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Mental Health and Wellbeing of Older Workers in Australia ARTICLE · APRIL 2015 DOI: 10.1093/workar/wav004
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Work, Aging and Retirement, 2015, Vol. 1, No. 2, pp. 202–213 doi:10.1093/workar/wav004 Advance Access publication March 9, 2015 Article
Mental Health and Wellbeing of Older Workers in Australia Miriam K. Forbes1, Karen M. Spence, Viviana M. Wuthrich, and Ronald M. Rapee Centre for Emotional Health, Macquarie University, Sydney, Australia 2109
A b st r a ct Population ageing has catalyzed worldwide social and political reform to encourage continued work and delayed retirement. These changes necessitate an understanding of the impact of working later in life on mental health and wellbeing. The aim of this study was to examine the relationship of age and work force status (working full time, part time, or retired) with mental health and wellbeing in Australian men and women past the average retirement age of 60. The effects of potential covariates (i.e., marriage, physical health, and financial stress) were also examined, and the impact of low qualification levels and physically demanding occupations were explored. A total of 2,149 men and women aged 60–79 from the 2007 National Survey of Mental Health and Wellbeing were included in the analyses. Results indicated that older age groups, people working part time, and men reported the best mental health and wellbeing outcomes. A minority of the significant main effects became be nonsignificant after controlling for marriage, financial stress, and physical health conditions in the models. Qualification levels and physically demanding occupations were not significant predictors for mental health and wellbeing. Taken together, these results suggest that there does not appear to be categorically beneficial or harmful outcomes for men and women working later in life, nor for those who have retired. People working part time later in life consistently reported the best mental health and wellbeing outcomes. Implications of the results are discussed in the context of the literature. The global proportion of people aged 60 and over is expected to double by 2050 (United Nations Department of Economic and Social Affairs—Population Division, 2013). The corresponding decline in the proportion of the population participating in the labor force will place financial pressure on government expenditure for health and social welfare spending and force the economic market to rely increasingly on older workers to maintain economic revenue (Butterworth, Gill, Rodgers, Anstey, Villamil, & Melzer, 2006; Schalk et al., 2010). The rising proportion of older adults in the population has catalyzed worldwide social and political reform to account for the major economic, social, and political consequences of this demographic shift. In order to increase rates of labor-force participation in older adults to avoid a stagnation in productivity, international (e.g., Duval, 2003) and Australian domestic policy responses (Commonwealth Department of Family and Community Services, 2002) encourage continued work and delayed retirement through pension restrictions and increasing pension and preservation ages (i.e., the age at which superannuation funds can be accessed; Melbourne Institute of Applied Economic and Social Research, 2014).
The Role of Work Status in Mental Health and Wellbeing While these changes are economically justified and driven, it is important to understand the implications for men and women who correspondingly delay retirement and continue to work later in life, particularly for their quality of life (QoL), mental health, and wellbeing. There are three directional theories as to how retirement might affect these outcomes: The first is that retirement might release workers from the pressure of their career and give them the opportunity and time to pursue other interests and activities, thus resulting in better mental health and wellbeing (Kim & Moen, 2002). This effect might be particularly pronounced for people with less social advantage, who are likely to have more stressful jobs and thus low morale that would improve with retirement (Kim & Moen, 2002). In support of the notion that retirement may have a positive impact on psychological wellbeing, some studies have found retirees to report less anxiety and distress and higher positive affect (Drentea, 2002), higher levels of morale (Kim & Moen, 2002), and better general mental health (Mein, Martikainen, Hemingway, Stansfeld, & Marmot, 2003). The second theory posits the opposite relationship: that is, that retirement may be related to loss of financial stability, a sense of purpose, and the social
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Mental Health and Wellbeing of Older Workers • 203 integration and support networks associated with working (Drentea, 2002). Accordingly, some research has linked retirement with higher prevalence of mental disorders (Butterworth et al., 2006), lower life satisfaction and happiness (Gall, Evans, & Howard, 1997), and lower QoL (Melbourne Institute of Applied Economic and Social Research, 2014). The third theory suggests that mental health and wellbeing during work life will remain relatively consistent into retirement (Kim & Moen, 2002). In support of this theory, some studies have found retirement to have no effect on mental health status (Costa, Zonderman, McCrae, Cornoni-Huntley, Locke, & Barbano, 1987; Midanik, Soghikian, Ransom, & Tekawa, 1995; Van Solinge, 2007). Overall, the findings are inconsistent and the relationship between work status and wellbeing is unclear. The discrepancies in studies’ findings are likely related to the heterogeneity in methodology; studies use different measures of adjustment, different designs, and focus on different demographics—often across a broad age range, and with a focus on men, or on specific subpopulations (e.g., Butterworth et al., 2006; Mein et al., 2003; Melzer, Buxton, & Villamil, 2004). The present study will investigate the role of work status in mental health and wellbeing in a nationally representative sample of men and women and focus on older workers. Very few studies have included the effects of part time work in examining mental health of ageing workers. Combining the three theories on the effects of retirement, part time work may act as a protective factor that lessens work-related stresses, allows more time for other pursuits, and simultaneously maintains the financial, autonomous, and social benefits of work. Part time work therefore needs to be considered and compared with the effects of working full time, and full time retirement.
