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Psychosocial risk factors for depressive disorders in late life
Psychosocial Risk Factors for Depressive Disorders in Late Life Martha L. Bruce This article summarizes research findings on psychosocial risk factors for late life depressive disorders. The article draws heavily on longitudinal cohort studies of welldefined, population-based samples with diagnostic measures that assess the risk of incident or new-onset depressive episodes. These studies have identified a number of significant psychosocial risk factors for late life depressive disorders, including life events and ongoing difficulties; death of a spouse or other loved one; medical illness and injuries; disability and functional decline; and lack of social contact. Additional evidence suggests that the impact of these psychosocial risk factors on depression can be enhanced or buffered by personal or environmental factors. Although many of these psychosocial risk factors are more prevalent among older than younger adults, it is not clear that their impact on the risk of depression differs by age. Methodological challenges to advancing research on psychosocial risk factors for late life depression are reviewed, including problems related to study designs, sample selection, and measurement. Biol Psychiatry 2002;52:175–184 © 2002 Society of Biological Psychiatry Key Words: Psychosocial, risk factor, depression, epidemiology, late life, geriatrics
Introduction
T
his article reviews the research evidence concerning the contribution of psychosocial factors to the risk of depression in late life. Psychosocial factors are loosely defined as factors related to the psychological or social environment and processes (Kelsey et al 1996). Psychosocial factors encompass many of life experiences as well as the environmental context in which individuals pursue their lives. Understanding the role of psychosocial factors in the risk of disease is important, as psychosocial factors may offer innovative avenues for prevention and treatment. From the Department of Psychiatry, Westchester Division, Weill Medical College of Cornell University, White Plains, New York. Address reprint requests to Martha L. Bruce, Ph.D., M.P.H., Weill Medical College of Cornell University, Westchester Division, Department of Psychiatry, 21 Bloomingdale Road, White Plains NY 10605. Received January 24, 2002; revised March 26, 2002; accepted April 3, 2002.
© 2002 Society of Biological Psychiatry
This review begins by first describing criteria for evaluating studies that investigate the contribution of psychosocial factors to the risk of late life depression. The next section reports the results of these studies, organized by type of risk factor. The third section briefly reports evidence on factors that modify the impact of psychosocial factors on the risk of depression, and the next section discusses the extent to which psychosocial risk factors vary across age groups. The final section summarizes the findings and concludes with a discussion of challenges faced by the next set of investigations.
Criteria for Weighing the Evidence Although numerous studies have reported correlational associations between various psychosocial factors and depressive diagnoses or symptoms in late life, only a few provide relatively unambiguous evidence that psychosocial factors contribute to the risk of depression. The criteria for “relatively unambiguous” draw on the basic principles of observational epidemiology, the science of the occurrence and distribution of disease and other health-related conditions in populations. Epidemiology identifies risk factors on the basis of their variations in disease frequency (Kelsey et al 1996; Kraemer et al 2001). If a psychosocial factor is associated longitudinally with an elevated or diminished probability of disease occurrence in a population—and this difference cannot be explained by confounding or methodological reasons— then the psychosocial factor is considered a risk factor. Identifying a psychosocial factor as being a risk factor for depression does not require knowledge of the underlying mechanism of action (which could be either biological or nonbiological). Often this elevated risk is expressed in the form of a ratio comparing the rate or risk of disorder onset in a subgroup of a population exposed to (or characterized by) the psychosocial factor to that in the subgroup not exposed. Two examples of commonly used ratios are the relative risk and the odds ratio. Despite the fairly straightforward definition of risk factors, each element of the equation poses challenges to identifying psychosocial risk factors for late life depression. Key operational elements include a study design that 0006-3223/02/$22.00 PII S0006-3223(02)01410-5
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reduces ambiguity about causal ordering, subject selection strategies that ensure representativeness of relevant populations, and meaningful measures of depression. Each of these elements is explicated briefly, below.
Design By definition, risk factors should exist or occur before the onset of the disease or condition of interest. For some diseases and risk factors, the temporal ordering between risk factor and disease onset is fairly obvious, even using cross-sectional data. For example, a factor such as gender, prescribed at birth, clearly predates onset of diseases in late life. Evidence that the onset of depression is higher in women than men would confirm that female gender is a risk factor for depression. Similarly, many conditions have fairly clear cut onsets (e.g., some infectious diseases or accidents), so that their temporal ordering relative to long-standing psychosocial risk factors can be fairly well determined even retrospectively. But for most potential risk factors and outcomes, establishing the temporal sequence is less obvious. The most clear-cut approach to establishing temporal ordering is to use longitudinal data where risk factors are measured in groups who are disease free. The direction and magnitude of risk by different categories are identified by comparing each group’s rate of disease onset over time. The major hurdle in using this design is that the studies are usually very costly, especially for diseases with low incidence rates or for uncommon risk groups, both of which necessitate large sample sizes and/or long follow-up periods. Other approaches to establishing causal ordering include using retrospective reports that attempt to identify the temporal sequencing of risk factors relative to disease onset (e.g., in cross-sectional or case-control designs). In general, retrospective reports are problematic, in that most individuals do not remember the timing of events with much accuracy, and their reports of putative risk factors can be biased by the disease outcome; for example, individuals with the disease may be more likely to remember psychosocial occurrences (often as an attempt to “explain” their condition). Such problems in establishing “which came first” are especially difficult in retrospective studies of depression, as many potential risk factors (e.g., life events, poor social relationships, bad health, disability) have also been identified as potential outcomes of depression (e.g., Bruce 2001). Further, depressive affect has been shown to color how individuals perceive and report psychosocial factors, so that, for example, individuals who are depressed report greater levels of disability relative to objective observation or informant reports compared to individuals who are not depressed (Morgado et al 1991).
Sample Equally important to risk factor research is the make-up of the sample being analyzed, because the sample determines the denominators used in the analysis. A study sample is defined both by the choice of study population and how members of that population are selected. The gold standard in epidemiology tends to be representative samples of a well-defined geographic population (either communitydwelling residents or the full population, including residents of nursing homes and other institutions). The use of probability sampling strategies ensures that the analytic results can be generalized to the total population of interest. The major advantage of sampling from populations rather than sampling from more narrowly defined target groups (e.g., health care patients or social service clients) or soliciting volunteers is that the relationships between risk factors and disease outcomes observed in population-based samples are generally free from distortion or biases associated with becoming members of those narrowly defined groups or from a subject’s choosing to volunteer. A second advantage of population-based samples is their typically greater heterogeneity in the distribution psychosocial risk factors. Disadvantages include the generally lower prevalence of both depression and risk factors, thereby reducing power and necessitating larger and expensive sample sizes. Specialized targeted groups do have potential usefulness for risk factor analyses. Groups defined by health or social services use (e.g., patients and clients) will usually differ from community-based samples both by the condition that prompted their use of the service (e.g., medical illness) as well as by psychosocial factors that influence help seeking, given the condition (Leaf et al 1988). To the extent that these factors (e.g., medical illness, willingness to seek care) are themselves associated with depression, the prevalence of depression will tend to be higher in these service-defined groups. This increased prevalence can be useful to risk factor analyses, because it increases power, a problem with population-based sampling. If the group is defined by a known, powerful risk factor, for example, recent myocardial infarction (MI) patients, the use of these subjects allows the investigator to identify risk factors while controlling for this first factor (in this example, MI). The problem with using samples from these specialized groups is that the observed relationship of psychosocial factors to onset depression may differ from that of the general population— usually because this relationship is affected in some way by the presence a variable that influences group membership. The risk of depression associated with a stressful life event may be greater (or less) among recent MI patients than among healthier older adults. The problem for many risk factor analyses is that
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Table 1. Summary of Studies that Meet Methodological Criteriaa for Risk Factor Analyses of Late Life Depressive Disorders Study
Author
Follow period
Depression measure
New Haven ECA
Bruce and Hoff 1994 Bruce et al 1990 Bruce and Kim 1992
1 year
DIS/DSM-III
Baltimore ECA
Chen et al 2000
13 years
DIS/DSM-III
Gospel Oaks
Prince et al 1998
1 year
SHORT-CARE
Amsterdam Study of the Elderly (AMSTEL) Sweden
Schoevers et al 2000
3 years
GSM-AGECAT
Forsell 2000
3 years
CPRS/DSM-IV
Sample sizes
Location
Ages (years)
5034 baseline 3698 follow-up 3170 at risk 1687 (ⱖ 65 at risk) 3481 baseline 1856 follow-up 654 baseline 451 follow-up 383 at risk 4051 baseline 2244 follow-up 1940 at risk 1810 baseline 927 follow-up 895 at risk
United States (region)
18 and over
United States (region)
18 and over
Great Britain (region)
65 and over
Netherlands (Amsterdam)
65– 84
Sweden (region)
75 and over
DIS, Diagnostic Interview Schedule (Robins et al 1980); DSM-III, Diagnostic and Statistical Manual of Mental Disorders, Third Edition (American Psychiatric Association 1980); DSM-IV, Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (American Psychiatric Association 1994); SHORT-CARE (Gurland et al 1984); GMS-AGECAT, Geriatric Mental State Examination— computerized system (Copeland et al 1986); CPRS, Comprehensive Psychopathological Rating Scale (Asberg et al 1978); ECA, Epidemiologic Catchment Area; SHORT-CARE, short comprehensive assessment and referral evaluation. a Representative sample of population-based older cohort; depression diagnoses assessed comparably at two or more time points.
the extent to which these relationship do or do not generalize to the population at large is not known.
