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Public Health School, Catholic University of Louvain, Brussels, Belgium and Erasmus University Medical Centre, Rotterdam, The Netherlands
Faculty of Economics and Applied Economics, Catholic University of Leuven, Belgium
University of Warwick Medical School, Coventry, UK
Public Health School, Catholic University of Louvain, Brussels, Belgium
Erasmus University Medical Centre, Rotterdam, The Netherlands
University of Liège, Belgium
Correspondence: Dr Vincent Lorant, School of Public Health, Faculty of Medicine, Université Catholique de Louvain, Clos Chapelle aux champs 30.41, 1200 Brussels, Belgium. Tel: +32 2 7643263; fax: +32 2 7643183; email: lorant{at}sesa.ucl.ac.be
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Aims To assess whether longitudinal change in socio-economic factors affects change of depression level.
Method In a prospective cohort study using the annual Belgian Household Panel Survey (1992–1999), depression was assessed using the Global Depression Scale. Socio-economic factors were assessed with regard to material standard of living, education, employment status and social relationships.
Results A lowering in material standard of living between annual waves was associated with increases in depressive symptoms and caseness of major depression. Life circumstances also influenced depression. Ceasing to cohabit with a partner increased depressive symptoms and caseness, and improvement in circumstances reduced them; the negative effects were stronger than the positive ones.
Conclusions The study showed a clear relationship between worsening socio-economic circumstances and depression.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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METHOD |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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The first eight waves of the survey (1992–1999) were used. In the first wave 8741 individuals were interviewed. Because loss to follow-up (at an annual rate of 13%) reduced the sample size over time, an average of 453 new participants were added each year. New participants came from two sources: individuals joining an already participating household, and new households being selected using the same sampling framework. The final sample comprised 11 909 individuals who each participated in an average of 4.6 waves, providing 54 941 observations.
Measures
Depression
Depression was assessed using a modified version of the global depression
scale of the Health and Daily Living Form (HDL;
Moos et al, 1990).
This self-administered symptom checklist was developed to evaluate the
presence and severity of symptoms of major depression, according to Research
Diagnostic Criteria (Spitzer et
al, 1978). The psychometric properties and scoring method of
the HDL scale have been described elsewhere
(Moos et al, 1990).
The HDL global depression scale comprises 18 items, has a good internal
reliability (Cronbach’s 
=0.94) and was highly correlated
(r=0.88) with the Beck Depression Inventory in a validation study
(Swindle et al,
1998). Following Bracke
(2000), caseness of major
depression was defined as the presence of depressed mood plus five additional
symptoms.
Socio-economic status
Following James S. Coleman’s rational choice social theory, Oakes
& Rossi (2003) defined
socio-economic status in relation to three types of resources: material
standard of living, skills and social relationships. Material standard of
living was measured by income, deprivation, poverty, and subjective financial
strain. We computed the monthly net equivalent household income using the
Organisation for Economic Cooperation and Development
(1982) equivalence scale. The
index of deprivation elaborated by Weich & Lewis
(1998a) was
calculated; this index allocates one point for each of the following:
Poverty was defined as living in a household with an income less than half of the population median income. Subjective financial strain was assessed by a question asking, ‘How well are you managing these days with your current income?’ Scores ranged from 0 (very well) to 5 (with great difficulty).
Skills were assessed by educational status and unemployment: education was quantified using the number of years of education and unemployment was coded as 1 if the individual was unemployed (and available for work) for more than 6 months in the past year and as 0 otherwise.
Social relationships were assessed by civic participation and living arrangements. Civic participation was defined as participation in voluntary associations (Harpham et al, 2002), scored as 1 for those who were currently members of at least one social organisation (such as a local community, cultural or sports organisation) or who were volunteer workers and as 0 otherwise. Living arrangements were coded as 1 for individuals living with a partner, including a spouse, and as 0 otherwise.
Questions concerning socio-economic status referred to the preceding year, whereas depression items referred to the 3 months prior to interview.
