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A Mixed-Methods Approach to Understanding Loneliness and Depression in Older Adults

A Mixed-Methods Approach to Understanding Loneliness and Depression in Older Adults Abstract Objectives. Depression in late life may be difficult to identify, and older adults often do not accept depression treatment offered. This article describes the methods by which we combined an investigator-defined definition of depression with a person-derived definition of depression in order to understand how older adults and their primary care providers overlapped and diverged in their ideas about depression. Methods. We recruited a purposive sample of 102 persons aged 65 years and older with and without significant depressive symptoms on a standardized assessment scale (Center for Epidemiologic Studies–Depression scale) from primary care practices and interviewed them in their homes. We applied methods derived from anthropology and epidemiology (consensus analysis, semi-structured interviews, and standardized assessments) in order to understand the experience and expression of late-life depression. Results. Loneliness was highly salient to older adults whom we asked to describe a depressed person or themselves when depressed. Older adults viewed loneliness as a precursor to depression, as self-imposed withdrawal, or as an expectation of aging. In structured interviews, loneliness in the week prior to interview was highly associated with depressive symptoms, anxiety, and hopelessness. Discussion. An improved understanding of how older adults view loneliness in relation to depression, derived from multiple methods, may inform clinical practice. THE purpose of this article is to describe ways that a deliberate blending of research methods can provide a better understanding of how older African American and White adults define and express the experience of depression in contrast to clinical definitions of depression. The Sociocultural Context of Depression study used mixed methods to combine an expert-derived definition of depression with a person-derived definition of depression in order to understand more fully ways to identify and treat older adults with depression. A second goal for the study was to compare findings on the experience of depression for older African American and White adults. Quantitative studies of the distribution of depression among older adults measure the duration, number, and presence or absence of symptoms according to a standardized definition of depression. However, quantitative studies might not be able to capture the contextual factors that affect the experience of depression or the meaning that depression has to the older person. Qualitative studies about depression can describe the experience of depression from the point of view of the person, but they cannot describe the distribution, magnitude, or frequency of that experience at a group or population level. By applying quantitative and qualitative approaches, we have constructed a strategy by which researchers can attempt to understand the experience of depression among older adults from different and complementary conceptual frameworks. In this article, we trace the concept of loneliness and its relationship to depression through three different data collection strategies in order to illustrate how research methods from diverse epistemologic traditions yield a more nuanced picture about the experience of depression in older adults than would be gained from a single research method. Depression in Late Life Depression is an important public health problem among older adults in the United States. Indeed, the World Health Organization has projected that major depression will be the second highest cause of disability after heart disease in all countries by 2020 (Murray & Lopez, 1996). According to the Diagnostic and Statistical Manual of Mental Disorders (4th edition; DSM–IV; American Psychiatric Association, 1994), a diagnosis of major depressive disorder (MDD) requires that five or more depressive symptoms be present, and those symptoms must persist for 2 or more weeks. Depressive symptoms include depressed mood, anhedonia (an inability to derive pleasure from pleasurable activities or events), significant change in weight or appetite, changes in sleep patterns, decreased concentration, decreased energy, inappropriate guilt or feelings of worthlessness, psychomotor agitation or retardation, and suicidal ideation. A diagnosis of MDD requires that either depressed mood or anhedonia be present. Among older adults, nonmajor depression (i.e., depression that contains some but not all of the features of MDD described in the DSM–IV) is also an important problem that leads to significant morbidity and disability. Studies have suggested that up to 14% of patients in primary care settings meet criteria for MDD (Leon et al., 1995), whereas estimates of the prevalence of subsyndromal depression among older adults in the primary care setting range as high as four times that of major depression (Gallo & Lebowitz, 1999). Depression that does not meet standard criteria for MDD is of particular importance for older adults because, whereas rates of MDD decline with advancing age, rates of depressive symptoms and suicide increase (Gallo & Lebowitz, 1999). Nonmajor depression is likely to coexist with other medical problems in older adults (Stockton, Gonzales, Stern, & Epstein, 2004; Zung, Broadhead, & Roth, 1993). Thus, persons with depressive symptoms are more likely to seek care from their primary care provider than from specialty mental health providers (Pincus, Davis, & McQueen, 1999), giving the primary care setting an important opportunity for identification of depression. Yet depression in older adults is often unrecognized and untreated or undertreated in the primary care setting. The U.S. Surgeon General has brought attention to the problem of unrecognized and untreated depression among older adults (U.S. Department of Health and Human Services, 1999). Older persons in primary care may not accept the diagnosis or treatment of depression, in part due to beliefs and explanatory models of illness that do not line up with the treatments most commonly offered by primary care physicians. Lack of recognition of depression and acceptance of depression treatment are particular concerns for older African Americans (Cooper-Patrick, Gallo, Gonzales, et al., 1999; Cooper-Patrick, Gallo, Powe, et al., 1999; Gallo, Marino, Ford, & Anthony, 1995). Gallo and colleagues (1995) found that African Americans were significantly less likely than Whites to have consulted with a specialist in mental health, even accounting for coincident psychiatric disorder, gender, and other covariates known to be associated with differential use of health care services. African Americans were less likely than Whites to accept antidepressant medications or individual counseling (Cooper-Patrick, Gallo, Gonzales, et al., 1999). Taken together, these studies highlight the importance of the primary health care sector for mental health care of older persons from minority groups and indicate that sociocultural context may influence how receptive persons from different ethnic groups may be to interventions. Yet, there is evidence from our recent work in the primary care setting that older African American adults must be more depressed and more disabled by their depression than Whites for their primary care doctors to diagnose them with depression (Gallo, Bogner, Morales, & Ford, 2005). Older adults may be inclined to describe depressive symptoms in terms of loneliness. The literature on the relationship between loneliness and depression in older adults emphasizes social support factors (Pinquart & Sorensen, 2001) and the growing number of losses that older adults experience (Alpass & Neville, 2003; Andersson, 1998; van Baarsen, 2002), personality factors such as neuroticism and extraversion or introversion (Long & Martin, 2000; Pinquart & Sorensen, 2001), and differences between the experience of being alone and feeling lonely (Adams, Sanders, & Auth, 2004). Interventions to address loneliness in older adults are aimed at increasing self-efficacy and addressing changing social networks (Blazer, 2002). In summary, depression is an important problem for older African American and White adults. Although older adults do seek care from their primary care providers for medical problems, they may not discuss depressive symptoms with their primary care providers, and they are even less likely to seek specialty mental health care. When older adults do discuss depressive symptoms with their provider, they may be inclined to use terms such as loneliness that are not part of the standard DSM–IV definition of depression. For these reasons, in the Sociocultural Context of Depression study, we wanted to explore the experience of depression among older African American and White adults from the perspective of the older person. Accessing the Emic Perspective Anthropology's insistence on the importance of culture opens up different ways of theorizing the meaning and effect of depression in different patient populations. As anthropologic research has shown, culture is an important determining factor in the experience and expression of psychiatric symptoms (Good, 1992; Kleinman, 1980; Kleinman & Good, 1985). Older adults in the United States represent both an age and cultural cohort with unique life experiences and sets of relationships, attitudes, and orientations toward mental health and health care services (Gallo, Anthony, & Muthen, 1994; Gallo & Lebowitz, 1999). The qualitative paradigm, familiar to anthropologists, uses an inductive approach to uncover principles, theories, patterns, and trends over time in naturally occurring circumstances. As such, a qualitative approach facilitates a more central role for stakeholders in the assessment of their opinions, preferences, and perceptions about mental illness and its treatment (Hohmann, 2002). Classic qualitative research methods such as in-depth informant interviews (Bernard, 2002; Weller, 1998) and newer strategies such as cultural consensus analysis (DeMunck & Sobo, 1998; Dressler, Balieiro, & Santos, 1998; Handwerker & Borgatti, 1998) can be used to elicit definitions and descriptions of depression independent of professionally defined criteria. Key to getting at the root of the difficulty in assessment and treatment of depression in older adults is not just an understanding of the experience of the illness but also an appreciation for what older adults conceive of as “depression” in the first place. Accordingly, we conducted a mixed-methods study that instantiated a “cultural epidemiology” (DiGiacomo, 1999; Weiss, 2001), melding epidemiologic and anthropologic perspectives. Our anthropologic analysis allowed us to access the emic, or insider, perspective of the research participants whereas the epidemiologic approach provided an etic, or external, scientific interpretation of depression. Although both perspectives are important to an understanding of the phenomenon of depression, integrating emic and etic perspectives at multiple levels allowed us to understand more clearly issues related to recognition, treatment, and adherence to treatment for depression. Mixing Methods We wished to demonstrate the utility of invoking both an anthropologic and epidemiologic approach to understanding depression in late life by exploring the way that different dimensions of the concept of loneliness emerged and intersected with concepts about depression by using qualitative and quantitative research strategies at multiple phases within the same study. We chose to focus on loneliness because the respondents in our study gave prominence to loneliness as a component of their view of depression. Because of the mixed design, we were able to use text and numeric data from the same sample to consider the meaning of loneliness to the respondents using methods derived from anthropology—cultural consensus analysis (Borgatti, 1999; Dressler, Baleiro, & Santos, 1999; Matthews, 2000; Romney, 1999; Romney, Batchelder, & Weller, 1987; Romney, Weller, & Batchelder, 1986), semi-structured interviews (Bernard, 1998, 2002; Weller, 1998; Weller & Romney, 1988), and an epidemiological approach using statistical inference based on closed-ended questions (Gallo, 1995). We conclude this article with a discussion of the lessons we have learned about the benefits and pitfalls of this approach to understanding late-life depression and of implications of our findings for intervention design. Methods Study Overview Rather than approach our study with one of several sequential mixed-methods designs (Creswell, 1995; Creswell, Clark, Gutmann, & Hanson, 2003; Creswell, Fetters, & Ivankova, 2004), we mixed quantitative and qualitative methods in an iterative fashion in the research design, interview strategy, analysis, and inference, giving equal weight to both sets of methods. The Sociocultural Context of Depression (“Spectrum II”) built upon survey data collected for a concurrent set of linked studies, entitled the Spectrum of Depression in Late Life (“Spectrum I”), described in the following paragraphs. We designed the Spectrum I studies in order to describe depression in late life that may not meet standard criteria for MDD (Bogner et al., 2004; Gallo, Bogner, Straton, et al., 2005). We screened 2,560 older adults for depression in their primary care doctors' offices. Of these, we asked 773 to participate, and 355 older adults agreed to participate in the Spectrum I studies and completed a baseline in-home assessment. We used standard measures to assess depression, anxiety, hopelessness, daily functioning, cognition, medical conditions, and personality (Table 1). (See Bogner et al., 2004, and Gallo, Bogner, Straton, et al., 2005, for a detailed discussion about the purpose, methods, and sample in Spectrum I.) We administered measures in person at baseline and 12 months post baseline and on the telephone at 3 months post baseline. At the completion of a 2- to 3-hr in-home interview, we asked participants whether they had anything to add about depression that had not been covered in the survey. Fifty-one percent of the respondents said there were additional relevant details about depression they wanted to add that explained or elaborated upon their fixed-choice responses. We viewed this as a signal from the participants that there were important aspects about their experiences with depression that were not being tapped by the original survey. We designed Spectrum II to give respondents an opportunity to express their views about depression and to integrate respondents' views with the structured responses they gave to the fixed-answer questions in Spectrum I. Study Sample We identified participants for Spectrum