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Mental health: depression, anxiety, and anger in the US

Introduction

Mental illness is one of many factors that can, by producing low levels of life satisfaction, erode happiness. Yet, as Clark et al. (2018) observe, many studies of life satisfaction ignore mental illness by implicitly assuming, quite wrongly, that unhappiness and mental illness are synonymous. Indeed, Clark et al. ask how much misery would be reduced if mental illness were to be eliminated. Their answer - based on the twin observations that persons with diagnosed mental illness were 0.1 points more likely to be in misery (than persons without such illness) and that, of the total population in the United States, 22% had diagnosed mental illness - was that misery would be reduced by 2.2 points, approximately a third of the total likelihood of being in misery. In another example from a different country, Borooah (2006) reported that in Northern Ireland, only 4% of those with severe mental health problems described themselves as happy and 60% described themselves as unhappy; equally tellingly, only 32% of those whose mental health problems were not severe described themselves as happy - the same proportion as those with severe heart problems who regarded themselves as happy.

Given the importance of mental illness, this study examines three specific aspects of mental ill health: depression, anxiety, and anger. The US National Network of Depression Centers estimated that US$210 billion were lost in the US due to serious mental illness; depression was the single biggest cause of disability in the US among those aged 15 to 44; and depression, along with family issues and stress, ranked among the top three workplace issues.1 The serious consequences of depression and anxiety are compounded by the numbers affected: about 16 million persons in the US (or 6.9% of adults) had at least one major depressive episode in 2013.2 Moreover, there is a significant gender bias to depression, anxiety, and anger, with women being much more likely than men to have experienced depression and anxiety but much less likely to have experienced anger (Nolen-Hoeksema, 2001; Shar- kin, 1993). A greater incidence of depression among women is also reflected in prescriptions for antidepressant medicines. For example, Albert (2015) reported that in Canada between 2007 and 2011 antidepressant medications were prescribed twice as often for women as for men.

In explaining why rates of depression are higher for women than for men, Nolen-Hoeksema (2001, 2003) distinguished between two effects, the quantification of which constitutes the fundamental purpose of this chapter. First, she argued that, compared to men, women might be more likely to be exposed to depression-inducing factors. Second, even when men and women were exposed to the same depression-inducing factors, women might be more likely than men to develop depression.

Given these two effects - generated, respectively, by gender differences in exposure and in response to depression-inducing factors - what is needed to explain differences in depression rates between women and men is an integrative model, encompassing both exposure and response effects. In addition, it would be useful to quantify how much of the observed difference in depression rates between men and women could be explained by differences between them in their exposure, and how much could be explained by differences between them in their response, to depression-inducing factors. The central purpose of this chapter is to build such a model and offer such quantification.

 
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