases such as hypertension. The recent emerging trends of prevention initiatives and government investment in programs to control noncommunicable diseases in Sri Lanka necessitate policy decisions and effective interventions such as health-promoting work settings. This needs evidence and policy-relevant recommendations. Hence, this study was carried out to determine the prevalence of job stress and explore the relationship between perceived job stress and presence of hypertension among senior officers and managerial assistants of government administration offices in a district of Sri Lanka.
Methods
Participants
A descriptive cross-sectional study was conducted among senior officers and managerial assistants attached to public administration offices in the Colombo District. In this district, there are 23 such offices. The total number of senior officers (SOs) and managerial assistants (MAs) attached to the institutions were 358 and 1231, respectively. The study population comprising full-time, permanent SOs and MAs between the ages of 30 to 60 years and employed for at least a period of 1 year or more in a similar government institution in a similar cadre post were selected for the study. Officers on maternity or other long leave and officers on prolonged (more than 1 month) steroid therapy confirmed by documented evidence were excluded from the study.
Sample size calculation to detect prevalence of hypertension was done assuming the prevalence of hypertension among adults was 20%,17 95% confidence interval, and a precision of 0.05 using the formula to detect a population proportion. Ten percent was added to account for nonresponse. Sample size calculation was done separately for SOs and MAs, since they were regarded as 2 study populations. Thus, the final calculated sample size for SOs and MAs was 275 and 760, respectively. A stratified simple random sampling technique was used to select the SOs and MAs. Stratification was done according to 23 institutions they are attached to, and the number needed to select from each institution was decided according to probability proportionate to the size (PPS). The required number of SOs and MAs from each institution was selected randomly based on the number allocated to each institution according to PPS. The latest updated version of the payroll was used as the sampling frame for the purpose of sampling, and the completeness was checked with an institutional name list prior to use. A unique ID number was given to all the eligible officers. Computer-generated random numbers were used to identify the study participants. The identified officers were met face to face and invited to participate in the study.
Ethical clearance was obtained from the Ethical Review Board of Faculty of Medicine, University of Colombo (EC-11-178-17.11.2011). Permission and consent were obtained from the Ministry of Public Administration and Home Affairs and all heads of institutions before commencement of the study. Written informed consent was obtained from all respondents after informing the following in the information sheet: the purpose, the objectives, and benefits to the occupational group by conducting this study.
A self-administered questionnaire (SAQ) was used to gather information, which consisted of 3 broad components: sociodemographic characteristics; work-related information, which included validated Sinhala Effort Reward Imbalance Questionnaire; and lifestyle-related correlates of hypertension. In addition to the SAQ, a data collection form was used to record blood pressure (BP), anthropometric measurements, fasting blood sugar values, and to record information on the past medical history and drug history of the participants.
To assess test–retest reliability, 10% of the questionnaires were readministered to randomly selected study participants 2 weeks after the initial data collection.
Measures
Blood Pressure
The BP measurements were done based on the American Heart Association BP measurement recommendations,18 which reduced the intraobserver error of BP measurements. All BP measurements were carried out by the principal investigator. Participants were allowed to sit for 5 minutes before measuring BP. The participants were asked to refrain from smoking or ingesting caffeine during the 30 minutes preceding the BP measurement. A cuff with a bladder that is 12–13 cm × 35 cm in size with a larger bladder for fat arms was used. The bladder within the cuff will encircle at least 80% of the arm. The cuff was placed at the heart level of the patient. The disappearance of Phase V Korotkoff sounds was used to measure the diastolic BP. Two BP readings were obtained separated by 1 minute. The average of these 2 values was taken.
When the first 2 readings differed by more than 5 mm Hg, an additional reading was taken and averaged. Classification of hypertension was done based on the classification of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-7).19 A person was considered to be a hypertensive if he/she was an already diagnosed case of hypertension and/or on treatment or with a current systolic BP of ≥140 mm Hg or diastolic BP ≥90 mm Hg (JNC-7 criteria).
