ORIGINAL ARTICLE
A Longitudinal Study of the Mental Health Impacts of Job Loss The Role of Socioeconomic, Sociodemographic, and Social Capital Factors Anna M. Ziersch, PhD, Fran Baum, PhD, Richard J. Woodman, PhD, Lareen Newman, PhD, and Gwyn Jolley, PhD
Objectives: To examine the role of socioeconomic, sociodemographic, and social capital factors in buffering or exacerbating the mental health impacts of job loss. Methods: A 2-year longitudinal cohort study of 300 workers experiencing job loss from a motoring manufacturer in Adelaide, South Australia. Data were collected on mental health (12-item version of the General Health Questionnaire) and socioeconomic, sociodemographic, and social capital factors. Analysis used linear mixed-effects regression. Results: Workers had poorer mental health than the general population. Female gender, less years at the plant, and not being partnered were associated with poorer mental health. The effects of financial status depended on current employment and levels of social support. Trust and social contact were associated with better mental health. Conclusion: A number of socioeconomic, sociodemographic, and social capital factors influence mental health in workers experiencing job loss, offering clues on how to support workers.
T
here is well-established evidence that job loss has negative effects on health, especially mental health.1–8 Nevertheless, less is known about the relative impact of socioeconomic, sociodemographic, and social capital factors in buffering or exacerbating these effects and the interaction between these factors, including potential changes over time. This article explores their role in the context of mass job loss after an automotive plant closure. Job loss affects mental and physical health through a number of pathways, including loss of income, job loss itself as a stressful life event, health-damaging behaviors that may result from unemployment, and the loss of social networks and work-related identity.9 Financial strain is one of the most important mediators between unemployment and poorer mental health.2,10–14 Reemployment may5,15 or may not16 help workers avoid the negative effects of sudden job loss, but even those who are quickly reemployed can still suffer if they cannot find similar jobs and pay.2 Education levels, occupation, and social class have also been linked to health outcomes of job loss and redundancy, with those with less education and in lower-skilled occupations having fewer reemployment prospects and worse health outcomes.13,17,18 Sociodemographic factors have been found to be relevant to the health impacts of job loss and unemployment. For example, research has found that men find job loss and unemployment more difficult than women, reflecting factors such as the pressure of being in
a traditional bread-winning role.13,17,19,20 Nevertheless, other studies have found women reporting worse mental health outcomes,11 no gender difference,21,22 or more mixed results.23 Research on marital status suggests that being married can be protective, especially for women.17 Mental health impacts of job loss and redundancy have been found to vary by age, with some studies suggesting older people fare worse24–26 but some evidence that middle-aged groups have the worst outcomes.19 Social capital has also been linked to mental and physical health,27–29 but the literature is theoretically and conceptually complex.30,31 Drawing on Bourdieu,32 we conceive social capital as operating at an individual level and it can be defined as the social resources available to an individual through social networks and is one means by which economic capital is reproduced in better-off groups in society. Key features of social capital include structural (social networks, social support, social and civic participation) and cognitive (trust) aspects.33 Research on social capital and work has tended to focus on how social capital helps in gaining employment34,35 and social capital within the workplace.36–39 Since work brings with it a social network and sense of identity and meaning,40 losing one’s job may well affect health and well-being because of the loss of these.41 Likewise, research also suggests that part of the loss of well-being that comes with unemployment results from losing work-related social support.41 The broader literature on social support and health suggests that those with greater social support in the face of major life events such as job loss might be buffered from its worst effects.42,43 To our knowledge, there has not previously been an assessment of the relevance of a range of social capital variables to the mental health effects of job loss. This study uses prospectively collected survey data from workers of an automotive plant in South Australia after its closure, to address the following research questions in relation to job loss: 1. How does the mental health of those experiencing recent job loss compare with that of the general population? 2. What socioeconomic, sociodemographic, and social capital factors are associated with mental health? 3. Does time since job loss or gender moderate the above associations?