Age, Retirement, Gender, and Mental Health and Wellbeing Older age is generally related to better mental health and wellbeing, and specifically with lower prevalence of common mental disorders (Andrews, Hall, Teesson, & Henderson, 1999; Melzer et al., 2004; Warr, 1992). Retiring younger is also independently related to increased mental and physical ill health. It is thus likely that young retirees may disproportionately represent people with physical and mental health problems, and that the remaining workforce is relatively healthy (Van Solinge, 2007). Correspondingly, existing research has found that retiring at an early age is related to particularly poor outcomes for men: British and Australian men who retired before the age of 65 have been found to have significantly higher rates of common mental disorders compared to age-matched men still working, and retirees over 65, after controlling for known covariates (Butterworth et al., 2006; Gill, Butterworth, Rodgers, Antsey, Villamil, & Melzer, 2006; Melzer et al., 2004). This finding may be because these individuals were required to retire early due to mental or physical health issues, or the poorer mental and physical health outcomes might be a result of early retirement. It is also possible that social norms and expectations prescribe an acceptable age at which to retire that negatively affects men who retire before this age. Regardless of the cause, this interaction between age and work status—particularly for young retirees—introduces bias for studies that examine the effects of workforce status over broad age groups (e.g., 45–75; Butterworth et al., 2006). A focus on workers over the normative retirement age could help to understand whether working late in life is related to positive or negative outcomes, after taking into account the effects of a normative age for retirement.
Adjustment to retirement is also likely to differ for men and women because gender structures work life and retirement experiences (Gall et al., 1997; Van Solinge, 2007), and men tend to have stronger work attachment (Vo et al., 2014). Correspondingly, in Butterworth and colleagues’ (2006) analysis of the 1997 Australian National Survey of Mental Health and Wellbeing, age interacted with labor force status to affect mental health for men, but not for women. This suggests a less central role of work for women in that cohort.
Potential Confounding Variables Wang, Henkens, and Van Solinge (2011) suggested that retirement adjustment is a longitudinal process during which adjustment fluctuates as a function of individual resources, such as physical health, financial resources, and social resources (e.g., marriage). The effects of these factors on the relationship between age and work status on mental health and QoL are thus another important consideration. Marriage is a protective factor for adjustment in retirement (Van Solinge, 2007; Wong & Earl, 2009); financial hardship is independently associated with mental health problems (Gill et al., 2006) and has also been found to predict life dissatisfaction and maladjustment in retirement (Gall et al., 1997; Kim & Moen, 2002; Wong & Earl, 2009); and poor physical health is related to maladjustment in retirement (Kim & Moen, 2002) and has been found to account for the differences in mental health between retirees and workers over 55 (Gill et al., 2006). Further, physical health deteriorates with age (Wong & Earl, 2009), and is also strongly related to mental health, particularly in older adults (Gall et al., 1997). Although these resources appear to be important in adjustment to retirement, they are also relevant to mental health across the life span (Kim & Moen, 2002). It is likely that the experience of financial stress or chronic physical conditions will amplify associations between work status and poor mental health and wellbeing, while marriage is likely to attenuate these relationships. Hence, it is important to examine the effects of these potential covariates on the relationships between work status and mental health and wellbeing in ageing workers.