Job Stress
The ERI questionnaire, which was in English (see the appendix), was translated into Sinhala and validated in a similar population prior to its use. The ERI questionnaire was a self-administered questionnaire and contained 23 items, each graded on a 5-point scale, in 3 domains that concerned the psychosocial aspects of work, namely, efforts, rewards, and overcommitment. The items efforts and rewards included 5 responses marked on a Likert-type scale and were answered in 2 steps. In the first step, the respondents were asked whether the item content described a typical experience in the workplace. If they agreed, they were asked about the level of distress. The overcommitment items were scored on a 4-point Likert-type scale ranging from strongly agree to strongly disagree.20 The responses to each item of efforts and reward were scored in 5-point scales. Accordingly, the possible range of scores for “effort” and “reward” was from 6 to 30 and from 11 to 55, respectively. A ratio between the “effort” and “reward” scales can be computed as below, and as the main purpose of statistical analyses is to test associations of ERI at work with health, the following procedures are currently recommended.21
Effort–reward ratio
This measurement provides an approximate of the mismatch between the efforts and the rewards. It is assumed that the imbalance between effort and reward is the cause for adverse health outcomes. A single score is calculated from the effort and reward scale sums for each respondent by using the formula e/(rc), where e is the sum of the effort items, r is the sum of the reward items, and c is the correction factor fixed at 0.454 if the numerator (efforts) contains 5 items or fixed at 0.545 if the numerator (efforts) contains 6 items. Therefore, a score equal to 1 indicates an even balance between the elements of effort and reward for that individual. Scores greater than 1 indicate an unfavorable ratio of effort to reward (high effort/low reward), and scores less than 1 indicate a favorable ratio of effort to reward (low effort/high reward).2
Effect of sum of score of single components on health
The sums of scores of each scale, namely, efforts, rewards, and overcommitment, could be used to analyze associations with health. Individuals with scores in the upper tertile are considered to have an excessive asymmetry between efforts and rewards at work, which may increase the likelihood of psychological stress.2
Other Variables
Height was measured using a microtoise steel tape and recorded to the nearest 0.5 cm. The subjects looked straight ahead with their head, back, and feet touching the vertical support.
Weight was measured without shoes on an electronic digital weighing scale to the nearest 100 g, and the scale was calibrated after each field session against a standard weights set. The body mass index (BMI) is calculated by weight in kilograms divided by the square of the height. The classification and cutoff points used were based on the anthropometry of adult Asians. A BMI of ≤18.49 kg/m2 was regarded as underweight, 18.50 kg/m2 to 23.00 kg/m2 as desirable, 23.01 kg/m2 to 27.50 kg/m2 as overweight, and ≥27.51 kg/m2 as obese.22
A flexible, nonelastic measuring tape was used to measure the waist and hip circumference. The recordings were made to the nearest 0.2 cm. The cutoff points for waist and waist–hip ratio given by the World Health Organization expert consultation for the classification of South Asians on obesity23 was used to classify the study participants.
All participants were investigated for diabetes mellitus by measuring fasting plasma glucose level using a venous blood sample. Two milliliters of venous blood was collected from each individual. The blood samples were obtained after an overnight fast of at least 8 hours. Dysglycemia was defined as either fasting blood sugar of >110 mg/dL and current use (within past 4 weeks) of insulin or oral hypoglycemic drugs.
The SAQ and data collection form were anonymous in order to maximize reliability with regard to information obtained. The ERI questionnaire was adopted and validated to be used in the Sri Lankan context (the 3 subscales, namely, efforts, rewards, and overcommitment, had Cronbach’s α coefficients of .80, .84, and .60, respectively). The designing of the tools was based on the STEP-wise approach to Surveillance (STEPS) of the NCD risk factor questionnaire24 and International Physical Activity Questionnaire (IPAQ).25 The SAQ was pretested among 15 randomly selected SOs and MAs from a district adjacent to Colombo. Administration of the SAQ was followed by a cognitive debriefing, and further revisions were made based on the suggestions, with special attention to the final wording of questions to ensure clarity and flow. The data collection was conducted during the period of May to December 2012.
Procedure
The main purpose of statistical analysis is to test associations of ERI at work with health. The detailed analysis of the ERI questionnaire that is currently recommended is given above.
Statistical analysis was conducted employing the software package SPSS (version 16). Using JNC-7 guidelines, the employees were identified as having hypertension or not. The prevalence of job stress and hypertension among SOs and MAs was calculated with the respective confidence intervals.