II (n = 102) from the pool of older adults who had participated in Spectrum I and who had agreed to be contacted and interviewed again. The full sample from Spectrum I included 355 adults aged 65 years and older; because of the sampling strategy, we enriched this sample with persons who scored above the threshold indicating significant depression on a standardized assessment scale, described later. The University of Pennsylvania Institutional Review Board granted permission to recontact and interview Spectrum I participants. We obtained a certificate of confidentiality from the Department of Health and Human Services as an additional confidentiality safeguard. We used a purposive sampling strategy in order to select participants for Spectrum II. We selected at random an initial subsample of 8 participants from the pool of available participants to be interviewed. After reviewing eight transcripts from this initial group, we began to form ideas for selection criteria for the next subsample. In the first group of transcripts, participants expressed a preference for a diagnosis of anxiety over a diagnosis of depression. Some denied the presence of depression but acknowledged the presence of anxiety and the need to treat it. Therefore, the sampling criteria for the next subsample included persons with high or low depression scores and high anxiety scores. Subsequent subsamples, based on ongoing discussion of transcripts, included persons with a family history of depression, persons for whom there was discordance between the level of depressive symptoms and the doctor's opinion about whether the person was depressed, men with relatively good physical functioning, and the oldest participants in Spectrum I. In addition to these sampling criteria, we purposively sampled to achieve an equal number of African American and White participants. Data Collection Our data consisted of both text and numbers, reflecting the differing epistemological frameworks we used. Here we discuss the strategies under three rubrics: freelisting; semi-structured interviews; and structured interviews employing standardized, closed-ended questions tapping into the constructs of interest. Freelisting At the start of each interview we collected freelists for cultural consensus analysis. We asked each respondent to provide a list of words that described “a person who is depressed” and “you when you are depressed, down in the dumps, or blue.” Cultural consensus analysis is a technique concerned with the extent to which members of a group share cultural knowledge within a particular domain (in this instance, depression) (Romney et al., 1986). Cultural consensus analysis gives insight into the cultural variation in the explanatory models used for disease. In this way, researchers can produce an estimate of the preferred cultural model for the domain in question. Coupled with semi-structured interviews, freelisting and cultural consensus analysis can yield a culturally defined conceptualization of depression that can be juxtaposed with responses to standardized instruments. We collected freelists before the semi-structured interview so that they would not be influenced by discussion that emerged during the interview. Semi-structured interviews In the Spectrum II semi-structured interviews we asked participants to respond to vignettes about depression and open-ended questions that were designed to obtain each individual's explanatory model for depression (Kleinman, 1980). As discrepancies between respondents' depression scores and their doctors' evaluation of their depression became apparent, we added questions about whether the respondent felt that their doctor understood how they felt in both a physical and emotional sense (Wittink, Barg, & Gallo, 2006). All interviews were conducted in the respondents' homes by one of four interviewers from a professional research firm who were trained and supervised by project staff. Interviewers digitally recorded the interviews and sent them to a transcriptionist who produced a verbatim recording of the interview. The transcriptionist removed identifying information, such as the names of individuals or places, during the transcription process. The actual identity of the participants and their related personal health information is retained by the research firm and cannot be known by the project investigators. Spectrum I structured interviews During the Spectrum I study, we obtained information on age, gender, self-identified ethnicity, marital status, living arrangements, and level of educational by using standard questions. We assessed depression status with the Center for Epidemiologic Studies–Depression scale. This is a screening instrument consisting of 20 items that is used to assess depressive symptoms (Devins, Orme, Costello, & Yitzchak, 1988; Radloff, 1977). The scale has high internal consistency and reliability, acceptable test–retest stability, and good construct validity in community-based and clinical samples (Weissman, Sholomskas, Pottenger, Prusoff, & Locke, 1977). We assessed anxiety by using the Beck Anxiety Inventory. Researchers developed this instrument in order to measure the severity of anxiety symptoms (Beck, Epstein, Brown, & Steer, 1988). The Beck Anxiety Inventory has been shown to be an appropriate instrument for measuring symptoms of anxiety in elders (Steer, Willman, Kay, & Beck, 1994). We assessed sadness and anhedonia at baseline by using standard questions (Wittchen, 1994). We included the Beck Hopelessness Scale included in the battery of instruments. This scale assesses factors (hopefulness about the future, a sense of giving up, and future anticipation or plans; Beck, Weissman, Lester, & Trexler, 1974) that have been found to be related to suicidal ideation (Hill, Gallagher, Thompson, & Ishida, 1988). We used the Medical Outcomes Study Short Form-36 in order to assess functional status (McHorney, 1996). Data Management and Analysis We used cultural consensus analysis to identify salient terms from the freelists that the respondents generated in order to define depression and to determine the extent to which that definition was shared by members of the group. We examined freelists to identify and group terms with similar meaning (e.g., loneliness and lonely were both entered as the word lonely). We entered freelists into ANTHROPAC (Borgatti, 1996) and used this to calculate a Smith's saliency score. The saliency score is a function of the frequency with which a word is mentioned and the average position on the list where the word falls. Words that are mentioned most frequently and early in most lists obtain the highest saliency scores. We then plotted saliency scores as a scree curve in order to identify natural breaking points in the scores (Cattell, 1965). We considered terms to have a high degree of salience if they fell above a natural breaking point in the scree curve or if they were mentioned spontaneously by at least 20% of the respondents. We derived a cultural consensus about depression by weighting each informant's input according to its relationship to the most likely answer from the group and then aggregating the weighted responses (Borgatti, 1996). ANTHROPAC produced eigenvalues and we used a minimum ratio of 3 to 1 for the first factor in relation to the second factor as a rule of thumb to satisfy the requirement that there be a unified concept about the domain among members of a group. We also used ANTHROPAC in order to generate a score that determined the extent to which each person was similar to other members of the group with respect to their perceptions about the domain of depression (Jaskyte & Dressler, 2004). Next we used grounded theory in our analysis of transcripts from semi-structured interviews. Two team members reviewed each transcript in toto and completed a “within-case” summary for discussion by the study team. Reviewers coded the transcripts broadly in order to identify the answers to four questions: (a) What is the cause of depression? (b) What is it like to be depressed? (c) What should you do for depression? and (d) How will the depression turn out? Other team members reviewed the broadly coded transcript sections, assigning more detailed codes to the text. After coders identified themes, they linked them to their speakers' quantitative attributes. We used the constant comparative method in order to identify themes (Boeije, 2002). For this analysis, we focused our attention on text broadly coded as “What is the cause of depression?” and “What is it like to be depressed?” in order to develop the themes we present here. In order to assess reliability of the team coding, an independent reviewer broadly coded 10 randomly selected transcripts. This assessment of intercoder reliability showed that 90% of the time (92% for “causes of depression” and 89% for “what is depression like”) the study team and the independent reviewer agreed upon codes assigned to the text. Trustworthiness of our data (Morrow, 2005) was accomplished at the data collection phase by regular debriefing of the interviewers to encourage standardized administration of the questions. During data analysis, we used several strategies to address trustworthiness, including immersion in the data through close multiple readings, discussion by the study team, searches for discrepant cases, and peer debriefing. Finally, we examined the extent to which depression measures and several other factors assessed in structured interviews overlapped with a closed-ended question about loneliness (“How often did you feel lonely in the past week?”). We calculated t tests or chi-square statistics as appropriate for continuous or categorical data (statistical analysis). We set alpha at.05 to denote statistical significance, recognizing that tests of statistical significance are approximations that serve as aids to interpretation and inference. Results Sample Characteristics In all, 102 persons participated in Spectrum II. We could not record one interview due to equipment failure and therefore we did not include it in the grounded theory analysis. The first 60 adults interviewed completed freelists (in subsequent interviews, 42 persons completed pile sorts, but we have focused on freelists for this analysis). Table 2 provides respondent characteristics. The characteristics of participants in Spectrum II who participated in qualitative interviews were not statistically significantly different from those of individuals who participated in Spectrum I, except for ethnicity. By design, approximately half of the people who participated in qualitative interviews self-identified as African American (46%)—compared to 34% of the participants in Spectrum I (p =.01)—so that we could explore whether there were ethnic differences in the experience and understanding of depression. Salient Terms Related to Depression We identified salient terms from 60 freelists for responses to “What words would you use to describe a depressed person?” and “What words would you use to describe yourself when you are depressed, down in the dumps, or feeling blue?” The most salient terms used by all 60 respondents to describe a depressed person included lonely, lack of interest, down, sad, and not talkative. By contrast, respondents described themselves when depressed by using the following words: sad, lonely, tired, anxious, depressed, and physical pain.Tables 3 and 4 provide the frequency of words mentioned by respondents, the average position of words on the lists, and saliency scores. Respondents mentioned lonely most frequently to describe a depressed person and to describe one's self when depressed, occurring on average as roughly the second and third word, respectively, in the lists. We should note that although lonely was mentioned by 37% of people describing a depressed person and 30% of those describing themselves when depressed, participants chose this word spontaneously from all possible descriptors, not from a list of limited options. Consensus analysis showed the extent to which individuals in the group agreed upon the set of terms that defined a depressed person and themselves when depressed. The level of agreement was expressed as a knowledge score. The average knowledge score for the definition of a depressed person was 0.864 (SD = 0.056). Examination of the eigenvalues revealed that the ratio of the first factor to the second factor was 41.8 to 1.3. The average knowledge score for the definition of themselves when they are depressed was 0.880 (SD = 0.064). For themselves when they are depressed, the ratio of the first factor to the second factor was 46.9 to 1.2. These eigenvalues indicated that one construct (one factor) underlay the word lists from the respondents and that all respondents generally concurred with that construct (the “one-culture” assumption; Handwerker & Borgatti, 1996, p. 569). The large initial eigenvalue for both freelists indicating one construct or factor (for a depressed person and for themselves when they are depressed) and the essentially equivalent mean knowledge scores on both lists for African American respondents (a depressed person, M =.80, SD =.073; themselves when they are depressed, M =.835, SD =.090) and White respondents (a depressed person, M =.824, SD =.073; themselves when they are depressed, M =.821, SD =.094) indicated that African American and White respondents mapped to the cultural model for depression expressed by all respondents. Salient Themes Related to Depression and Loneliness Because loneliness played such a significant role in the definition of depression in the freelists, we examined the semi-structured interviews in order to better understand the concept of loneliness as it related to older adults' concepts about depression. In all, 74 of 101 participants linked loneliness spontaneously to depression. Participants spoke about loneliness and depression in three ways: loneliness is a natural and inevitable part of aging, lonely people withdraw and are responsible for their loneliness, and loneliness is a gateway to depression. There were no explicit patterns among themes with relation to self-reported ethnicity, marital status, depression status, or gender. Loneliness as a natural part of aging was tied to loss of friends and family, usually through death or abandonment. For example, in response to a vignette, one widowed African American woman who was not depressed said, “I mean, you're older like that, you feel lonely. You don't have no husband, nobody but yourself. All the kids married and you just a lonely person.” Respondents commented that lonely people withdraw and may bring on their own isolation. A married White woman who was not depressed stated, “They feel lonely … They do that to themselves. They want to be alone. I mean, they don't get out and get with other people.” Withdrawal leads to excessive