Outcome Measures As noted above, this review of psychosocial risk factors focuses on the risk of depressive disorders. Although the extent to which depression is better conceptualized and measured as a diagnosis or spectrum of symptoms is debated, the more narrow focus on diagnosed depression is useful in summarizing risk factors for a more homogeneously defined and potentially clinical useful outcome. Despite important advances in the assessment of depression diagnoses in nonpatient samples using (Frances 2000; Regier 2000; Spitzer 2000), challenges remain in developing valid and reliable approaches to assessing depressive disorders in the large, population-based samples needed for risk factor analyses, especially among older adults, in whom failing memory or cognitive decline, fear of stigma, and medical illness can influence reports of depressive illness (see, for example, Hasin and Link 1988; Knauper and Wittchen 1994; Parker 1987). To identify risk factors, analyses are generally conducted only among members of the sample who are disease-free, making them “at risk” for the outcome. The definition of “disease-free” depends on the nature of the disease. Because depression is a chronic condition characterized by an episodic course, different analytic strategies for identifying risk factors are possible. The most conservative is assessment of true depression incidence
(i.e., first onset) among individuals who at baseline have no history of depression. Alternatively, risk factors can be identified for new episodes (i.e., new onsets) of depression among individuals who are not experiencing a depressive episode at baseline. Given the chronic nature of depression, analyses of new episodes generally control for past history of depression, although past history of depression tends to be under-reported in studies of late life (Parker 1987).
Summary of Criteria The following review of known psychosocial risk factors for late life depression will concentrate on studies that meet the gold standard criteria: data from longitudinal cohort studies of well-defined, population-based samples, with diagnostic measures in which the analyses are conducted either among older adults without any reported history of depression (i.e., incidence analyses) or among adults who are free of depression at baseline and where past depression history is modeled as a covariate (i.e., new episode analyses). Studies that include younger as well as older adults are reported if potential age-specific effects of psychosocial risk factors are considered. Only a handful of studies meet these criteria; their characteristics are summarized in Table 1. The discussion of their findings, below, is augmented by findings from case-control studies that have applied careful selection criteria and from longitudinal studies of well-defined samples using symptom measures.
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Strongest Evidence
notion that health events may or may not be aggregated into a composite measure of life events suggests the importance of investigating specific types of events. Many sentinel events, however, such as nursing home admission or other forms of relocation, are commonly experienced by older adults but occur too infrequently at the population level in a given period of time to provide sufficient statistical power for determining their contribution to the risk of depression in longitudinal studies of depression diagnoses. The literature on those specific events and other psychosocial factors that do meet these standards is reviewed below.
Negative Life Events and Ongoing Difficulties Many specific psychosocial risk factors for depression fall under the general category of life stressors. The impact of life stressors, especially life events, on the risk of depression has received a great deal of study for a long time. As summarized by McLean and Link (1994), the two major conceptualizations of how life events affect mental health include: 1) life events as disruptive experiences that necessitate changes and readjustment (Dohrenwend et al 1978; Holmes and Rahe 1967); and 2) life events as meaningful experiences that arouse negative emotions (Brown 1989; Brown and Harris 1978). Although researchers often differentiate life events from chronic stressors, also termed ongoing difficulties, the distinction between the two is not well defined, and newer research suggests that the duration is only one of several dimensions of life experiences that may be relevant to the risk of depression (Wheaton 1999). Some of these key characteristics include the degree to which events or conditions are perceived as undesirable, the extent to which they are in the control of the individuals, the strength of magnitude of the event, the extent to which they are life threatening, and their duration. Measuring life events and chronic difficulties is challenging in all age groups. Part of the problem is that the universe of specific events and difficulties is huge, so that ensuring that a set of measures captures all potential events and their relevant characteristics can be time consuming and expensive. An equally important consequence of the huge pool of potential events is that aggregate measures of life stressors are almost always measured retrospectively. Studies using retrospective assessments of life events can not rule out the possibility that an observed association between reported life events and current depression is a function of reporting or recall bias. The empirical data on aggregate measures of life events and depression are inconsistent. Among population-based studies, aggregated life events reported at follow-up are associated with new depression episodes in two studies (Cervilla and Prince 1997; Chen et al 2000). Case-control studies of diagnosed depression have mixed results, with some (Brilman and Ormel 2001; Murphy 1982) but not all (Mazure et al 2002) reporting an association between depressive diagnoses and life events. Longitudinal studies of depressive symptoms are similarly inconclusive (Glass et al 1997; Kennedy et al 1990). To some extent, differences in the results may reflect discrepancies in how health status was treated by the analyses; in general, life events have a greater association with depression when health events were included as a life event rather than controlled separately in the analysis. The
Medical Illness A strong and consistently observed risk factor of major depression in late life is medical illness, especially diseases of the cardiovascular system (see Krishnan 2002, in this issue). Although medical illness’s biological contribution to depression has received a great deal of attention, the role of medical illness as a psychosocial risk factor is also important. Similar to other life events, onset or exacerbation of severe or life-threatening illness may be profoundly meaningful and may often engender considerable disruption to one’s normal life. For some older adults, health-related events, such as newly diagnosed cancer, heart attacks, or hip fractures symbolize advanced aging and the sense of mortality. Like more clearly defined social events, medical illness and other health related events can precipitate hospitalization, reduction in social activities, increased disability, shifts in the nature of social relationships, and residential relocation. Epidemiologic analyses of longitudinal communitybased data of older adults suggest that composite measures of medical illness increase the risk of depressive disorders by 3.0 over 1 year (Prince et al 1998) and continue to exert risk over longer time, controlling for past history of depression, death of a spouse, demographic factors, and other potential psychosocial factors (Schoevers et al 2000). Community-based, prospective studies of onset or change in depressive symptoms report similar findings (Geerlings et al 2000; Kennedy et al 1990), as do casecontrol studies of depressive disorders in late life (Mazure et al, 2002; Murphy 1982). Data implicating medical burden as a risk factor for depression pose clear research challenges, perhaps most important being the need to develop strategies to differentiate biological from psychosocial contributions of medical illness (Dew 1998). This goal is hampered in part by the common use of generally nonspecific and unreliable measures of medical status in community samples. Most of the indicators of medical burden in prospective community-based studies lack specificity in terms of type of
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conditions, their severity or stage, as well as lack of any knowledge of the prodromal health status. Similar to bereavement, medical illness often prompts a series of other events (e.g., nursing home admission) and ongoing difficulties (e.g., chronic disability) that may also contribute to the risk of major depression. Given the high prevalence of medical burden in older adults and its strong, albeit complex, contribution to the risk of depression, controlling for variables related to physical health or medical burden is essential to analyses of the contribution of any psychosocial factors to the risk of late life depression.
Bereavement/Death of Loved One The death of one’s spouse or other loved one is consistently and strongly associated with subsequent depression across numerous studies. Among longitudinal studies of population-based samples using diagnostic criteria, the risk of depression associated with the death of one’s spouse is estimated as high as 24.3 during the first year (Bruce et al 1990), 9.0 during the first 2 years (Turvey et al 1999), and 3.1 during the first 3 years (Schoevers et al 2000), controlling for past history of depression. Studies of self-reported symptoms scales also find strong relationships between death of a spouse and onset or increase in depressive symptoms (e.g, Oxman et al 1992; Turvey et al 1999). Despite the consistency of these data, understanding how bereavement increases the risk for depression poses at least two major research challenges. First, many normal expressions of grieving mirror clinical symptoms of depression. The point (either in duration or severity) where normal grief is labeled pathologic varies by culture and through history, as evidenced by changing criteria in different versions of the DSM. The research challenge is ensuring that both sociocultural and clinical notions of grieving and depression are integrated into analyses and their interpretations. Second, the death of a spouse is among the more clearly specified life events, yet these events vary in numerous ways, ranging from the circumstances leading up to the death (e.g, years of illness, perhaps accompanied by caregiving demands) to the events following the death (e.g., relocation, financial strain, changes in role expectations and activities, social isolation) (Gallagher et al 1981–1982). The death itself may be expected or unanticipated, traumatic, or relatively peaceful. The scant available data are highly inconclusive but do suggest that this line of inquiry could be useful. For example, data on caregiving spouses indicate that caregivers who experienced strain before their spouses’s death were not at increased risk of depressive symptoms in contrast to
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noncaregiving spouses or caregivers who did not report strain (Schulz et al 2001). On the other hand, Turvey et al (1999) found that reported “expectedness of death” did not significantly predict depression among surviving spouses. Understanding these qualitative differences and the chains of precursors and sequellae may be useful to predicting who is at risk for pathologic grief or might benefit from preventive interventions.
