Taking time seriously: a call to action in mental health research.

542434 research-article2014

HSBXXX10.1177/0022146514542434Journal of Health and Social BehaviorGeorge

Leonard I. Pearlin Award Paper

Taking Time Seriously: A Call to Action in Mental Health Research

Journal of Health and Social Behavior 2014, Vol. 55(3) 251–264 © American Sociological Association 2014 DOI: 10.1177/0022146514542434 jhsb.sagepub.com

Linda K. George1

Abstract Sociological research on mental health focuses on a multitude of dynamic processes, including changes in psychological symptoms or the onset of a mental disorder, the course and outcome of mental health problems, and the associations of mental health with a wide variety of time-varying social risk and protective factors. I argue that scholars studying mental health have, thus far, only scratched the surface of the temporal dynamics upon which mental health and illness rest. Two broad research issues are reviewed to illustrate important temporal issues that have been neglected or understudied in mental health research: (1) specific dimensions of temporality, which focus on dynamic processes at the individual level, and (2) the age-period-cohort model, which focuses on mental health at the population level. Priority topics for future research that takes time seriously are recommended.

Keywords age-period-cohort, life course, social risk factors, stress process theory Although the sociology of mental health has been enriched by several important theories, none has been more significant or contributed more to our understanding of the social antecedents of mental health problems than stress process theory. Developed by Leonard Pearlin and colleagues, stress process theory provides an elegant model of the social structural roots of psychological distress and unease (Pearlin et al. 1981). It is a deceptively simple theory, comprised of four constructs. Social roles and other forms of social location (e.g., race, gender) are exogenous variables that put individuals at greater or lesser risk of stressors, which, in turn, increase the risk of mental distress and disorder. The final construct, social and social-psychological resources, refers to factors that can mediate or moderate the effects of stress on mental health. During the more than three decades since its introduction, stress process theory has been the theoretical foundation of hundreds—perhaps thousands—of studies. Both measures of its core constructs and the statistical methods used to test the model have expanded and increased in sophistication. Throughout, stress

process theory has been and remains the primary conceptual foundation for studying the relationships between social factors and mental health. At about the same time that stress process theory was introduced, the life course perspective was gaining advocates and empirical inquiries by a largely separate group of sociologists. Spearheaded by Glen Elder’s now-classic Children of the Great Depression (1974), scholars studying specific segments of the life course (e.g., the transition to adulthood) began to devote increased attention to time—biographical time, historical time, and their intersections. Longitudinal studies had been common for decades, although most were based on data covering relatively short periods of time. A large proportion of studies on the stress process model are now based on longitudinal data (e.g., Cronkite and Moos 1984; Noh and 1

Duke University, Durham, NC, USA

Corresponding Author: Linda K. George, Department of Sociology, Duke University, Box 90088, Durham, NC 27708-0088, USA. E-mail: [email protected]

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Avison 1996), the importance of which was emphasized by Pearlin and colleagues. Although these studies advanced our understanding of the links between stress and mental health, using longitudinal data is not synonymous with performing life course research. Two major differences between relatively routine longitudinal research and life course research reflect the substantially greater emphasis on time in the latter. First, although transitions and other short-term changes are relevant, the focus of life course research is on longer trajectories of change and stability that often incorporate multiple transitions. Second, life course research focuses on historical, as well as biographical, time. For example, life course scholars question the ways in which historical circumstances affect the changes observed in routine longitudinal studies (e.g., are the observed patterns cohort-specific or significantly affected by recent historical events?). I have argued that the life course perspective is not a theory per se (e.g., George 2003, 2013). Rather, the life course perspective rests on a set of core principles that focus on time, the intersections of life domains that are typically studied separately, and balancing structural determinism with human agency. Consequently, the life course perspective is most useful when it is combined with a well-articulated theory. Many scholars have integrated life course perspectives with preexisting theories, including stress process theory at both the conceptual (e.g., George 2003; Pearlin et al. 2005) and empirical levels (e.g., Goosby 2013; Walsemann, Gee, and Geronimus 2010). Despite a sizeable and growing body of research that integrates stress process theory— and other theories relevant to the study of mental health—with life course principles, this paper rests on the contention that mental health researchers have not yet taken time as seriously as is merited. I discuss two broad research topics that have not received the attention they deserve. First, I review several specific dimensions of time that are relevant to mental health research but are understudied. Second, I discuss the contributions that would result from increased attention to the age-period-cohort model in mental health research. I use examples of relevant research to the extent possible. Inevitably, however, much of the discussion focuses on priority issues for future research.