introspection and a consequent lack of productivity or agency. The same White woman said, “I think you need to involve yourself with people. You have to get around people … because more or less you can let it roll off your back when you are involved with other people, you know? But I've cut myself off from everybody. I think that's what happened.” Another White woman who was not depressed commented, “I think when you are lonely, you are more or less focused on yourself, and it makes it worse.” Respondents also saw loneliness as a gateway to depression. Participants viewed depression as a serious outcome to the feeling of loneliness. Implicit in this was the idea that unaddressed loneliness progresses into a more serious state of depression. In response to a vignette about a person who had symptoms of depression, one married White man who was not depressed stated, “[The person in the vignette] is feeling alone and there's nothing, no good reason for him to get out of bed in the morning, which … leads itself to a mental illness problem.” In response to a different vignette, a divorced White woman who was not depressed stated that “because of [the woman in the vignette's] loneliness, she started to isolate herself. She's so lonely; she's kind of pushing herself into that state.” Statistical Analysis of Depression and Loneliness Compared with respondents who did not report having felt lonely in the past week, persons who reported having felt lonely in the week prior to interview were more likely to self-identify as African American, more likely to have attained an educational level less than high school, and less likely to be married (Table 5). In general, persons who reported loneliness were more depressed, anxious, and hopeless, and were more likely to report sadness and anhedonia, than individuals who did not report loneliness. They were also more likely to have Medical Outcomes Study Short Form-36 scores indicating worse functioning. Discussion Our study calls attention to the feeling of loneliness that older adults tie closely to concepts of depression. Loneliness appears to map onto standard assessments of depression and hopelessness. In open-ended interviews, older adults in our study were able to explain how they linked loneliness and depression. Participants did not simply equate depression with loneliness. Instead, they saw loneliness as leading to depression. Older adults in our study drew on their ideas about the meaning of aging in saying that loneliness was closely related to aging. Nevertheless, they saw loneliness as something that was the responsibility of the individual to avoid. Limitations Several study limitations require comment. As with all qualitative data, the nature and amount of information the respondents offered depended upon their interactions with the interviewers, the circumstances surrounding their interview, and their motivations for participating in the study. Although interviewers trained rigorously in the intent of each question, their engagement with each person, their increasing comfort over time with the interview experience, and their own communication styles may have affected the responses in unknown ways. Similarly, because all interviews took place in the respondents' homes where their responses were less tied to their role as patient, participants' responses were likely to be different from responses they might have given in a doctor's office or other location. People who agree to participate in a research study in their home may be lonelier than those who do not agree to participate. Thus it is possible that the importance of the construct of loneliness in the context of depression may have been greater among participants in our study than in the general population. In addition, we must also acknowledge the possibility that respondents may have recalled response categories from the structured survey interviews in Spectrum I and that these recollections may have influenced their responses to the Spectrum II open-ended questions about depression. Finally, all of the respondents resided in or around one large mid-Atlantic city. Geographic location is likely to affect responses to questions about aging, loneliness, and depression. The experience of aging in this region is likely to be different from aging in a geographic location with greater or fewer numbers of older people. Despite these limitations, we believe our study results have both clinical relevance and methodological significance. Linking Loneliness and Depression Results from each of the three data collection strategies in this study provide a different lens for ways that older adults link loneliness and depression (Figure 1). Results from the freelists and consensus analysis emphasize the primary place that loneliness holds in the way that older adults define depression in others and in themselves. Cultural consensus analysis revealed that a single cultural model accounted for the terms provided by participants in the freelisting task. Loneliness was highly salient to the older adults in the sample and was equally salient for African American and White respondents. High consensus, reflected in the knowledge scores about a definition of depression that highlights loneliness, belies a common set of experiences related to the domain of depression. Findings from the grounded theory approach reinforce the importance of loneliness as a construct central to the experience of late-life depression and clarify the meaning of loneliness for older people. Older adults appear to consider loneliness as something that happens with aging or as a precursor to depression. Medication may not seem to be appropriate to an older adult who construes loneliness as related to aging or for whom loneliness has not reached the depressive stage. We plan to test this hypothesis in future work. When depression is experienced as loneliness it may be classified, by older adults and by their doctors, as a normal outcome of aging. As such, depression as loneliness is unlikely to trigger further diagnostic assessment by the primary care physician. Linking loneliness to deliberate social withdrawal implies a negative judgment of those who withdraw and precludes social assistance by family members and friends. Viewing loneliness as a gateway or precursor to depression connotes the idea of a continuum of depression. Loneliness is the mild form, but left unaddressed it can lead to mental illness. Loneliness may be a more acceptable, less stigmatizing way to express depressive symptoms. We found in our survey data that persons who said they had been lonely in the week prior to interview were more likely to be depressed, anxious, and hopeless, and were more likely to report more functional impairment, than persons who did not report loneliness. This is consistent with other studies that have found depression to be highly associated with loneliness (Adams et al., 2004; Eisses et al., 2004; Osborn et al., 2003; Prince, Harwood, Blizard, Thomas, & Mann, 1997; Stek, Gussekloo, Beekman, van Tilberg, & Westendrop, 2004). Although loneliness was reported more commonly among African Americans (56%) than Whites (44%), the difference in proportions did not reach conventional levels of statistical significance. Nevertheless, the observation deserves comment in the light of the freelisting and thematic results. The freelisting results indicate that African American and White respondents defined depression in similar ways and considered loneliness as highly salient in that definition. The lack of any particular pattern in the semi-structured interviews with regard to the meaning of loneliness for African American and White respondents provides further evidence that loneliness as depression does not differ in an obvious way between groups. How, then, to explain our survey findings that African Americans endorse loneliness more often than Whites in this sample? In epidemiologic surveys, African Americans have either the same or lower prevalence of depression as Whites (Gallo, Royall, & Anthony, 1993). Furthermore, African Americans are also less likely than Whites to report sadness on standardized instruments for depression (Cooper et al., 2003; Gallo, Cooper-Patrick, & Lesikar, 1998). So even though the construct of loneliness may have the same relationship to depression for African Americans as for Whites, African Americans may be more likely to assent to loneliness. Perhaps loneliness is felt to be less stigmatizing, or cultural factors may play a role. For example, African Americans generally place great importance on extended family ties (Dressler, Hoeppner, & Pitts, 1985), so the absence of significant social and familial relationships may be experienced as profound loss. Extending Inference Across Conceptual Frameworks So far, we have interpreted the findings from the results of consensus analysis, grounded theory, and statistical analysis of standardized instruments in relation to loneliness and depression as if each were carried out in isolation. The juxtaposition of data collection strategies derived from different epistemological frameworks and illustrated in Figure 1 suggests that in addition to making inferences within rows of the figure, we may make inferences across the rows of the figure (i.e., metainference). Tashakkori defined metainference (or integrated mixed inference) as an inference “developed through the integration of the inferences that are obtained on the basis of QUAL and QUAN strands of a mixed methods study” (Tashakkori & Teddlie, 2003, p. 710). Our study involved both hypothesis generation (induction) and hypothesis testing (deduction), the hallmark of mixed inference (Erzberger & Kelle, 2003; Wittink et al., 2006). Here, we want to step back to consider what inference we draw from our study of loneliness and its relationship with depression. We wish to emphasize that we do not construe our task of metainference as reconciling or triangulating what we have learned from consensus analysis, grounded theory, and statistical analysis of the data. From the outset, our orientation did not focus on pitting one method against another, obviating the need to discuss the results in terms of contrasting methods that reflect an underlying core or latent variable explaining the results. Freelisting allowed the respondents to guide us to the importance of loneliness to their notion of depression. Standardized questionnaires allowed us to observe the overlap among etic-derived constructs. An open-ended interview style allowed respondents to elaborate on how they fit loneliness and depression together, reducing the need for us to speculate from our statistical analysis on the meaning to the respondents of the correlation between loneliness and depression. Specifically, respondents viewed loneliness as a precursor to depression. We think the study explicates how loneliness and depression are related in a more complete, if more complex, way than if we had been tied to one epistemological framework. Findings with respect to ethnicity are an example. If we had employed survey methods only, we would have been tied to reporting increased levels of loneliness in African Americans without the characterization of salience and themes that brought further depth to the analysis across ethnic groups. Methodological Implications Blending of anthropologic and epidemiologic methods is not a matter of simply adding and stirring to mix, so to speak. Rather, throughout this project we had to work through significant theoretical and practical obstacles. Members of the interdisciplinary research team, trained in highly variant schools of epidemiologic, clinical, and anthropologic modes of research, had to settle on a mutually coherent approach to data gathering, analysis, and synthesis. In group meetings, we returned repeatedly to debates over the existence, nature, and status of truth as it pertained to our research. We found that the language we used often sounded similar but meant very different things. We differed at times about the appropriate journals in which to publish, the style in which to write, and the professional meetings at which to present our work. Nevertheless, the conceptual work required for mixed, integrated studies promised to allow both etic and emic perspectives to inform clinical practice, and to allow ways we could use findings like these to design mental health services to be more attentive to the experience and cultural models of older adults. Relevance of a Mixed-Methods Approach for Design of Interventions Recognition and treatment of depression in older adults presents a number of challenges. Key among them is identifying the presence of depressive symptoms along with comorbid illnesses, and, once identified, offering treatment that is acceptable to that individual. If clinicians are to design and present treatments for depression to older adults that are congruent with elders' understanding of depression, obtaining a subjective description of depression and treatment from the older adult who is experiencing it is an important step (Hohmann, 2002). Attending to and interpreting older adults' descriptions of loneliness as a cause of depression and linking it in clinical interactions with resulting depressive symptoms might provide a more culturally appropriate explanation that resonates with older adults' beliefs. Furthermore, such a description provides insight into the personal, experiential, social, and cultural meaning of depression in older adults and adds to the information gained from an epidemiologic approach, which enables researchers to understand the relationship among key variables as they relate to the definition and risk of depression (Rubinstein, 1992). Clinical interventions that address social factors such as social isolation and loneliness, so central to the older adults' understandings of depression, might result in improved engagement with treatment (Wittink et al., 2005). In the face of patient thinking that loneliness is a precursor to depression, treatment that incorporates social factors might be best couched in terms of preventing “progression” to depression. At the same time, clinicians need to recognize that loneliness may be a manifestation of depression and not merely a precursor. It is not clear whether the description of loneliness we have presented is limited to this cohort of older adults born between World War I and World War II. We might speculate that the older persons in our sample aged during a time when families became more geographically disparate. Social institutions and the distribution of social capital have changed such that individuals are more likely to recreate and worship alone (Putnam, 2000). Thus, because the participants linked loneliness inextricably with aging, we might expect that as future cohorts age, they will express similar