Disability Disability and functional decline in late life generally reflect some combination of physical, cognitive, and psychological impairments (Bruce 2001). They also serve as psychosocial risk factors for late-life depression. Onset disability, for example from a stroke or hip fracture, is a life event profoundly disruptive to previous behavior. Both new and prolonged disabilities bring losses of independence and productivity; in our own study of older, medically ill patients receiving visiting nurse services, some patients express feelings of worthlessness about their new inability to perform role activities. For community-dwelling, older adults, the presence of disabilities (measured by activities of daily living limitations) increases the risk of depression by 4.2 over 1 year, controlling for demographic, medical, and other psychosocial risk factors (Prince et al 1998). In a 3-year follow-up study of depression-free, older residents of Amsterdam, baseline as well as new-onset disability increased the risk of new-onset depression, controlling for past depressive history, chronic disease, and bereavement (Schoevers et al 2000). Homebound status (generally indicating more severe disability and health problems) similarly is associated with increased risk of first onset major depression (Bruce and Hoff 1994). Communitybased, prospective studies similarly report that disability and declining functional status are associated with increased risk of onset or worsening of depressive symptoms, controlling for poor health status and demographic variables (e.g., Geerlings et al 2000; Kennedy et al 1990). These findings are mirrored in case-control studies of depressive disorders in late life (Mazure et al 2002; Murphy 1982). The complexity of disability as a construct, relative to some other psychosocial factors, poses considerable challenges to risk-factor analyses. In particular, disability is a good example of a psychosocial risk factor for depression for which the evidence is equally strong that depression contributes to the risk of disability. Disentangling how their unique contributions to each other is made even more difficult as both share other biological, psychological, and social risk factors. Further, the various dimensions of disability, for example differences in actual versus per-
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ceived behavior or actual behavior versus perceived ability (Bruce 2001), may contribute uniquely to the risk of depression; of particular importance, then, is finding ways to measure and differentiate the unique affect of each aspect of disability on risk of depression. And like each of the other psychosocial risk factors discussed above, disability can contribute to the risk of other risk factors, such as declining health or social isolation, so that understanding the chain of events leading to depression may be important.
Trauma The class of events labeled traumatic events are by definition the most extreme forms of life events. Traumatic events differ from other life events by being outside the scope of common or expected life experience (Beiser 1998). The “horrible and unthinkable” can be categorized on several spectrums, for example the extent to which an event involves full communities (e.g., war) as opposed to being limited to one or a few individuals (e.g., rape), is brief or prolonged, is instigated by humans (e.g., arson) versus those occurring by nature (e.g., tornados), and, if human-made, is intentional (e.g., hi-jackings) as opposed to accidental (e.g., plane crashes). Common wisdom suggests that the large-group, intentional events are the most stressful. Examples include most wars, the Nazi concentration camps, and the terrorist attacks on the World Trade Center. There has been little formal research on the impact of these kinds of traumatic experiences on the lives of older adults. Because the most extreme form of these events are uncommon and unexpected, prospective studies can rarely capture their impact unless a mass event occurs by chance during the course of an ongoing study (Beiser 1998). Anecdotal accounts are contradictory, suggesting either that the life circumstances of many older adults make them particularly vulnerable to traumatic events or that their lifetime of experiences make them particularly resilient. Research into the response of older adults to traumatic events, such as the terrorist attacks of September 11, 2001, may be useful to understanding the vulnerability of older—and younger—adults to adverse outcomes from life events generally.
Social Support Social relationships, ranging from social isolation to social support, have long been implicated in risk of depression. In their review of conceptual and empirical literature on social support and mental health, Turner and Turner (1999) summarize several essential points relevant to studies of risk factors for late life depression. First, social support is a multifactorial construct that involves various
dimensions of perceptions, structure, and behavior. Although most kinds of social support, including help and assistance (e.g., “instrumental support”), may have good consequences on health, the most robust findings involve perceived support, also called emotional support. Second, empirical studies suggest both that social support may directly enhance psychological well-being (and reduce the risk for depression) and that social support may act in the specific context of social stressors by reducing or buffering their risk of depression. In studies of the risk of late life depression, lack of social contact is associated with increased risk of onset depression in some (Bruce and Hoff 1994; Prince et al 1998) but not all (Schoevers et al 2000) longitudinal analyses. Similarly inconsistent findings have been recorded in longitudinal studies of the depressive symptoms (Kennedy et al 1990; Oxman et al 1992). These inconsistent findings point, in part, to the complex nature of social support variables in studies of late life, especially as perceptions of support involve both long-term notions of self in relationship to society as well as a person’s more immediate social circumstances. These social circumstances may fluctuate as friends and family re-locate or die as well as other age-related changes in social networks. The inconsistent findings may also reflect unresolved problems in measuring different aspects of social relationships, including the extent to which social isolation and support are external stressors versus aspects of an individual’s own social functioning.
Modifying Factors Despite the strong evidence that psychosocial factors contribute longitudinally to the risk of depression, the majority of individuals who experience specific psychosocial risk factors do not become depressed. One possible explanation for this observation is that our measures of psychosocial risk factors do not adequately specify the aspect of the factor that increases risk. This explanation is being addressed in an ongoing fashion by efforts to improve measures or further specificity. Another possible explanation is that some risk factors only work— or work more strongly—in the context of another variable. This other variable modifies the risk associated with a psychosocial factor for depression. In statistical terms, modifying factors interact with the psychosocial risk factor on depression risk (Baron and Kenny 1986; Kraemer et al 2001). Modifying variables may conceptually increase the vulnerability of an individual to the impact of the risk factor or buffer its effect. At the very least, knowledge of modifying variables is useful by helping specify more narrowly groups at risk for late life depression. When modifying factors are themselves alter-
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able, they become potential targets for reducing the risk of depression. Many investigators have identified variables that modify the impact of psychosocial risk factors on depression, although fewer of these studies meet the methodological criteria of prospective studies of elderly adults with good measures of depression diagnoses. Putative modifying variables range from genetic indicators to other psychosocial variables. For example, Kendler and colleagues have identified genetic factors that appear to modify the impact of life events on depression risk (Kendler et al 2001). Various studies have indicated that psychosocial factors can have a different risk for depression in women as compared to men. For example, two studies have observed a greater risk of depression associated with marital separation or divorce on men than women (Bruce and Kim 1992; Kendler et al 2001). In their community-based, case-control study, Ormel et al (2001) observed a stronger impact of stressful life events among older adults who also scored high on neuroticism or reported ongoing difficulties. Similarly, in a case-control study of late life depression, Mazure et al (2002) found that stressful life events were associated with depression only when the nature of the event matched the personality type of the individual. In their study, interpersonal events were associated with depression only among individuals who report sociotropy, whereas achievement-related events were associated with depression only among individuals who report autonomy.
Age Differences The focus of this review has been the contribution of psychosocial factors to the risk of depressive disorders in late life. A final question is the extent to which psychosocial risk factors for depression are different— either in terms of exposure or impact—in older compared to younger adults.
Age Variation in the Prevalence of Psychosocial Risk Factors The clearest evidence of an age difference in psychosocial risk factor research is that the distribution of psychosocial factors varies by age. Many of the factors reviewed above reflect experiences and conditions that are more prevalent among the old than the young. Widowhood, medical events, disability, and declining social support systems are not inevitable but are increasingly likely as adults age. In the same vein, this review has not mentioned many of the life events particularly relevant to the circumstances and social roles of younger adults, such as job loss or divorce. The different distribution of specific types of events across age groups underscores the difficulty of constructing a
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meaningful composite measure of life events that could be used across the life course.
Age Variation in the Impact of Psychosocial Risk Factors Less evident is whether or not the impact of psychosocial risk factors differs by age. Several rationales have been offered for possible age differences. One notion is that “age normative” life events have a smaller impact on depression than less normative or expected events. Other hypotheses suggest that the distribution of modifying variables varies by age. For example, advanced age may improve self-esteem and maturation that protects older adults against the impact of psychosocial risk factors (Gove et al 1989). Conversely, aging sometimes results in the loss of a sense of personal control over one’s environment or losses in social support resources, losses that might reduce the ability of older adults to buffer the negative impact of psychosocial risk factors (Krause 1999; Rodin 1986). But the actual data supporting each of these hypotheses are not convincing, in part because the unequal distribution of risk factor by age results in insufficient power to test the hypothesis in some age groups (e.g., widowhood is a rare event in population-based studies of the young) in longitudinal studies of depressive disorders. Perhaps more convincing are the data suggesting that ongoing difficulties have a smaller effect on the risk of depression in older than younger adults. For example, Forsell (2000) found that no baseline psychosocial factor predicted depression after controlling for past history. Similarly, among older adults with no reported depressive symptoms, Kennedy et al (1990) also found that onset depressive symptoms were not associated with baseline (mostly ongoing and persistent) psychosocial factors at baseline but were associated with changes over the follow-up period (e.g., life events). These findings are consistent with the survival hypothesis: as individuals age into late life and survive exposure to long-term, difficult situations without succumbing to depression, the likelihood of a first onset depression associated with those difficulties declines.