METHODS From the Southgate Institute for Health, Society and Equity (Profs Ziersch and Baum and Drs Newman and Jolley) and Flinders Centre for Epidemiology & Biostatistics (Prof Woodman), Flinders University, Adelaide, South Australia. The research was supported by the South Australian Department of Health and the South Australian Department of Families and Communities through the Human Services Research and Innovation Program, and the Australian Research Council (ARC) Linkage Program (LP0562288). The authors also acknowledge support from a National Health and Medical Research Council Capacity Building Grant (324724), the ARC Federation Fellowship of Prof Fran Baum, and the ARC Future Fellowship of A/Prof Anna Ziersch. The authors declare no conflicts of interest. Address correspondence to: Anna M. Ziersch, PhD, Southgate Institute for Health, Society and Equity, Flinders University, GPO Box 2100, Adelaide, South Australia, Australia 5001 ([email protected]). C 2014 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0000000000000193
714
Setting In April 2004, Mitsubishi Motors Australia Limited (MMAL) announced the downsizing and eventual closure of parts of its South Australian engine and assembly plants in Adelaide, South Australia, with approximately 700 “involuntary” redundancies and 400 “voluntary” redundancies, commencing in June 2004. Most workers received relatively generous redundancy packages based on length of employment at MMAL. This study tracked the workers after their job loss over 2 years using three surveys. The study was approved by the Flinders University Social and Behavioural Ethics Committee.
Procedure Recruitment commenced at the beginning of the main wave of redundancies. Sensitivities from MMAL about the closure meant JOEM r Volume 56, Number 7, July 2014
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 56, Number 7, July 2014
that workers were recruited through MMAL-administered mail-outs to all workers (to meet ethical requirements), researcher site visits, and “snowballing” where participants recommended to researchers other MMAL workers who might be interested in participating in the study. An initial cohort of 371 workers was recruited with face-toface structured surveys administered from March to November 2005 (survey 1) and telephone surveys administered approximately 6 and 18 months later (surveys 2 and 3). A total of 316 workers completed survey 2 and 300 completed survey 3, an 81% retention rate. The outcome of interest was mental health, assessed using the 12-item version of the General Health Questionnaire (GHQ),44 which is commonly used in studies of unemployment and job loss.17 Scores range from 0 to 12, with higher scores indicating worse mental health. Sociodemographic and socioeconomic variables measured at each survey included age (years), gender, education level (secondary school or less, trade certificate/diploma, and university degree or higher), and relationship status (married/de facto and other). Selfreported financial management was dichotomized into “managing comfortably/very comfortably” or “getting by/finding it difficult/ very difficult.” On the basis of the literature, we would expect women, those with higher education, in a partnered relationship and financially managing comfortably to fare better. Specific employment-related variables included years worked in the factory, redundancy status (voluntary or involuntary), occupation (recoded into “professional” and “nonprofessional”), and employment (recoded into four categories: “still employed in the plant,” “reemployed,” “unemployed,” and “no longer in the labor force”). The literature suggests that those in professional occupations and those currently employed would have better mental health. Social capital variables included group participation, social contact, trust, and social support, which have been used extensively in previous research.45,46 We expected individuals with higher levels of these components to have better mental health. Group participation was assessed as either weekly/monthly or less-frequent (occasionally, rarely, or never) participation in a community group within the last 12 months. Principal component analysis revealed two underlying constructs relating to social contact with friends and family. Social contact with friends (face-to-face, e-mail, and/or telephone) was coded as “regular” (at least once a week) versus “less regular” (“less than once a week”). Social contact with family was coded similarly. Social support was derived using five questions; how many people participants could ask (none, 1 to 2, 3 to 4, and 5+) for help to discuss personal problems, practical help, to borrow money to pay a large bill, get information and advice looking for a new job, or information on managing finances. Principal component analysis indicated one overriding component (eigenvalue more than 1) that explained 50% of the variance in the data; overall KMO = 0.80 and principal component loadings 0.63 to 0.75. The derived score for this component was used in the analysis. The survey measured three separate aspects of generalized trust by asking agreement with three statements: “Generally speaking, people in Australia can be trusted,” “Generally speaking, you can trust governments,” and “Generally speaking, you can trust big business.” The first question represents level of trust in the public, a key aspect of social capital. The other two trust questions were included as both federal and state governments provided substantial financial support for workers, while MMAL also offered the redundant workers a reasonably good package. Possible responses ranged from 1 (strongly disagree) to 5 (strongly agree). Principal component analysis of the three items indicated one underlying component that explained 64% of the variability in the data and was used for the analysis; overall KMO = 0.7, principal component loadings 0.75 to 0.84.
