The depression and marital status relationship is modified by both age and gender
Introduction
According to the Global Burden of Disease Study 2015 major depression is a leading cause of years lived with disability globally, coming third after lower back and neck pain, and sense organ diseases, respectively (GBD, 2015 Collaborators). Major depression can be the cause or the result of social, psychological and biological factors such as age and sex Studies have repeatedly shown that the prevalence of depression is higher in women than men worldwide (Kessler et al., 2015). In terms of age, the prevalence of depression decreases steadily with advancing age, and although depression is more prevalent in young women compared to men, this prevalence difference decreases with age and in no longer evident in people over 75 years old (Patten et al., 2016).
One key social factor that modifies depression is marital status, and research has repeatedly shown that married people have better mental health than those who are single, widowed, separated and divorced (Bebbington 1987; Jang et al., 2009; Bulloch et al., 2009; LaPierre, 2009). It has also been shown that the relationship between marital disruption and depression is bidirectional (Bulloch et al., 2009). That being married is protective for depression is not an exclusively Western phenomenon as depression is also relatively high in unmarried Japanese (Inaba et al., 2005). A meta-analysis of people > (or = ) 55 years old showed that being unmarried is a significant risk factor for depression in late life (Yan et al., 2011). However in Koreans aged 75–85 years no relationship between marital status and depression was found for women, whereas divorced and widowed men had higher rates of depressive symptoms than their married counterparts (Jang et al., 2009). Taken together these studies show that there is a complex relationship between age, sex, marital status and depression. In this study we examined how age and sex modify modify the association of marital status with major depression as an outcome. Interaction terms in regression models were used to quantify the modification observed, such that the term interaction is used in the remainder of this paper to refer to the resulting subgroup variations. Use of a very large data set enabled us to examine married and common-law categories separately, whereas these are usually combined in existing studies. Similarly we were able to study widowed, separated and divorced categories which are also often combined.
Section snippets
Methods
This study used the cross-sectional data files collected in an early cycle of the National Population Health Survey (1996), the general health surveys of the Canadian Community Health Survey (2000, 2003, 2005, 2007/2008, 2009/2010, 2011/2012 and 2013), plus the two mental health Canadian Community Health Survey of 2002 and 2012 (Table 1). These surveys used a complex multistage sampling procedure to obtain a representative sample of the Canadian population. First geographical clusters were
Results
First we performed a 1-step analysis on the pooled data from all 10 surveys. We were able to include all 5 categories of marital status (Table 2) and we calculated the odds ratios of depression in each category, and estimated the influence of sex and age. A significant interaction was found for age and sex (OR = 0.99, 95% CI 0.99–1.00) indicating that the effect of sex became weaker with age. Similarly significant interactions were found for all 5 marital statuses, providing statistical
Discussion
Here we report a series of interactions of age and sex with different categories of marital status. All of the interactions found in the initial 1-step analysis were reproduced in the 2-step analysis (Table 2) which helps to confirm their robustness. Since some of the latter have wider CIs they are more conservative and may be considered the principal findings of our study since the replicate sampling weights used in each estimate were unmodified and therefore may more effectively adjust for
Contributors
Authors Williams and Lavorato conducted the analysis of the Statistics Canada data. Authors Bulloch and Patten wrote the manuscript. All authors contributed to and have approved the final manuscript.
Role of funding
This work was supported by an operating grant from the Canadian Institutes of Health Research (MOP-130415), the Hotchkiss Brain Institute and the Alberta Mental Health and Addictions Strategic Clinical Network, none of whom had any involvement the planning or execution of the research.
Acknowledgements
The analysis was conducted at the Prairie Regional Data Centre, a part of the Canadian Research Data Centre Network (CRDCN). The services provided by the CRDCN are made possible by the financial or in-kind support of the SSHRC, the CIHR, the CFI, Statistics Canada and participating universities. The views expressed in this paper do not necessarily reflect the views of CRDCN or of its partners. This work was supported by an operating grant from the Canadian Institutes of Health Research (MOP-
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