The χ2 test was performed to assess the relationship between job stress and hypertension as well as relationship of other correlates with hypertension. The correlates that were significantly associated with hypertension were used in logistic regression models for multivariate analysis. During multivariate analysis, the dependent variable was hypertension status of the participants as decided according to the JNC-7 guidelines for the classification of hypertension. Those who were having hypertension were coded as “1,” and those without hypertension were coded as “0.” All variables were entered into the logistic regression model as dichotomous variables. Risk level for each dichotomous variable was identified, level 1 indicating “high risk” and level 0 indicating “low risk.” “Low risk” category of each variable was identified by the lower proportion of hypertension seen in that category. The odds ratios (ORs) with confidence intervals (CIs) were calculated to quantify the strength of association between hypertension and correlates. The significant level was considered as P ≤ .05. The risk was expressed as OR and its 95% CI.
Results
Of the 275 SOs invited for the study, 272 responded. Therefore, the response rate of SOs was 98.9%. Of the 760 MAs invited, 739 responded; hence, the response rate for MAs was 97.2%. Selected sociodemographic and occupational characteristics of SOs and MAs are given in Table 1.
Table 1.
Sociodemographic/Economic and Occupational Characteristics of the Senior Officers (n = 272) and Managerial Assistants (n = 739).
Senior Officers
|
Managerial Assistants
|
|||
---|---|---|---|---|
Variable | n | % | n | % |
Age (years) | ||||
30–39 | 108 | 39.7 | 320 | 43.3 |
40–49 | 73 | 26.8 | 228 | 30.8 |
50–60 | 91 | 33.5 | 191 | 25.9 |
Gender | ||||
Female | 156 | 57.4 | 578 | 77.3 |
Male | 116 | 42.6 | 168 | 22.7 |
Level of education | ||||
G.C.E. Ordinary Level passed | 0 | 0 | 17 | 2.3 |
G.C.E. Advanced Level passed | 9 | 3.3 | 100 | 13.5 |
Technical/diploma/vocational training | 128 | 47.1 | 397 | 53.7 |
University degree | 24 | 8.8 | 64 | 8.7 |
Postgraduate degree | 111 | 40.8 | 161 | 21.8 |
Average monthly salary (Rs) | ||||
10 000–29 000 | 135 | 49.6 | 712 | 96.3 |
30 000–49 000 | 107 | 39.4 | 27 | 3.7 |
≥50 000 | 30 | 11.0 | 0 | 0 |
Family income (Rs) | ||||
≤20 000 | 4 | 1.5 | 32 | 4.3 |
21 000–59 000 | 160 | 58.8 | 612 | 82.8 |
60 000–99 000 | 80 | 29.4 | 72 | 9.8 |
≥100 000 | 28 | 10.3 | 23 | 3.1 |
Occupational characteristics | ||||
Duration of service in the current workplace (years) | ||||
≤5 | 178 | 65.4 | 457 | 61.8 |
6–10 | 64 | 23.5 | 214 | 29 |
11–20 | 19 | 7.1 | 40 | 5.4 |
≥21 | 11 | 4 | 28 | 3.8 |
Average work hours per week (hours) | ||||
40 | 60 | 22.1 | 255 | 34.5 |
41–50 | 159 | 58.5 | 417 | 56.4 |
≥51 | 53 | 19.4 | 67 | 9.1 |
Commuting distance (km) | ||||
≤10 | 67 | 24.6 | 200 | 27.1 |
11–20 | 106 | 39 | 191 | 25.8 |
21–30 | 39 | 14.3 | 113 | 15.3 |
31–40 | 19 | 7 | 70 | 9.5 |
≥41 | 41 | 15.1 | 165 | 22.3 |
Grade of work | ||||
Special grade | 58 | 21.3 | 22 | 3 |
Grade I | 89 | 32.7 | 189 | 25.6 |
Grade II | 56 | 20.6 | 305 | 41.3 |
Grade III | 69 | 25.4 | 223 | 30.1 |
Prevalence of Job stress
The prevalence of job stress (JS) based on effort–rewards imbalance among SOs and MAs was 74.6% (95% CI = 69.4% to 79.8%) and 80.5% (95% CI = 77.6% to 83.4%), respectively. The prevalence of overcommitment among SOs and MAs was 35.3% (95% CI = 29.8% to 41.2%) and 29% (95% CI = 25.7% to 32.3%), respectively. Statistically significant difference was observed between the prevalence of effort–reward ratio (z = 2.03; P = .05) and overcommitment (z = 1.9; P = .05) among SOs and MAs (Table 2).
