Health and Pink Collar Work

Similar documents
Details of the design and recruitment of the participants in the studies included in our meta-

Aneurin Bevan Health Board. Living Well, Living Longer: Inverse Care Law Programme


Focus on hip fracture: Trends in emergency admissions for fractured neck of femur, 2001 to 2011

14 Effort, reward and effort-reward-imbalance in the nursing profession in Europe

Workplace as an origin of health inequalities

EPSRC Care Life Cycle, Social Sciences, University of Southampton, SO17 1BJ, UK b

Trends in Consultation Rates in General Practice 1995 to 2006: Analysis of the QRESEARCH database.

Measuring the relationship between ICT use and income inequality in Chile

Appendix. We used matched-pair cluster-randomization to assign the. twenty-eight towns to intervention and control. Each cluster,

Do quality improvements in primary care reduce secondary care costs?

Increased mortality associated with week-end hospital admission: a case for expanded seven-day services?

Scottish Hospital Standardised Mortality Ratio (HSMR)

Public Health Skills and Career Framework Multidisciplinary/multi-agency/multi-professional. April 2008 (updated March 2009)

Utilisation patterns of primary health care services in Hong Kong: does having a family doctor make any difference?

Avoidable Hospitalisation

Statistical Analysis of the EPIRARE Survey on Registries Data Elements

NHS Grampian Equal Pay Monitoring Report

All In A Day s Work: Comparative Case Studies In The Management Of Nursing Care In A Rural Community

Do GPs sick-list patients to a lesser extent than other physician categories? A population-based study

A Study on AQ (Adversity Quotient), Job Satisfaction and Turnover Intention According to Work Units of Clinical Nursing Staffs in Korea

Original Article Nursing workforce in very remote Australia, characteristics and key issuesajr_

Higher Education Students and Qualifiers at Scottish Institutions

Telephone triage systems in UK general practice:

EVALUATION of NHS Health Check PLUS COMMUNITY OUTREACH PROGRAMME in Greenwich

NUTRITION SCREENING SURVEY IN THE UK AND REPUBLIC OF IRELAND IN 2010 A Report by the British Association for Parenteral and Enteral Nutrition (BAPEN)

Responses of pharmacy students to hypothetical refusal of emergency hormonal contraception


Aging in Place: Do Older Americans Act Title III Services Reach Those Most Likely to Enter Nursing Homes? Nursing Home Predictors

National Health Promotion in Hospitals Audit

Primary medical care new workload formula for allocations to CCG areas

NUTRITION SCREENING SURVEYS IN HOSPITALS IN NORTHERN IRELAND,

SCHOOL - A CASE ANALYSIS OF ICT ENABLED EDUCATION PROJECT IN KERALA

Cardiovascular Disease Prevention and Control: Interventions Engaging Community Health Workers

UK Renal Registry 20th Annual Report: Appendix A The UK Renal Registry Statement of Purpose

ESRC/NIHR funded PhD studentship in Health Economics. ESRC Doctoral Training Centre - University College London

EuroHOPE: Hospital performance

Excess mortality among people with serious mental illness: a quality issue. Veena Raleigh Senior Fellow, The King s Fund

Final Report ALL IRELAND. Palliative Care Senior Nurses Network

Commentary for East Sussex

Summary of Findings. Data Memo. John B. Horrigan, Associate Director for Research Aaron Smith, Research Specialist

STEUBEN COUNTY HEALTH PROFILE. Finger Lakes Health Systems Agency, 2017

NURSING CARE IN PSYCHIATRY: Nurse participation in Multidisciplinary equips and their satisfaction degree

UK Renal Registry 13th Annual Report (December 2010): Appendix A The UK Renal Registry Statement of Purpose

3. Q: What are the care programmes and diagnostic groups used in the new Formula?

Disparities in Primary Health Care Experiences Among Canadians With Ambulatory Care Sensitive Conditions

Type of intervention Secondary prevention of heart failure (HF)-related events in patients at risk of HF.