Statistical Analysis The STATA statistical software package was used for all analysis (version 12.0, Stata Corp, College Station, TX) except the prin-
Longitudinal Study of the Mental Health Impacts of Job Loss
cipal component analysis for which we used SPSS version 19.0. Baseline characteristics of the subjects were described using mean ± SD for normally distributed continuous variables and median (interquartile range) for non-normally distributed continuous variables. Linear mixed models were used to estimate the association between subject sociodemographic and social capital characteristics and GHQ scores. These models use all available data over the followup, handle differences in length of follow-up at each wave, and take into account the fact that repeated measures on the same individual are correlated. We fitted the intercept as a random effect, thereby allowing for individual differences in GHQ scores at baseline. Variables that were significant at P < 0.1 in univariate linear mixed model analysis were considered for inclusion in multivariate models. Interaction terms assessed for inclusion were those between time and all other covariates, and those between gender, employment status, years worked at Mitsubishi, financial situation, and each of the covariates. In model 1, we assessed the effects of time since job loss, adjusted for age and gender. The final multivariate linear mixed model (model 2) included fixed-effect terms for time, time-squared, age, gender, years worked at Mitsubishi, relationship status, financial situation, redundancy status, employment status, group participation, socializing with friends, trust, and social support. In model 2, we also included all significant variables or their interactions with interaction terms considered significant at the level of P < 0.01. These were “time × gender,” “employment status × financial situation,” and “social support × financial situation.” In a sensitivity analysis, we included the effect of self-rated health, which although significant did not substantively change the results of model 2.
RESULTS A total of 300 participants (271 men and 29 women) participated in all three interviews. A detailed breakdown of the workforce demographics was not available from MMAL. Nevertheless, the sample reflected the generally understood workforce of the plant, being largely middle aged to older men engaged in nonprofessional occupations, and also mirrored the relative proportion of professionals employed in the automotive industry in general (21% in 200647 ). There was at least some missing data for specific variables at all three interviews for 12 subjects who were not included in the regression analysis. Table 1 describes the characteristics of the 300 participants at interview 1. Mean ages for men and women were 47.8 ± 10.3 years and 49.0 ± 10.1 years (P = 0.55), respectively. Median GHQ score was slightly higher for men than for women (P = 0.05) who had a fairly uniform distribution of GHQ scores (Fig. 1). Almost half the male workers had less than a high school level of education (45.8%). Most were lower-skilled and married. Approximately one third of workers had found employment elsewhere by the time of their first interview, and approximately 20% socialized with friends less than once per week. Overall, 42.3% reported financially either “getting by” or “having difficulty,” with