connections. However, because the experience of loneliness also appears related to social and cultural factors such as housing, interpersonal relationships, and family structure, as these social and cultural institutions evolve, the experience of loneliness in later life may evolve as well. Older adults may describe depressive symptoms in words that are different from those of physicians. Luborsky and Riley asked nursing home residents to talk about what the word depression meant to them (Luborsky & Riley, 1997), finding that participants drew a connection between the experience of depression and the sense of being part of the outside world. Specifically, nondepressed older adults used terms related to mood to describe depression, whereas depressed persons tended to talk about feelings of isolation rather than affect. Social isolation was seen as a cause of depression as discussed by participants in our sample. Loneliness as an “idiom of distress” was also described by O'Nell (1996, p. 77) in her study of depression among the Flathead people of Montana. For members of the Flathead community, loneliness was equated with depression and was experienced as feeling outside of the normative system of social exchange. The salience of loneliness as a signal of depression recalls the notion of anomie described by Durkheim (1951) in 19th century France, in which individuals felt so disengaged from society that they took their own lives. Despite the salience of loneliness to how older adults think about depression, loneliness has not received much attention. Loneliness may interact with depression to increase mortality (Stek et al., 2005) and has been associated with neuroendocrine, cardiovascular, and inflammatory marker responses (Steptoe, Owen, Kunz-Ebrecht, & Brydon, 2004). Loneliness must be considered not just in terms of social isolation, but as an emotional condition in its own right deserving of further study (Cohen, 2000). In summary, our mixed-methods approach to understanding the relationship between loneliness and depression in older adults allows us to make several metainferences. First, it allows us to appreciate the dimensional quality of the construct of loneliness. Loneliness is perceived as both cause and effect of depression. A small amount of it is perceived as normal but unpleasant, whereas a large amount is connected to mental illness. Our approach has also given us insight into the linkages between loneliness and aging stereotypes. Acceptance of loneliness in an older person as “to be expected” minimizes the suffering and morbidity that might accrue from a possibly treatable depression. Decision Editor: Charles F. Longino Jr. Figure 1. Open in new tabDownload slide Overarching conceptual framework, methods, and results. Solid figure to the right represents metainference across frameworks (see text for details). DSM–IV = Diagnostic and Statistical Manual of Mental Disorders (4th edition) Figure 1. Open in new tabDownload slide Overarching conceptual framework, methods, and results. Solid figure to the right represents metainference across frameworks (see text for details). DSM–IV = Diagnostic and Statistical Manual of Mental Disorders (4th edition) Table 1. Standardized Questionnaires Used in Spectrum I. Variable . Initial In-Office Contact . Baseline In-Home Assessment . 3-Month Telephone Assessment . 12-Month In-Home Assessment . Depressive symptoms     Center for Epidemiologic Studies–Depression scale X X X X     Composite International Diagnostic Interview–Depression section X X Somatic illness and function     Medicines and medical conditions X X X     Medical Outcomes Study Short Form-36 X X X Cognitive responses     Beck Anxiety Scale X X     Beck Hopelessness Scale X X Cognition     Mini-Mental State Examination X X     Executive functioning         Initial letter verbal fluency test X X         Trails X X         Clock drawing X X         Digit symbol substitution X X     Telephone Interview for Cognitive Status–modified X     Brief Test of Attention X X     Hopkins Verbal Learning Test X X Self-rated cognition     Memory Functioning Questionnaire X X Personality     NEO Five Factor Inventory X Other measures     Demographic information X     Living arrangements X X     Alcohol, CAGE<--?1--> <--?2-->Questionnaire X X     Use of health care services X X X     Life events X X X     Social network and support X     Family history of dementia/depression X     Apolipoprotein E genotyping X Variable . Initial In-Office Contact . Baseline In-Home Assessment . 3-Month Telephone Assessment . 12-Month In-Home Assessment . Depressive symptoms     Center for Epidemiologic Studies–Depression scale X X X X     Composite International Diagnostic Interview–Depression section X X Somatic illness and function     Medicines and medical conditions X X X     Medical Outcomes Study Short Form-36 X X X Cognitive responses     Beck Anxiety Scale X X     Beck Hopelessness Scale X X Cognition     Mini-Mental State Examination X X     Executive functioning         Initial letter verbal fluency test X X         Trails X X         Clock drawing X X         Digit symbol substitution X X     Telephone Interview for Cognitive Status–modified X     Brief Test of Attention X X     Hopkins Verbal Learning Test X X Self-rated cognition     Memory Functioning Questionnaire X X Personality     NEO Five Factor Inventory X Other measures     Demographic information X     Living arrangements X X     Alcohol, CAGE<--?1--> <--?2-->Questionnaire X X     Use of health care services X X X     Life events X X X     Social network and support X     Family history of dementia/depression X     Apolipoprotein E genotyping X Notes: NEO = neuroticism, extraversion and openness to experience. Open in new tab Table 1. Standardized Questionnaires Used in Spectrum I. Variable . Initial In-Office Contact . Baseline In-Home Assessment . 3-Month Telephone Assessment . 12-Month In-Home Assessment . Depressive symptoms     Center for Epidemiologic Studies–Depression scale X X X X     Composite International Diagnostic Interview–Depression section X X Somatic illness and function     Medicines and medical conditions X X X     Medical Outcomes Study Short Form-36 X X X Cognitive responses     Beck Anxiety Scale X X     Beck Hopelessness Scale X X Cognition     Mini-Mental State Examination X X     Executive functioning         Initial letter verbal fluency test X X         Trails X X         Clock drawing X X         Digit symbol substitution X X     Telephone Interview for Cognitive Status–modified X     Brief Test of Attention X X     Hopkins Verbal Learning Test X X Self-rated cognition     Memory Functioning Questionnaire X X Personality     NEO Five Factor Inventory X Other measures     Demographic information X     Living arrangements X X     Alcohol, CAGE<--?1--> <--?2-->Questionnaire X X     Use of health care services X X X     Life events X X X     Social network and support X     Family history of dementia/depression X     Apolipoprotein E genotyping X Variable . Initial In-Office Contact . Baseline In-Home Assessment . 3-Month Telephone Assessment . 12-Month In-Home Assessment . Depressive symptoms     Center for Epidemiologic Studies–Depression scale X X X X     Composite International Diagnostic Interview–Depression section X X Somatic illness and function     Medicines and medical conditions X X X     Medical Outcomes Study Short Form-36 X X X Cognitive responses     Beck Anxiety Scale X X     Beck Hopelessness Scale X X Cognition     Mini-Mental State Examination X X     Executive functioning         Initial letter verbal fluency test X X         Trails X X         Clock drawing X X         Digit symbol substitution X X     Telephone Interview for Cognitive Status–modified X     Brief Test of Attention X X     Hopkins Verbal Learning Test X X Self-rated cognition     Memory Functioning Questionnaire X X Personality     NEO Five Factor Inventory X Other measures     Demographic information X     Living arrangements X X     Alcohol, CAGE<--?1--> <--?2-->Questionnaire X X     Use of health care services X X X     Life events X X X     Social network and support X     Family history of dementia/depression X     Apolipoprotein E genotyping X Notes: NEO = neuroticism, extraversion and openness to experience. Open in new tab Table 2. Characteristics of Spectrum I Participants Who Did and Did Not Participate in Spectrum II. Characteristic . Spectrum I only (n = 253) . Spectrum II (n = 102) . p . Average age in years, M (SD) 75.1 (5.8) 75.8 (6.4) .310 Female 195 (77) 75 (74) .530 African American 73 (29) 47 (46) .002 Married 96 (38) 45 (44) .320 Less than high school education 106 (42) 38 (37) .380 Center for Epidemiologic Studies–Depression scale score, M (SD) 14.9 (10.8) 14.3 (12.4) .690 Felt lonely in the week prior to interviewa (0 = no days, 3 = all days) 0.9 (1.2) 0.9 (1.2) .970 Lives alone 96 (38) 39 (38) .900 Characteristic . Spectrum I only (n = 253) . Spectrum II (n = 102) . p . Average age in years, M (SD) 75.1 (5.8) 75.8 (6.4) .310 Female 195 (77) 75 (74) .530 African American 73 (29) 47 (46) .002 Married 96 (38) 45 (44) .320 Less than high school education 106 (42) 38 (37) .380 Center for Epidemiologic Studies–Depression scale score, M (SD) 14.9 (10.8) 14.3 (12.4) .690 Felt lonely in the week prior to interviewa (0 = no days, 3 = all days) 0.9 (1.2) 0.9 (1.2) .970 Lives alone 96 (38) 39 (38) .900 Note: SD = standard deviation. All values given are n (%) unless otherwise noted. aWhere 0 = no days, 3 = all days. Open in new tab Table 2. Characteristics of Spectrum I Participants Who Did and Did Not Participate in Spectrum II. Characteristic . Spectrum I only (n = 253) . Spectrum II (n = 102) . p . Average age in years, M (SD) 75.1 (5.8) 75.8 (6.4) .310 Female 195 (77) 75 (74) .530 African American 73 (29) 47 (46) .002 Married 96 (38) 45 (44) .320 Less than high school education 106 (42) 38 (37) .380 Center for Epidemiologic Studies–Depression scale score, M (SD) 14.9 (10.8) 14.3 (12.4) .690 Felt lonely in the week prior to interviewa (0 = no days, 3 = all days) 0.9 (1.2) 0.9 (1.2) .970 Lives alone 96 (38) 39 (38) .900 Characteristic . Spectrum I only (n = 253) . Spectrum II (n = 102) . p . Average age in years, M (SD) 75.1 (5.8) 75.8 (6.4) .310 Female 195 (77) 75 (74) .530 African American 73 (29) 47 (46) .002 Married 96 (38) 45 (44) .320 Less than high school education 106 (42) 38 (37) .380 Center for Epidemiologic Studies–Depression scale score, M (SD) 14.9 (10.8) 14.3 (12.4) .690 Felt lonely in the week prior to interviewa (0 = no days, 3 = all days) 0.9 (1.2) 0.9 (1.2) .970 Lives alone 96 (38) 39 (38) .900 Note: SD = standard deviation. All values given are n (%) unless otherwise noted. aWhere 0 = no days, 3 = all days. Open in new tab Table 3. Salient Words Obtained in the Freelisting Task: “What Words Would You Use to Describe a Depressed Person?”. Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Lonely 1 21 37 2.6 0.266 Lack of interest 2 20 35 4.2 0.188 Down 3 14 25 4.1 0.143 Sad 6 9 16 1.9 0.134 Not talkative 4 12 21 4.2 0.122 Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Lonely 1 21 37 2.6 0.266 Lack of interest 2 20 35 4.2 0.188 Down 3 14 25 4.1 0.143 Sad 6 9 16 1.9 0.134 Not talkative 4 12 21 4.2 0.122 Note: Data from Spectrum II (2002–2004). Open in new tab Table 3. Salient Words Obtained in the Freelisting Task: “What Words Would You Use to Describe a Depressed Person?”. Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Lonely 1 21 37 2.6 0.266 Lack of interest 2 20 35 4.2 0.188 Down 3 14 25 4.1 0.143 Sad 6 9 16 1.9 0.134 Not talkative 4 12 21 4.2 0.122 Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Lonely 1 21 37 2.6 0.266 Lack of interest 2 20 35 4.2 0.188 Down 3 14 25 4.1 0.143 Sad 6 9 16 1.9 0.134 Not talkative 4 12 21 4.2 0.122 Note: Data from Spectrum II (2002–2004). Open in new tab Table 4. Salient Words Obtained in the Freelisting Task: “What Words Would You Use to Describe Yourself When You Are Depressed, Down in the Dumps, or Feeling Blue?”. Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Sad 2 15 26 2.4 0.200 Lonely 1 17 30 3.4 0.193 Tired 3 13 23 3.3 0.161 Anxious 4 11 19 3.4 0.130 Depressed 6 10 18 2.9 0.119 Physical pain 16 6 11 1.5 0.094 Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Sad 2 15 26 2.4 0.200 Lonely 1 17 30 3.4 0.193 Tired 3 13 23 3.3 0.161 Anxious 4 11 19 3.4 0.130 Depressed 6 10 18 2.9 0.119 Physical pain 16 6 11 1.5 0.094 Note: Data from Spectrum II (2002–2004). Open in new tab Table 4. Salient Words Obtained in the Freelisting Task: “What Words Would You Use to Describe Yourself When You Are Depressed, Down in the Dumps, or Feeling Blue?”. Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Sad 2 15 26 2.4 0.200 Lonely 1 17 30 3.4 0.193 Tired 3 13 23 3.3 0.161 Anxious 4 11 19 3.4 0.130 Depressed 6 10 18 2.9 0.119 Physical pain 16 6 11 1.5 0.094 Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Sad 2 15 26 2.4 0.200 Lonely 1 17 30 3.4 0.193 Tired 3 13 23 3.3 0.161 Anxious 4 11 19 3.4 0.130 Depressed 6 10 18 2.9 0.119 Physical pain 16 6 11 1.5 0.094 Note: Data from Spectrum II (2002–2004). Open in new tab Table 5. Relationship Among Loneliness, Personal Characteristics, Depression, and Functioning. Characteristic . Lonely in the Week Prior to Interview (n = 41) . Not Lonely in the Week Prior to Interview (n = 60) . p . Sociodemographic characteristics     Age in years 76.8 (6.4) 75.1 (6.3) .179     Female, n (%) 33 (80) 42 (70) .241     African American, n (%) 23 (56) 23 (38) .080     Less than high school education, n (%) 21 (51) 16 (27) .012     Married, n (%) 11 (27) 34 (57) .003 Psychological status     Center for Epidemiologic Studies–Depression scale modified 22.9 (12.1) 8.2 (8.3) <.001     Beck Anxiety Index 14.0 (10.2) 5.4 (5.3) <.001     Beck Hopelessness Scale 5.6 (4.5) 3.1 (2.3) <.001     Sadness, n (%) 30 (75) 19 (32) <.001     Anhedonia, n (%) 16 (39) 8 (13) .003 Functioning (Medical Outcomes Study Short Form-36)     General health score 41.8 (20.3) 53.8 (17.8) .002     Physical functioning score 48.9 (27.4) 69.6 (28.5) <.001     Social functioning score 55.2 (31.0) 79.8 (25.4) <.001     Body pain score 51.3 (23.6) 56.1 (24.1) .327     Role physical score 29.3 (35.8) 56.3 (39.8) .001     Role emotional score 53.7 (46.5) 88.6 (28.9) .001 Characteristic . Lonely in the Week Prior to Interview (n = 41) . Not Lonely in the Week Prior to Interview (n = 60) . p . Sociodemographic characteristics     Age in years 76.8 (6.4) 75.1 (6.3) .179     Female, n (%) 33 (80) 42 (70) .241     African American, n (%) 23 (56) 23 (38) .080     Less than high school education, n (%) 21 (51) 16 (27) .012     Married, n (%) 11 (27) 34 (57) .003 Psychological status     Center for Epidemiologic Studies–Depression scale modified 22.9 (12.1) 8.2 (8.3) <.001     Beck Anxiety Index 14.0 (10.2) 5.4 (5.3) <.001     Beck Hopelessness Scale 5.6 (4.5) 3.1 (2.3) <.001     Sadness, n (%) 30 (75) 19 (32) <.001     Anhedonia, n (%) 16 (39) 8 (13) .003 Functioning (Medical Outcomes Study Short Form-36)     General health score 41.8 (20.3) 53.8 (17.8) .002     Physical functioning score 48.9 (27.4) 69.6 (28.5) <.001     Social functioning score 55.2 (31.0) 79.8 (25.4) <.001     Body pain score 51.3 (23.6) 56.1 (24.1) .327     Role physical score 29.3 (35.8) 56.3 (39.8) .001     Role emotional score 53.7 (46.5) 88.6 (28.9) .001 Notes: All values given are M (SD) unless otherwise noted. Data from Spectrum I (2001–2003). SD = standard deviation. Open in new tab Table 5. Relationship Among Loneliness, Personal Characteristics, Depression, and Functioning. Characteristic . Lonely in the Week Prior to Interview (n = 41) . Not Lonely in the Week Prior to Interview (n = 60) . p . Sociodemographic characteristics     Age in years 76.8 (6.4) 75.1 (6.3) .179     Female, n (%) 33 (80) 42 (70) .241     African American, n (%) 23 (56) 23 (38) .080     Less than high school education, n (%) 21 (51) 16 (27) .012     Married, n (%) 11 (27) 34 (57) .003 Psychological status     Center for Epidemiologic Studies–Depression scale modified 22.9 (12.1) 8.2 (8.3) <.001     Beck Anxiety Index 14.0 (10.2) 5.4 (5.3) <.001     Beck Hopelessness Scale 5.6 (4.5) 3.1 (2.3) <.001     Sadness, n (%) 30 (75) 19 (32) <.001     Anhedonia, n (%) 16 (39) 8 (13) .003 Functioning (Medical Outcomes Study Short Form-36)     General health score 41.8 (20.3) 53.8 (17.8) .002     Physical functioning score 48.9 (27.4) 69.6 (28.5) <.001     Social functioning score 55.2 (31.0) 79.8 (25.4) <.001     Body pain score 51.3 (23.6) 56.1 (24.1) .327     Role physical score 29.3 (35.8) 56.3 (39.8) .001     Role emotional score 53.7 (46.5) 88.6 (28.9) .001 Characteristic . Lonely in the Week Prior to Interview (n = 41) . Not Lonely in the Week Prior to Interview (n = 60) . p . Sociodemographic characteristics     Age in years 76.8 (6.4) 75.1 (6.3) .179     Female, n (%) 33 (80) 42 (70) .241     African American, n (%) 23 (56) 23 (38) .080     Less than high school education, n (%) 21 (51) 16 (27) .012     Married, n (%) 11 (27) 34 (57) .003 Psychological status     Center for Epidemiologic Studies–Depression scale modified 22.9 (12.1) 8.2 (8.3) <.001     Beck Anxiety Index 14.0 (10.2) 5.4 (5.3) <.001     Beck Hopelessness Scale 5.6 (4.5) 3.1 (2.3) <.001     Sadness, n (%) 30 (75) 19 (32) <.001     Anhedonia, n (%) 16 (39) 8 (13) .003 Functioning (Medical Outcomes Study Short Form-36)     General health score 41.8 (20.3) 53.8 (17.8) .002     Physical functioning score 48.9 (27.4) 69.6 (28.5) <.001     Social functioning score 55.2 (31.0) 79.8 (25.4) <.001     Body pain score 51.3 (23.6) 56.1 (24.1) .327     Role physical score 29.3 (35.8) 56.3 (39.8) .001     Role emotional score 53.7 (46.5) 88.6 (28.9) .001 Notes: All values given are M (SD) unless otherwise noted. Data from Spectrum I (2001–2003). SD = standard deviation. Open in new tab The Spectrum Study was supported by Grants MH62210-01, MH62210-01S1, and MH67077-01 from the National Institute of Mental Health. Dr. Wittink was supported by a National Research Service Award (MH019931-08A1). Dr. Bogner is a Robert Wood Johnson Foundation Clinical Faculty Scholar (2004–2008). Drs. Gallo, Wittink, and Bogner were also supported by National Institute of Mental Health Awards K24 MH070407, K23 MH073658, and K23 MH67671. We would like to thank the anonymous reviewers for their careful reading and helpful suggestions for this article. References Adams, K. B., Sanders, S., Auth, E. A. ( 2004 ). Loneliness and depression in independent living retirement communities: Risk and resilience factors. Aging & Mental Health , 8 , 475 -485. Alpass, F. M., Neville, S. ( 2003 ). Loneliness, health and depression in older males. Aging & Mental Health , 7 , 212 -216. American Psychiatric Association. ( 1994 ). 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Theories on coping with loss: The impact of social support and self-esteem on adjustment to emotional and social loneliness following a partner's death in later life. Journal of Gerontology: Social Sciences , 57B , S33 -S42. Weiss, M. G. ( 2001 ). Cultural epidemiology: An introduction and overview. Anthropology & Medicine , 8 ,(1), 5 -28. Weissman, M. M., Sholomskas, D., Pottenger, M., Prusoff, B. A., Locke, B. Z. ( 1977 ). Assessing depressive symptoms in five psychiatric populations: A validation study. American Journal of Epidemiology , 106 , 203 -214. Weller, S. C. ( 1998 ). Structured interviewing and questionnaire construction. In H. R. Bernard (Ed.), Handbook of methods in cultural anthropology (pp. 365–409). Walnut Creek, CA: AltaMira Press. Weller, S. C., Romney, A. K. ( 1988 ). Systematic data collection. Newbury Park, CA: Sage. Wittchen, H. U. ( 1994 ). Reliability and validity studies of the WHO-Composite International Diagnostic Interview (CIDI): A critical review. Journal of Psychiatric Research , 28 , 57 -84. Wittink, M. N., Barg, F. K., Gallo, J. J. ( 2006 ). The unwritten rules of talking to doctors about depression. Annals of Family Medicine , 4 , 302 -309. Wittink, M. N., Oslin, D., Knott, K. A., Coyne, J. C., Gallo, J. J., Zubritsky, C., et al. ( 2005 ). Personal characteristics and depression-related attitudes of older adults and participation in stages of implementation of a multi-site effectiveness trial (PRISM-E). International Journal of Geriatric Psychiatry , 20 , 927 -937. Zung, W. W., Broadhead, W. E., Roth, M. E. ( 1993 ). Prevalence of depressive symptoms in primary care. Journal of Family Practice , 37 , 337 -344. Author notes Departments of 1Family Medicine and Community Health and 2 Anthropology, University of Pennsylvania, Philadelphia. 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Oxford University Press
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The Gerontological Society of America
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1079-5014
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1758-5368
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10.1093/geronb/61.6.S329
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Abstract

Abstract Objectives. Depression in late life may be difficult to identify, and older adults often do not accept depression treatment offered. This article describes the methods by which we combined an investigator-defined definition of depression with a person-derived definition of depression in order to understand how older adults and their primary care providers overlapped and diverged in their ideas about depression. Methods. We recruited a purposive sample of 102 persons aged 65 years and older with and without significant depressive symptoms on a standardized assessment scale (Center for Epidemiologic Studies–Depression scale) from primary care practices and interviewed them in their homes. We applied methods derived from anthropology and epidemiology (consensus analysis, semi-structured interviews, and standardized assessments) in order to understand the experience and expression of late-life depression. Results. Loneliness was highly salient to older adults whom we asked to describe a depressed person or themselves when depressed. Older adults viewed loneliness as a precursor to depression, as self-imposed withdrawal, or as an expectation of aging. In structured interviews, loneliness in the week prior to interview was highly associated with depressive symptoms, anxiety, and hopelessness. Discussion. An improved understanding of how older adults view loneliness in relation to depression, derived from multiple methods, may inform clinical practice. THE purpose of this article is to describe ways that a deliberate blending of research methods can provide a better understanding of how older African American and White adults define and express the experience of depression in contrast to clinical definitions of depression. The Sociocultural Context of Depression study used mixed methods to combine an expert-derived definition of depression with a person-derived definition of depression in order to understand more fully ways to identify and treat older adults with depression. A second goal for the study was to compare findings on the experience of depression for older African American and White adults. Quantitative studies of the distribution of depression among older adults measure the duration, number, and presence or absence of symptoms according to a standardized definition of depression. However, quantitative studies might not be able to capture the contextual factors that affect the experience of depression or the meaning that depression has to the older person. Qualitative studies about depression can describe the experience of depression from the point of view of the person, but they cannot describe the distribution, magnitude, or frequency of that experience at a group or population level. By applying quantitative and qualitative approaches, we have constructed a strategy by which researchers can attempt to understand the experience of depression among older adults from different and complementary conceptual frameworks. In this article, we trace the concept of loneliness and its relationship to depression through three different data collection strategies in order to illustrate how research methods from diverse epistemologic traditions yield a more nuanced picture about the experience of depression in older adults than would be gained from a single research method. Depression in Late Life Depression is an important public health problem among older adults in the United States. Indeed, the World Health Organization has projected that major depression will be the second highest cause of disability after heart disease in all countries by 2020 (Murray & Lopez, 1996). According to the Diagnostic and Statistical Manual of Mental Disorders (4th edition; DSM–IV; American Psychiatric Association, 1994), a diagnosis of major depressive disorder (MDD) requires that five or more depressive symptoms be present, and those symptoms must persist for 2 or more weeks. Depressive symptoms include depressed mood, anhedonia (an inability to derive pleasure from pleasurable activities or events), significant change in weight or appetite, changes in sleep patterns, decreased concentration, decreased energy, inappropriate guilt or feelings of worthlessness, psychomotor agitation or retardation, and suicidal ideation. A diagnosis of MDD requires that either depressed mood or anhedonia be present. Among older adults, nonmajor depression (i.e., depression that contains some but not all of the features of MDD described in the DSM–IV) is also an important problem that leads to significant morbidity and disability. Studies have suggested that up to 14% of patients in primary care settings meet criteria for MDD (Leon et al., 1995), whereas estimates of the prevalence of subsyndromal depression among older adults in the primary care setting range as high as four times that of major depression (Gallo & Lebowitz, 1999). Depression that does not meet standard criteria for MDD is of particular importance for older adults because, whereas rates of MDD decline with advancing age, rates of depressive symptoms and suicide increase (Gallo & Lebowitz, 1999). Nonmajor depression is likely to coexist with other medical problems in older adults (Stockton, Gonzales, Stern, & Epstein, 2004; Zung, Broadhead, & Roth, 1993). Thus, persons with depressive symptoms are more likely to seek care from their primary care provider than from specialty mental health providers (Pincus, Davis, & McQueen, 1999), giving the primary care setting an important opportunity for identification of depression. Yet depression in older adults is often unrecognized and untreated or undertreated in the primary care setting. The U.S. Surgeon General has brought attention to the problem of unrecognized and untreated depression among older adults (U.S. Department of Health and Human Services, 1999). Older persons in primary care may not accept the diagnosis or treatment of depression, in part due to beliefs and explanatory models of illness that do not line up with the treatments most commonly offered by primary care physicians. Lack of recognition of depression and acceptance of depression treatment are particular concerns for older African Americans (Cooper-Patrick, Gallo, Gonzales, et al., 1999; Cooper-Patrick, Gallo, Powe, et al., 1999; Gallo, Marino, Ford, & Anthony, 1995). Gallo and colleagues (1995) found that African Americans were significantly less likely than Whites to have consulted with a specialist in mental health, even accounting for coincident psychiatric disorder, gender, and other covariates known to be associated with differential use of health care services. African Americans were less likely than Whites to accept antidepressant medications or individual counseling (Cooper-Patrick, Gallo, Gonzales, et al., 1999). Taken together, these studies highlight the importance of the primary health care sector for mental health care of older persons from minority groups and indicate that sociocultural context may influence how receptive persons from different ethnic groups may be to interventions. Yet, there is evidence from our recent work in the primary care setting that older African American adults must be more depressed and more disabled by their depression than Whites for their primary care doctors to diagnose them with depression (Gallo, Bogner, Morales, & Ford, 2005). Older adults may be inclined to describe depressive symptoms in terms of loneliness. The literature on the relationship between loneliness and depression in older adults emphasizes social support factors (Pinquart & Sorensen, 2001) and the growing number of losses that older adults experience (Alpass & Neville, 2003; Andersson, 1998; van Baarsen, 2002), personality factors such as neuroticism and extraversion or introversion (Long & Martin, 2000; Pinquart & Sorensen, 2001), and differences between the experience of being alone and feeling lonely (Adams, Sanders, & Auth, 2004). Interventions to address loneliness in older adults are aimed at increasing self-efficacy and addressing changing social networks (Blazer, 2002). In summary, depression is an important problem for older African American and White adults. Although older adults do seek care from their primary care providers for medical problems, they may not discuss depressive symptoms with their primary care providers, and they are even less likely to seek specialty mental health care. When older adults do discuss depressive symptoms with their provider, they may be inclined to use terms such as loneliness that are not part of the standard DSM–IV definition of depression. For these reasons, in the Sociocultural Context of Depression study, we wanted to explore the