Table 2.
Distribution of Employees by Presence of Job Stress.
Status of Occupational Stress | SOs (n = 272) | MAs (n = 739) | P Value |
---|---|---|---|
High effort–rewards ratio | 203 (74.6%); 95% CI = 69.4–79.8 | 595 (80.5%); 95% CI = 77.6–83.4 | z = 2.03; P = .05 |
Over commitment | 96 (35.3%); 95% CI = 29.8–41.2 | 214 (29%); 95% CI = 25.7–32.3 | z = 1.9; P = .05 |
High efforts | 98 (36%); 95% CI = 30.3–41.7 | 304 (41.1%); 95% CI = 37.6–44.6 | z = −1.5; P = .14 |
Low rewards | 102 (37.5%); 95% CI = 31.8–43.2 | 216 (29%); 95% CI = 25.7–32.3 | z = −0.6; P = .5 |
Abbreviations: SO, senior officer; MA, managerial assistant; CI, confidence interval.
Prevalence of Hypertension
The crude prevalence of hypertension based on classification of the JNC-7 criteria among 30- to 60-year-old SOs and MAs attached to government public administration offices was 32.4 per hundred population (95% CI = 26.8–37.9) and 29.4 per hundred population (95% CI = 26.2–32.7), respectively. The age- and sex-adjusted prevalence of hypertension among 30- to 60-year-old SOs and MAs attached to the aforementioned offices was 32.9 per hundred population with a 95% CI of 27.4 to 38.6, and 33.01 per hundred population with a 95% CI of 29.6 to 36.4, respectively. The observed differences between the 2 percentages among SOs and MAs were not statistically significant (P > .05).
Stress and Hypertension
When considering job stress assessed using the ERI ratio, which measured the imbalance, it was significantly higher (P < .05) among SOs diagnosed as having hypertension, 78.4% (n = 69). Considering the MAs, 84.1% (n = 138) diagnosed as having hypertension reported as having effort–reward imbalance and was a statistically significant (P < .05) correlate of hypertension (Table 3).
Table 3.
Efforts–Reward Imbalance and Hypertension Among Senior Officers and Managerial Assistants.
Variable | Senior Officers
|
Managerial Assistants
|
||||
---|---|---|---|---|---|---|
Hypertensive, n (%) | Unadjusted OR | Adjusted ORa | Hypertensive, n (%) | Unadjusted OR | Adjusted ORa | |
High ERI ratiob | 69 (78.4) | 1.4 (0.7–2.5) | 2.8 (1.1–7.4) | 138 (84.1) | 1.4 (0.9–2.2) | 2.2 (1.1–4.2) |
Overcommitmentb | 47 (53.4) | 1.5 (0.9–2.4) | 2.5 (1.1–5.6) | 96 (44.2) | 1.4 (1–2.1) | — |
High effortsb | 42 (47.7) | 2.1 (1.2–3.5) | 2.5 (1.2–5.3) | 111 (51.2) | 2.6 (1.8–3.7) | 3.0 (1.9–4.8) |
Low rewardsb | 51 (58.0) | 1.3 (0.8–2.2) | — | 140 (64.5) | 1.1 (0.7–1.3) | — |
Abbreviations: OR, odds ratio; CI, confidence interval.
Odds ratios (95% CIs) for hypertension were estimated using multiple logistic regression analysis with control for the univariate analysis the following: age >40 years, male sex, Rs >50 000 per month average monthly salary, high body mass index/waist hip ratio, dysglycemia, self-reported dyslipidemia, positive family history, current smoking, current alcohol consumption, physical inactivity, energy dense diet, <5 servings per day fruit and vegetable consumption, >5 g per day salt consumption, lesser commuting distance (<20 km per day), higher grade of work (Special Grade and Grade I), high occupational stress as measured by ERI ratio, high efforts, and overcommitment, and a health promotional work setting, with the low-strain group serving as the reference.
Classified according to the ERI model *e/(r/c); e is the sum score of the effort scale; r is the sum score of the reward scale; c is a correction factor. Correction factor is the ratio between the number of items included in the effort scale and the number of items in the rewards scale. The correction factor is fixed at 0.454 if the numerator (efforts) contains 5 items. The correction factor is fixed at 0.545 if the numerator (efforts) contains 6 items.20 See the appendix—not retained in the final model.