Statistical analyses
In order to assess the extent of changes in both socio-economic status and
depression, we computed a longitudinal variance ratio (the longitudinal
variance divided by the total variance). These ratios range in principle from
0 to 1, and reflect the relative magnitude of longitudinal (within-individual)
variance to cross-sectional (between-individual) variance. A ratio of 1 would
indicate no between-individual variance and that all variance in a given
variable over the course of the study was due to longitudinal
(within-individual) change. A ratio of 0 would imply that there was no change
over time and that all variance was cross-sectional (between individuals).
To account for clustering at the individual level, a standard fixed-effect model was used and is estimated by taking the difference between each observation at time t and its average 7-year value for both socio-economic and depression variables (Hsiao, 1986). As a consequence the analysis focused on longitudinal changes in socio-economic factors and in depression. The choice of a fixed-effect model, as against a random-effects model, is supported by the Hausman test (m=366.1, P <0.0001) (Hsiao, 1986). For analysis concerning caseness of major depression, we used a conditional logistic regression, which is the equivalent of a fixed-effect model for a binary response. Because women are more vulnerable to low socio-economic status than men (Lorant et al, 2003), we compared results according to gender by a t-test.
In a longitudinal model, loss to follow-up could result in bias if poor people and those with depression are more likely to be lost to follow-up than those who are well off and not depressed. To allow for a correction of this selection bias, an inverse Mill’s ratio was estimated by a probit regression and then included in the model as an additional explanatory variable. We used the Heckman selection model adapted by Wooldridge for panel-data fixed-effect models (Wooldridge, 1995).
Because the effect of income on depression has been shown to be greater among those on the lowest incomes, we stratified the analysis by income groups and tested for statistically significant interactions (Weich et al, 2001). Moreover, to distinguish between the effects of improving and worsening socio-economic status on rates of depression, we compared each of these groups against a reference group defined as individuals with no change on any given socio-economic measure. We used an F-test in order to test whether improvement had a different effect from deterioration in absolute value. All estimations were carried out with SAS version 9 for UNIX.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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The estimates of the fixed-effect models are shown in Table 2. The left-hand part of the table is related to depression scores and the right-hand side addresses the case of major depression. All coefficients are bivariate and controlled only for the inverse Mill’s ratio. An increase of subjective financial strain (e.g. from ‘with difficulty’ to ‘with great difficulty’) or in deprivation was associated with statistically significant changes in both depression score and the likelihood of being a case of major depression. Becoming poor resulted in a statistically significant increase in depression score (but not in cases of major depression). Increase in income or becoming unemployed were associated neither with a change in depression score nor with a change in cases of major depression. Changing civic participation was associated with lower depression score only, to a statistically significant degree. Change in living arrangements was associated with change in both depression score and change in cases of major depression.
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There were statistically significant gender differences. Change in subjective financial strain increased the depression score to a greater extent for women than for men (women ß=0.48 v. men ß=0.23; t=4.2, P<0.001). Change in poverty had a greater effect among women compared with men (women ß=0.47 v. men ß=0.19; t=2.2, P=0.01). Embarking on a cohabiting relationship reduced depression more among women than among men (women ß=–1.23 v. men ß=–0.43; t=–3.45, P<0.01). These results were similar when running a multiplicative model with gender as an interaction term. On stratifying by income, subjective financial strain had a greater effect for individuals living in households with below-median income compared with those in households with above-median incomes (ß=0.42 v. ß=0.32, P<0.05, t=1.7). Similarly, the association between depressive symptoms and change in deprivation reached statistical significance for individuals in low-income households but not for those in higher-income households (ß=0.21 v. ß=0.02, P<0.01, t=2.5). Change in partnership had a greater effect for individuals in low-income households than for those with higher incomes (ß=–1.14 v. ß=–0.44, P<0.001, t=3.2).