experience of depression among older African American and White adults from the perspective of the older person. Accessing the Emic Perspective Anthropology's insistence on the importance of culture opens up different ways of theorizing the meaning and effect of depression in different patient populations. As anthropologic research has shown, culture is an important determining factor in the experience and expression of psychiatric symptoms (Good, 1992; Kleinman, 1980; Kleinman & Good, 1985). Older adults in the United States represent both an age and cultural cohort with unique life experiences and sets of relationships, attitudes, and orientations toward mental health and health care services (Gallo, Anthony, & Muthen, 1994; Gallo & Lebowitz, 1999). The qualitative paradigm, familiar to anthropologists, uses an inductive approach to uncover principles, theories, patterns, and trends over time in naturally occurring circumstances. As such, a qualitative approach facilitates a more central role for stakeholders in the assessment of their opinions, preferences, and perceptions about mental illness and its treatment (Hohmann, 2002). Classic qualitative research methods such as in-depth informant interviews (Bernard, 2002; Weller, 1998) and newer strategies such as cultural consensus analysis (DeMunck & Sobo, 1998; Dressler, Balieiro, & Santos, 1998; Handwerker & Borgatti, 1998) can be used to elicit definitions and descriptions of depression independent of professionally defined criteria. Key to getting at the root of the difficulty in assessment and treatment of depression in older adults is not just an understanding of the experience of the illness but also an appreciation for what older adults conceive of as “depression” in the first place. Accordingly, we conducted a mixed-methods study that instantiated a “cultural epidemiology” (DiGiacomo, 1999; Weiss, 2001), melding epidemiologic and anthropologic perspectives. Our anthropologic analysis allowed us to access the emic, or insider, perspective of the research participants whereas the epidemiologic approach provided an etic, or external, scientific interpretation of depression. Although both perspectives are important to an understanding of the phenomenon of depression, integrating emic and etic perspectives at multiple levels allowed us to understand more clearly issues related to recognition, treatment, and adherence to treatment for depression. Mixing Methods We wished to demonstrate the utility of invoking both an anthropologic and epidemiologic approach to understanding depression in late life by exploring the way that different dimensions of the concept of loneliness emerged and intersected with concepts about depression by using qualitative and quantitative research strategies at multiple phases within the same study. We chose to focus on loneliness because the respondents in our study gave prominence to loneliness as a component of their view of depression. Because of the mixed design, we were able to use text and numeric data from the same sample to consider the meaning of loneliness to the respondents using methods derived from anthropology—cultural consensus analysis (Borgatti, 1999; Dressler, Baleiro, & Santos, 1999; Matthews, 2000; Romney, 1999; Romney, Batchelder, & Weller, 1987; Romney, Weller, & Batchelder, 1986), semi-structured interviews (Bernard, 1998, 2002; Weller, 1998; Weller & Romney, 1988), and an epidemiological approach using statistical inference based on closed-ended questions (Gallo, 1995). We conclude this article with a discussion of the lessons we have learned about the benefits and pitfalls of this approach to understanding late-life depression and of implications of our findings for intervention design. Methods Study Overview Rather than approach our study with one of several sequential mixed-methods designs (Creswell, 1995; Creswell, Clark, Gutmann, & Hanson, 2003; Creswell, Fetters, & Ivankova, 2004), we mixed quantitative and qualitative methods in an iterative fashion in the research design, interview strategy, analysis, and inference, giving equal weight to both sets of methods. The Sociocultural Context of Depression (“Spectrum II”) built upon survey data collected for a concurrent set of linked studies, entitled the Spectrum of Depression in Late Life (“Spectrum I”), described in the following paragraphs. We designed the Spectrum I studies in order to describe depression in late life that may not meet standard criteria for MDD (Bogner et al., 2004; Gallo, Bogner, Straton, et al., 2005). We screened 2,560 older adults for depression in their primary care doctors' offices. Of these, we asked 773 to participate, and 355 older adults agreed to participate in the Spectrum I studies and completed a baseline in-home assessment. We used standard measures to assess depression, anxiety, hopelessness, daily functioning, cognition, medical conditions, and personality (Table 1). (See Bogner et al., 2004, and Gallo, Bogner, Straton, et al., 2005, for a detailed discussion about the purpose, methods, and sample in Spectrum I.) We administered measures in person at baseline and 12 months post baseline and on the telephone at 3 months post baseline. At the completion of a 2- to 3-hr in-home interview, we asked participants whether they had anything to add about depression that had not been covered in the survey. Fifty-one percent of the respondents said there were additional relevant details about depression they wanted to add that explained or elaborated upon their fixed-choice responses. We viewed this as a signal from the participants that there were important aspects about their experiences with depression that were not being tapped by the original survey. We designed Spectrum II to give respondents an opportunity to express their views about depression and to integrate respondents' views with the structured responses they gave to the fixed-answer questions in Spectrum I. Study Sample We identified participants for Spectrum II (n = 102) from the pool of older adults who had participated in Spectrum I and who had agreed to be contacted and interviewed again. The full sample from Spectrum I included 355 adults aged 65 years and older; because of the sampling strategy, we enriched this sample with persons who scored above the threshold indicating significant depression on a standardized assessment scale, described later. The University of Pennsylvania Institutional Review Board granted permission to recontact and interview Spectrum I participants. We obtained a certificate of confidentiality from the Department of Health and Human Services as an additional confidentiality safeguard. We used a purposive sampling strategy in order to select participants for Spectrum II. We selected at random an initial subsample of 8 participants from the pool of available participants to be interviewed. After reviewing eight transcripts from this initial group, we began to form ideas for selection criteria for the next subsample. In the first group of transcripts, participants expressed a preference for a diagnosis of anxiety over a diagnosis of depression. Some denied the presence of depression but acknowledged the presence of anxiety and the need to treat it. Therefore, the sampling criteria for the next subsample included persons with high or low depression scores and high anxiety scores. Subsequent subsamples, based on ongoing discussion of transcripts, included persons with a family history of depression, persons for whom there was discordance between the level of depressive symptoms and the doctor's opinion about whether the person was depressed, men with relatively good physical functioning, and the oldest participants in Spectrum I. In addition to these sampling criteria, we purposively sampled to achieve an equal number of African American and White participants. Data Collection Our data consisted of both text and numbers, reflecting the differing epistemological frameworks we used. Here we discuss the strategies under three rubrics: freelisting; semi-structured interviews; and structured interviews employing standardized, closed-ended questions tapping into the constructs of interest. Freelisting At the start of each interview we collected freelists for cultural consensus analysis. We asked each respondent to provide a list of words that described “a person who is depressed” and “you when you are depressed, down in the dumps, or blue.” Cultural consensus analysis is a technique concerned with the extent to which members of a group share cultural knowledge within a particular domain (in this instance, depression) (Romney et al., 1986). Cultural consensus analysis gives insight into the cultural variation in the explanatory models used for disease. In this way, researchers can produce an estimate of the preferred cultural model for the domain in question. Coupled with semi-structured interviews, freelisting and cultural consensus analysis can yield a culturally defined conceptualization of depression that can be juxtaposed with responses to standardized instruments. We collected freelists before the semi-structured interview so that they would not be influenced by discussion that emerged during the interview. Semi-structured interviews In the Spectrum II semi-structured interviews we asked participants to respond to vignettes about depression and open-ended questions that were designed to obtain each individual's explanatory model for depression (Kleinman, 1980). As discrepancies between respondents' depression scores and their doctors' evaluation of their depression became apparent, we added questions about whether the respondent felt that their doctor understood how they felt in both a physical and emotional sense (Wittink, Barg, & Gallo, 2006). All interviews were conducted in the respondents' homes by one of four interviewers from a professional research firm who were trained and supervised by project staff. Interviewers digitally recorded the interviews and sent them to a transcriptionist who produced a verbatim recording of the interview. The transcriptionist removed identifying information, such as the names of individuals or places, during the transcription process. The actual identity of the participants and their related personal health information is retained by the research firm and cannot be known by the project investigators. Spectrum I structured interviews During the Spectrum I study, we obtained information on age, gender, self-identified ethnicity, marital status, living arrangements, and level of educational by using standard questions. We assessed depression status with the Center for Epidemiologic Studies–Depression scale. This is a screening instrument consisting of 20 items that is used to assess depressive symptoms (Devins, Orme, Costello, & Yitzchak, 1988; Radloff, 1977). The scale has high internal consistency and reliability, acceptable test–retest stability, and good construct validity in community-based and clinical samples (Weissman, Sholomskas, Pottenger, Prusoff, & Locke, 1977). We assessed anxiety by using the Beck Anxiety Inventory. Researchers developed this instrument in order to measure the severity of anxiety symptoms (Beck, Epstein, Brown, & Steer, 1988). The Beck Anxiety Inventory has been shown to be an appropriate instrument for measuring symptoms of anxiety in elders (Steer, Willman, Kay, & Beck, 1994). We assessed sadness and anhedonia at baseline by using standard questions (Wittchen, 1994). We included the Beck Hopelessness Scale included in the battery of instruments. This scale assesses factors (hopefulness about the future, a sense of giving up, and future anticipation or plans; Beck, Weissman, Lester, & Trexler, 1974) that have been found to be related to suicidal ideation (Hill, Gallagher, Thompson, & Ishida, 1988). We used the Medical Outcomes Study Short Form-36 in order to assess functional status (McHorney, 1996). Data Management and Analysis We used cultural consensus analysis to identify salient terms from the freelists that the respondents generated in order to define depression and to determine the extent to which that definition was shared by members of the group. We examined freelists to identify and group terms with similar meaning (e.g., loneliness and lonely were both entered as the word lonely). We entered freelists into ANTHROPAC (Borgatti, 1996) and used this to calculate a Smith's saliency score. The saliency score is a function of the frequency with which a word is mentioned and the average position on the list where the word falls. Words that are mentioned most frequently and early in most lists obtain the highest saliency scores. We then plotted saliency scores as a scree curve in order to identify natural breaking points in the scores (Cattell, 1965). We considered terms to have a high degree of salience if they fell above a natural breaking point in the scree curve or if they were mentioned spontaneously by at least 20% of the respondents. We derived a cultural consensus about depression by weighting each informant's input according to its relationship to the most likely answer from the group and then aggregating the weighted responses (Borgatti, 1996). ANTHROPAC produced eigenvalues and we used a minimum ratio of 3 to 1 for the first factor in relation to the second factor as a rule of thumb to satisfy the requirement that there be a unified concept about the domain among members of a group. We also used ANTHROPAC in order to generate a score that determined the extent to which each person was similar to other members of the group with respect to their perceptions about the domain of depression (Jaskyte & Dressler, 2004). Next we used grounded theory in our analysis of transcripts from semi-structured interviews. Two team members reviewed each transcript in toto and completed a “within-case” summary for discussion by the study team. Reviewers coded the transcripts broadly in order to identify the answers to four questions: (a) What is the cause of depression? (b) What is it like to be depressed? (c) What should you do for depression? and (d) How will the depression turn out? Other team members reviewed the broadly coded transcript sections, assigning more detailed codes to the text. After coders identified themes, they linked them to their speakers' quantitative attributes. We used the constant comparative method in order to identify themes (Boeije, 2002). For this analysis, we focused our attention on text broadly coded as “What is the cause of depression?” and “What is it like to be depressed?” in order to develop the themes we present here. In order to assess reliability of the team coding, an independent reviewer broadly coded 10 randomly selected transcripts. This assessment of intercoder reliability showed that 90% of the time (92% for “causes of depression” and 89% for “what is depression like”) the study