Psychosocial Risk Factors: Summary and Challenges for Future Research The research summarized here has identified a number of significant psychosocial risk factors for late life depressive disorders, including life events and ongoing difficulties; death of a spouse or other loved one; medical illness and injuries; disability and functional decline; and lack of social contact. The effects of these psychosocial factors have been demonstrated in well-designed, longitudinal
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studies. For the most part, the effects have persisted while controlling for at least some type of biological or physical health status variable. Preliminary evidence suggests that the impact of these psychosocial risk factors on depression can be enhanced or buffered by personal or environmental factors. Although many of these psychosocial risk factors are more prevalent among older than younger adults, it is not clear that their impact on the risk of depression differs by age. Risk factors suggest but do not confirm a causal relationship. Evidence of risk often poses additional question about the specificity of the relationship (e.g., can the components of the psychosocial variable that contribute to risk be more closely specified?) and the potential causal mechanisms (e.g., what biological, psychological, and social factors mediate the relationships between the observed risk factor and the disease outcomes?). Such questions point to needed additional research. But, as suggested throughout this review, the kinds of data needed to answer this next generation of questions pose a number of research challenges. Perhaps the most concrete hurdle is that risk factor research meeting the rigorous criteria required by observational epidemiology is expensive and time consuming, especially in studies of low incident events, such as first-onset depression in community-dwelling, older adults. Innovative methodologies are needed to advance research on psychosocial risk factors for late life depression. For example, multistage stratified sampling designs that preserve the representativeness of populations but generate samples enriched with high numbers of high-risk subjects generally offer a more economical approach to achieving adequate statistical power than simple random samples (Shrout and Newman 1989). Questions about the pathway between a given risk factor and depression may be appropriately addressed by sampling only from welldefined, high-risk groups—if the sample’s representativeness to their counterparts at the population level can be determined. A possible alternative to observational design is the use of intervention studies to confirm or discover risk factors. In this case, when successful interventions designed to manipulate psychosocial factors (e.g., enhance social contact, reduce disability) further result in changed risk for depression, these findings provide evidence of the contribution of these psychosocial factors to the risk of depression. Again, however, a key step to ensuring that findings from such intervention studies meaningfully contribute to knowledge of psychosocial risk factors is determining the extent to which the study’s sample can be generalized to other populations of older adults. A common theme in discussing each of the psychosocial risk factors is how frequently each is linked in a chain of events. For example, health problems increase the risk
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of disability, which increases the risk of social isolation. These chains are not inevitable, but common enough that it may be useful for investigators to examine the impact of specific sequences of events or different composite sets of conditions on the risk of depression. The inter-relatedness among psychosocial risk factors also has research implications for the timing of follow-up assessments as shorter time spans between assessments may be needed to capture the sequencing of events or the unique impact of each occurrence on the risk of depression. Thorough understanding of the contribution of psychosocial factors to the risk of depression demands more knowledge of how psychosocial factors work with, and in the context of, biological variables. A first and feasible step is the addition of meaningful biological measures to rigorously designed studies of psychosocial risk factors and the addition of meaningful psychosocial risk factors to biological studies. Better measures should make it possible to specify the unique and joint contributions of psychosocial factors with far greater precision than currently possible. Even with better measures, however, our current designs may well not capture some of the long-term relationships between biological and psychosocial factors that lead to depression in late life. Nor do they permit continued investigation into the clinical course and outcomes of these depressions. A more ambitious goal is to add these measures to long-term, population-based studies of midlife or even younger populations, following their risk for depressive disorders into late life as they shape and respond to life’s vicissitudes (Vaillant and Mukamal 2001). Finally, a key motivation for conducting risk factors analyses is to help shape preventive and treatment interventions for depression in late life. The portraits of the frail, socially isolated, medically burdened and disabled older person at risk for depression provide us targets for prevention activities, as well as identify high-risk groups for selective interventions. Can we, for example, reduce the level of disability associated with a progressive medical condition, or at least enhance a person’s psychological capacity to cope with new disability? Armed with knowledge of risk factors for late-life depression, the next challenge is to determine which psychosocial risk factors are malleable enough to serve as intervention targets, determine effective biological or psychosocial approaches to changing these risk factors, and ultimately implement strategies to reduce the burden of late-life depression in this vulnerable population.
This work was supported by grant no. KO2MH01634. Aspects of this work were presented at the conference, “Unmet Needs in Diagnosis and Treatment of Mood Disorders in Late Life,” October 9 –10, 2001 in Washington, DC. The conference was sponsored by the
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National Depressive and Manic-Depressive Association (National DMDA) through unrestricted educational grants provided by Abbott Laboratories, AstraZeneca, Forest Laboratories, GlaxoSmithKline, Janssen Pharmaceutica, Eli Lilly and Company, Merck & Co., National Institute of Mental Health, Organon, Pfizer Inc, and Wyeth-Ayerst Laboratories.
Dohrenwend BS, Krasnoff L, Askenasy AR, Dohrenwend BP (1978): Exemplification of a method for scaling life events: The PERI life events scale. J Health Soc Behav 19:205–229.
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American Psychiatric Association (1980): Diagnostic and Statistical Manual of Mental Disorders, Third Edition. Washington, DC: American Psychiatric Association. American Psychiatric Association (1994): Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Washington, DC: American Psychiatric Association. Åsberg M, Perris C, Schalling D, Sedvall G (1978): The CPRS— development and application of a psychiatric rating scale. Acta Psychiatr Scand Suppl 271, pp 5–27. Baron RM, Kenny DA (1986): The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51:1173–1182. Beiser M (1998): Extreme situations. In: Dohrenwend BP, editor. Adversity, Stress, and Psychopathology. New York: Oxford University Press, pp 9 –11. Brilman EI, Ormel J (2001): Life events, difficulties and onset of depressive episodes in late life. Psychol Med 31:859 –869. Brown GW (1989): Life events and measurement. In: Brown GW, Harris T, editors. Life Events and Illness. New York: Guilford, pp 3– 45. Brown GW, Harris T (1978): Social Origins of Depression. New York: Free Press. Bruce ML (2001): Depression and disability in late life: Directions for future research. Am J Geriatr Psychiatry 9:102–112. Bruce ML, Hoff RA (1994): Social and physical health risk factors for first onset major depressive disorder in a community sample. Soc Psychiatry Psychiatr Epidemiol 29:165–171. Bruce ML, Kim KM (1992): Differences in the effects of divorce on major depression in men and women. Am J Psychiatry 149:914 –917. Bruce ML, Kim KM, Leaf PJ, Jacobs S (1990): Depressive episodes and dysphoria resulting from conjugal bereavement in a prospective community sample. Am J Psychiatry 147:608 –611. Cervilla JA, Prince MJ (1997): Cognitive impairment and social distress as different pathways to depression in the elderly: A cross-sectional study. Int J Geriatr Psychiatry 12:995–1000. Chen LS, Eaton WW, Gallo JJ, Nestadt G, Crum RM (2000): Empirical examination of current depression categories in a population-based study: Symptoms, course, and risk factors. Am J Psychiatry 157:573–580. Copeland JRM, Dewey ME, Griffiths-Jones HM (1986): A computerized psychiatric diagnostic system and case nomenclature for elderly subjects: GMS and AGECAT. Psychol Med 16:89 –99. Dew MA (1998): Psychiatric disorder in the context of physical illness. In: Dohrenwend BP, editor. Adversity, Stress, and Psychopathology. New York: Oxford University Press, 177– 218.
Forsell Y (2000): Predictors of depression, anxiety, and psychotic symptoms in a very elderly population: Data from a 3 year follow-up study. Soc Psychiatry Psychiatr Epidemiol 35:259 –263.