no significant difference between genders. Among the 291 workers with a measured GHQ score at baseline, 131 (44%) had a score of 2 or more (Fig. 1),48 indicating significantly greater psychiatric morbidity than that of the general Australian population with similar mean age (19.1%; P < 0.001)49 and also of South Australians (32%; P < 0.001) (South Australian Department of Health, unpublished data, 2005). By gender, 42% of male workers had a score of 2 or more compared with 62% of women (P = 0.04) (Table 1). The overall mean ± SD GHQ score was 2.60 ± 3.35, and the mean GHQ scores by gender were 2.48 ± 3.29 for men and 3.71 ± 3.78 for women, which were both significantly higher than the mean scores for Australians living in capital cities (0.86 [P < 0.001] and 1.07 [P = 0.001], respectively).49
C 2014 American College of Occupational and Environmental Medicine
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
715
JOEM r Volume 56, Number 7, July 2014
Ziersch et al
TABLE 1. Subject Characteristics at Year 1 (First Interview) Men (n = 271)
Women (n = 29)
47.8 ± 10.3 1 (0–4) 42 210/61 17 (12–25) 12.6
49.0 ± 10.1 3 (0–10) 62 23/6 10 (8.5–16) 24.1
0.55 0.05 0.04 0.82 0.0002 0.08
45.8 42.4 11.8
79.3 10.3 10.3
0.002
76.4 23.6
93.1 6.9
0.04
10.4 81.1 8.5 32.8
10.3 72.4 17.2 65.5
9.2 37.3 18.1 12.9 22.5
6.9 34.5 17.2 20.7 20.7
43.5 56.5 0.04 ± 0.99 −0.01 ± 1.02 20.0 20.0
31.0 69.0 −0.37 ± 1.02 0.06 ± 0.85 20.7 6.9
Age, mean ± SD, yrs GHQ score, median (IQR) GHQ score of 2 or more, % Left Mitsubishi already, n (yes/no) Years worked at Mitsubishi, median (IQR) Self-rated general health fair or poor, % Education, % Less than high school Certificate Degree or above Occupation in Mitsubishi, % Lower skilled Professional Relationship status, % Never married Married or partnered Divorced/separated/widowed Redundancy, voluntary, % Employment status, % Self-employed Employed Unemployed Not in labor force Still in the plant Financial status, % Getting by/difficult Comfortable Social support (standardized factor score), mean ± SD Trust (standardized factor score), mean ± SD Social contact with friends
A Longitudinal Study of the Mental Health Impacts of Job Loss The Role of Socioeconomic, Sociodemographic, and Social Capital Factors Anna M. Ziersch, PhD, Fran Baum, PhD, Richard J. Woodman, PhD, Lareen Newman, PhD, and Gwyn Jolley, PhD
Objectives: To examine the role of socioeconomic, sociodemographic, and social capital factors in buffering or exacerbating the mental health impacts of job loss. Methods: A 2-year longitudinal cohort study of 300 workers experiencing job loss from a motoring manufacturer in Adelaide, South Australia. Data were collected on mental health (12-item version of the General Health Questionnaire) and socioeconomic, sociodemographic, and social capital factors. Analysis used linear mixed-effects regression. Results: Workers had poorer mental health than the general population. Female gender, less years at the plant, and not being partnered were associated with poorer mental health. The effects of financial status depended on current employment and levels of social support. Trust and social contact were associated with better mental health. Conclusion: A number of socioeconomic, sociodemographic, and social capital factors influence mental health in workers experiencing job loss, offering clues on how to support workers.