Physically Demanding Work and Qualification Levels There has been much media attention and speculation on the effects of an increase in pension age for workers with less qualifications and/ or physically demanding occupations. While there is little research on this topic, people with more qualifications tend to work later in life (Burtless, 2013; Wong & Earl, 2009), perhaps due to their more stable employment, good physical health, and superior work conditions (Alpass, 2013); these circumstances are likely associated with positive mental health and wellbeing. Similarly, physical work gets harder as physical capacities decline with age, and physically demanding jobs produce more rapid physiological ageing and more health problems (Van Solinge, 2007), which is likely related to poorer mental health and wellbeing. Therefore qualification levels and the physicality of different occupations and are likely to affect the relationships between work status and psychological health in older adulthood, but to our knowledge, these relationships have not been examined in the literature to date.
The Present Study In short, the mental health and wellbeing of older adults is an increasingly important area for research. The aim of the present study is to
204 • M. K. Forbes et al. examine the impact of age, work status (i.e., retired, part time, or full time work), and gender in older adults (i.e., those past the average Australian retirement age of 60) on mental health and wellbeing outcomes, in a large nationally representative survey of the Australian population (The 2007 Australian National Survey of Mental Health and Wellbeing). The effects of potential covariates (i.e., marriage, poor physical health, and financial stress) will be examined, and the influence of qualification levels and physically demanding occupations will be explored in secondary analyses. Based on previous research, it is hypothesized that: Hypothesis 1. Older age groups will have better mental health and wellbeing than younger age groups. Hypothesis 2. People working part time will have better mental health and wellbeing, compared to people who are retired or working full time. Hypothesis 3. The relationship between work status and mental health will be stronger for men than for women (i.e., there will be a significant two-way interaction between work status and gender). Hypothesis 4. Highly qualified men and women working later in life will have better mental health and wellbeing than less highly qualified workers. Hypothesis 5. Work in physically demanding roles will be related to poorer wellbeing for older adults, compared to work in nonphysically demanding roles.
M et h o d
Participants and Procedure
The 2007 Australian National Survey of Mental Health and Wellbeing (NSMHWB) was conducted by the Australian Bureau of Statistics (ABS) within a stratified, multi-stage probability sample of adults aged 16–85 years living in private dwellings in Australia, excluding very remote areas. The contents of the survey are described below and the details of the methods have been described elsewhere (Slade, Johnston, Oakley-Browne, Andrews, & Whiteford, 2009). In total, 8,841 people participated in the voluntary survey, which represented a 60% response rate. Because the focus of this study was on older workers, the present study began by including all participants over the age of 60 (n = 2515), which is the average retirement age in Australia (Melbourne Institute of Applied Economic and Social Research, 2014). Participants aged 80 and over (n = 355) were excluded, as very few were working part time (n = 9) and none were working full time, so comparisons on the key independent variable of work status could not be made. Participants with severe cognitive impairment (n = 3)—as measured by scores under 23 on the Mini Mental State Examination (Folstein, Folstein, & McHugh, 1975)—were also excluded to ensure the validity of retrospective recall responses. Finally, people who were looking for work were excluded from analyses (n = 8) because they could not be included in the retired, part time or full time working group. The final sample included 2,149 adults aged 60–79, and 49.6% (n = 1,066) were women.
Measures The 2007 NSMHWB provides information on the prevalence of selected lifetime and 12-month mental disorders from the ICD-10 and DSM-IV, including anxiety disorders (e.g., generalized anxiety disorder [GAD]), affective disorders (e.g., depression), and substance use disorders (e.g., harmful alcohol use), based on a modified version of the World Mental Health Survey Initiative version of the World Health Organization’s Composite International Diagnostic Interview (WMHCIDI 3.0). It also provides information on the level of impairment associated with mental health problems, as well as demographic and socioeconomic characteristics. The measures extracted from the survey that were used in the present analyses are described in detail later.
