provides summary information on the 42 publications included in this study. This table is presented in lieu of the standard meta-analysis forest plot because of space limitations and the inherent difficulty in garnering data heterogeneity information from a plot that contains 235 point estimates and confidence intervals. The mean relative risk from each of the 42 publications, however, was included in in the interest of providing information from which some heterogeneity observations might be made. The forest plot is available from the authors on request.
provides descriptive statistics on the 235 mortality risk estimates included in this study. Data were obtained from 42 studies, published between 1984 and 2008, covering 15 countries (mostly in Europe and North America), and representing more than 20 million persons. The majority of persons analyzed were men, and almost all were of working age at baseline. The average follow-up duration across all studies was 9.02 years. Of the HRs analyzed, the mean 5-year impact factor was 5.59 and the mean number of citations received per year since publication was 2.68. The mean score on the Newcastle-Ottawa Scale was 7.76.
Distribution of mortality risk estimates in the analysis by selected variables.
presents the results of a number of meta-analyses (See for sample size information). All analyses were stratified by the level of statistical adjustment of the risk estimate. Persons who experienced unemployment were significantly more likely to die than the comparison group. The mean unadjusted HR was 2.08 (95% confidence interval [CI], 1.77-2.43; n = 40 risk estimates); age-adjusted HR, 1.59 (95% CI, 1.42-1.77; n = 75); and HR adjusted for age and additional covariates, 1.63 (95% CI, 1.49-1.79; n = 120). These results show that unemployment is associated with a 63% higher risk of mortality in studies controlling for covariates. also shows that the exclusion of data where either the death rate or the standard error had to be estimated does not alter the direction, magnitude, or level of statistical significance of the mean HRs.
Tests of heterogeneity and sample size information for the meta-analyses reported in .
Subgroup Meta-analyses and Meta-regression Analyses
As described at the end of the methods section, data on the prevalence of high BMI, smoking, drinking, drug use, or other health factors was not available for analysis. However, comparisons between the subset of our data where health was directly controlled (n=45 HRs) or where health-related behaviors were controlled (n=27 HRs) and the remaining data still provides results relevant to the debate between the coping hypothesis and the latent sickness hypothesis. presents the results of the meta-regression analyses, which provide a multivariate test for differences between key sub-groups. Model 1 shows that there was no significant difference in HR magnitude between studies that controlled for any measure of health and the remaining studies (p = 0.1236). Model 3, however, shows that the mean HR was 24% lower for studies that controlled for one or more health behaviors, when compared to the remaining studies (p = .0159). These results suggest that health behaviors may confound the unemployment-mortality association to some degree. However, the results also indicate that pre-existing health behaviors and conditions do not account for 100% of the relationship between unemployment and mortality (see the discussion for more on this issue).
Bivariate and multivariate meta-regression analyses predicting the magnitude of the effect of unemployment on mortality.a
Previous studies suggested that gender is a key moderating variable for the unemployment-mortality association. Preliminary examinations of individual studies revealed qualitative differences between the magnitude of HRs for men and for women, suggesting that women and men be analyzed separately. shows that unemployment was associated with an increased risk of death when HRs were adjusted for age and additional covariates. However, gender-specific analyses show that the magnitude of the association was greater for men (HR, 1.78; 95% CI, 1.56-2.02; n = 54 HRs) than for women (HR, 1.37; 95% CI, 1.17-1.60; n = 36). Model 3 of confirms that the proportion of a sample that is male had a significant impact on the magnitude of the HR. The risk of death for men was 37% higher than that for women (p < .001).
Previous research has also suggested that age may moderate the association between unemployment and mortality. We therefore also conducted sub-group analyses based on average age at baseline. As shown in , unemployment was associated with a 73% increased risk of all-cause mortality for people under the age of 40 years who were in their early careers (HR, 1.73;95% CI, 1.41-2.11; n = 29) and a 77% increased risk for those between the ages of 40 and 50 years who were in mid-career (HR, 1.77; 95% CI, 1.59-1.98; n = 70). The association was substantially reduced for those between the ages of 50 and 65 years who were near the end of their working careers (HR, 1.25; 95% CI, 1.03-1.52; n = 19). The results of the meta-regression analysis (Model 3 of ) show a significant effect for mean age (a 6% decrease for each additional 10 years; p = .0165) confirm this finding, with HR magnitude being approximately equal between the youngest and the middle age group (p = .4394) but 26% lower for the oldest age group (p = .0016).