Gallagher DE, Thompson LW, Peterson JA (1981–1982): Psychosocial factors affecting adaptation to bereavement in the elderly. Inter J Aging Hum Dev 14:79 –95. Geerlings SW, Beekman ATF, Deeg DJH, Van Tilburg W (2000): Physical health and the onset and persistence of depression in older adults: An eight-wave prospective community-based study. Psychol Med 30:369 –380. Glass TA, Kasl SV, Berkman LF (1997): Stressful life events and depressive symptoms among the elderly. Evidence from a prospective community study. J Aging Health 9:70 –89. Gove WR, Ortega ST, Style CB (1989): The maturation and role perspective on aging and self through the adult years: An empirical evaluation. Am J Sociol 94:1117–1145. Gurland B, Golden RR, Teresi JA, Challop J (1984): The SHORT-CARE: An efficient instrument for the assessment of depression, dementia and disability. J Gerontol 39:166 –169. Hasin D, Link B (1988): Age and recognition of depression: Implications for a cohort effect in major depression. Psychol Med 18:683–688. Holmes TH, Rahe RH (1967): The social readjustment rating scale. J Psychosom Res 11:213–218. Kelsey JL, Whittemore AS, Evans AS, Thompson WD (1996): Methods in Observational Epidemiology. New York: Oxford University Press. Kendler KS, Thornton LM, Prescott CA (2001): Gender differences in the rates of exposure to stressful life events and sensitivity to their depressogenic effects. Am J Psychiatry 158:587–593. Kennedy GJ, Kelman HR, Thomas C (1990): The emergence of depressive symptoms in late life: The importance of declining health and increasing disability. J Community Health 15:93– 104. Knauper B, Wittchen HU (1994): Diagnosing major depression in the elderly: Evidence for response bias in standardized diagnostic interviews? J Psychiatr Res 28:147–164. Kraemer HC, Stice E, Kazdin A, Offord D, Kupfer D (2001): How do risk factors work together? Mediators, moderators, and independent, overlapping, and proxy risk factors. Am J Psychiatry 158:848 –856. Krause N (1999): Mental disorder in late life: Exploring the influence of stress and socioeconomic status. In: Anshensel CS, Phelan JC, editors. Handbook of the Sociology of Mental Health. New York: Kluwer, pp 183–208. Krishnan KRR (2002): Biological Risk Factors in Late Life Depression. Biol Psychiatry 52:185–192. Leaf PJ, Bruce ML, Tischler GL, Freeman DH, Weissman MM, Myers JK (1988): Factors affecting the utilization of specialty and general medical mental health services. Med Care 26:9 – 26.
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Mazure CM, Maciejewski PK, Jacobs SC, Bruce ML (2002): Stressful life events interacting with cognitive-personality styles to predict late onset, major depression. Am J Geriatr Psychiatry 10:297–304. McLean DE, Link BG (1994): Unraveling complexity: Strategies to refine concepts, measures, and research designs in the study of life events and mental health. In: Avison WR, Gotlib IH, editors. Stress and Mental Health. Contemporary Issues and Prospects for the Future. New York: Plenum Press, pp 15– 42. Morgado A, Smith M, Lecrubier Y, Widlocher D (1991): Depressed subjects unwittingly overreport poor social adjustment which they reappraise when recovered. J Nerv Ment Dis 179:614 –619. Murphy E (1982): Social origins of depression in old age. Br J Psychiatry 141:135–142. Ormel J, Oldehinkel AJ, Brilman EI (2001): The interplay and etiological continuity of neuroticism, difficulties, and life events in the etiology of major and subsyndromal, first and recurrent depressive episodes in later life. Am J Psychiatry 158:885–891. Oxman TE, Berkman LJ, Kasl S, Freeman DH Jr, Barrett J (1992): Social support and depressive symptoms in the elderly. Am J Epidemiol 135:356 –368. Parker G (1987): Are the lifetime prevalence estimates in the ECA study accurate? Psychol Med 17:275–282. Prince MJ, Harwood RH, Thomas A, Mann AH (1998): A prospective population-based cohort study of the effects of disablement and social milieu on the onset and maintenance of late-life depression. The Gospel Oak Project VII. Psychol Med 28:337–350.
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Regier DA (2000): Limitations of diagnostic criteria and assessment instruments for mental disorders. Arch Gen Psychiatry 57:109 –115. Robins LN, Helzer JE, Croughan J, Ratcliff KS (1980): National Institute of Mental Health Diagnostic Interview Schedule: Its history, characteristics, and validity. Arch Gen Psychiatry 38:381–389. Rodin J (1986): Aging and health: Effects of the sense of control. Science 233:1271–1275. Schoevers RA, Beekman AT, Deeg DJ, Geerlings MI, Jonker C, Van Tilburg W (2000): Risk factors for depression in later life: Results of a prospective community based study. J Affect Disord 459:127–137. Schulz R, Beach SR, Lind B, Martire LM, Zdaniuk B, Hirsch C, et al (2001): Involvement in caregiving and adjustment to death of a spouse: Findings from the caregiver health effects study. JAMA 285:3123–3129. Shrout PE, Newman S (1989): Design of two-phase prevalence surveys of rare disorders. Biometrics 45:549 –555. Spitzer RL (2000): Diagnosis and need for treatment are not the same. Arch Gen Psychiatry 57:120. Turner RJ, Turner JB (1999): Social integration and support. In: Aneshensel CS, Phelan JC, editors. Handbook of Sociology of Mental Health. New York: Kluwer Academic, pp 301–319. Turvey CL, Carney C, Arndt S, Wallace RB, Herzog R (1999): Conjugal loss and syndromal depression in a sample of elders aged 70 years and over. Am J Psychiatry 156:1596 –1601. Vaillant GE, Mukamal K (2001): Successful aging. Am J Psychiatry 158:839 –847. Wheaton B (1999): Social stress. In: Aneshensel CS, Phelan JC, editors. Handbook of Sociology of Mental Health. New York: Kluwer Academic, pp 277–300.
Introduction
T
his article reviews the research evidence concerning the contribution of psychosocial factors to the risk of depression in late life. Psychosocial factors are loosely defined as factors related to the psychological or social environment and processes (Kelsey et al 1996). Psychosocial factors encompass many of life experiences as well as the environmental context in which individuals pursue their lives. Understanding the role of psychosocial factors in the risk of disease is important, as psychosocial factors may offer innovative avenues for prevention and treatment. From the Department of Psychiatry, Westchester Division, Weill Medical College of Cornell University, White Plains, New York. Address reprint requests to Martha L. Bruce, Ph.D., M.P.H., Weill Medical College of Cornell University, Westchester Division, Department of Psychiatry, 21 Bloomingdale Road, White Plains NY 10605. Received January 24, 2002; revised March 26, 2002; accepted April 3, 2002.
© 2002 Society of Biological Psychiatry
This review begins by first describing criteria for evaluating studies that investigate the contribution of psychosocial factors to the risk of late life depression. The next section reports the results of these studies, organized by type of risk factor. The third section briefly reports evidence on factors that modify the impact of psychosocial factors on the risk of depression, and the next section discusses the extent to which psychosocial risk factors vary across age groups. The final section summarizes the findings and concludes with a discussion of challenges faced by the next set of investigations.
Criteria for Weighing the Evidence Although numerous studies have reported correlational associations between various psychosocial factors and depressive diagnoses or symptoms in late life, only a few provide relatively unambiguous evidence that psychosocial factors contribute to the risk of depression. The criteria for “relatively unambiguous” draw on the basic principles of observational epidemiology, the science of the occurrence and distribution of disease and other health-related conditions in populations. Epidemiology identifies risk factors on the basis of their variations in disease frequency (Kelsey et al 1996; Kraemer et al 2001). If a psychosocial factor is associated longitudinally with an elevated or diminished probability of disease occurrence in a population—and this difference cannot be explained by confounding or methodological reasons— then the psychosocial factor is considered a risk factor. Identifying a psychosocial factor as being a risk factor for depression does not require knowledge of the underlying mechanism of action (which could be either biological or nonbiological). Often this elevated risk is expressed in the form of a ratio comparing the rate or risk of disorder onset in a subgroup of a population exposed to (or characterized by) the psychosocial factor to that in the subgroup not exposed. Two examples of commonly used ratios are the relative risk and the odds ratio. Despite the fairly straightforward definition of risk factors, each element of the equation poses challenges to identifying psychosocial risk factors for late life depression. Key operational elements include a study design that 0006-3223/02/$22.00 PII S0006-3223(02)01410-5
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reduces ambiguity about causal ordering, subject selection strategies that ensure representativeness of relevant populations, and meaningful measures of depression. Each of these elements is explicated briefly, below.
Design By definition, risk factors should exist or occur before the onset of the disease or condition of interest. For some diseases and risk factors, the temporal ordering between risk factor and disease onset is fairly obvious, even using cross-sectional data. For example, a factor such as gender, prescribed at birth, clearly predates onset of diseases in late life. Evidence that the onset of depression is higher in women than men would confirm that female gender is a risk factor for depression. Similarly, many conditions have fairly clear cut onsets (e.g., some infectious diseases or accidents), so that their temporal ordering relative to long-standing psychosocial risk factors can be fairly well determined even retrospectively. But for most potential risk factors and outcomes, establishing the temporal sequence is less obvious. The most clear-cut approach to establishing temporal ordering is to use longitudinal data where risk factors are measured in groups who are disease free. The direction and magnitude of risk by different categories are identified by comparing each group’s rate of disease onset over time. The major hurdle in using this design is that the studies are usually very costly, especially for diseases with low incidence rates or for uncommon risk groups, both of which necessitate large sample sizes and/or long follow-up periods. Other approaches to establishing causal ordering include using retrospective reports that attempt to identify the temporal sequencing of risk factors relative to disease onset (e.g., in cross-sectional or case-control designs). In general, retrospective reports are problematic, in that most individuals do not remember the timing of events with much accuracy, and their reports of putative risk factors can be biased by the disease outcome; for example, individuals with the disease may be more likely to remember psychosocial occurrences (often as an attempt to “explain” their condition). Such problems in establishing “which came first” are especially difficult in retrospective studies of depression, as many potential risk factors (e.g., life events, poor social relationships, bad health, disability) have also been identified as potential outcomes of depression (e.g., Bruce 2001). Further, depressive affect has been shown to color how individuals perceive and report psychosocial factors, so that, for example, individuals who are depressed report greater levels of disability relative to objective observation or informant reports compared to individuals who are not depressed (Morgado et al 1991).