Age group
The relationship between mental health problems and age has been found to be U-shaped (Byles, Gallienne, Blyth, & Banks, 2012), and this relationship has been found to vary by work status (e.g., Butterworth et al., 2006). Age was consequently split into categories to allow for the relationships between mental health and work status to vary across age groups in combinations of linear and/or nonlinear patterns, as well as to allow for group comparisons. Therefore, age was analyzed in brackets of 5 years as follows: 60–64 years (coded 0), 65–69 years (coded 1), 70–74 years (coded 2), and 75–79 years (coded 3). These groups were derived based on other studies (e.g., Butterworth et al., 2006).
Work status
Respondents specified whether they were working full time, part time, looking for work (i.e., unemployed), or not in the labor force, as well as the number of hours usually worked per week in all jobs. Retirement status was not explicitly collected in the survey. Therefore, in keeping with past research (Butterworth et al., 2006), those who were not in the labor force and worked zero hours per week were classified in this study as retired. People who reported working up to 34 hr per week were classified as part time, and those working 35 hr or more per week were classified as full time. Those who reported that they were looking for work were excluded from the analyses as described previously. The categories were coded as retired (0), working part time (1), and working full time (2).
Disorder diagnosis
Previous studies have used the prevalence of all common mental disorders as an indicator of mental health in retirement, but this approach will also likely capture lifetime psychopathology that is unrelated to retirement specifically. In the context of research on the possible effects of work force status, a focus on common mental disorders with late-life onset and responsiveness to life stressors (e.g., GAD and depressive episodes; Blazer, 2003; Chou, 2009) may be more appropriate. A dichotomous 12-month diagnosis variable was thus computed based on the assessment of depressive disorders and GAD in the NSMHWB. Participants that met the criteria for a DSM-IV-TR or ICD-10 diagnosis for dysthymia, any depressive episode (i.e., minor, moderate, or major), any unipolar depressive disorder, or GAD in the previous 12 months were given a code of “1.” Those who had not met criteria for any of these disorders were coded as “0.”
Psychological distress
The Kessler Psychological Distress Scale (K10, Kessler et al., 2002) is an established ten-item measure of nonspecific psychological distress
Mental Health and Wellbeing of Older Workers • 205 and depressive symptoms experienced in the past 4 weeks (e.g., “During the last 30 days, about how often did you feel hopeless?”). Each of the items were scored from None of the time (1) to All of the time (5), and summed to give a possible range of 10–50 (Andrews & Slade, 2001).
Self-reported QoL
The Delighted to Terrible Scale (cf. Andrews & Withey, 1976) is a single-item QoL measure that asks about respondents’ QoL over the previous year and how they feel about the future, and has seven response options ranging from Delighted (1) to Terrible (7).
Financial stress
The experience of household financial problems in the past 12 months (e.g., “Could not pay electricity, gas or telephone bills on time” or “pawned or sold something”) was transformed into a dichotomous variable coded as yes (1) or no (0): any participant that reported experiencing any of the seven listed financial problems in the past 12 months was classified as having experienced financial stress. These items were used to represent financial stress rather than annual income, which is highly related to work status (Palmore, Fillenbaum, & George, 1984).
Marriage
Registered marital status was transformed into a dichotomous married (1) or not married (i.e., never married, widowed, divorced, or separated) (0) variable to account for the protective effects of current marriage.
Physical health
The experience of any chronic physical health condition (e.g., cancer, arthritis, back or neck pain, asthma, or diabetes) in the past 12 months was transformed into a dichotomous variable, coded as no conditions (0) or current conditions (1). This relatively objective measure of physical health was used instead of subjective physical health, as health problems are expected and normative for older age groups (Costa et al., 1987), which would confound the effects of age.
Qualification level
Respondents were asked the level of their highest non-school qualification, which was transformed into a dichotomous variable: Participants with no nonschool qualifications, and with basic postsecondary qualifications (i.e., up to and including a Certificate IV; Australian Qualifications Framework [AQF] Levels 1–4) were classified as the not highly qualified (0) group, and those with higher postsecondary qualifications (i.e., at or above an Advanced Diploma or Diploma; AQF Levels 5–10) were classified as highly qualified (1). This distinction was made based on the AQF, which lists Level 5 as the first level where graduates are deemed to have skills for paraprofessional work (AQF Council, 2013). Participants with a “level not determined” response (n = 10 men, n = 9 women) were classified as missing and excluded from analyses that included the qualification level variable.