A Brief, but Necessary Detour Before we address temporal issues, one caveat merits attention. For more than two decades, there

has been substantial discussion—and often controversy—about the utility and meaning of psychiatric diagnoses. Some of the giants of the field are critical of psychiatric diagnoses and argue against using them on conceptual and methodological grounds (Mirowsky and Ross 1989). Other outstanding scholars describe the conceptual and empirical complexities of categorical versus dimensional measures and conclude that both symptom scales and psychiatric diagnoses can be useful (Cowgill and Sonuga-Barke 2012; Kessler 2002; Wheaton 2001). Distinctions among measures of mental health problems will not be made here. Depending on the point being made, I refer to psychiatric diagnoses, symptom scales, psychological distress, and the inclusive category of mental health problems. The issues raised here are relevant to the study of mental health, however it is measured. I view both dichotomous measures of psychiatric diagnoses and continuous measures of psychiatric symptoms as useful and valid, depending on the research question and the intended audience. I have argued that symptom and diagnostic measures are analogous to continuous measures of income and dichotomous measures of poverty (Tweed and George 1989). I have used both diagnostic and symptom measures and repeatedly find that the same risk and protective factors are associated with both symptom scales and diagnostic measures. Other investigators have observed this pattern as well (e.g., Schnittker 2012; Turner and Beiser 1990), reinforcing Wheaton’s (2001) empirical finding that categorical and dimensional measures of mental health are not qualitatively different.

The Many Faces of Time Time can be conceptualized and measured in multiple ways. I will review five dimensions of time that are relevant to the sociology of mental health but have not been fully exploited.

Length of Exposure One important dimension of time is length of exposure, which focuses attention on the extent to which time spent in a given state affects outcomes of interest. A tacit assumption in much social science research is that the longer individuals are exposed to specific social conditions, the greater the likelihood that the exposure will affect an outcome of interest. In mental health research, for example, it is generally assumed that exposure to chronic


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George stressors will have a stronger impact on psychiatric problems than exposure to acute stressors. And yet, we know very little about how long individuals must be exposed to specific risk factors before they experience the onset or worsening of psychiatric symptoms or disorder. Nor is much known about the forms of the relationships between length of exposure to risk and protective factors and mental health outcomes. Stress researchers have contributed most to our understanding of the links between length of exposure to risk factors and mental health outcomes, although it has been a lengthy process. For decades, stress research focused on either recent life events or conditions that were assumed to be chronic without measuring length of exposure (e.g., job stress, financial strain). Later, investigators developed stress measures such as lifetime trauma (e.g., Trappler, Cohen, and Tulloo 2007), cumulative stress (Godin et al. 2005), and operant stress (Avison and Turner 1988). These measures incorporate long-term exposure to stress, and empirical findings confirm that they are more strongly related to mental health problems and better mediate the effects of social status variables on mental health outcomes than do less time-inclusive stress measures (e.g., Turner, Wheaton, and Lloyd 1995). In a related vein, several studies demonstrate that persistent poverty has more negative effects on mental health than single or intermittent episodes of poverty (e.g., Evans and Kim 2007; McLeod and Shanahan 1996). Despite these promising results, we know little about how length of exposure conditions the effects of most social risk factors on mental health outcomes. The same logic applies to social factors that protect mental health. The positive association between social support and psychiatric symptoms is well-established, but little is known about how length of exposure to social support affects its relationship with mental health outcomes. Little also is known about the forms that best describe the relationships between length of exposure to risk or protective factors and mental health outcomes. Are there dose-response relationships between some risk factors and mental health outcomes? Or are there risk and protective factors for which length of exposure levels off, after which continued exposure no longer alters the likelihood of mental health problems? Undoubtedly, the forms of the relationships between length of exposure to risk or protective factors and mental health outcomes differ across specific risk and protective factors and across mental health outcomes.

Duration-dependence Duration-dependence is a fascinating temporal pattern in which the effects of time change depending on length of time in a specific state or environment. For example, the likelihood of marrying (for the first time) exhibits a clear pattern of durationdependence in the United States. Between the ages of approximately 18 and 40, every year that one is unmarried increases the odds of marrying the following year. After the age of 40 or so, however, every year that one is unmarried decreases the odds of marrying the following year. Duration-dependence has been examined in some areas of social and behavioral research, including residential relocation (Gordon and Molho 1995), unemployment (van den Berg and van Ours 1996), and obesity (Daouli et al. 2014). All three outcomes showed patterns of duration dependence in which the likelihood of positive change (i.e., moving, getting a job, and losing weight) increased for some time, after which additional time was associated with decreased likelihood of change. Duration-dependence has received little attention in mental health research, but some studies point to its usefulness. Research that my colleagues and I conducted demonstrates that duration-dependence applies to the odds of recovering from an episode of major depressive disorder (MDD) (Bosworth et al. 2002; Steffens et al. 2005). This research was based on samples of individuals who were clinically diagnosed as having MDD at baseline. Study participants were interviewed and screened for MDD approximately every 6 months for up to 48 months after the baseline assessment. Because MDD is a dichotomous measure, we traced patterns of chronicity, recovery, and relapse over time. We observed clear duration-dependence in recovery from the index episode. For two years after baseline, the odds of recovery increased significantly at each time of measurement. For individuals who had not recovered within 24 months, however, their odds of recovery decreased at every subsequent time of measurement. Reversals in the effects of predictors on an outcome are the most dramatic examples of durationdependence. More subtle forms involve the strengthening or weakening of the effects of predictors as duration increases. For example, in another study of recovery from MDD, McLeod and colleagues reported that the effects of negative reactions from spouses and the patient’s level of education became stronger as duration of the depressive episode increased. In contrast, the