Sample Equally important to risk factor research is the make-up of the sample being analyzed, because the sample determines the denominators used in the analysis. A study sample is defined both by the choice of study population and how members of that population are selected. The gold standard in epidemiology tends to be representative samples of a well-defined geographic population (either communitydwelling residents or the full population, including residents of nursing homes and other institutions). The use of probability sampling strategies ensures that the analytic results can be generalized to the total population of interest. The major advantage of sampling from populations rather than sampling from more narrowly defined target groups (e.g., health care patients or social service clients) or soliciting volunteers is that the relationships between risk factors and disease outcomes observed in population-based samples are generally free from distortion or biases associated with becoming members of those narrowly defined groups or from a subject’s choosing to volunteer. A second advantage of population-based samples is their typically greater heterogeneity in the distribution psychosocial risk factors. Disadvantages include the generally lower prevalence of both depression and risk factors, thereby reducing power and necessitating larger and expensive sample sizes. Specialized targeted groups do have potential usefulness for risk factor analyses. Groups defined by health or social services use (e.g., patients and clients) will usually differ from community-based samples both by the condition that prompted their use of the service (e.g., medical illness) as well as by psychosocial factors that influence help seeking, given the condition (Leaf et al 1988). To the extent that these factors (e.g., medical illness, willingness to seek care) are themselves associated with depression, the prevalence of depression will tend to be higher in these service-defined groups. This increased prevalence can be useful to risk factor analyses, because it increases power, a problem with population-based sampling. If the group is defined by a known, powerful risk factor, for example, recent myocardial infarction (MI) patients, the use of these subjects allows the investigator to identify risk factors while controlling for this first factor (in this example, MI). The problem with using samples from these specialized groups is that the observed relationship of psychosocial factors to onset depression may differ from that of the general population— usually because this relationship is affected in some way by the presence a variable that influences group membership. The risk of depression associated with a stressful life event may be greater (or less) among recent MI patients than among healthier older adults. The problem for many risk factor analyses is that
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Table 1. Summary of Studies that Meet Methodological Criteriaa for Risk Factor Analyses of Late Life Depressive Disorders Study
Author
Follow period
Depression measure
New Haven ECA
Bruce and Hoff 1994 Bruce et al 1990 Bruce and Kim 1992
1 year
DIS/DSM-III
Baltimore ECA
Chen et al 2000
13 years
DIS/DSM-III
Gospel Oaks
Prince et al 1998
1 year
SHORT-CARE
Amsterdam Study of the Elderly (AMSTEL) Sweden
Schoevers et al 2000
3 years
GSM-AGECAT
Forsell 2000
3 years
CPRS/DSM-IV
Sample sizes
Location
Ages (years)
5034 baseline 3698 follow-up 3170 at risk 1687 (ⱖ 65 at risk) 3481 baseline 1856 follow-up 654 baseline 451 follow-up 383 at risk 4051 baseline 2244 follow-up 1940 at risk 1810 baseline 927 follow-up 895 at risk
United States (region)
18 and over
United States (region)
18 and over
Great Britain (region)
65 and over
Netherlands (Amsterdam)
65– 84
Sweden (region)
75 and over
DIS, Diagnostic Interview Schedule (Robins et al 1980); DSM-III, Diagnostic and Statistical Manual of Mental Disorders, Third Edition (American Psychiatric Association 1980); DSM-IV, Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (American Psychiatric Association 1994); SHORT-CARE (Gurland et al 1984); GMS-AGECAT, Geriatric Mental State Examination— computerized system (Copeland et al 1986); CPRS, Comprehensive Psychopathological Rating Scale (Asberg et al 1978); ECA, Epidemiologic Catchment Area; SHORT-CARE, short comprehensive assessment and referral evaluation. a Representative sample of population-based older cohort; depression diagnoses assessed comparably at two or more time points.
the extent to which these relationship do or do not generalize to the population at large is not known.
Outcome Measures As noted above, this review of psychosocial risk factors focuses on the risk of depressive disorders. Although the extent to which depression is better conceptualized and measured as a diagnosis or spectrum of symptoms is debated, the more narrow focus on diagnosed depression is useful in summarizing risk factors for a more homogeneously defined and potentially clinical useful outcome. Despite important advances in the assessment of depression diagnoses in nonpatient samples using (Frances 2000; Regier 2000; Spitzer 2000), challenges remain in developing valid and reliable approaches to assessing depressive disorders in the large, population-based samples needed for risk factor analyses, especially among older adults, in whom failing memory or cognitive decline, fear of stigma, and medical illness can influence reports of depressive illness (see, for example, Hasin and Link 1988; Knauper and Wittchen 1994; Parker 1987). To identify risk factors, analyses are generally conducted only among members of the sample who are disease-free, making them “at risk” for the outcome. The definition of “disease-free” depends on the nature of the disease. Because depression is a chronic condition characterized by an episodic course, different analytic strategies for identifying risk factors are possible. The most conservative is assessment of true depression incidence
(i.e., first onset) among individuals who at baseline have no history of depression. Alternatively, risk factors can be identified for new episodes (i.e., new onsets) of depression among individuals who are not experiencing a depressive episode at baseline. Given the chronic nature of depression, analyses of new episodes generally control for past history of depression, although past history of depression tends to be under-reported in studies of late life (Parker 1987).
Summary of Criteria The following review of known psychosocial risk factors for late life depression will concentrate on studies that meet the gold standard criteria: data from longitudinal cohort studies of well-defined, population-based samples, with diagnostic measures in which the analyses are conducted either among older adults without any reported history of depression (i.e., incidence analyses) or among adults who are free of depression at baseline and where past depression history is modeled as a covariate (i.e., new episode analyses). Studies that include younger as well as older adults are reported if potential age-specific effects of psychosocial risk factors are considered. Only a handful of studies meet these criteria; their characteristics are summarized in Table 1. The discussion of their findings, below, is augmented by findings from case-control studies that have applied careful selection criteria and from longitudinal studies of well-defined samples using symptom measures.
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Strongest Evidence
notion that health events may or may not be aggregated into a composite measure of life events suggests the importance of investigating specific types of events. Many sentinel events, however, such as nursing home admission or other forms of relocation, are commonly experienced by older adults but occur too infrequently at the population level in a given period of time to provide sufficient statistical power for determining their contribution to the risk of depression in longitudinal studies of depression diagnoses. The literature on those specific events and other psychosocial factors that do meet these standards is reviewed below.
Negative Life Events and Ongoing Difficulties Many specific psychosocial risk factors for depression fall under the general category of life stressors. The impact of life stressors, especially life events, on the risk of depression has received a great deal of study for a long time. As summarized by McLean and Link (1994), the two major conceptualizations of how life events affect mental health include: 1) life events as disruptive experiences that necessitate changes and readjustment (Dohrenwend et al 1978; Holmes and Rahe 1967); and 2) life events as meaningful experiences that arouse negative emotions (Brown 1989; Brown and Harris 1978). Although researchers often differentiate life events from chronic stressors, also termed ongoing difficulties, the distinction between the two is not well defined, and newer research suggests that the duration is only one of several dimensions of life experiences that may be relevant to the risk of depression (Wheaton 1999). Some of these key characteristics include the degree to which events or conditions are perceived as undesirable, the extent to which they are in the control of the individuals, the strength of magnitude of the event, the extent to which they are life threatening, and their duration. Measuring life events and chronic difficulties is challenging in all age groups. Part of the problem is that the universe of specific events and difficulties is huge, so that ensuring that a set of measures captures all potential events and their relevant characteristics can be time consuming and expensive. An equally important consequence of the huge pool of potential events is that aggregate measures of life stressors are almost always measured retrospectively. Studies using retrospective assessments of life events can not rule out the possibility that an observed association between reported life events and current depression is a function of reporting or recall bias. The empirical data on aggregate measures of life events and depression are inconsistent. Among population-based studies, aggregated life events reported at follow-up are associated with new depression episodes in two studies (Cervilla and Prince 1997; Chen et al 2000). Case-control studies of diagnosed depression have mixed results, with some (Brilman and Ormel 2001; Murphy 1982) but not all (Mazure et al 2002) reporting an association between depressive diagnoses and life events. Longitudinal studies of depressive symptoms are similarly inconclusive (Glass et al 1997; Kennedy et al 1990). To some extent, differences in the results may reflect discrepancies in how health status was treated by the analyses; in general, life events have a greater association with depression when health events were included as a life event rather than controlled separately in the analysis. The
Medical Illness A strong and consistently observed risk factor of major depression in late life is medical illness, especially diseases of the cardiovascular system (see Krishnan 2002, in this issue). Although medical illness’s biological contribution to depression has received a great deal of attention, the role of medical illness as a psychosocial risk factor is also important. Similar to other life events, onset or exacerbation of severe or life-threatening illness may be profoundly meaningful and may often engender considerable disruption to one’s normal life. For some older adults, health-related events, such as newly diagnosed cancer, heart attacks, or hip fractures symbolize advanced aging and the sense of mortality. Like more clearly defined social events, medical illness and other health related events can precipitate hospitalization, reduction in social activities, increased disability, shifts in the nature of social relationships, and residential relocation. Epidemiologic analyses of longitudinal communitybased data of older adults suggest that composite measures of medical illness increase the risk of depressive disorders by 3.0 over 1 year (Prince et al 1998) and continue to exert risk over longer time, controlling for past history of depression, death of a spouse, demographic factors, and other potential psychosocial factors (Schoevers et al 2000). Community-based, prospective studies of onset or change in depressive symptoms report similar findings (Geerlings et al 2000; Kennedy et al 1990), as do casecontrol studies of depressive disorders in late life (Mazure et al, 2002; Murphy 1982). Data implicating medical burden as a risk factor for depression pose clear research challenges, perhaps most important being the need to develop strategies to differentiate biological from psychosocial contributions of medical illness (Dew 1998). This goal is hampered in part by the common use of generally nonspecific and unreliable measures of medical status in community samples. Most of the indicators of medical burden in prospective community-based studies lack specificity in terms of type of
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conditions, their severity or stage, as well as lack of any knowledge of the prodromal health status. Similar to bereavement, medical illness often prompts a series of other events (e.g., nursing home admission) and ongoing difficulties (e.g., chronic disability) that may also contribute to the risk of major depression. Given the high prevalence of medical burden in older adults and its strong, albeit complex, contribution to the risk of depression, controlling for variables related to physical health or medical burden is essential to analyses of the contribution of any psychosocial factors to the risk of late life depression.