Physically demanding occupations
Occupation was independently coded by three of the authors (M.F., K.S., V.W.) to classify occupations as not physically demanding (0) somewhat physically demanding (1), and physically demanding (2). The
eight categories specified on the Basic Confidentialised Unit Record File (CURF) were clarified with reference to the more detailed 51 categories on the Expanded CURF. Two of the three raters agreed on the classification of every category, and upon further consideration the third rater came to the same conclusion. The occupations were rated as follows: managers, professionals, and clerical and administrative workers were not considered physical; community and personal service workers and sales workers were classified as somewhat physical; and technicians and trades workers, machinery operators and drivers, and laborers were classified as physical occupations. One difficulty that the raters faced was the inclusion of Farmers in the managers group in the original survey. Consequently, all analyses were run with and without the managers included in the “not physically demanding” group. This did not impact the significance or interpretation of the analyses, so farmers were retained in the nonphysically demanding group as part of the managers group in all analyses. The effects of a physical occupation could only be examined in the context of people currently working (full time or part time), and so were based on small sample sizes (n = 321 men, and n = 188 women), with particularly small cell sizes in older age groups.
Data Analysis Microdata were provided by the Australian Bureau of Statistics in the form of household and person Basic CURFs. These datasets were merged, using the person identifier in each data set in order to include the financial stress variable in the analyses. The data were analyzed using Statistical Package for the Social Science (SPSS) Version 22 for Macintosh. The variables were calculated as described in the Measures section, with a focus on isolating constructs of interest. Where possible, variables were transformed to dichotomous predictors to examine the specific effect of a variable of interest (e.g., the experience of financial stress). This method results in a loss of some detailed information (e.g., the specific types of financial stress experienced), but avoids the loss of statistical power that results from examining all available levels of a nominal variable (e.g., examining each type of financial stress separately). It is important to note that the categories in nominal variables are not equivalent, so their inclusion as count variables (e.g., number of types of financial stresses experienced) is not valid. Age group, work status, and gender represented the primary independent variables; disorder diagnosis, psychological distress, and self-reported QoL represented the dependent variables; financial stress, marriage, and physical health were examined as potential covariates of the primary relationships; and qualification level and physically demanding work represented secondary covariates of interest. Descriptive and bivariate statistics were computed before running the primary analyses. All analyses examining diagnosis as an outcome variable were conducted using multiple logistic regression with indicator coding. All analyses examining psychological distress and QoL as the outcome variables were conducted using the univariate general linear model (GLM) procedure in SPSS. Age group was treated as a continuous ordinal variable, and difference contrasts were used to compare age groups (i.e., each group was compared to the preceding age group), and simple contrasts were used for work status (“retired” was used as the reference group) and gender (women represented the reference group). All of the covariates were categorical variables, for which simple contrasts were used, and the group coded “0” was used
206 • M. K. Forbes et al. as the reference group. Bonferroni adjustments were used for post hoc multiple comparisons and estimated mean pairwise comparisons. The multicollinearity and linearity of the logit assumptions were met for the logistic regression analyses, but the homogeneity of variances and normality assumptions were violated in the GLM analyses. These distributions were anticipated due to the nature of the variables, and the F-statistic is robust to these violations (Lindman, 1974), but the results should be interpreted with caution nonetheless. The multiple logistic regression and GLM analyses were weighted by “person weight,” which indicates how many people in the Australian population are represented by each response. The raw weight provided by the ABS was divided by the sum of the weights for the present sample, and multiplied by the real sample size (n = 2,149). This accounted for the method of sample selection and the chance of the respondent being selected, as well as retaining accurate significance testing based on the sample size, rather than the 3,038,388 adults in the Australian population that this group represented. After univariate and bivariate descriptive statistics were calculated, analyses were run separately for each outcome variable, in order to examine the unique relationships of age and work status with different aspects of mental health and wellbeing. The main effects of age group, work status, and gender were examined for each outcome variable (Model 1; Hypotheses 1 and 2) before their interactions were added (Model 2; Hypothesis 3). The potential covariates of financial stress, marriage and physical health were then included—after the removal of any nonsignificant interaction effects in Model 2—to examine the direction and significance of their effects on the relationship of age group and work status with each outcome variable (Model 3). These variables were included after the main effects—rather than before— to examine how they affect the key relationships of interest. Given the overlap of some of the constructs (e.g., physical health and mental health), the inclusion of these covariates in the models before the main effects would account for much of the variance in the dependent variables, and preclude the evaluation of the primary relationships of interest (i.e., age group, work status, and gender with mental health and wellbeing). The unique effects of qualification levels and physically demanding jobs on the effects of age group, work status, and gender were also explored (Model 4; Hypotheses 4 and 5). An alpha level of .01 was used for bivariate relationships, given the multiple comparisons and large sample size. An alpha level of .05 was used for testing Models 1–4, given the a priori hypotheses.