team and the independent reviewer agreed upon codes assigned to the text. Trustworthiness of our data (Morrow, 2005) was accomplished at the data collection phase by regular debriefing of the interviewers to encourage standardized administration of the questions. During data analysis, we used several strategies to address trustworthiness, including immersion in the data through close multiple readings, discussion by the study team, searches for discrepant cases, and peer debriefing. Finally, we examined the extent to which depression measures and several other factors assessed in structured interviews overlapped with a closed-ended question about loneliness (“How often did you feel lonely in the past week?”). We calculated t tests or chi-square statistics as appropriate for continuous or categorical data (statistical analysis). We set alpha at.05 to denote statistical significance, recognizing that tests of statistical significance are approximations that serve as aids to interpretation and inference. Results Sample Characteristics In all, 102 persons participated in Spectrum II. We could not record one interview due to equipment failure and therefore we did not include it in the grounded theory analysis. The first 60 adults interviewed completed freelists (in subsequent interviews, 42 persons completed pile sorts, but we have focused on freelists for this analysis). Table 2 provides respondent characteristics. The characteristics of participants in Spectrum II who participated in qualitative interviews were not statistically significantly different from those of individuals who participated in Spectrum I, except for ethnicity. By design, approximately half of the people who participated in qualitative interviews self-identified as African American (46%)—compared to 34% of the participants in Spectrum I (p =.01)—so that we could explore whether there were ethnic differences in the experience and understanding of depression. Salient Terms Related to Depression We identified salient terms from 60 freelists for responses to “What words would you use to describe a depressed person?” and “What words would you use to describe yourself when you are depressed, down in the dumps, or feeling blue?” The most salient terms used by all 60 respondents to describe a depressed person included lonely, lack of interest, down, sad, and not talkative. By contrast, respondents described themselves when depressed by using the following words: sad, lonely, tired, anxious, depressed, and physical pain.Tables 3 and 4 provide the frequency of words mentioned by respondents, the average position of words on the lists, and saliency scores. Respondents mentioned lonely most frequently to describe a depressed person and to describe one's self when depressed, occurring on average as roughly the second and third word, respectively, in the lists. We should note that although lonely was mentioned by 37% of people describing a depressed person and 30% of those describing themselves when depressed, participants chose this word spontaneously from all possible descriptors, not from a list of limited options. Consensus analysis showed the extent to which individuals in the group agreed upon the set of terms that defined a depressed person and themselves when depressed. The level of agreement was expressed as a knowledge score. The average knowledge score for the definition of a depressed person was 0.864 (SD = 0.056). Examination of the eigenvalues revealed that the ratio of the first factor to the second factor was 41.8 to 1.3. The average knowledge score for the definition of themselves when they are depressed was 0.880 (SD = 0.064). For themselves when they are depressed, the ratio of the first factor to the second factor was 46.9 to 1.2. These eigenvalues indicated that one construct (one factor) underlay the word lists from the respondents and that all respondents generally concurred with that construct (the “one-culture” assumption; Handwerker & Borgatti, 1996, p. 569). The large initial eigenvalue for both freelists indicating one construct or factor (for a depressed person and for themselves when they are depressed) and the essentially equivalent mean knowledge scores on both lists for African American respondents (a depressed person, M =.80, SD =.073; themselves when they are depressed, M =.835, SD =.090) and White respondents (a depressed person, M =.824, SD =.073; themselves when they are depressed, M =.821, SD =.094) indicated that African American and White respondents mapped to the cultural model for depression expressed by all respondents. Salient Themes Related to Depression and Loneliness Because loneliness played such a significant role in the definition of depression in the freelists, we examined the semi-structured interviews in order to better understand the concept of loneliness as it related to older adults' concepts about depression. In all, 74 of 101 participants linked loneliness spontaneously to depression. Participants spoke about loneliness and depression in three ways: loneliness is a natural and inevitable part of aging, lonely people withdraw and are responsible for their loneliness, and loneliness is a gateway to depression. There were no explicit patterns among themes with relation to self-reported ethnicity, marital status, depression status, or gender. Loneliness as a natural part of aging was tied to loss of friends and family, usually through death or abandonment. For example, in response to a vignette, one widowed African American woman who was not depressed said, “I mean, you're older like that, you feel lonely. You don't have no husband, nobody but yourself. All the kids married and you just a lonely person.” Respondents commented that lonely people withdraw and may bring on their own isolation. A married White woman who was not depressed stated, “They feel lonely … They do that to themselves. They want to be alone. I mean, they don't get out and get with other people.” Withdrawal leads to excessive introspection and a consequent lack of productivity or agency. The same White woman said, “I think you need to involve yourself with people. You have to get around people … because more or less you can let it roll off your back when you are involved with other people, you know? But I've cut myself off from everybody. I think that's what happened.” Another White woman who was not depressed commented, “I think when you are lonely, you are more or less focused on yourself, and it makes it worse.” Respondents also saw loneliness as a gateway to depression. Participants viewed depression as a serious outcome to the feeling of loneliness. Implicit in this was the idea that unaddressed loneliness progresses into a more serious state of depression. In response to a vignette about a person who had symptoms of depression, one married White man who was not depressed stated, “[The person in the vignette] is feeling alone and there's nothing, no good reason for him to get out of bed in the morning, which … leads itself to a mental illness problem.” In response to a different vignette, a divorced White woman who was not depressed stated that “because of [the woman in the vignette's] loneliness, she started to isolate herself. She's so lonely; she's kind of pushing herself into that state.” Statistical Analysis of Depression and Loneliness Compared with respondents who did not report having felt lonely in the past week, persons who reported having felt lonely in the week prior to interview were more likely to self-identify as African American, more likely to have attained an educational level less than high school, and less likely to be married (Table 5). In general, persons who reported loneliness were more depressed, anxious, and hopeless, and were more likely to report sadness and anhedonia, than individuals who did not report loneliness. They were also more likely to have Medical Outcomes Study Short Form-36 scores indicating worse functioning. Discussion Our study calls attention to the feeling of loneliness that older adults tie closely to concepts of depression. Loneliness appears to map onto standard assessments of depression and hopelessness. In open-ended interviews, older adults in our study were able to explain how they linked loneliness and depression. Participants did not simply equate depression with loneliness. Instead, they saw loneliness as leading to depression. Older adults in our study drew on their ideas about the meaning of aging in saying that loneliness was closely related to aging. Nevertheless, they saw loneliness as something that was the responsibility of the individual to avoid. Limitations Several study limitations require comment. As with all qualitative data, the nature and amount of information the respondents offered depended upon their interactions with the interviewers, the circumstances surrounding their interview, and their motivations for participating in the study. Although interviewers trained rigorously in the intent of each question, their engagement with each person, their increasing comfort over time with the interview experience, and their own communication styles may have affected the responses in unknown ways. Similarly, because all interviews took place in the respondents' homes where their responses were less tied to their role as patient, participants' responses were likely to be different from responses they might have given in a doctor's office or other location. People who agree to participate in a research study in their home may be lonelier than those who do not agree to participate. Thus it is possible that the importance of the construct of loneliness in the context of depression may have been greater among participants in our study than in the general population. In addition, we must also acknowledge the possibility that respondents may have recalled response categories from the structured survey interviews in Spectrum I and that these recollections may have influenced their responses to the Spectrum II open-ended questions about depression. Finally, all of the respondents resided in or around one large mid-Atlantic city. Geographic location is likely to affect responses to questions about aging, loneliness, and depression. The experience of aging in this region is likely to be different from aging in a geographic location with greater or fewer numbers of older people. Despite these limitations, we believe our study results have both clinical relevance and methodological significance. Linking Loneliness and Depression Results from each of the three data collection strategies in this study provide a different lens for ways that older adults link loneliness and depression (Figure 1). Results from the freelists and consensus analysis emphasize the primary place that loneliness holds in the way that older adults define depression in others and in themselves. Cultural consensus analysis revealed that a single cultural model accounted for the terms provided by participants in the freelisting task. Loneliness was highly salient to the older adults in the sample and was equally salient for African American and White respondents. High consensus, reflected in the knowledge scores about a definition of depression that highlights loneliness, belies a common set of experiences related to the domain of depression. Findings from the grounded theory approach reinforce the importance of loneliness as a construct central to the experience of late-life depression and clarify the meaning of loneliness for older people. Older adults appear to consider loneliness as something that happens with aging or as a precursor to depression. Medication may not seem to be appropriate to an older adult who construes loneliness as related to aging or for whom loneliness has not reached the depressive stage. We plan to test this hypothesis in future work. When depression is experienced as loneliness it may be classified, by older adults and by their doctors, as a normal outcome of aging. As such, depression as loneliness is unlikely to trigger further diagnostic assessment by the primary care physician. Linking loneliness to deliberate social withdrawal implies a negative judgment of those who withdraw and precludes social assistance by family members and friends. Viewing loneliness as a gateway or precursor to depression connotes the idea of a continuum of depression. Loneliness is the mild form, but left unaddressed it can lead to mental illness. Loneliness may be a more acceptable, less stigmatizing way to express depressive symptoms. We found in our survey data that persons who said they had been lonely in the week prior to interview were more likely to be depressed, anxious, and hopeless, and were more likely to report more functional impairment, than persons who did not report loneliness. This is consistent with other studies that have found depression to be highly associated with loneliness (Adams et al., 2004; Eisses et al., 2004; Osborn et al., 2003; Prince, Harwood, Blizard, Thomas, & Mann, 1997; Stek, Gussekloo, Beekman, van Tilberg, & Westendrop, 2004). Although loneliness was reported more commonly among African Americans (56%) than Whites (44%), the difference in proportions did not reach conventional levels of statistical significance. Nevertheless, the observation deserves comment in the light of the freelisting and thematic results. The freelisting results indicate that African American and White respondents defined depression in similar ways and considered loneliness as highly salient in that definition. The lack of any particular pattern in the semi-structured interviews with regard to the meaning of loneliness for African American and White respondents provides further evidence that loneliness as depression does not differ in an obvious way between groups. How, then, to explain our survey findings that African Americans endorse loneliness more often than Whites in this sample? In epidemiologic surveys, African Americans have either the same or lower prevalence of depression as Whites (Gallo, Royall, & Anthony, 1993). Furthermore, African Americans are also less likely than Whites to report sadness on standardized instruments for depression (Cooper et al., 2003; Gallo, Cooper-Patrick, & Lesikar, 1998). So even though the construct of loneliness may have the same relationship to depression for African Americans as for Whites, African Americans may be more likely to assent to loneliness. Perhaps loneliness is felt to be less stigmatizing, or cultural factors may play a role. For example, African Americans generally place great importance on extended family ties (Dressler, Hoeppner, & Pitts, 1985), so the absence of significant social and familial relationships may be experienced as profound loss. Extending Inference Across Conceptual Frameworks So far, we have interpreted the findings from the results of consensus analysis, grounded