Bereavement/Death of Loved One The death of one’s spouse or other loved one is consistently and strongly associated with subsequent depression across numerous studies. Among longitudinal studies of population-based samples using diagnostic criteria, the risk of depression associated with the death of one’s spouse is estimated as high as 24.3 during the first year (Bruce et al 1990), 9.0 during the first 2 years (Turvey et al 1999), and 3.1 during the first 3 years (Schoevers et al 2000), controlling for past history of depression. Studies of self-reported symptoms scales also find strong relationships between death of a spouse and onset or increase in depressive symptoms (e.g, Oxman et al 1992; Turvey et al 1999). Despite the consistency of these data, understanding how bereavement increases the risk for depression poses at least two major research challenges. First, many normal expressions of grieving mirror clinical symptoms of depression. The point (either in duration or severity) where normal grief is labeled pathologic varies by culture and through history, as evidenced by changing criteria in different versions of the DSM. The research challenge is ensuring that both sociocultural and clinical notions of grieving and depression are integrated into analyses and their interpretations. Second, the death of a spouse is among the more clearly specified life events, yet these events vary in numerous ways, ranging from the circumstances leading up to the death (e.g, years of illness, perhaps accompanied by caregiving demands) to the events following the death (e.g., relocation, financial strain, changes in role expectations and activities, social isolation) (Gallagher et al 1981–1982). The death itself may be expected or unanticipated, traumatic, or relatively peaceful. The scant available data are highly inconclusive but do suggest that this line of inquiry could be useful. For example, data on caregiving spouses indicate that caregivers who experienced strain before their spouses’s death were not at increased risk of depressive symptoms in contrast to
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noncaregiving spouses or caregivers who did not report strain (Schulz et al 2001). On the other hand, Turvey et al (1999) found that reported “expectedness of death” did not significantly predict depression among surviving spouses. Understanding these qualitative differences and the chains of precursors and sequellae may be useful to predicting who is at risk for pathologic grief or might benefit from preventive interventions.
Disability Disability and functional decline in late life generally reflect some combination of physical, cognitive, and psychological impairments (Bruce 2001). They also serve as psychosocial risk factors for late-life depression. Onset disability, for example from a stroke or hip fracture, is a life event profoundly disruptive to previous behavior. Both new and prolonged disabilities bring losses of independence and productivity; in our own study of older, medically ill patients receiving visiting nurse services, some patients express feelings of worthlessness about their new inability to perform role activities. For community-dwelling, older adults, the presence of disabilities (measured by activities of daily living limitations) increases the risk of depression by 4.2 over 1 year, controlling for demographic, medical, and other psychosocial risk factors (Prince et al 1998). In a 3-year follow-up study of depression-free, older residents of Amsterdam, baseline as well as new-onset disability increased the risk of new-onset depression, controlling for past depressive history, chronic disease, and bereavement (Schoevers et al 2000). Homebound status (generally indicating more severe disability and health problems) similarly is associated with increased risk of first onset major depression (Bruce and Hoff 1994). Communitybased, prospective studies similarly report that disability and declining functional status are associated with increased risk of onset or worsening of depressive symptoms, controlling for poor health status and demographic variables (e.g., Geerlings et al 2000; Kennedy et al 1990). These findings are mirrored in case-control studies of depressive disorders in late life (Mazure et al 2002; Murphy 1982). The complexity of disability as a construct, relative to some other psychosocial factors, poses considerable challenges to risk-factor analyses. In particular, disability is a good example of a psychosocial risk factor for depression for which the evidence is equally strong that depression contributes to the risk of disability. Disentangling how their unique contributions to each other is made even more difficult as both share other biological, psychological, and social risk factors. Further, the various dimensions of disability, for example differences in actual versus per-
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ceived behavior or actual behavior versus perceived ability (Bruce 2001), may contribute uniquely to the risk of depression; of particular importance, then, is finding ways to measure and differentiate the unique affect of each aspect of disability on risk of depression. And like each of the other psychosocial risk factors discussed above, disability can contribute to the risk of other risk factors, such as declining health or social isolation, so that understanding the chain of events leading to depression may be important.
Trauma The class of events labeled traumatic events are by definition the most extreme forms of life events. Traumatic events differ from other life events by being outside the scope of common or expected life experience (Beiser 1998). The “horrible and unthinkable” can be categorized on several spectrums, for example the extent to which an event involves full communities (e.g., war) as opposed to being limited to one or a few individuals (e.g., rape), is brief or prolonged, is instigated by humans (e.g., arson) versus those occurring by nature (e.g., tornados), and, if human-made, is intentional (e.g., hi-jackings) as opposed to accidental (e.g., plane crashes). Common wisdom suggests that the large-group, intentional events are the most stressful. Examples include most wars, the Nazi concentration camps, and the terrorist attacks on the World Trade Center. There has been little formal research on the impact of these kinds of traumatic experiences on the lives of older adults. Because the most extreme form of these events are uncommon and unexpected, prospective studies can rarely capture their impact unless a mass event occurs by chance during the course of an ongoing study (Beiser 1998). Anecdotal accounts are contradictory, suggesting either that the life circumstances of many older adults make them particularly vulnerable to traumatic events or that their lifetime of experiences make them particularly resilient. Research into the response of older adults to traumatic events, such as the terrorist attacks of September 11, 2001, may be useful to understanding the vulnerability of older—and younger—adults to adverse outcomes from life events generally.
Social Support Social relationships, ranging from social isolation to social support, have long been implicated in risk of depression. In their review of conceptual and empirical literature on social support and mental health, Turner and Turner (1999) summarize several essential points relevant to studies of risk factors for late life depression. First, social support is a multifactorial construct that involves various
dimensions of perceptions, structure, and behavior. Although most kinds of social support, including help and assistance (e.g., “instrumental support”), may have good consequences on health, the most robust findings involve perceived support, also called emotional support. Second, empirical studies suggest both that social support may directly enhance psychological well-being (and reduce the risk for depression) and that social support may act in the specific context of social stressors by reducing or buffering their risk of depression. In studies of the risk of late life depression, lack of social contact is associated with increased risk of onset depression in some (Bruce and Hoff 1994; Prince et al 1998) but not all (Schoevers et al 2000) longitudinal analyses. Similarly inconsistent findings have been recorded in longitudinal studies of the depressive symptoms (Kennedy et al 1990; Oxman et al 1992). These inconsistent findings point, in part, to the complex nature of social support variables in studies of late life, especially as perceptions of support involve both long-term notions of self in relationship to society as well as a person’s more immediate social circumstances. These social circumstances may fluctuate as friends and family re-locate or die as well as other age-related changes in social networks. The inconsistent findings may also reflect unresolved problems in measuring different aspects of social relationships, including the extent to which social isolation and support are external stressors versus aspects of an individual’s own social functioning.