High perceived efforts was significantly associated (P = .05) with hypertension among SOs and MAs. Among the SOs who were diagnosed to have hypertension, nearly half (47.7%; n = 42) had perceived higher efforts at work, while among MAs, 51.2% (n = 111) with hypertension had high perceived efforts.
In multivariate analysis, job stress measured by high effort–reward imbalance, high efforts, and overcommitment were significant occupation-related correlates among SOs. Having high ERI ratio increased the risk of hypertension by 3-fold (OR = 2.8; 95% CI = 1.1–7.4). The presence of high perceived efforts (OR = 2.5; 95% CI = 1.2–5.3) and overcommitment (OR = 2.5; 95% CI = 1.1–5.6) were other significant correlates of hypertension. Effort–rewards imbalance (OR = 2.2; 95% CI = 1.1–4.2) and perceived high efforts (OR = 3.02; 95% CI = 1.9–4.8) increased the risk of hypertension among MAs (Table 3).
Discussion
To our knowledge, no other study has analyzed the 2 components of the ERI model, effort–reward imbalance and overcommitment, in relation to hypertension in South Asia. Data were obtained from standardized, psychometrically validated questionnaire and were collected under standardized conditions. The Sri Lankan administrators are essentially white-collar workers; thus, the question of physical extrinsic effort was excluded during validation of the ERI questionnaire in the Sri Lankan context.
The prevalence based on effort–reward imbalance among SOs and MAs was high, and possible explanations would be while they are exerting high efforts the rewards in terms of salary, promotions, and appreciation would be low. Since they are all public sector employees, job security would not pose a threat. The ERI ratio was higher among SOs as expected, this finding is consistent with studies done elsewhere.26,27 The variability in the ERI ratios and scores among these 2 job categories could be due to career perspectives, expectations, and personal challenges.
In our study, the prevalence of hypertension among employees having effort–reward imbalance was above 75% for both categories of workers. Overcommitted employees too had a higher prevalence of hypertension. People with high overcommitment too will exaggerate efforts for their need for approval and rewards, and a discrepancy between efforts and rewards would aggravate stress.7
Job stress as measure by ERI was a significant correlate of hypertension among SOs and MAs. Hence, a combination of higher efforts and lower rewards increased the risk of hypertension among administrators.
The Whitehall II study, a cohort study conducted among British civil servants, reports significant effect of effort–rewards imbalance on all coronary heart disease incidence7 and adds evidence to the current report. The results of our study confirm previous findings in a similar cross-sectional design that ERI is associated with hypertension. Siegrist13 reports that high efforts–rewards imbalance significantly (P < .05) increased the risk of hypertension among middle managers (OR = 6.8; 95% CI = 1.7–26.6), and Peter et al,28 in a study conducted among Swedish men and women, report high efforts–rewards ratio increased the risk of hypertension equally among men (OR = 1.69; 95% CI = 1.13–2.53) and women (OR = 1.57; 95% CI = 0.9–2.7). High perceived effort was a significant correlate of hypertension among the administrators, and this supports the findings of the Whitehall II study, which was a follow-up study.7
In spite of the likelihood that job stress is a risk factor for hypertension,7,28 the strength of association from different studies cannot be directly compared as the methods of assessing job stress and the population studied differed from one study to another.29
The strengths of our study are the following: to our knowledge this is the first study to test effort–rewards imbalance as a correlate of hypertension in South Asia. In the current study, many confounding factors and effect modifiers were controlled for during analysis. Poor psychosocial conditions usually measured by socioeconomic status (SES) are related with poor psychosocial characteristics and unhealthy behaviors. In the current study, we adjusted for grade of work and family income, both of which predict the SES, which would weaken the argument that association of stress and health outcomes is merely a result of confounding by SES.7
There are some limitations that should be considered when interpreting the findings of this study. This study was conducted in the government administration offices in the Colombo district and the results may not be applicable to all administrative employees in the country. However, since all the government administrative offices in the district of Colombo were included in the study, the findings of this study are applicable to all SOs and MAs of administrative offices in the said district. The present study identified correlates of hypertension through a cross-sectional comparative study design. This precluded the assessment of the temporal relationship between hypertension and associated factors; therefore, no causal inference can be drawn. Therefore, further research using a longitudinal design should be undertaken to identify the effect of job stress on hypertension.