Maternal and Child Health North Carolina Division of Public Health, Women's and Children's Health Section

An Evaluation of Health Improvements for. Bowen Therapy Clients

Integrated approaches to worker health, safety and wellbeing: Review Update

DAHL: Demographic Assessment for Health Literacy. Amresh Hanchate, PhD Research Assistant Professor Boston University School of Medicine

Nurse Staffing and Quality in Rural Nursing Homes

Cranbrook a healthy new town: health and wellbeing strategy

Fleet and Marine Corps Health Risk Assessment, 02 January December 31, 2015

COMPARATIVE PROGRAM ON HEALTH AND SOCIETY 2001/2 WORKING PAPER WORKING PAPER

NHS WORKFORCE RACE EQUALITY STANDARD 2017 DATA ANALYSIS REPORT FOR NATIONAL HEALTHCARE ORGANISATIONS

UK GIVING 2012/13. an update. March Registered charity number

Independent Sector Nurses in 2007

Ninth National GP Worklife Survey 2017

Making an impact on the public's health and wellbeing in England: Emerging Approaches and Lessons

STEUBEN COUNTY HEALTH PROFILE

EQUALITY AND DIVERSITY DATA ANALYSIS WORKFORCE INFORMATION SUMMARY REPORT

Improving the accessibility of employment and training opportunities for rural young unemployed

Domiciliary non-invasive ventilation for recurrent acidotic exacerbations of COPD: an economic analysis Tuggey J M, Plant P K, Elliott M W

What organisations can do to improve women's ability to achieve their potential. Chief Medical Officer Professor Dame Sally C Davies FRS FMedSci

A Model of Health for Family Caregivers. Flo Weierbach, RN, MPH, PhD East Tennessee State University College of Nursing

MONROE COUNTY HEALTH PROFILE. Finger Lakes Health Systems Agency, 2017

RUPRI Center for Rural Health Policy Analysis Rural Policy Brief

Quality of care in family planning services in Senegal and their outcomes

Statistical methods developed for the National Hip Fracture Database annual report, 2014

London Councils: Diabetes Integrated Care Research

Health service availability and health seeking behaviour in resource poor settings: evidence from Mozambique

Leicestershire Partnership NHS Trust Summary of Equality Monitoring Analyses of Service Users. April 2015 to March 2016

Evaluation of an independent, radiographer-led community diagnostic ultrasound service provided to general practitioners

alpha-opha Health Equity Workgroup Health Equity Indicators Draft for Consultation February 8, 2013

Health Equity Audit NHS Health Checks in central Lancashire

ONTARIO PUBLIC HEALTH STANDARDS

Implementing race equality in the NHS: what next?

Predicting Transitions in the Nursing Workforce: Professional Transitions from LPN to RN

As part. findings. appended. Decision

Type D Personality, Self-Resilience, and Health- Promoting Behaviors in Nursing Students

Policy Brief. Nurse Staffing Levels and Quality of Care in Rural Nursing Homes. rhrc.umn.edu. January 2015

Is Thailand's Health System Recovering from Economic Crisis? Developing Indicators to Monitor Equity

2014 Census of Tasmanian General Practices. Tasmania Medicare Local Limited ABN

The effect of skill-mix on clinical decision-making in NHS Direct

LIVINGSTON COUNTY HEALTH PROFILE. Finger Lakes Health Systems Agency, 2017

Outcomes benchmarking support packs: CCG level

Title:The impact of physician-nurse task-shifting in primary care on the course of disease: a systematic review

Supplementary Material Economies of Scale and Scope in Hospitals

Study population The study population comprised patients requesting same day appointments between 8:30 a.m. and 5 p.m.

Service Proposal Guide. Medical Outreach Indigenous Chronic Disease Program

Birmingham Solihull and the Black Country Area Team

Module 3 Identifying Health Problems

Physiotherapy outpatient services survey 2012

Chicago Scholarship Online Abstract and Keywords. U.S. Engineering in the Global Economy Richard B. Freeman and Hal Salzman

ONTARIO COUNTY HEALTH PROFILE. Finger Lakes Health Systems Agency, 2017

Carers and Employment: Socioeconomic Data from the 2011 and 2016 Irish Censuses

Nursing skill mix and staffing levels for safe patient care

PG snapshot Nursing Special Report. The Role of Workplace Safety and Surveillance Capacity in Driving Nurse and Patient Outcomes

SOCIO-ECONOMIC EFFECT OF TELECOMMUNICATION GROWTH IN NIGERIA: AN EXPLORATORY STUDY

Transcription:

Health and Pink Collar Work Subhashis Basu 1, Giles Ratcliffe 2, Mark Green 2 Corresponding Author: Dr Subhashis Basu Specialist Registrar in Occupational Medicine Sheffield Occupational Health Service Northern General Hospital Sheffield United Kingdom S5 7AU Subhashis.basu@sth.nhs.uk Mr Giles Ratcliffe Specialist Registrar in Public Health School of Health and Related Research (ScHARR) The University of Sheffield Regent Court 30 Regent Street Sheffield S1 4DA Dr Mark Green Research Associate in Public Health School of Health and Related Research (ScHARR) The University of Sheffield Regent Court 30 Regent Street Sheffield S1 4DA Funding: None Conflicts of Interest: There are no conflicts of interest to declare Keywords: Occupation, health status, public health

Abstract (250) Introduction In recent years, there has been a decline in the manufacturing sector of the United Kingdom economy with a corresponding growth of service-orientated pink collar jobs in some regions. Whilst the health outcomes of white and blue collar workers are well-established, less is known about this emerging pink collar group. Aims The aim of this study was to identify the long-term health outcomes of pink collar workers in comparison to their white-collar counterparts across a range of indicators. Methods Area-level percentages for white, pink and blue collar workers were derived from routinely collected employment data in a northern English town. Area-level health data pertaining to male and female life expectancy; respiratory deaths; and deaths from cardiovascular and circulatory causes (all-age and under 75) were obtained from the local authority and public health observatory. Multivariate regression analyses were performed to assess relationships between job collar and health. Results When adjusted for deprivation, there was a protective relationship for deaths for circulatory disease under age 75 and increasing percentages of pink collar workers within an area in comparison to white collar workers. Other relationships between collar status and health outcomes were not statistically-significant. Conclusions The reasons underlying the protective effect of pink collar status for deaths from circulatory disease is uncertain and merits further study. Possibilities include differences in job strain and lifestyle behaviours. Our work has a number of limitations and longitudinal studies with detailed exposure data should assess the long-term health outcomes of these workers using agreed definitions.

MAIN PAPER (2027) Introduction Although there has been much work examining the relationship between measures of socioeconomic status (SES) such as income and education and their relationship with health; until recently less attention has been paid to the role of occupation. The complex interplay of these three measures of SES has been the subject of much study, and findings analysing their relative influence in determining health have been inconclusive. A large German study suggested that the effect of income upon mortality was more important than education or occupation, whereas a Norwegian study attributed observed health inequalities more heavily towards education. 1,2 This has led some to suggest that these three metrics should not be used interchangeably in social science research. 3 When examining inequalities in health by occupation alone, it is apparent that there are significant differences between different job grades. The Whitehall II study of 10,308 British civil servants established that the long-term health outcomes of white collar workers are superior to those of blue collar workers. 4 Several follow-up studies of the Whitehall cohort have examined differences in work exposures and their association with ill-health, such as high job strain and an increased incidence of coronary heart disease. 5 International studies including the Belgian Job Stress Project, Danish MONICA II study and Finnish Longitudinal Study on Municipal Employees have demonstrated a negative relationship between occupational psychosocial hazards and health. 6,7 In recent years however, many western economies such as that in the United Kingdom (UK) have shifted away from an industry-based model towards service provisions. In some regions, this has led to a rapid growth of service sector jobs in the economy. 8 The term pink collar work was initially coined after the Second World War to describe jobs that were traditionally the preserve of women. Such roles included administrative, clerical, assistant and secretarial work. Nonetheless, it has been recognised that in modern times such occupations are no longer the preserve of women, and the term has been applied more widely to refer to all service sector jobs. 9 One difficulty in studying characteristics of pink collar work is the absence of a standardised definition. Some roles once considered pink collar work such as teaching and nursing have now gained professional status in many countries and are now considered white collar work. Despite these issues, it is plausible that with the continued decline of manufacturing seen in many developed countries, the size of this workforce will expand. 10 It seems reasonable therefore that occupational and public health professionals should gain an understanding of the epidemiological features of this emerging group, including an understanding of health outcomes. This could, for example, shed light

upon the need for workplace and wider public health interventions to address ill-health. This study explores the distribution and area-level health outcomes associated with pink collar work in a northern English town and compares and contrasts these with those of white and blue collar work.