The socio-economic variables that were significant in Table 2 were categorised into three groups: no change (reference group), reduction in socio-economic status and increase in socio-economic status. Results (Table 3) showed that reduced financial strain had a positive effect on depression score whereas increased strain had a negative effect. The effect, in absolute value, of a reduction in financial strain was smaller than the effect for an increased strain (F=10.9, P<0.001). Reduction in poverty reduced depression score whereas an increase in poverty led to an increase of depression score. Although the effect of a reduction in poverty was higher, in absolute value, than the effect of an increase in poverty, this difference was not statistically different (F=1.4, P>0.05). Similar results were found for deprivation and income: the effect of worsening conditions was greater, in absolute value, than the effect of an improvement, but the tests were not statistically significant (deprivation F= 2.2; P>0.05; income F=1.2, P>0.05). Finally, the effect of ceasing to live with a partner was greater in absolute terms (ß=0.94) than starting to live with a partner (ß=–0.44) and the difference of the two coefficients, in absolute value, was statistically different (F=4.8, P=0.03). Finally, we carried out a multivariate analysis (results not shown) in which we jointly tested whether the worsening effects of financial strain, poverty, deprivation, income, civic participation and living arrangements were different from the improving effect of the same variables: the test was significant (F=3.6, P<0.01). Turning to cases of major depression, we found similar results: increases in financial strain or in deprivation raised the risk of depression. Ceasing to cohabit also increased the risk of depression. Improved socio-economic circumstances had no significant effect on the risk of depression.
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Our analyses focused on changes in socio-economic status and changes in depression occurring during the same year. However, it could be that a change in depression is due to an earlier change in socio-economic circumstances. Additional analyses indicated (results not shown) that changes in financial strain, in poverty and in deprivation in the previous year had no significant effect on current changes in depression.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Our results are also consistent with other experimental and longitudinal studies. A natural experiment in North Carolina showed a small and borderline significant effect of moving out of poverty on emotional symptoms (Costello et al, 2003), and a British longitudinal study showed that a decrease in income had a slight impact on General Health Questionnaire score (Benzeval & Judge, 2001). Ceasing to cohabit with a partner increased the level and the risk of depression, particularly among women. This is consistent with previous longitudinal studies of marital transition (Hope et al, 1999; Wu & Hart, 2002). Our study adds to this previous body of knowledge that these effects are greater among women and among individuals of lower socio-economic status. Moreover, because we have excluded time-invariant features, our results also support the notion that the risk of depression attached to such transitions is probably not a result of some personal vulnerability or lack of resilience.
The lack of association between unemployment and depression contrasts with studies of the mental health consequences of unemployment, which show that unemployed individuals are more at risk of major depression than those who are employed (Lennon, 1995). However, the results of longitudinal studies are mixed. Although loss of a job has been shown to be a predictor of depression in the Alameda follow-up study (Kaplan et al, 1987), this finding was not replicated in two more recent longitudinal studies (Bromberger & Matthews, 1994; Weich & Lewis, 1998a). The divergence in results between cross-sectional and longitudinal studies has already been highlighted and has been explained by specific characteristics that make some individuals unable to maintain employment (Bromberger & Matthews, 1994). Another possible explanation has to do with the timing of data collection: because our analyses used data collected at annual intervals we might have missed short-term fluctuations in mental health occurring between assessments. It is possible, although unlikely, that we have underestimated the effects of changes in employment status if there were significant numbers of participants who moved into and out of work between waves. Indeed, previous evidence suggests that the risk of depression increases steadily for 6 months after the individual becomes unemployed, then reaches a plateau and is reversed almost immediately on finding work (Warr & Jackson, 1985). Given that we had only one observation a year, this selection effect of unemployment on depression might thus have been underestimated.
Limitations
Our measures of depression and socio-economic status have limitations. The
results are thus vulnerable to the drawbacks of some symptoms inventories.
Previous research suggests that association between low socio-economic status
and major depression is greatest when the latter is addressed using
standardised clinical interviews rather than self-report questionnaires
(Miech et al, 1999;
Turner & Lloyd, 1999).