T
here is well-established evidence that job loss has negative effects on health, especially mental health.1–8 Nevertheless, less is known about the relative impact of socioeconomic, sociodemographic, and social capital factors in buffering or exacerbating these effects and the interaction between these factors, including potential changes over time. This article explores their role in the context of mass job loss after an automotive plant closure. Job loss affects mental and physical health through a number of pathways, including loss of income, job loss itself as a stressful life event, health-damaging behaviors that may result from unemployment, and the loss of social networks and work-related identity.9 Financial strain is one of the most important mediators between unemployment and poorer mental health.2,10–14 Reemployment may5,15 or may not16 help workers avoid the negative effects of sudden job loss, but even those who are quickly reemployed can still suffer if they cannot find similar jobs and pay.2 Education levels, occupation, and social class have also been linked to health outcomes of job loss and redundancy, with those with less education and in lower-skilled occupations having fewer reemployment prospects and worse health outcomes.13,17,18 Sociodemographic factors have been found to be relevant to the health impacts of job loss and unemployment. For example, research has found that men find job loss and unemployment more difficult than women, reflecting factors such as the pressure of being in
a traditional bread-winning role.13,17,19,20 Nevertheless, other studies have found women reporting worse mental health outcomes,11 no gender difference,21,22 or more mixed results.23 Research on marital status suggests that being married can be protective, especially for women.17 Mental health impacts of job loss and redundancy have been found to vary by age, with some studies suggesting older people fare worse24–26 but some evidence that middle-aged groups have the worst outcomes.19 Social capital has also been linked to mental and physical health,27–29 but the literature is theoretically and conceptually complex.30,31 Drawing on Bourdieu,32 we conceive social capital as operating at an individual level and it can be defined as the social resources available to an individual through social networks and is one means by which economic capital is reproduced in better-off groups in society. Key features of social capital include structural (social networks, social support, social and civic participation) and cognitive (trust) aspects.33 Research on social capital and work has tended to focus on how social capital helps in gaining employment34,35 and social capital within the workplace.36–39 Since work brings with it a social network and sense of identity and meaning,40 losing one’s job may well affect health and well-being because of the loss of these.41 Likewise, research also suggests that part of the loss of well-being that comes with unemployment results from losing work-related social support.41 The broader literature on social support and health suggests that those with greater social support in the face of major life events such as job loss might be buffered from its worst effects.42,43 To our knowledge, there has not previously been an assessment of the relevance of a range of social capital variables to the mental health effects of job loss. This study uses prospectively collected survey data from workers of an automotive plant in South Australia after its closure, to address the following research questions in relation to job loss: 1. How does the mental health of those experiencing recent job loss compare with that of the general population? 2. What socioeconomic, sociodemographic, and social capital factors are associated with mental health? 3. Does time since job loss or gender moderate the above associations?
METHODS From the Southgate Institute for Health, Society and Equity (Profs Ziersch and Baum and Drs Newman and Jolley) and Flinders Centre for Epidemiology & Biostatistics (Prof Woodman), Flinders University, Adelaide, South Australia. The research was supported by the South Australian Department of Health and the South Australian Department of Families and Communities through the Human Services Research and Innovation Program, and the Australian Research Council (ARC) Linkage Program (LP0562288). The authors also acknowledge support from a National Health and Medical Research Council Capacity Building Grant (324724), the ARC Federation Fellowship of Prof Fran Baum, and the ARC Future Fellowship of A/Prof Anna Ziersch. The authors declare no conflicts of interest. Address correspondence to: Anna M. Ziersch, PhD, Southgate Institute for Health, Society and Equity, Flinders University, GPO Box 2100, Adelaide, South Australia, Australia 5001 ([email protected]). C 2014 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0000000000000193
714
Setting In April 2004, Mitsubishi Motors Australia Limited (MMAL) announced the downsizing and eventual closure of parts of its South Australian engine and assembly plants in Adelaide, South Australia, with approximately 700 “involuntary” redundancies and 400 “voluntary” redundancies, commencing in June 2004. Most workers received relatively generous redundancy packages based on length of employment at MMAL. This study tracked the workers after their job loss over 2 years using three surveys. The study was approved by the Flinders University Social and Behavioural Ethics Committee.