R e s u lts
Descriptive Statistics and Bivariate Relationships
The descriptive statistics are displayed in Table 1, and the bivariate relationships are presented in Table 2. Independent samples t-tests, chi-square analyses with adjusted standardized residuals, one-way analyses of variance (ANOVAs), and Pearson correlations were used to examine bivariate relationships. For the sake of brevity, the bivariate relationships are not reported in full. The key bivariate relationships of interest are briefly summarized below. All of the relationships were in the expected directions.
Independent variables and covariates
The proportion of married people was significantly lower in older age groups, and older age groups were also less likely to be highly qualified.
People who were retired were more likely to have a chronic physical health condition, and less likely to be highly qualified, compared to those working part time or full time. Women were less likely to be married than men; follow-up analyses showed that women were significantly more likely to be widowed, but did not have significant differences across other registered marital statuses. Of those people still working, women were less likely to be working in physically demanding roles than men.
Covariates and dependent variables
Married people were significantly less likely to report a disorder, reported better QoL, and lower psychological distress. People with financial problems or chronic physical health conditions were more likely to have a disorder diagnosis, reported poorer QoL, and higher psychological distress. Highly qualified people reported less psychological distress and better QoL, but there were no significant relationships with probability of disorder diagnosis. Physically demanding work was not independently related to mental health and wellbeing.
Model 1 The main effects of age group, work status, and gender were examined for psychological distress (Table 3), self-reported QoL (Table 4), and disorder diagnosis (Table 5).
Age group
Older age groups reported significantly lower levels of distress and had lower odds of a disorder diagnosis, compared to younger adults. More specifically, custom hypothesis tests showed that each age group reported significantly less distress than the preceding age group, and was .74 times as likely to have a diagnosis. Age group was not related to QoL.
Work status
Work status was a significant main effect for all three mental health and wellbeing outcomes. Custom hypothesis tests showed that people working full time or part time reported significantly less psychological distress than people who were retired, and that people who were working part time had better self-reported QoL than people who were retired. Similarly, people working part time were .39 times as likely to have a disorder diagnosis than people who were retired.
Gender
Gender was a significant main effect for all three outcomes. Women reported higher levels of psychological distress, poorer QoL, and were more likely to have a disorder diagnosis, compared to men.
Model 2 Interactions between the main effects in Model 1 were added in Model 2. While the models remained significant, none of the two-way or three-way interactions between work status, age group, and gender were significant predictors for any of the outcome variables. The interaction terms were consequently removed to test Model 3.
Model 3 A model that retained the main effects of age group, work status, and gender and included the potential covariates of marriage, financial
Mental Health and Wellbeing of Older Workers • 207 Table 1. Descriptive Statistics for the Included Sample; for Women; and for Men Variable (Possible Range)
Total (n = 2,149)
Women (n = 1,066)
Men (n = 1,083)
Age (60–79) Marital status—married Marital status—widowed Work status—retired Work status—part time Work status—full time 12-month disorder diagnosis Psychological distressb (10–50) Quality of lifeb (1–7) Highly qualified Physical or somewhat physical job 12-month physical health condition Experiencing financial stress
67.94 (5.82) 1494 (69.5%) 308 (14.3%) 1633 (76.0%) 268 (12.5%) 248 (11.5%) 126 (5.8%) 13.32 (4.82) 2.68 (1.05) 435 (20.3%) 234 (10.9%) 1552 (72.2%) 119 (5.5%)
68.21 (5.90) 656 (61.6%) 244 (22.9%) 874 (82.0%) 143 (13.5%) 48 (4.5%) 80 (7.5%) 13.74 (5.16) 2.75 (1.03) 207 (19.4%) 68 (6.4%) 796 (74.7%) 67 (6.3%)
67.67 (5.73) 838a (77.3%) 64 (5.9%) 759a (70.1%) 125 (12.5%) 199a (18.4%) 46a (4.2%) 12.91a (4.41) 2.61a (1.06) 228 (21.0%) 166a (15.3%) 756 (69.8%) 52 (4.8%)
Note. Mean (SD) or number (% within column). a Independent samples t-tests and chi-square distributions showed significant gender differences p < .01. b Higher scores indicate greater distress and poorer quality of life, respectively.