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higher odds of young individuals recovering from MDD weakened over time (McLeod, Kessler, and Landis 1992). Many questions relevant to mental health could be asked about duration-dependence. It appears, for example, that persons suffering from psychotic disorders often exhibit duration-dependence with regard to taking prescribed psychotropic drugs. Evidence suggests that patients often comply with medications for some time after they are prescribed but later stop taking them (Fenton, Blyler, and Heinssen 1997). Although this pattern is frequently observed, it has not been described or analyzed as duration-dependence. Some stressors may also exhibit duration-dependence. There may be duration-dependence in the odds of remarrying after divorce or widowhood or in the likelihood of moving out of poverty.

Timing and Critical Periods The general hypothesis underlying studies of timing is that specific events, experiences, and environments will have different effects depending on the age at which they occur. Some evidence, for example, suggests that the psychological distress resulting from widowhood is greater for women widowed in middle-age than for women widowed later in life (Ball 1976). This age-related vulnerability is partially reversed, however, as it appears that middle-aged widows recover from the distress of spousal loss more quickly than their older counterparts (Barrett 2000). Divorce exhibits a somewhat different age pattern in that it generates higher levels of depressive symptoms in middle-aged women than in young and older divorcees (LaPierre 2009). Overall, little is known about the extent to which the effects of social risk and protective factors differ by age. It is implicitly assumed that stressors increase the risk of mental health problems for adults of all ages. Similarly, factors that decrease the risk of psychiatric symptoms and disorders are tacitly assumed to be equally protective throughout adulthood. Testing the validity of these assumptions is relatively simple, requiring only the estimation of interactions between age and social risk and protective factors. Critical periods are closely related to timing. The concept of critical periods is used primarily by developmental psychologists who argue that if specific developmental tasks are not completed at appropriate ages, subsequent development will be delayed or precluded (Bailey et al. 2001). Most

research on critical periods focuses on conditions in utero and in early childhood. Social structures and processes, however, can also facilitate or interfere with critical periods that are relevant to mental health. Elder’s (1974) groundbreaking study of Children of the Great Depression followed schoolage children and adolescents during the Great Depression and throughout most of their adult lives. A primary finding of this classic study was that a few years difference in age at the time of the Great Depression had lifelong consequences for socioeconomic success, family formation, and mental health. Study participants who were adolescents at the time of the Great Depression experienced lower levels of socioeconomic achievement and increased psychological distress throughout adulthood than their younger peers who experienced the same deprivations. Those who were adolescents during the Great Depression faced constricted opportunities as they entered young adulthood, exacting heavy costs that diminished their life chances across adulthood. Those who were younger during the Great Depression came of age when the opportunity structure had opened considerably, facilitating socioeconomic, marital, and psychological success throughout adulthood. Similarly, studies of World War II veterans indicate that men who entered the war at earlier ages fared substantially better in postwar economic achievements, marital quality, and mental health than men who were drafted in their late twenties or later (Elder, Shanahan, and Clipp 1994). Men who were older at the time of military service experienced disruptions to their careers, marriages, and life plans that were difficult to overcome after discharge. Younger men were able to complete education, begin careers, marry, and/or start families without disruptions after their military service ended. This age difference reverses, however, when the outcome of interest is mental health. Hastings (1991) reports that veterans who entered military service at young ages reported more emotional problems in 1950 than their older counterparts, although this age difference disappeared when veterans were interviewed in 1960. Evidence from a variety of fields suggests that early adulthood is pivotal in establishing the foundations of the adult life course. Disruptions and deprivations experienced at that critical period are difficult to overcome and tend to establish the “intercept” from which adult life course trajectories emerge. Critical periods at other points in the life course are likely, although they may affect either a narrower range of outcomes or different


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George outcomes. For example, some stressors may have more deleterious effects in later life than earlier in the life course. Financial losses, for example, may generate greater psychological distress in late life than at earlier ages because of limited opportunities for recouping economic assets. Clearly, the boundary between timing and critical periods is ambiguous. The overarching points are that the effects of social risk and protective factors are likely to vary by age and that social structures and processes can lay the foundation for these age differences.