However, because there is a monotonic relationship between symptom severity
and risk of major depression (Kendler
& Gardner, 1998), problems of this sort are unlikely to have
significantly affected our results. Further studies should replicate our
analysis with clinical interview schedules. Our socio-economic status
variables might also have limitations, particularly civic participation
– a concept that has recently been widely debated
(McKenzie et al,
2002). Our measure is defined at the individual level and captures
bonding relationships. As such it does not fully describe either the civic
participation of the community (such as collective efficacy) or the resources
provided by social policies.
A second limitation arises from the modest baseline participation rate and from the attrition rate, which might have made the sample increasingly upward-biased in terms of socio-economic status and downward-biased in terms of depression. External validation of the Belgian Households Panel Survey suggested that the baseline sample did reflect correctly the Belgian population in terms of age, gender and household type distribution (Jacobs & Marynissen, 1993). Moreover, baseline participation should not be a major issue here, as we were interested in longitudinal effects and not in cross-sectional inference. However, it is also possible that some personality traits might be related to both a lower baseline participation rate and a stronger association between socio-economic status and depression, particularly for individuals having poorer coping styles. Also, the study of attrition rates showed that attrition was higher in low-status individuals. Our analysis took care to correct for such bias and the loss to follow-up remains similar to that of panels in other European countries (Peracchi, 2002). Nevertheless, underestimation of the longitudinal effect of socio-economic status cannot be totally ruled out. Although a previous study has shown that such underestimation was slight (de Graaf et al, 2000), we must remain cautious regarding the precise size of our estimations.
Third, the principal aim of this study was to estimate the effect of change in socio-economic status on the change in depression. As such, the risk factors of interest were those that were most likely to change during the interval between assessments. Given that the mean age of the sample at baseline was 46 years, there was not much longitudinal variance in education. This should not be viewed as implying that lack of education is not an important determinant of psychopathology, rather that our sample displayed little longitudinal variance. Besides, our study took as a starting point the causation assumption, consistent with previous studies (Dohrenwend et al, 1992; Ritsher et al, 2001; Costello et al, 2003). However, selection cannot be totally ruled out because, for example, depression 3 months before interview could lead to loss of job the week before the interview, or because depressed mood at the time of interview could lead respondents to rate their circumstances (such as their financial strain) more pessimistically. Given the temporality of our measurement, we must remain cautious regarding the part of the association that could be the result of a selection effect.
Finally, the context might have influenced our results, particularly Belgium’s performance in promoting equity. On the one hand, Belgium has a welfare system that performs well in avoiding poverty in comparison with other European countries (Heady et al, 2001). On the other hand, educational segregation in Belgium appears to be greater than elsewhere (Gorard & Smith, 2004). Cross-national comparison suggests that Belgium has a mental health inequality that is close to the average inequality in the EU (Lorant et al, 2005).
Implications
This study should be extended in order to identify more groups that are
placed at greater risk of depression or, conversely, that are protected. After
all, the majority of people who live in poverty, or are confronted with a
sudden drop in their income, do not develop depression. Further studies should
investigate protective factors such as religion, culture, self-esteem and
coping styles.
Because a short-term change in financial strain or poverty is associated with higher depression level, our results suggest that improving social and economic circumstances on a short-term basis would have an effect on mental health inequalities. This should be considered in the design of strategies to tackle such inequalities, particularly income maintenance policies that might help to alleviate the effect of worsening socio-economic circumstances. These could include microcredit schemes (Patel & Kleinman, 2003), local economic development (Costello et al, 2003) and policies aimed at improving women’s participation in the labour market (Gordon & Shaw, 1999).
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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REFERENCES |
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Received for publication November 27, 2005. Revision received September 18, 2006. Accepted for publication October 27, 2006.
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Psychiatric Bulletin | Advances in Psychiatric Treatment | All RCPsych Journals |