Methods Our study was ecological in design. Area-level health outcome data for Rotherham residents were obtained at Medium-Super Output Area (MSOA) level over a five year period (2006-2010 inclusive). Super Output Areas are geographical regions developed following the 2001 census in England to facilitate the calculation of population-level neighborhood statistics such as socio-economic deprivation, crime and health data. On average across the country, each MSOA contains 7,200 residents according to the Office for National Statistics Mid-2012 Estimates in England. 5 Rotherham has 33 MSOAS and at the time of the study, population sizes ranged from 5,036 to 10,936 residents. Health data relating to life expectancy and deaths due to cardiovascular (CHD), circulatory (CVD) and respiratory (RESP) disease was collected and calculated as Indirectly-Age Standardised Ratios (IASR s all ages) for each MSOA. Stratified data was also available for CHD and CVD deaths for those aged under 75. Causes of death were coded using the International Classification of Diseases Version 10 (ICD-10 Version 2010). 6 Individual-level employment data for Rotherham residents (2010) was obtained from the local council. Workers were aggregated at MSOA level into three major categories: white collar (professional/managerial/executive); pink collar and blue collar (trade/manual). In the absence of a standardised definition, in our study we defined pink collar workers as those employed in assistant, customer service, entertainment, sales industry, administrative and personal service orientated work. 7 Jobs which were once considered pink-collar work such as registered nurse and teacher was classified as white-collar since such roles are affiliated with professional bodies in the United Kingdom. The distribution of percentages of pink-collar workers by MSOA was mapped using ESRI ArcGIS 10. 8 The relationship between residents occupational collar and health outcomes at area level was assessed using seperate linear regression models. In our model, we assigned pink and blue-collar groups as predictor variables and allocated white-collar workers as the reference group given their established superior health outcomes. We wished to determine the influence of residents deprivation status in the relationship between occupational collar and health. Area-level Indices of Multiple Deprivation (IMD) scores calculated by the Office for National Statistics in the UK are a composite of several domains including income; employment status; health deprivation and disability; educational skills and barriers to housing. The highest weighting however is given to income and in the absence of detailed education data, we

only included the income domain as a third predictor variable in our regression model. This is because we did not wish to introduce further confounding by including two separate measures of employment and health. Multicolinearity was assessed using scatter plots and Pearson s correlation coefficients. It should be noted that our analysis identified that residents deprivation status and education level were highly correlated (Pearson s coefficient >0.8), adding further weight to our decision to exclude the latter domain as an additional predictor variable. Statistical analysis was performed using IBM SPSS Version 22.0. 9 A weighted least- squares adjustment was made to account for variable MSOA populations in Rotherham. Statistical significance was set at the 0.05 level. Ethical approval was not required as the study used publically available, non-person identifiable data and was considered normal occupational health practice.

Results 86,928 Rotherham residents were registered as in-work in 2010. Using our occupational classifications 36,079 (41.5%) individuals were employed in white-collar, 23,860 (27.5%) in pinkcollar and 26,989 (31.0%) in blue-collar jobs. In total, 60% of the pink collar workforce were female. The spatial distribution of the pink collar workforce is shown in Figure 1. The central white belt extended diagonally across the map represents the town centre suggesting that the number of pink collar workers as a proportion of all individuals in employment is relatively low in these MSOAs. The highest concentrations (coloured black) are seen in the areas adjacent to the town centre with concentrations in rural settings located at the outskirts of the map. Insert Figure 1 here Table 1 shows the results from our regression analysis assessing the relationship between pink-collar status and health at area-level. Although statistically significant relationships were identified for pink-collar work and respiratory deaths, CHD deaths under 75 and CVD deaths under 75; only the relationship with CVD deaths under 75 remained significant following adjustment for IMD. The negative β co-efficient suggests that pink collar work has a protective effect in comparison to whitecollar work for this health outcome, indicating that for each unit increase in pink-collar workers there is a corresponding 3.45 unit decrease in circulatory deaths under the age of 75 at MSOA level. Insert Table 1 here Table 2 displays the results of the regression assessing the relationship between blue-collar work and health. Statistically-significant relationships for all health outcomes in relation to white-collar work were identified in the unadjusted analysis which disappeared when IMD was included in the model. Insert Table 2 here