Turning Points or Milestones A turning point or milestone occurs when a specific event, experience, or transition changes the direction of a preexisting trajectory in a permanent or longterm direction. Substantial evidence indicates that early mental illness has significant consequences for adult achievements and mental health. Mental health problems in childhood and/or adolescence have profound negative effects on educational attainment, adult occupational status, and lifetime earnings (Breslau et al. 2008; Marcotte and Wilcox-Gok 2001; McLeod and Fettes 2007). The effects of early mental illness on family formation include decreased odds of marriage for persons suffering from early psychotic disorders (e.g., Walkup and Gallagher 1999), earlier age of marriage for persons with affective and substance use disorders (Forthofer et al. 1996), earlier parenthood (Woodward and Fergusson 2001), and higher odds of divorce (Wade and Pevalin 2004). And, of course, early mental illness is a potent predictor of mental illness later in adulthood (Harrington et al. 1990). These studies were based either on cross-sectional data, in which adolescents or young adults with and without histories of mental illness are compared, or on longitudinal research in which children or adolescents are followed over time to young adulthood. This research provides valuable information about the effects of early mental illness on young adult outcomes. It does not, however, compare the life course consequences of mental illness experienced early versus later in the life course. Years ago, my colleagues and I compared individuals who experienced early (age 25 and younger) and later (age 26 and older) onset of psychiatric disorder. Age of onset was measured either currently (for a first episode) or retrospectively. Those who reported early onset of mental illness reported significantly lower socioeconomic status (SES) and poorer family outcomes than their age peers with no history of mental illness (Turnbull

et al. 1990). In contrast, SES and family characteristics of those who reported later onset of mental illness did not significantly differ from those of persons who reported no history of mental illness. Our study had significant limitations, particularly the use of retrospective data to measure age of onset of mental illness. Nonetheless, these results suggest that early onset of mental illness may be a consequential turning point in the life course, whereas later onset is not, at least with regard to the outcomes studied. Even earlier than our study, Huffine and Clausen (1979) followed the careers of men who were in mental hospitals at their baseline interviews. Follow-up interviews were administered periodically for 5 to 20 years. The investigators found that men who had solid work histories prior to the onset of mental illness suffered no occupational penalties as a result of their psychiatric illnesses and/or psychiatric hospitalizations. Indeed, they maintained orderly careers even when psychiatric symptoms persisted or recurred. In contrast, men who had unstable occupational histories prior to hospitalization also had unstable careers after discharge from the hospital—a pattern that persisted throughout follow-up interviews. Huffine and Clausen did not focus directly on age, nor did they statistically model time. Nonetheless, their results suggest that mental illness can become a life course milestone or anchor that affects life chances for decades. It also is likely that severe traumas may be a turning point in subsequent life course patterns of mental health problems. Research on the effects of severe stressors such as sexual assault and combat experience suggests that they may increase the odds of mental health problems not only in the short term but also across the entire adult life course. Identifying turning points poses substantial methodological challenges. Most important, it is not possible to identify a milestone prospectively. It is only in retrospect that the point at which a trajectory significantly changes direction can be observed. In addition, specific analytic strategies are required to estimate the prevalence of milestones across the life course. To estimate the prevalence of milestones, a form of latent class or growth mixture models can be used to determine whether a trajectory that fits the temporal pattern of a milestone is statistically defensible. Despite the methodological complexities involved, efforts to determine the role of turning points or milestones in mental health across the life course are merited. Despite the broad array of temporal issues relevant to social factors and mental illness, substantial


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methodological challenges remain. In particular, although a number of longitudinal panel studies covering significant portions of the life course are now available, the designs of these studies are seldom ideal for the purposes of modeling time. Many research questions could be best addressed if times of measurement were linked to specific events, both personal and historical. In studies of the effects of marital dissolution on mental health, for example, it would be ideal to have measurements at specific intervals both prior and subsequent to spousal loss (e.g., one or two years prior to divorce/ widowhood and at one-year intervals thereafter). Extant surveys, however, are not linked to specific events, leaving considerable heterogeneity in the time periods “before and after” events of interest. The intervals between times of measurement also can be problematic. Consider panel data in which study participants are interviewed every two years. For research questions in which change is hypothesized to be rapid, two years may be too long to be optimal. A note about causal inference also is in order. I have argued elsewhere that the concepts of social causation and social selection have little meaning for life course scholars (George 2013). We are more interested in tracing and estimating the relationships between trajectories of variables of interest than sorting out “chicken and egg” issues. Nonetheless, panel data with multiple times of measurement needed for trajectory analysis permit investigators to establish temporal order between presumed antecedents and outcomes and to model reciprocal effects using structural equation models.