Other life stressors such as family life stressors, financial burden, and health burden were not assessed in the current study. Due to the lack of data on other life stressors, it was not possible to examine the potential confounding effects of the above and thus would affect the causal interpretation of results. Hence, a future study could conduct a comprehensive evaluation of stress with multiple domains and its effect on health.
This study was also limited to those currently working, which could have contributed to a healthy worker effect.30 According to the study design, all subjects on prolong leave were excluded from analyses. It is possible that some subjects were on prolonged leave due to work-related ill-health. In future studies, it could be useful to measure the latest work conditions for early retired or those on prolonged sick leave.
Conclusion and Recommendation
This study indicates that the prevalence of job stress among SOs and MAs attached to government administrative offices is relatively high and a serious problem. More than three quarters of the population suffered from efforts–rewards imbalance and suggests there is a significant level of imbalance between efforts the employees exert and the rewards received in return among administrators. This calls for urgent prevention and control measures for job stress among all employees in the aforementioned offices. The management should seek for and be aware of excessive efforts and render necessary support at work as balance should be gained between the effort expended and rewards received. In addition, the management should be trained to encourage workers and appreciate and support them at work, as it is best that rewards be improved rather efforts decreases.7
There was a significantly high prevalence of hypertension among the subjects with high job stress. Furthermore, the study identified job stress as an independent and a strong contributory factor for hypertension mediating through efforts and rewards imbalance and high efforts at the workplace. Clinicians should be made aware of the association of job stress and increased risk of hypertension when assessing a patient with hypertension, especially uncontrolled hypertension, so that appropriate preventive measures could be recommended. It would be beneficial to conduct research to identify prevalence and determinants of job stress and its association with other cardiovascular diseases. This should include a broad range of employees and worksites to ensure the generalizability of the research findings.
Based on the current evidence, it can be recommended to implement effective preventive strategies and interventions for prevention of job stress, especially by establishing health- promotion policies at the workplace.
Acknowledgments
We thank Professor Johannes Siegrist at University of Düsseldorf for granting permission to adopt and validate the Effort–Reward Imbalance Questionnaire to the Sri Lankan context. The authors gratefully acknowledge the time and effort given by all the institutions and participants to make this study a success.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by in part by the Noncommunicable Disease Unit, Ministry of Health Sri Lanka, and the ASCEND Program which is funded by the Fogarty International Center at the United States’ National Institutes of Health (NIH), under Award Number D43TW008332 (ASCEND Research Network).
Appendix
The Original 23-Item Effort–Rewards Imbalance (ERI) Mode
Effort | |
---|---|
ERI 1 | I have constant time pressure due to a heavy work load. |
ERI 2 | I have many interruptions and disturbances while performing my job. |
ERI 3 | I have a lot of responsibility in my job. |
ERI 4 | I am often pressured to work overtime. |
ERI 5 | My job is physically demanding. |
ERI 6 | Over the past few years, my job has become more and more demanding. |
Reward | |
ERI 7 | I receive the respect I deserve from my superiors. |
ERI 8 | I receive the respect I deserve from my colleagues. |
ERI 9 | I experience adequate support in difficult situations. |
ERI 10 | I am treated unfairly at work. |
ERI 11 | My job promotion prospects are poor. |
ERI 12 | I have experienced or I expect to experience an undesirable change in my work situation. |
ERI 13 | My employment security is poor. |
ERI 14 | My current occupational position adequately reflects my education and training. |
ERI 15 | Considering all my efforts and achievements, I receive the respect and prestige I deserve at work. |
ERI 16 | Considering all my efforts and achievements, my job promotion prospects are adequate. |
ERI 17 | Considering all my efforts and achievements, my salary/income is adequate. |
Overcommitment | |
OC 1 | I get easily overwhelmed by time pressures at work. |
OC 2 | As soon as I get up in the morning I start thinking about work problems. |
OC 3 | When I get home, I can easily relax and “switch off” work. |
OC 4 | People close to me say I sacrifice too much for my job. |
OC 5 | Work rarely lets me go, it is still on my mind when I go to bed. |
OC 6 | If I postpone something that I was supposed to do today I’ll have trouble sleeping at night. |
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The contents of this report are solely the responsibility of the authors, and do not necessarily represent the official views of the National Institutes of Health.