Discussion As far as we are aware, this is the first study to outline the health outcomes associated with pink collar work in a systematic fashion. Our findings corroborate existing knowledge of the social stratification of health outcomes in that at a population-level, blue-collar workers experience worse health outcomes than their white-collar counterparts. 4 After adjustment for deprivation, the statistically significant differences between the two groups which were observed for all measured health outcomes (apart from circularity deaths under 75 and pink collar workers) disappear, suggesting income disparity is a key underlying explanation. Differences in health outcomes between pink and blue collar workers were assessed against the health outcomes of white collar workers rather than directly against each other. Prior to adjustment for deprivation, there were statistically significant differences in three health outcomes between pink and white collar workers. For two, respiratory deaths and deaths from coronary heart disease under the age of 75, adjustment for deprivation explained our findings. Nonetheless, a significant difference in population deaths from circulatory disease under the age of 75 remained. The negative standardised Beta co-efficient value suggests that for each unit increase in the proportion of pink collar workers within an MSOA, there is a corresponding 3.45 unit decrease in deaths from circulatory disease. The reasons underlying the protective effect of pink collar work status are unclear but may well be multi-factorial. Possible explanations include differences in lifestyle factors such as smoking rates, dietary behaviours, physical activity and sedentary behaviours for which our data was either incomplete or not available. There are also likely to be differences between groups in nature of work itself such as occupational stress. Features of job strain such as demand-control model, effortreward imbalance and organisational justice have received much attention in the literature, particularly in relation to white and blue-collar work. 6, 17 Another contributing factor could be that disparities of income and educational attainment traditionally seen between different occupational classes, such as in the nine major groups of the Standard Occupational Classification system, 18 do not apply in the same way to modern white, pink and blue collar workers. The collared groups may be more heterogeneous, and thus these disparities become blurred. In any case, longitudinal studies with comprehensive exposure data will likely provide more detailed information to address these questions. From a population-health perspective, this type of analysis can supplement existing data which outline the likely long-term health outcomes associated with belonging to specific occupational groups. Broad categorisations such as those applied here may be more useful in risk profiling and

developing strategic interventions aimed at improving the health of a large workforce at area-level both in and outside the immediate workplace. A number of avenues already exist through which such measures can be implemented. For example, local occupational health and wellbeing agendas provide one such opportunity. A lever through which occupational and public health professionals can work together is the Joint Strategic Needs Assessment (JSNA). This is a document produced by local governments in England outlining multi-disciplinary strategies for addressing key populationhealth concerns. We would encourage occupational health professionals to contribute their ideas and experience to such work. Our study has a number of limitations. It is ecological and thus it is not possible to attribute causality in discussing our observed associations. We also were unable to match data regarding the agestructure of the workforce to employment data, since the former was only available at population level. In the absence of this information, we performed our analyses assuming that age profiles were similar across the three collared groups. Although in our comparison of health outcomes of blue collar workers against white collar, findings were consistent with previous research; the implications for our analysis of pink collar worker are uncertain. A longitudinal study with access to age-stratified rates for population-level health outcomes would shed more light on this. We have also assumed that the recorded job reflects an individual s main lifetime type of work which will have introduced further bias. Finally, our employment and health data has been aggregated on an area basis and thus has limited application at individual-level. Key Points There has been a growth in the size of pink collar workforces in many Western economies but their health demographics are unknown Our study suggests that status as a pink collar worker has a protective effect against death from circulatory disease under the age of 75 as compared to white collar status Further work should explore whether age stratification of the workforce and health outcomes identifies similar protective effects

Figures and Tables Figure 1: Distribution of Pink Collar Workers by MSOA

Health Outcome Standardised β Coefficient (U) 95% Confidence Interval (U) -0.122 to P- Value (U) Standardised β Coefficient (A) Male Life Expectancy 0.073 0.267 0.452-0.068 Female Life -0.108 to Expectancy 0.117 0.342 0.298 0.032 Respiratory -8.423 to Deaths All 0.142 Ages -4.141 0.050-2.763 CHD Deaths -3.772 to All Ages -0.080 3.612 0.965 1.144 CHD Deaths -10.764 to - <75-5.625 0.488 0.033-2.680 Circulatory -3.822 to Deaths All 1.386 Ages -1.218 0.347-0.359 Circulatory -8.303 to - Deaths <75-5.865 1.621 0.005-3.453 Table 1: Health outcomes for Pink-Collar Work 95% Confidence Interval (A) -0.224 to P- Value (A) 0.088 0.377-0.199 to 0.263 0.777-7.233 to 1.708 0.216-2.699 to 4.986 0.547-7.419 to 2.056 0.257-3.071 to 2.352 0.788-6.766 to - 0.141 0.042 U = Unadjusted; A=Adjusted for Deprivation Status