Opportunities Afforded by Age, Period, Cohort Analysis The vexing conundrum posed by separating the effects of age, period, and cohort has been known for more than half a century. During this time, multiple analytic strategies were offered for unraveling the interdependence among these three temporal dimensions. None of them is ideal because they all require introducing an assumption that may be untenable. Nonetheless, reasonable strategies for simultaneously disentangling the effects of age, period, and cohort are now available. All three concepts are temporal. Age, of course, refers to patterns that emerge as the result of the passage of time and are expected to generalize across time and space. Period effects are simply time-of-measurement effects. A period

effect occurs if an event or a condition in the larger environment changes the outcome of interest in a way that is distinct from a preexisting pattern or trajectory. Period effects typically either level off and create a new statistical norm or end and the outcome reverts to its original pattern. By definition, a period effect affects people of all ages, although it may do so to varying degrees. Cohort refers to people who experience an event at about the same time. For our purposes, cohorts will refer to birth cohorts—groups of individuals born at different historical times. A cohort effect occurs when people born at or about the same time are demonstrably different from those born before and after them. A cohort effect can occur because of a compositional characteristic (e.g., an unusually large or small cohort) or, more often, because people experience historical or social conditions that differentiate them from persons born before and after them. Age, period, and cohort (APC) effects are not explanations for the patterns they reveal. Rather, they provide initial information from which specific hypotheses about their underlying dynamics can be crafted and tested. Knowing whether a pattern reflects the dynamics of aging, period, or cohort is an essential first step in understanding a phenomenon of interest.

APC-related Research to Date Population distributions of mental health problems differ by age, period, and cohort. Even without sophisticated APC analyses, considerable evidence suggests that all three dynamics are at work. Much of this research is dated, however, and very little is based on statistical models intended to simultaneously parse out age changes, period effects, and cohort differences. Of particular concern is the fact that most previous research that claims to reveal age or cohort effects is based on cross-sectional data. Age and cohort effects are confounded in cross-sectional studies, however. In this section, I briefly review previous research addressing age, period, and/or cohort effects in mental health.

Cohort Differences Beginning in the late 1980s, investigators hypothesized that recent cohorts of young adults were experiencing higher rates of MDD and/or depressive symptoms than were earlier cohorts. Comparison of age differences in the prevalence of


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George mental health problems in cross-sectional studies conducted at different times was one strategy used to test this hypothesis. The hypothesis of increasingly distressed young adults over time was generally supported (e.g., Collishaw et al. 2004; Lewinsohn et al. 1993), although this approach has significant methodological limitations (e.g., differences in samples and measures across studies). Introduction of the Diagnostic Interview Survey (DIS)—later modified as the Composite International Diagnostic Interview (CIDI)—provided another strategy for testing this hypothesis. The DIS was first used in the multisite Epidemiologic Catchment Area (ECA) studies and generated estimates of current, recent, and lifetime prevalence of MDD. ECA investigators tested the cohort difference hypothesis using lifetime prevalence rates of participants 18 years and older. At baseline, birth years of ECA participants ranged from roughly 1890 to 1960. Results supported the hypothesis in that older cohorts reported lower rates of MDD in their teens and early twenties than did more recent cohorts (Burke et al. 1991; Klerman and Weissman 1989). The same pattern was observed for drug abuse/dependence, as well as smaller, but similar cohort differences for alcohol abuse/dependence and obsessive-compulsive disorder (Burke et al. 1991). Similar findings, also based on retrospective reports of age of onset, were reported by Kessler and colleagues (2005), using data from the National Comorbidity Survey Replication. The use of lifetime diagnoses based on retrospective data has been strongly questioned, however, with some critics attributing the purported cohort effect to memory loss in older cohorts and selective mortality.

Age Effects Since the introduction of the DIS, an extensive body of research has focused on age changes in psychiatric diagnoses and symptoms, especially MDD and depressive symptoms. The hypothesis that guided early research predicted that older adults would be at greater risk of MDD and higher levels of depressive symptoms because of the role losses and health declines characteristic of later life. Although this hypothesis makes intuitive sense, it has received no empirical support with regard to age differences in MDD. Researchers consistently report that rates of psychiatric disorders in general and MDD in particular are lower among older adults than middle-aged and younger adults (Kessler et al. 2010b, using data from the WHO World Mental Health Survey; Kessler et al. 2010a, using data from the National Comorbidity

Survey Replication; and Wade and Cairney 1997, using data from the Canadian National Population Health Survey). Results have been inconsistent, however, for the relationship between age and depressive symptoms. In bivariate analyses, most investigators report higher levels of depressive symptoms for older than middle-aged adults (e.g., Blazer et al. 1991; Kessler et al. 1992; Mirowsky and Ross 1992; Schieman, Van Gundy, and Taylor 2001; Wade and Cairney 1997). Other investigators, however, report the opposite—that depressive symptoms are lower among older adults than among the middle-aged or, especially, young adults (e.g., Blazer, Hughes, and George 1987; Schieman, Van Gundy, and Taylor 2002). In some studies, controlling on demographic characteristics, social roles, physical disability, and other variables, however, an originally positive relationship between age and depressive symptoms reverses to negative (Blazer et al. 1991; Schieman et al. 2002; Wade and Cairney 1997). This suggests that the losses and physical declines common in later life suppress a “true” negative relationship between age and depressive symptoms.