Procedure Recruitment commenced at the beginning of the main wave of redundancies. Sensitivities from MMAL about the closure meant JOEM r Volume 56, Number 7, July 2014
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 56, Number 7, July 2014
that workers were recruited through MMAL-administered mail-outs to all workers (to meet ethical requirements), researcher site visits, and “snowballing” where participants recommended to researchers other MMAL workers who might be interested in participating in the study. An initial cohort of 371 workers was recruited with face-toface structured surveys administered from March to November 2005 (survey 1) and telephone surveys administered approximately 6 and 18 months later (surveys 2 and 3). A total of 316 workers completed survey 2 and 300 completed survey 3, an 81% retention rate. The outcome of interest was mental health, assessed using the 12-item version of the General Health Questionnaire (GHQ),44 which is commonly used in studies of unemployment and job loss.17 Scores range from 0 to 12, with higher scores indicating worse mental health. Sociodemographic and socioeconomic variables measured at each survey included age (years), gender, education level (secondary school or less, trade certificate/diploma, and university degree or higher), and relationship status (married/de facto and other). Selfreported financial management was dichotomized into “managing comfortably/very comfortably” or “getting by/finding it difficult/ very difficult.” On the basis of the literature, we would expect women, those with higher education, in a partnered relationship and financially managing comfortably to fare better. Specific employment-related variables included years worked in the factory, redundancy status (voluntary or involuntary), occupation (recoded into “professional” and “nonprofessional”), and employment (recoded into four categories: “still employed in the plant,” “reemployed,” “unemployed,” and “no longer in the labor force”). The literature suggests that those in professional occupations and those currently employed would have better mental health. Social capital variables included group participation, social contact, trust, and social support, which have been used extensively in previous research.45,46 We expected individuals with higher levels of these components to have better mental health. Group participation was assessed as either weekly/monthly or less-frequent (occasionally, rarely, or never) participation in a community group within the last 12 months. Principal component analysis revealed two underlying constructs relating to social contact with friends and family. Social contact with friends (face-to-face, e-mail, and/or telephone) was coded as “regular” (at least once a week) versus “less regular” (“less than once a week”). Social contact with family was coded similarly. Social support was derived using five questions; how many people participants could ask (none, 1 to 2, 3 to 4, and 5+) for help to discuss personal problems, practical help, to borrow money to pay a large bill, get information and advice looking for a new job, or information on managing finances. Principal component analysis indicated one overriding component (eigenvalue more than 1) that explained 50% of the variance in the data; overall KMO = 0.80 and principal component loadings 0.63 to 0.75. The derived score for this component was used in the analysis. The survey measured three separate aspects of generalized trust by asking agreement with three statements: “Generally speaking, people in Australia can be trusted,” “Generally speaking, you can trust governments,” and “Generally speaking, you can trust big business.” The first question represents level of trust in the public, a key aspect of social capital. The other two trust questions were included as both federal and state governments provided substantial financial support for workers, while MMAL also offered the redundant workers a reasonably good package. Possible responses ranged from 1 (strongly disagree) to 5 (strongly agree). Principal component analysis of the three items indicated one underlying component that explained 64% of the variability in the data and was used for the analysis; overall KMO = 0.7, principal component loadings 0.75 to 0.84.
Statistical Analysis The STATA statistical software package was used for all analysis (version 12.0, Stata Corp, College Station, TX) except the prin-
Longitudinal Study of the Mental Health Impacts of Job Loss
cipal component analysis for which we used SPSS version 19.0. Baseline characteristics of the subjects were described using mean ± SD for normally distributed continuous variables and median (interquartile range) for non-normally distributed continuous variables. Linear mixed models were used to estimate the association between subject sociodemographic and social capital characteristics and GHQ scores. These models use all available data over the followup, handle differences in length of follow-up at each wave, and take into account the fact that repeated measures on the same individual are correlated. We fitted the intercept as a random effect, thereby allowing for individual differences in GHQ scores at baseline. Variables that were significant at P < 0.1 in univariate linear mixed model analysis were considered for inclusion in multivariate models. Interaction terms assessed for inclusion were those between time and all other covariates, and those between gender, employment status, years worked at Mitsubishi, financial situation, and each of the covariates. In model 1, we assessed the effects of time since job loss, adjusted for age and gender. The final multivariate linear mixed model (model 2) included fixed-effect terms for time, time-squared, age, gender, years worked at Mitsubishi, relationship status, financial situation, redundancy status, employment status, group participation, socializing with friends, trust, and social support. In model 2, we also included all significant variables or their interactions with interaction terms considered significant at the level of P < 0.01. These were “time × gender,” “employment status × financial situation,” and “social support × financial situation.” In a sensitivity analysis, we included the effect of self-rated health, which although significant did not substantively change the results of model 2.