Modifying Factors Despite the strong evidence that psychosocial factors contribute longitudinally to the risk of depression, the majority of individuals who experience specific psychosocial risk factors do not become depressed. One possible explanation for this observation is that our measures of psychosocial risk factors do not adequately specify the aspect of the factor that increases risk. This explanation is being addressed in an ongoing fashion by efforts to improve measures or further specificity. Another possible explanation is that some risk factors only work— or work more strongly—in the context of another variable. This other variable modifies the risk associated with a psychosocial factor for depression. In statistical terms, modifying factors interact with the psychosocial risk factor on depression risk (Baron and Kenny 1986; Kraemer et al 2001). Modifying variables may conceptually increase the vulnerability of an individual to the impact of the risk factor or buffer its effect. At the very least, knowledge of modifying variables is useful by helping specify more narrowly groups at risk for late life depression. When modifying factors are themselves alter-
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able, they become potential targets for reducing the risk of depression. Many investigators have identified variables that modify the impact of psychosocial risk factors on depression, although fewer of these studies meet the methodological criteria of prospective studies of elderly adults with good measures of depression diagnoses. Putative modifying variables range from genetic indicators to other psychosocial variables. For example, Kendler and colleagues have identified genetic factors that appear to modify the impact of life events on depression risk (Kendler et al 2001). Various studies have indicated that psychosocial factors can have a different risk for depression in women as compared to men. For example, two studies have observed a greater risk of depression associated with marital separation or divorce on men than women (Bruce and Kim 1992; Kendler et al 2001). In their community-based, case-control study, Ormel et al (2001) observed a stronger impact of stressful life events among older adults who also scored high on neuroticism or reported ongoing difficulties. Similarly, in a case-control study of late life depression, Mazure et al (2002) found that stressful life events were associated with depression only when the nature of the event matched the personality type of the individual. In their study, interpersonal events were associated with depression only among individuals who report sociotropy, whereas achievement-related events were associated with depression only among individuals who report autonomy.
Age Differences The focus of this review has been the contribution of psychosocial factors to the risk of depressive disorders in late life. A final question is the extent to which psychosocial risk factors for depression are different— either in terms of exposure or impact—in older compared to younger adults.
Age Variation in the Prevalence of Psychosocial Risk Factors The clearest evidence of an age difference in psychosocial risk factor research is that the distribution of psychosocial factors varies by age. Many of the factors reviewed above reflect experiences and conditions that are more prevalent among the old than the young. Widowhood, medical events, disability, and declining social support systems are not inevitable but are increasingly likely as adults age. In the same vein, this review has not mentioned many of the life events particularly relevant to the circumstances and social roles of younger adults, such as job loss or divorce. The different distribution of specific types of events across age groups underscores the difficulty of constructing a
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meaningful composite measure of life events that could be used across the life course.
Age Variation in the Impact of Psychosocial Risk Factors Less evident is whether or not the impact of psychosocial risk factors differs by age. Several rationales have been offered for possible age differences. One notion is that “age normative” life events have a smaller impact on depression than less normative or expected events. Other hypotheses suggest that the distribution of modifying variables varies by age. For example, advanced age may improve self-esteem and maturation that protects older adults against the impact of psychosocial risk factors (Gove et al 1989). Conversely, aging sometimes results in the loss of a sense of personal control over one’s environment or losses in social support resources, losses that might reduce the ability of older adults to buffer the negative impact of psychosocial risk factors (Krause 1999; Rodin 1986). But the actual data supporting each of these hypotheses are not convincing, in part because the unequal distribution of risk factor by age results in insufficient power to test the hypothesis in some age groups (e.g., widowhood is a rare event in population-based studies of the young) in longitudinal studies of depressive disorders. Perhaps more convincing are the data suggesting that ongoing difficulties have a smaller effect on the risk of depression in older than younger adults. For example, Forsell (2000) found that no baseline psychosocial factor predicted depression after controlling for past history. Similarly, among older adults with no reported depressive symptoms, Kennedy et al (1990) also found that onset depressive symptoms were not associated with baseline (mostly ongoing and persistent) psychosocial factors at baseline but were associated with changes over the follow-up period (e.g., life events). These findings are consistent with the survival hypothesis: as individuals age into late life and survive exposure to long-term, difficult situations without succumbing to depression, the likelihood of a first onset depression associated with those difficulties declines.
Psychosocial Risk Factors: Summary and Challenges for Future Research The research summarized here has identified a number of significant psychosocial risk factors for late life depressive disorders, including life events and ongoing difficulties; death of a spouse or other loved one; medical illness and injuries; disability and functional decline; and lack of social contact. The effects of these psychosocial factors have been demonstrated in well-designed, longitudinal
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studies. For the most part, the effects have persisted while controlling for at least some type of biological or physical health status variable. Preliminary evidence suggests that the impact of these psychosocial risk factors on depression can be enhanced or buffered by personal or environmental factors. Although many of these psychosocial risk factors are more prevalent among older than younger adults, it is not clear that their impact on the risk of depression differs by age. Risk factors suggest but do not confirm a causal relationship. Evidence of risk often poses additional question about the specificity of the relationship (e.g., can the components of the psychosocial variable that contribute to risk be more closely specified?) and the potential causal mechanisms (e.g., what biological, psychological, and social factors mediate the relationships between the observed risk factor and the disease outcomes?). Such questions point to needed additional research. But, as suggested throughout this review, the kinds of data needed to answer this next generation of questions pose a number of research challenges. Perhaps the most concrete hurdle is that risk factor research meeting the rigorous criteria required by observational epidemiology is expensive and time consuming, especially in studies of low incident events, such as first-onset depression in community-dwelling, older adults. Innovative methodologies are needed to advance research on psychosocial risk factors for late life depression. For example, multistage stratified sampling designs that preserve the representativeness of populations but generate samples enriched with high numbers of high-risk subjects generally offer a more economical approach to achieving adequate statistical power than simple random samples (Shrout and Newman 1989). Questions about the pathway between a given risk factor and depression may be appropriately addressed by sampling only from welldefined, high-risk groups—if the sample’s representativeness to their counterparts at the population level can be determined. A possible alternative to observational design is the use of intervention studies to confirm or discover risk factors. In this case, when successful interventions designed to manipulate psychosocial factors (e.g., enhance social contact, reduce disability) further result in changed risk for depression, these findings provide evidence of the contribution of these psychosocial factors to the risk of depression. Again, however, a key step to ensuring that findings from such intervention studies meaningfully contribute to knowledge of psychosocial risk factors is determining the extent to which the study’s sample can be generalized to other populations of older adults. A common theme in discussing each of the psychosocial risk factors is how frequently each is linked in a chain of events. For example, health problems increase the risk
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of disability, which increases the risk of social isolation. These chains are not inevitable, but common enough that it may be useful for investigators to examine the impact of specific sequences of events or different composite sets of conditions on the risk of depression. The inter-relatedness among psychosocial risk factors also has research implications for the timing of follow-up assessments as shorter time spans between assessments may be needed to capture the sequencing of events or the unique impact of each occurrence on the risk of depression. Thorough understanding of the contribution of psychosocial factors to the risk of depression demands more knowledge of how psychosocial factors work with, and in the context of, biological variables. A first and feasible step is the addition of meaningful biological measures to rigorously designed studies of psychosocial risk factors and the addition of meaningful psychosocial risk factors to biological studies. Better measures should make it possible to specify the unique and joint contributions of psychosocial factors with far greater precision than currently possible. Even with better measures, however, our current designs may well not capture some of the long-term relationships between biological and psychosocial factors that lead to depression in late life. Nor do they permit continued investigation into the clinical course and outcomes of these depressions. A more ambitious goal is to add these measures to long-term, population-based studies of midlife or even younger populations, following their risk for depressive disorders into late life as they shape and respond to life’s vicissitudes (Vaillant and Mukamal 2001). Finally, a key motivation for conducting risk factors analyses is to help shape preventive and treatment interventions for depression in late life. The portraits of the frail, socially isolated, medically burdened and disabled older person at risk for depression provide us targets for prevention activities, as well as identify high-risk groups for selective interventions. Can we, for example, reduce the level of disability associated with a progressive medical condition, or at least enhance a person’s psychological capacity to cope with new disability? Armed with knowledge of risk factors for late-life depression, the next challenge is to determine which psychosocial risk factors are malleable enough to serve as intervention targets, determine effective biological or psychosocial approaches to changing these risk factors, and ultimately implement strategies to reduce the burden of late-life depression in this vulnerable population.
This work was supported by grant no. KO2MH01634. Aspects of this work were presented at the conference, “Unmet Needs in Diagnosis and Treatment of Mood Disorders in Late Life,” October 9 –10, 2001 in Washington, DC. The conference was sponsored by the
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National Depressive and Manic-Depressive Association (National DMDA) through unrestricted educational grants provided by Abbott Laboratories, AstraZeneca, Forest Laboratories, GlaxoSmithKline, Janssen Pharmaceutica, Eli Lilly and Company, Merck & Co., National Institute of Mental Health, Organon, Pfizer Inc, and Wyeth-Ayerst Laboratories.
Dohrenwend BS, Krasnoff L, Askenasy AR, Dohrenwend BP (1978): Exemplification of a method for scaling life events: The PERI life events scale. J Health Soc Behav 19:205–229.
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