Time of Measurement Effects Very little research explicitly examines period effects in mental health. Research examining the effects of natural and man-made disasters on mental health may provide implicit information about period effects. I will use Hurricane Katrina as an example. Using Web of Science, I identified more than 500 journal articles that examined the mental health consequences of Hurricane Katrina. I scanned approximately 100 of them. Two patterns caught my attention. First, almost all studies reported high rates of mental health problems among the victims of Hurricane Katrina. Most studies examined symptoms of posttraumatic stress disorder (PTSD), although anxiety and depressive symptoms were also examined frequently. Fewer studies examined diagnostic measures of PTSD, generalized anxiety disorder, and MDD. Second, the vast majority of studies had severe methodological problems. Most studies were timelimited to several months or one to two years following Hurricane Katrina. Especially problematic, the absence of predisaster measures of mental health precludes conclusions about the extent to which Hurricane Katrina was responsible for increases in psychiatric disorders or symptoms. Most studies compared the prevalence of post-Katrina mental


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health problems with prevalence rates reported in samples not exposed to a recent natural disaster. Although these comparisons may be better than nothing, they are methodologically very poor. A few studies were able to compare rates of mental health problems post-Katrina to samples from the same geographic area that were assessed prior to the hurricane (e.g., Galea et al. 2007). Regardless of the basis of comparison, high rates of psychiatric disorders and symptoms were reported for hurricane victims. Comparisons of prevalence rates of psychiatric disorders shortly after and two to five years postKatrina suggest that rates of PTSD, anxiety, and major depression decreased somewhat but remained high relative to those in nonaffected samples (McLaughlin et al. 2011; Paxson et al. 2012). Although this body of research is suggestive, it fails to document a statistically defensible period effect because of the absence of predisaster assessments of mental health. Another relevant body of research examines the effects of economic downturns on mental health. A recent review of two decades of research on the mental health consequences of unemployment examined the effects of economic contraction at both the individual and population levels (GoldmanMellor, Saxton, and Catalano 2010). The authors conclude that at the individual level, job loss generates a moderate but significant increase in mental health problems, including anxiety and depressive disorders/symptoms, suicide, alcohol abuse, and illicit drug use. Population studies typically use the unemployment rate to measure economic contraction. The relationships between unemployment rate and suicide and alcohol abuse are moderate and significant, but relationships vary across studies for depression and anxiety. Studies of economic downturns and mental health outcomes are generally methodologically stronger than studies of the mental health consequences of disasters because the former examine changes in mental health (i.e., they include measures of mental health prior to job loss or increases in unemployment).

True APC Analyses I identified three studies that model the simultaneous effects of age, period, and cohort on mental health outcomes. These studies illustrate the potential of APC analysis to describe mental health at the population level. Roberts, Lee, and Roberts (1991) examined trends in the prevalence of depressive symptoms in the Alameda County Study. They used three waves

of data (the minimum required for an APC analysis) spanning 18 years. The times of measurement were 1965, 1974, and 1983. The results indicated the presence of all three effects. Age and cohort effects were similar: Depressive symptoms were highest among the oldest participants, and earlier cohorts reported higher levels of symptoms at each time of measurement. Levels of depressive symptoms at all ages and for all cohorts were markedly higher in 1974 than in 1965 and 1983, indicating a period effect. No explanation for the period effect was provided. Interestingly, however, Klerman and Weissman (1989) also reported a sharp spike in the prevalence of MDD in 1974, although they did not report a decline after that. Sacker and Wiggins (2002) examined inequalities in depressive symptoms between 1981 and 2000 using British data from cohorts born in 1958 and 1970. Although their data included only two cohorts, they reported substantially higher levels of depressive symptoms among the 1970 cohort than among the 1958 cohort—both at each time of measurement and when the cohorts were compared at the same ages. The investigators also observed a period effect in which gender and SES differences narrowed from 1981 to 2000 in both cohorts. The period effect for gender resulted from levels of depressive symptoms decreasing in women and increasing in men over time. The narrowing of SES differences resulted from decreases in symptoms among low-SES individuals over time. More recently, Yang (2007) used latent growth curve analysis to examine age and cohort trajectories of depressive symptoms among adults age 65 and older at baseline. There were four times of measurement over 10 years. Disentangling age changes from cohort differences proved to be very important. Without taking cohort differences into account, it appeared that depressive symptoms increased with age. When cohort was taken into account, however, older cohorts exhibited lower levels of depressive symptoms and steeper declines in depressive symptoms over time than younger cohorts. Although levels of depressive symptoms varied over time, no clear period effect was observed. Yang’s results differ substantially from Roberts and colleagues’ findings, especially with regard to the cohort effect observed. Note, however, that the Alameda County Study included adults of all ages and Yang’s sample was restricted to persons age 65 and older at baseline. In summary, available research suggests that age, period, and cohort are related to population levels of mental health. Few studies, however, attempt to