Health Outcome Standardised β Coefficient (U) 95% Confidence Interval (U) -0.427 to - P- Value (U) Standardised β Coefficient (A) Male Life Expectancy -0.341 0.256 0.000 0.963 Female Life -0.408 to - Expectancy -0.309 0.210 0.000 0.102 Respiratory 3.291 to Deaths All 7.060 Ages 5.175 0.000 1.802 CHD Deaths 1.479 to All Ages 3.103 4.728 0.001 0.109 CHD Deaths 4.320 to <75 6.581 8.842 0.000 0.632 Circulatory 1.725 to Deaths All 4.016 Ages 2.870 0.000 0.768 Circulatory 4.395 to Deaths <75 5.865 7.335 0.000 2.171 Table 2: Health outcomes for Blue-Collar Work 95% Confidence Interval (A) -0.151 to P- Value (A) 0.158 0.963-0.33 to 0.126 0.369-2.623 to 6.226 0.412-3.694 to 3.911 0.954-5.319 to 4.056 0.785-1.915 to 3.451 0.563-1.107 to 5.449 0.186 U = Unadjusted; A=Adjusted for Deprivation Status

References 1. Geyer S and Peter R. Income, occupational position, qualification and health inequalities competing risks? Journal of Epidemiology and Community Health 2000; 54: 299-305. 2. Rognerud MA and Zahl PH. Social inequalities in mortality: changes in the relative importance of income, education and household size over a 27-year period. The European Journal of Public Health 2006; 16: 62-68. 3. Geyer S, Hemström Ö, Peter R and Vågerö D. Education, income, and occupational class cannot be used interchangeably in social epidemiology. Empirical evidence against a common practice. Journal of Epidemiology and Community Health 2006; 60: 804-810 4. Marmot MG, Davey Smith G, Stansfield S, et al. "Health Inequalities among British civil servants: the Whitehall II study". Lancet 1991; 337: 1387 1393. doi:10.1016/0140-6736(91)93068-k. 5. Kuper H and Marmot M. Job strain, job demands, decision latitude, and risk of coronary heart disease within the Whitehall II study. Journal of epidemiology and community health, 2003; 57:147-153. 6. von Bonsdorff MB, Seitsamo J, von Bonsdorff ME, Ilmarinen J, Nygård CH and Rantanen T. Job strain among blue-collar and white-collar employees as a determinant of total mortality: a 28-year population-based follow-up. BMJ open 2012; 2(2). 7. Kivimäki M, Virtanen M, Elovainio M, Kouvonen A, Väänänen A and Vahtera J. Work stress in the etiology of coronary heart disease a meta-analysis. Scandinavian journal of work, environment & health 2006; 32: 431-442. 8. Coe NM and Jones A. A new geography of the UK economy? Commentary on the publication of the economic geography of the UK. The Geographical Journal 2011; 177: 149-154. 9. Hodson R, Sullivan TA. The Social Organisation of Work. Wadsworth: USA, 2012. 10. Clark D. Urban Decline (Routledge Revivals). Routledge: United Kingdom, 2013. 11. Office for National Statistics. Super Output Areas. Available at http://www.ons.gov.uk/ons/guide-method/geography/beginners-guide/census/superoutput-areas--soas-/index.html. Accessed 15 April 2014. 12. Jordan H, Roderick P, Martin D. The Index of Multiple Deprivation 2000 and accessibility effects on health. Journal of epidemiology and community health 2004; 58: 250-257. 13. Casey C. The changing contexts of work. In: Boud D, Garrick J. Understanding Learning at Work. Routledge: New York, 1999.

14. Briggs D. The Role of GIS: Coping With Space (And Time) in Air Pollution Exposure Assessment. Journal of Toxicology and Environmental Health 2006; 68: 1243-1261. 15. Office for National Statistics. English Indices of Deprivation 2010. Available at: https://www.gov.uk/government/publications/english-indices-of-deprivation-2010. Accessed 11 July 2014. 16. Coakes SJ, Steed L. SPSS: Analysis without anguish using SPSS version 14.0 for Windows. John Wiley & Sons, Inc. 17. Hanebuth D, Meinel M and Fischer JE. Health-related quality of life, psychosocial work conditions, and absenteeism in an industrial sample of blue-and white-collar employees: a comparison of potential predictors. Journal of Occupational and Environmental Medicine 2006; 48: 28-37.