RESULTS A total of 300 participants (271 men and 29 women) participated in all three interviews. A detailed breakdown of the workforce demographics was not available from MMAL. Nevertheless, the sample reflected the generally understood workforce of the plant, being largely middle aged to older men engaged in nonprofessional occupations, and also mirrored the relative proportion of professionals employed in the automotive industry in general (21% in 200647 ). There was at least some missing data for specific variables at all three interviews for 12 subjects who were not included in the regression analysis. Table 1 describes the characteristics of the 300 participants at interview 1. Mean ages for men and women were 47.8 ± 10.3 years and 49.0 ± 10.1 years (P = 0.55), respectively. Median GHQ score was slightly higher for men than for women (P = 0.05) who had a fairly uniform distribution of GHQ scores (Fig. 1). Almost half the male workers had less than a high school level of education (45.8%). Most were lower-skilled and married. Approximately one third of workers had found employment elsewhere by the time of their first interview, and approximately 20% socialized with friends less than once per week. Overall, 42.3% reported financially either “getting by” or “having difficulty,” with no significant difference between genders. Among the 291 workers with a measured GHQ score at baseline, 131 (44%) had a score of 2 or more (Fig. 1),48 indicating significantly greater psychiatric morbidity than that of the general Australian population with similar mean age (19.1%; P < 0.001)49 and also of South Australians (32%; P < 0.001) (South Australian Department of Health, unpublished data, 2005). By gender, 42% of male workers had a score of 2 or more compared with 62% of women (P = 0.04) (Table 1). The overall mean ± SD GHQ score was 2.60 ± 3.35, and the mean GHQ scores by gender were 2.48 ± 3.29 for men and 3.71 ± 3.78 for women, which were both significantly higher than the mean scores for Australians living in capital cities (0.86 [P < 0.001] and 1.07 [P = 0.001], respectively).49
C 2014 American College of Occupational and Environmental Medicine
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
715
JOEM r Volume 56, Number 7, July 2014
Ziersch et al
TABLE 1. Subject Characteristics at Year 1 (First Interview) Men (n = 271)
Women (n = 29)
47.8 ± 10.3 1 (0–4) 42 210/61 17 (12–25) 12.6
49.0 ± 10.1 3 (0–10) 62 23/6 10 (8.5–16) 24.1
0.55 0.05 0.04 0.82 0.0002 0.08
45.8 42.4 11.8
79.3 10.3 10.3
0.002
76.4 23.6
93.1 6.9
0.04
10.4 81.1 8.5 32.8
10.3 72.4 17.2 65.5
9.2 37.3 18.1 12.9 22.5
6.9 34.5 17.2 20.7 20.7
43.5 56.5 0.04 ± 0.99 −0.01 ± 1.02 20.0 20.0
31.0 69.0 −0.37 ± 1.02 0.06 ± 0.85 20.7 6.9
Age, mean ± SD, yrs GHQ score, median (IQR) GHQ score of 2 or more, % Left Mitsubishi already, n (yes/no) Years worked at Mitsubishi, median (IQR) Self-rated general health fair or poor, % Education, % Less than high school Certificate Degree or above Occupation in Mitsubishi, % Lower skilled Professional Relationship status, % Never married Married or partnered Divorced/separated/widowed Redundancy, voluntary, % Employment status, % Self-employed Employed Unemployed Not in labor force Still in the plant Financial status, % Getting by/difficult Comfortable Social support (standardized factor score), mean ± SD Trust (standardized factor score), mean ± SD Social contact with friends