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George parse out the effects of age, period, and cohort using statistically defensible analytic methods. Increased use of APC analysis is especially important for research on mental health because of the need to distinguish between age changes and cohort effects and because period effects are rarely observed without explicit attempts to identify them. As is true for individual-level studies of temporality, APC research also faces methodological challenges. Data appropriate for APC analyses are not as plentiful as are panel data for tracing individual temporal patterns because data including multiple cohorts, ages, and time measurements are needed. Many longitudinal panels, for example, are restricted to a fairly narrow range of cohorts (although there are exceptions, such as the Health and Retirement Study). Times of measurement also can fit poorly with hypothesized period effects. Natural and man-made disasters typically don’t “line up” well with the schedules of ongoing surveys, making it difficult to reveal the true trajectory of the outcome of interest before, during, and after the event expected to trigger a period change. APC analyses can be performed on repeated cross-sections of data (which is problematic for individuallevel temporal analyses), such as the General Social Survey. As yet, however, the extent to which APC analyses of repeated cross-sectional data and those of longitudinal panel data yield the same or similar results is not known.

Taking Time Seriously in Future Research I have argued in this paper that the sociology of mental health would profit from taking time more seriously. Every aspect of mental health is dynamic—the onset of symptoms or disorder, the symptom/disorder course and outcome, and changes in risk and protective factors. Moreover, changes in mental health and the factors that increase or reduce the risk of mental health problems occur at both the individual and population levels. This paper focused on two major components of the dynamics of mental health: dimensions of temporality that are relevant primarily at the individual level and the use of ageperiod-cohort analysis to understand mental health trends at the population level.

Dimensions of Temporality Little is known about the individual-level dynamics of temporality. Conventional between-person analyses that examine the relationships between

risk and/or protective factors and mental health using data with two or three times of measurement (e.g., using baseline variables to predict changes in mental health between baseline and Time 2) simply cannot tell us much about the complexities of time. Fine-grained analyses of length of exposure and duration-dependence are needed to better understand how and when time spent in specific conditions affects mental health outcomes. Studies of timing and critical periods are needed to help explain why specific social risk and protective factors predict mental health for some types of people at some parts of the life course but not others. Research also is needed to identify the conditions under which social risk factors are sufficiently severe that they generate a milestone or turning point in life course trajectories of mental illness, as well as research that identifies the conditions under which mental illness itself becomes a turning point in the life course. Research infrastructure is now sufficiently sophisticated that dynamic processes and time itself can be modeled. Multiple data sets are now available that include a wide range of variables relevant to the sociology of mental health and multiple times of measurement over significant proportions of the life course. Sophisticated statistical models permitting analysis of trajectories of independent and dependent variables are widely available. The direct, indirect, and interactive effects of time can be modeled. As noted previously, substantial methodological challenges remain. Nonetheless, the time is ripe for taking time seriously in studies of social factors and mental health.

Age, Period, Cohort Analysis APC analysis provides valuable opportunities to document and to understand, if not explain, the forces that account for the dynamics of mental health at the population level. All three components of the APC model are relevant to the sociology of mental health. It is tempting to assume that age changes, especially in late adulthood, result from biological processes. And, of course, there are age-related decreases in biological reserve, memory, and cognition that are independent of period and cohort. Virtually ignored are ways that social structure may create age-related patterns of physical and mental health. Age norms can create age-based patterns that are independent of period and cohort. Stable age-based public policies also can generate age effects that span multiple periods and cohorts.


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Recent research demonstrates this with regard to the impact of Medicare on health service utilization. Medicare is a virtually universal health insurance policy for persons age 65 and older in the United States. Trajectories of health care utilization that encompass the years before and after receipt of Medicare show that the volume and cost of medical care increase dramatically for Americans age 65 and 66 and then decrease considerably before increasing at advanced ages (Hadley and Waidmann 2006; McWilliams et al. 2007). Clearly, many adults postpone diagnostic tests and elective procedures until they receive Medicare—a pattern especially common among older adults who lacked health insurance prior to Medicare. Observers who do not know about Medicare might look at this age pattern of medical care and conclude that one’s 65th birthday is accompanied by sudden and precipitous physical decline when, in fact, the pattern reflects rational responses to an age-based policy. To my knowledge, the extent to which receipt of Medicare affects the use and cost of mental health services has not been studied, but this is a prime candidate for inquiry. Period effects also merit more attention from mental health scholars. The effects of sudden societal disruptions and more gradual cultural changes on mental health deserve additional inquiry. As previously noted, natural and man-made disasters are obvious candidates for generating period effects. An interesting, unaddressed question is how widespread a secular change must be before a pattern can be legitimately labeled as a period effect. It is appears, for example, that a period effect of increased psychiatric symptoms occurred in areas directly affected by Hurricane Katrina but did not occur for the U.S. population as a whole. Another man-made disaster, with more widespread effects, was the terrorist attacks of 9/11. Research shows, not surprisingly, high levels of PTSD, affective, and anxiety symptoms as well as high rates of the diagnosis of PTSD after 9/11 among Manhattan residents and volunteers (e.g., Neria et al. 2013). Other studies report increased levels of psychiatric symptoms after 9/11 in U.S. areas far from Manhattan and not directly affected by the disaster there (e.g., Silver et al. 2013). Indeed, evidence suggests that 9/11 also harmed mental health in Europe and Australia (e.g., Smits et al. 2006). A priority for future research is identifying the conditions under which man-made and natural disasters have limited versus widespread consequences for mental health. Social and health policies and social movements also have the potential to generate demonstrable