While follow-up duration has not often been explored in the literature as a moderating factor, preliminary examinations of individual studies suggested that the association between unemployment and mortality may change as time passes. Sub-group analyses based on follow-up duration () show that people who experienced unemployment had a 73% higher risk of death during the first 5 years of follow-up (HR, 1.73; 95% CI, 1.44-2.06; n = 30). The elevation of risk of death remained approximately the same when the follow-up duration averaged 5 to 10 years (HR, 1.76; 95% CI, 1.55-2.00; p <.001; n = 47) but then decreased to a 42% elevation of risk in studies with a follow-up of more than 10 years (HR, 1.42; 95% CI, 1.22-1.64; n = 43). However, the meta-regression results indicate that there was no significant trend associated with follow-up duration (p = .3476).
Furthermore, the type of comparison group used may also have an effect on the magnitude of the mean HR. Preliminary comparisons of individual studies confirmed this, leading us to also examine sub-groups results based on the type of comparison group used. The mean HR was much higher when the comparison group was employed persons only (HR, 1.75; 95% CI, 1.54-1.98; ) than when the comparison group was the general population (HR, 1.24; 95% CI, 1.01-1.51). The results of meta-regression analysis () confirm this, showing that HRs were 32% lower when the general population was used as the comparison group (p < .001). also shows that the risk of death was marginally lower when studies excluded persons not in the labor force (HR, 1.60; 95% CI, 1.45-1.76) than when studies included a mixture of unemployed persons and those who were not in the labor force (HR, 1.73; 95% CI, 1.46-2.04). However, the meta-regression analyses () show that when unemployed persons were combined with persons not in the labor force the HR increased by 46% (p < .001) once other study-level factors were controlled.
The between-groups Cochrane's Q for the meta-analysis of all 235 HRs was statistically significant (p=.0149) and the I2 statistic was quite high (I2, 76.2; 95% CI, 22.1-92.8), indicating that important moderating variables exist and supporting the decisions to use random effects models and conduct sub-group meta-analyses. As shown in , the Q-tests for these subgroup meta-analyses were statistically significant only for statistically-unadjusted HRs. In all of the remaining subgroup analyses however, Q-tests and I2 tests were non-significant, indicating that heterogeneity was adequately accounted for by the use of a random effects model. Since the discussion of the meta-analysis focused on HRs adjusted for age and additional covariates, the results discussed above are not an artifact of heterogeneity in the data.
To be conservative however, meta-regressions were used to examine other possible sources of heterogeneity in the data. The model fit statistics for Model 3 of (R2, .3702; p<.001 for the Cochrane's Q of the model) indicate that this model captured a very substantial portion of the heterogeneity in the data. Nevertheless, the unexplained heterogeneity variance component for this and the other models shown in was highly significant (each p<.001), confirming the need to use a random effects model for all analyses.
As reported earlier, health behaviors, sex, mean age, and the composition of the case and control groups moderate the mean HR. Model 3 of shows that other significant moderators include the time elapsed between the end of baseline and the beginning of follow-up (a 6% increase in risk for each additional year; p = .0006), whether the risk estimate was adjusted for age (a 16% decrease when age was controlled; p = .0159), and whether the risk estimate was adjusted for socioeconomic status (a 13% decrease when SES was well-controlled; p = .0265). While HRs from the United States and the Scandinavian nations are over-represented in the data, the results do not seem to be biased by this factor as there was no significant difference in HR magnitude between either region and the remaining nations (p = .7707 and p = .9216, respectively).
Of the 235 HRs, 93 were statistically-adjusted for age or had an age range smaller or equal to 35 years, did not use the general population as the control group, did not include persons not in the labor force in the case group, were from studies with less than a one year gap between the end of baseline and the beginning of follow-up, and were from studies in which men and women were analyzed separately. These 93 HRs were then grouped according to sex and age group, the resulting six sub-groups subjected separately to meta-analysis (see ). The mean HR among women under the age of 40 was 1.73 (95% CI, 1.41-2.11; n, 19), was 1.34 (95% CI, 1.15-1.56; n, 14) when the mean age was 40 to 49.9 years, and was 0.94 (95% CI, 0.80-1.11; n, 9) when the mean age was 50 years or above. The mean HR among men under the age of 40 was 1.95 (95% CI, 1.69-2.26; n, 26), was 1.86 (95% CI, n, 14) when mean age was 40 to 49.9 years, and was 1.17 (95% CI, 1.00-1.36; n, 11) when the mean age was greater than or equal to 50 years. In all six meta-analyses, the Q-test was not significant and the I2 statistic was not significantly different from zero, indicating homogeneity in the data. The high correspondence between these six more conservative meta-analyses and the full sample meta-analyses reported in further confirm that heterogeneity in the sub-group data was not a major problem.
Meta-analyses stratified by gender and age a