References
- 1.Belkic KL, Landsbergis PA, Schnall PL, Baker D. Is job strain a major source of cardiovascular disease risk? Scand J Work Environ Health. 2004;30:85–128. doi: 10.5271/sjweh.769. [DOI] [PubMed] [Google Scholar]
- 2.Siegrist J, Marmot M. Health inequalities and the psychosocial environment—two scientific challenges. Soc Sci Med. 2004;58:1463–1473. doi: 10.1016/S0277-9536(03)00349-6. [DOI] [PubMed] [Google Scholar]
- 3.Karasek R, Baker D, Marxer F, Ahlbom A, Theorell T. Job decision latitude, job demands, and cardiovascular disease: a prospective study of Swedish men. Am J Public Health. 1981;71:694–705. doi: 10.2105/ajph.71.7.694. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Hwang GS, Choi JW, Choi SH, et al. Effects of a tailored health promotion program to reduce cardiovascular disease risk factors among middle-aged and advanced-age bus drivers. Asia Pac J Public Health. 2012;24:117–127. doi: 10.1177/1010539510373140. [DOI] [PubMed] [Google Scholar]
- 5.Kivimäki M, Leino-Arjas P, Luukkonen R, Riihimäi H, Vahtera J, Kirjonen J. Work stress and risk of cardiovascular mortality: prospective cohort study of industrial employees. BMJ. 2002;325:857. doi: 10.1136/bmj.325.7369.857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Chandola T, Britton A, Brunner E, et al. Work stress and coronary heart disease: what are the mechanisms? Eur Heart J. 2008;29:640–648. doi: 10.1093/eurheartj/ehm584. [DOI] [PubMed] [Google Scholar]
- 7.Kuper H, Singh-Manoux A, Siegrist J, Marmot M. When reciprocity fails: effort–reward imbalance in relation to coronary heart disease and health functioning within the Whitehall II study. Occup Environ Med. 2002;59:777–784. doi: 10.1136/oem.59.11.777. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Huda B, Rusli B, Naing L, Winn T, Tengku M, Rampal K. Job strain and its associated factors among lecturers in the School of Medical Sciences, Universiti Sains Malaysia and Faculty of Medicine, Universiti Kebangsaan Malaysia. Asia Pac J Public Health. 2004;16:32–40. doi: 10.1177/101053950401600106. [DOI] [PubMed] [Google Scholar]
- 9.Landsbergis PA, Schnall PL, Warren K, Pickering TG, Schwartz JE. Association between ambulatory blood pressure and alternative formulations of job strain. Scand J Work Environ Health. 1994;20:349–363. doi: 10.5271/sjweh.1386. [DOI] [PubMed] [Google Scholar]
- 10.Vrijkotte TGM, van Doornen LJP, de Geus EJC. Effects of work stress on ambulatory blood pressure, heart rate, and heart rate variability. Hypertension. 2000;35:880–886. doi: 10.1161/01.hyp.35.4.880. [DOI] [PubMed] [Google Scholar]
- 11.Wilkinson RG, Marmot MG. Social Determinants of Health: The Solid Facts. Geneva, Switzerland: World Health Organization; 2003. [Google Scholar]
- 12.Karasek R, Brisson C, Kawakami N, Houtman I, Bongers P, Amick B. The Job Content Questionnaire (JCQ): an instrument for internationally comparative assessments of psychosocial job characteristics. J Occup Health Psychol. 1998;3:322–325. doi: 10.1037//1076-8998.3.4.322. [DOI] [PubMed] [Google Scholar]
- 13.Siegrist J. Adverse health effects of high-effort/low-reward conditions. J Occup Health Psychol. 1996;1:27–41. doi: 10.1037//1076-8998.1.1.27. [DOI] [PubMed] [Google Scholar]
- 14.Tsutsumi A, Kayaba K, Nagami M, et al. The effort-reward imbalance model: experience in Japanese working population. J Occup Health. 2002;44:398–407. [Google Scholar]
- 15.Calnan M, Wadsworth E, May M, Smith A, Wainwright D. Job strain, effort-reward imbalance, and stress at work: competing or complementary models? Scand J Public Health. 2004;32:84–93. doi: 10.1080/14034940310001668. [DOI] [PubMed] [Google Scholar]