period effects. Evidence clearly demonstrates that the introduction of Medicare and Medicaid altered the distribution of health care and health itself for older adults and, to a lesser extent, for the poor. The introduction of the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSMIII) permanently changed the prevalence and distributions of psychiatric disorders in the United States. And although there was a substantial lag, the Surgeon General’s report on the dangers of smoking and related legislation and taxation affected the prevalence of smoking and changed it from a luxury associated with high income to predominantly the habit of the socially and economically disadvantaged. Cohort differences in mental health merit additional inquiry. Cohort differences are the most complex and arguably the most interesting of the three types of population dynamics because a vast number of social factors increase or decrease the likelihood of mental health problems—and these factors can vary widely across cohorts depending on historical conditions and cultural trends. For example, one potentially relevant cohort effect relevant for mental health is the average age at first marriage in the United States. According to the U.S. Census, age at first marriage between 1890 and 2010 exhibits a U-shaped curve for both men and women. In 1890, the average age at first marriage for men was 26.4 years. Age at first marriage then decreased to its lowest level of 24.0 in 1950 and then increased consistently to its highest level of 28.4 in 2010 (U.S. Census Bureau 2012). Corresponding ages for women were 23.5 in 1890, 20.4 in 1950, and 26.8 in 2010. Strong evidence indicates that married adults average significantly fewer psychiatric symptoms than the unmarried (e.g., LaPierre 2009; Wade and Pevalin 2004). This suggests that compared with earlier cohorts, current cohorts of young adults may experience more psychiatric symptoms while in their twenties. Thus, the cohort effect of higher levels of psychiatric symptoms over the past few decades may be explained in part by lower rates of marriage during young adulthood. The broader issue, however, is that societal trends may create cohort effects in mental health.

The Need to Reframe Existing Theory With appropriate data sources and statistical techniques widely available, a major remaining obstacle to taking time seriously in the sociology of mental health is the lack of relevant theory.


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George Although several important theories inform sociological research on mental health, time is not a key element of those theories. Extant theories seldom lead to testable hypotheses about length of exposure or critical periods at the individual level or to disentangling age and cohort effects at the population level. The life course perspective sensitizes researchers to the importance of biographical and historical time but provides little guidance for incorporating time into extant theories of the social factors and processes that affect the risk of mental health problems. Extant theories can be modified to better incorporate time, however. For example, Scott Lynch and I attempted to demonstrate how differential exposure and differential vulnerability might be conceptualized and measured in a withinperson design, rather than the conventional between-person design of comparing individuals who differ in levels of stress (George and Lynch 2003; Lynch and George 2002). Other theory-based research questions need to be reframed to incorporate hypotheses about time. Consider, for example, the well-established positive relationships between religious participation, especially religious service attendance, and physical and mental health (see Koenig, King, and Carson 2012 for a recent review). Virtually all of this research is based on between-person designs. That is, we know that people who frequently attend religious services are generally healthier than people who never attend religious services. We do not know, however, whether transitioning from a religious nonattender to a religious attender improves or protects health—or how long it takes for the health benefits to manifest. Addressing these issues would require a within-person design that traces trajectories of religious participation with health trajectories. At present, theories about the health benefits of religious participation and other forms of social integration provide no justification for hypotheses about the characteristics of individuals who move from nonattenders to attenders or the length of time required before religious participation has a demonstrable effect on health. The lack of attention to time in sociological theories that inform research on mental health (and other topics) is not a result of it being ignored by classic theorists. Among a multitude of quotable comments in The Sociological Imagination, C. Wright Mills stated, “No social study that does not come back to the problems of biography, of history, and of their intersections within a society has completed its intellectual journey” (1959:3). His statement is the crux of the life course perspective. Note his claim that no

study can afford to neglect these issues. Thus, there is much to be learned about the sociology of mental health by taking time seriously.

AUTHOR’S NOTE A version of this article was presented at the Leonard I. Pearlin Award ceremony of the American Sociological Association’s Mental Health Section in New York City, New York, in August 2013.

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Author Biography Linda K. George is Professor of Sociology and Associate Director, Center for the Study of Aging and Human Development at Duke University. Her research examines the effects of social factors—including socioeconomic status, stress, and social integration—on physical and mental health outcomes across the life course.


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