stress, and physical health was tested for the outcomes of psychological distress (Table 3), self-reported QoL (Table 4), and disorder diagnosis (Table 5).
Covariate main effects
Married people reported better QoL, and were two-thirds as likely to have a disorder diagnosis, compared to others; marriage was not a significant predictor for psychological distress. People experiencing chronic physical health problems or financial stress reported higher psychological distress, poorer QoL, and were three- and four-times (respectively) as likely to have a disorder diagnosis.
Effects of covariates in Model 3
The inclusion of these covariates in predicting psychological distress did not change the significance or direction of any of the main effects in Model 1 (Table 3 and Figure 1). However, their inclusion in predicting QoL weakened the main effect of gender in Model 1, which became nonsignificant. Based on the bivariate relationships summarized earlier, it is likely this effect was driven by the protective factor of marriage, which was less common for women, and was a strong predictor of better QoL. Age group remained a non-significant predictor of QoL, and work status remained a significant predictor (Table 4 and Figure 2). After the inclusion of the covariates, the main effect of work status was no longer a significant predictor for a 12-month disorder diagnosis (Table 5). Based on the bivariate relationships, it appears that the poorer physical health in retirees may have attenuated this relationship by accounting for the higher rates of disorders in people who were retired. The main effects of age group and gender remained significant.
Model 4 The effects of qualification levels and physically demanding jobs, in addition to the main effects of age group, work status, and gender were also examined. None of the models that examined the effect of qualification levels or physically demanding work were significant (Tables 3–5).
Discussion This study examined the relationships of age group, work force status, and gender with mental health and wellbeing in Australians over the age of 60. The effects of marriage, financial stress, physical health, qualification levels, and physically demanding occupations on these relationships were also examined. Taken together, the results suggest that being older or male, and working part time are protective factors for mental health and wellbeing in adults past the average retirement age of 60 years. The specific hypotheses for the study received mixed support, and the results are discussed in the context of the literature below. The first hypothesis was supported, as older age groups had less psychological distress and lower rates of GAD and depression, after controlling for work status and gender. These effects remained after the inclusion of potential covariates in the models, which suggests a robust effect of age for these mental health outcomes. Age group was not significantly related to self-reported QoL. These results give a general indication of higher or equivalent wellbeing in older age groups compared to younger age groups, which is consistent with the broader literature (Andrews et al., 1999; Melzer et al., 2004; Warr, 1992), and demonstrates that similar relationships found in previous research were not solely driven by the poorer mental health and wellbeing reported by middle-aged adults (e.g., aged 45–60; Butterworth et al., 2006; Melzer et al., 2004). The second hypothesis was also supported: work status was significantly related to all of the mental health and wellbeing outcomes, and part time work was a robust protective factor, particularly in comparison to full-time retirement. Working full time and being retired were generally associated with equivalent mental health. As such, retirement was neither associated with higher levels of mental health and wellbeing (cf. Drentea, 2002; Kim & Moen, 2002; Mein et al., 2003), nor lower levels (cf. Butterworth et al., 2006; Gall et al., 1997). The experience of chronic physical health conditions attenuated the relationship between work status and mental disorder diagnosis, which appears to be due to the high rates of physical health conditions reported in retirees. In contrast, part time work remained a robust protective factor for psychological distress and QoL after accounting for covariates. The
4.879 (3), .181a 1.37 (2), .504a 2.26 (1), .133a 50.64 (1),