- 16.Ostry AS, Hershler R, Chen L, Hertzman C. A longitudinal study comparing the effort-reward imbalance and demand-control models using objective measures of physician utilization. Scand J Public Health. 2004;32:456–463. doi: 10.1080/14034940410028190. [DOI] [PubMed] [Google Scholar]
- 17.Wijewardene K, Mohideen MR, Mendis S, et al. Prevalence of hypertension, diabetes and obesity: baseline findings of a population based survey in four provinces in Sri Lanka. Ceylon Medical Journal. 2005;50(2):62–70. doi: 10.4038/cmj.v50i2.1571. [DOI] [PubMed] [Google Scholar]
- 18.Pickering TG, Hall JE, Appel LJ, et al. Recommendations for blood pressure measurement in humans and experimental animals: part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Hypertension. 2005;45:142–161. doi: 10.1161/01.HYP.0000150859.47929.8e. [DOI] [PubMed] [Google Scholar]
- 19.Chobanian AV, Bakris GL, Black HR, et al. Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension. 2003;42:1206–1252. doi: 10.1161/01.HYP.0000107251.49515.c2. [DOI] [PubMed] [Google Scholar]
- 20.Siegrist J, Starke D, Chandola T, et al. The measurement of effort–reward imbalance at work: European comparisons. Soc Sci Med. 2004;58:1483–1499. doi: 10.1016/S0277-9536(03)00351-4. [DOI] [PubMed] [Google Scholar]
- 21.Siegrist J. Psychometric Properties of the Effort-Reward Imbalance Questionnaire. Düsseldorf, Germany: Düsseldorf University; 2013. [Accessed July 23, 2015]. http://www.uniklinik-duesseldorf.de/fileadmin/Datenpool/einrichtungen/institut_fuer_medizinische_soziologie_id54/ERI/PsychometricProperties.pdf. [Google Scholar]
- 22.World Health Organization. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–163. doi: 10.1016/S0140-6736(03)15268-3. [DOI] [PubMed] [Google Scholar]
- 23.World Health Organization. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation. Geneva, Switzerland: World Health Organization; 2011. pp. 8–11. [Google Scholar]
- 24.Bonita R, De Courten M, Dwyer T, Jamrozik K, Winkelmann R. Surveillance of Risk Factors for Noncommunicable Diseases: The WHO STEPwise Approach: Summary. Geneva, Switzerland: World Health Organization; 2001. [Accessed July 23, 2015]. http://whqlibdoc.who.int/hq/2001/WHO_NMH_CCS_01.01.pdf. [Google Scholar]
- 25.Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB. [DOI] [PubMed] [Google Scholar]
- 26.Msaouel P, Keramaris NC, Apostolopoulos AP, et al. The effort-reward imbalance questionnaire in Greek: translation, validation and psychometric properties in health professionals. J Occup Health. 2012;54:119–130. doi: 10.1539/joh.11-0197-oa. [DOI] [PubMed] [Google Scholar]
- 27.Tsutsumi A, Nagami M, Morimoto K, Matoba T. Responsiveness of measures in the effort–reward imbalance questionnaire to organizational changes: a validation study. J Psychosom Res. 2002;52:249–256. doi: 10.1016/s0022-3999(02)00291-x. [DOI] [PubMed] [Google Scholar]
- 28.Peter R, Alfredsson L, Hammar N, Siegrist J, Theorell T, Westerholm P. High effort, low reward, and cardiovascular risk factors in employed Swedish men and women: baseline results from the WOLF Study. J Epidemiol Community Health. 1998;52:540–547. doi: 10.1136/jech.52.9.540. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kawakami N, Haratani T, Araki S. Job strain and arterial blood pressure, serum cholesterol, and smoking as risk factors for coronary heart disease in Japan. Int Arch Occup Environ Health. 1998;71:429–432. doi: 10.1007/s004200050302. [DOI] [PubMed] [Google Scholar]
- 30.Howe GR, Chiarelli AM, Linsay JP. Components and modifiers of the healthy worker effect: evidence from three occupational cohorts and Implications for industrial compensation. Am J Epidemiol. 1988;128:1364–1375. doi: 10.1093/oxfordjournals.aje.a115089. [DOI] [PubMed] [Google Scholar]