theory, and statistical analysis of standardized instruments in relation to loneliness and depression as if each were carried out in isolation. The juxtaposition of data collection strategies derived from different epistemological frameworks and illustrated in Figure 1 suggests that in addition to making inferences within rows of the figure, we may make inferences across the rows of the figure (i.e., metainference). Tashakkori defined metainference (or integrated mixed inference) as an inference “developed through the integration of the inferences that are obtained on the basis of QUAL and QUAN strands of a mixed methods study” (Tashakkori & Teddlie, 2003, p. 710). Our study involved both hypothesis generation (induction) and hypothesis testing (deduction), the hallmark of mixed inference (Erzberger & Kelle, 2003; Wittink et al., 2006). Here, we want to step back to consider what inference we draw from our study of loneliness and its relationship with depression. We wish to emphasize that we do not construe our task of metainference as reconciling or triangulating what we have learned from consensus analysis, grounded theory, and statistical analysis of the data. From the outset, our orientation did not focus on pitting one method against another, obviating the need to discuss the results in terms of contrasting methods that reflect an underlying core or latent variable explaining the results. Freelisting allowed the respondents to guide us to the importance of loneliness to their notion of depression. Standardized questionnaires allowed us to observe the overlap among etic-derived constructs. An open-ended interview style allowed respondents to elaborate on how they fit loneliness and depression together, reducing the need for us to speculate from our statistical analysis on the meaning to the respondents of the correlation between loneliness and depression. Specifically, respondents viewed loneliness as a precursor to depression. We think the study explicates how loneliness and depression are related in a more complete, if more complex, way than if we had been tied to one epistemological framework. Findings with respect to ethnicity are an example. If we had employed survey methods only, we would have been tied to reporting increased levels of loneliness in African Americans without the characterization of salience and themes that brought further depth to the analysis across ethnic groups. Methodological Implications Blending of anthropologic and epidemiologic methods is not a matter of simply adding and stirring to mix, so to speak. Rather, throughout this project we had to work through significant theoretical and practical obstacles. Members of the interdisciplinary research team, trained in highly variant schools of epidemiologic, clinical, and anthropologic modes of research, had to settle on a mutually coherent approach to data gathering, analysis, and synthesis. In group meetings, we returned repeatedly to debates over the existence, nature, and status of truth as it pertained to our research. We found that the language we used often sounded similar but meant very different things. We differed at times about the appropriate journals in which to publish, the style in which to write, and the professional meetings at which to present our work. Nevertheless, the conceptual work required for mixed, integrated studies promised to allow both etic and emic perspectives to inform clinical practice, and to allow ways we could use findings like these to design mental health services to be more attentive to the experience and cultural models of older adults. Relevance of a Mixed-Methods Approach for Design of Interventions Recognition and treatment of depression in older adults presents a number of challenges. Key among them is identifying the presence of depressive symptoms along with comorbid illnesses, and, once identified, offering treatment that is acceptable to that individual. If clinicians are to design and present treatments for depression to older adults that are congruent with elders' understanding of depression, obtaining a subjective description of depression and treatment from the older adult who is experiencing it is an important step (Hohmann, 2002). Attending to and interpreting older adults' descriptions of loneliness as a cause of depression and linking it in clinical interactions with resulting depressive symptoms might provide a more culturally appropriate explanation that resonates with older adults' beliefs. Furthermore, such a description provides insight into the personal, experiential, social, and cultural meaning of depression in older adults and adds to the information gained from an epidemiologic approach, which enables researchers to understand the relationship among key variables as they relate to the definition and risk of depression (Rubinstein, 1992). Clinical interventions that address social factors such as social isolation and loneliness, so central to the older adults' understandings of depression, might result in improved engagement with treatment (Wittink et al., 2005). In the face of patient thinking that loneliness is a precursor to depression, treatment that incorporates social factors might be best couched in terms of preventing “progression” to depression. At the same time, clinicians need to recognize that loneliness may be a manifestation of depression and not merely a precursor. It is not clear whether the description of loneliness we have presented is limited to this cohort of older adults born between World War I and World War II. We might speculate that the older persons in our sample aged during a time when families became more geographically disparate. Social institutions and the distribution of social capital have changed such that individuals are more likely to recreate and worship alone (Putnam, 2000). Thus, because the participants linked loneliness inextricably with aging, we might expect that as future cohorts age, they will express similar connections. However, because the experience of loneliness also appears related to social and cultural factors such as housing, interpersonal relationships, and family structure, as these social and cultural institutions evolve, the experience of loneliness in later life may evolve as well. Older adults may describe depressive symptoms in words that are different from those of physicians. Luborsky and Riley asked nursing home residents to talk about what the word depression meant to them (Luborsky & Riley, 1997), finding that participants drew a connection between the experience of depression and the sense of being part of the outside world. Specifically, nondepressed older adults used terms related to mood to describe depression, whereas depressed persons tended to talk about feelings of isolation rather than affect. Social isolation was seen as a cause of depression as discussed by participants in our sample. Loneliness as an “idiom of distress” was also described by O'Nell (1996, p. 77) in her study of depression among the Flathead people of Montana. For members of the Flathead community, loneliness was equated with depression and was experienced as feeling outside of the normative system of social exchange. The salience of loneliness as a signal of depression recalls the notion of anomie described by Durkheim (1951) in 19th century France, in which individuals felt so disengaged from society that they took their own lives. Despite the salience of loneliness to how older adults think about depression, loneliness has not received much attention. Loneliness may interact with depression to increase mortality (Stek et al., 2005) and has been associated with neuroendocrine, cardiovascular, and inflammatory marker responses (Steptoe, Owen, Kunz-Ebrecht, & Brydon, 2004). Loneliness must be considered not just in terms of social isolation, but as an emotional condition in its own right deserving of further study (Cohen, 2000). In summary, our mixed-methods approach to understanding the relationship between loneliness and depression in older adults allows us to make several metainferences. First, it allows us to appreciate the dimensional quality of the construct of loneliness. Loneliness is perceived as both cause and effect of depression. A small amount of it is perceived as normal but unpleasant, whereas a large amount is connected to mental illness. Our approach has also given us insight into the linkages between loneliness and aging stereotypes. Acceptance of loneliness in an older person as “to be expected” minimizes the suffering and morbidity that might accrue from a possibly treatable depression. Decision Editor: Charles F. Longino Jr. Figure 1. Open in new tabDownload slide Overarching conceptual framework, methods, and results. Solid figure to the right represents metainference across frameworks (see text for details). DSM–IV = Diagnostic and Statistical Manual of Mental Disorders (4th edition) Figure 1. Open in new tabDownload slide Overarching conceptual framework, methods, and results. Solid figure to the right represents metainference across frameworks (see text for details). DSM–IV = Diagnostic and Statistical Manual of Mental Disorders (4th edition) Table 1. Standardized Questionnaires Used in Spectrum I. Variable . Initial In-Office Contact . Baseline In-Home Assessment . 3-Month Telephone Assessment . 12-Month In-Home Assessment . Depressive symptoms     Center for Epidemiologic Studies–Depression scale X X X X     Composite International Diagnostic Interview–Depression section X X Somatic illness and function     Medicines and medical conditions X X X     Medical Outcomes Study Short Form-36 X X X Cognitive responses     Beck Anxiety Scale X X     Beck Hopelessness Scale X X Cognition     Mini-Mental State Examination X X     Executive functioning         Initial letter verbal fluency test X X         Trails X X         Clock drawing X X         Digit symbol substitution X X     Telephone Interview for Cognitive Status–modified X     Brief Test of Attention X X     Hopkins Verbal Learning Test X X Self-rated cognition     Memory Functioning Questionnaire X X Personality     NEO Five Factor Inventory X Other measures     Demographic information X     Living arrangements X X     Alcohol, CAGE<--?1--> <--?2-->Questionnaire X X     Use of health care services X X X     Life events X X X     Social network and support X     Family history of dementia/depression X     Apolipoprotein E genotyping X Variable . Initial In-Office Contact . Baseline In-Home Assessment . 3-Month Telephone Assessment . 12-Month In-Home Assessment . Depressive symptoms     Center for Epidemiologic Studies–Depression scale X X X X     Composite International Diagnostic Interview–Depression section X X Somatic illness and function     Medicines and medical conditions X X X     Medical Outcomes Study Short Form-36 X X X Cognitive responses     Beck Anxiety Scale X X     Beck Hopelessness Scale X X Cognition     Mini-Mental State Examination X X     Executive functioning         Initial letter verbal fluency test X X         Trails X X         Clock drawing X X         Digit symbol substitution X X     Telephone Interview for Cognitive Status–modified X     Brief Test of Attention X X     Hopkins Verbal Learning Test X X Self-rated cognition     Memory Functioning Questionnaire X X Personality     NEO Five Factor Inventory X Other measures     Demographic information X     Living arrangements X X     Alcohol, CAGE<--?1--> <--?2-->Questionnaire X X     Use of health care services X X X     Life events X X X     Social network and support X     Family history of dementia/depression X     Apolipoprotein E genotyping X Notes: NEO = neuroticism, extraversion and openness to experience. Open in new tab Table 1. Standardized Questionnaires Used in Spectrum I. Variable . Initial In-Office Contact . Baseline In-Home Assessment . 3-Month Telephone Assessment . 12-Month In-Home Assessment . Depressive symptoms     Center for Epidemiologic Studies–Depression scale X X X X     Composite International Diagnostic Interview–Depression section X X Somatic illness and function     Medicines and medical conditions X X X     Medical Outcomes Study Short Form-36 X X X Cognitive responses     Beck Anxiety Scale X X     Beck Hopelessness Scale X X Cognition     Mini-Mental State Examination X X     Executive functioning         Initial letter verbal fluency test X X         Trails X X         Clock drawing X X         Digit symbol substitution X X     Telephone Interview for Cognitive Status–modified X     Brief Test of Attention X X     Hopkins Verbal Learning Test X X Self-rated cognition     Memory Functioning Questionnaire X X Personality     NEO Five Factor Inventory X Other measures     Demographic information X     Living arrangements X X     Alcohol, CAGE<--?1--> <--?2-->Questionnaire X X     Use of health care services X X X     Life events X X X     Social network and support X     Family history of dementia/depression X     Apolipoprotein E genotyping X Variable . Initial In-Office Contact . Baseline In-Home Assessment . 3-Month Telephone Assessment . 12-Month In-Home Assessment . Depressive symptoms     Center for Epidemiologic Studies–Depression scale X X X X     Composite International Diagnostic Interview–Depression section X X Somatic illness and function     Medicines and medical conditions X X X     Medical Outcomes Study Short Form-36 X X X Cognitive responses     Beck Anxiety Scale X X     Beck Hopelessness Scale X X Cognition     Mini-Mental State Examination X X     Executive functioning         Initial letter verbal fluency test X X         Trails X X         Clock drawing X X         Digit symbol substitution X X     Telephone Interview for Cognitive Status–modified X     Brief Test of Attention X X     Hopkins Verbal Learning Test X X Self-rated cognition     Memory Functioning Questionnaire X X Personality     NEO Five Factor Inventory X Other measures     Demographic information X     Living arrangements X X     Alcohol, CAGE<--?1--> <--?2-->Questionnaire X X     Use of health care services X X X     Life events X X X     Social network and support X     Family history of dementia/depression X     Apolipoprotein E genotyping X Notes: NEO = neuroticism, extraversion and openness to experience. Open in new tab Table 2. Characteristics of Spectrum I Participants Who Did and Did Not Participate in Spectrum II. Characteristic . Spectrum I only (n = 253) . Spectrum II (n = 102) . p . Average age in years, M (SD) 75.1 (5.8) 75.8 (6.4) .310 Female 195 (77) 75 (74) .530 African American 73 (29) 47 (46) .002 Married 96 (38) 45 (44) .320 Less than high school education 106 (42) 38 (37) .380 Center for Epidemiologic Studies–Depression scale score, M (SD) 14.9 (10.8) 14.3 (12.4) .690 Felt lonely in the week prior to interviewa (0 = no days, 3 = all days) 0.9 (1.2) 0.9 (1.2) .970 Lives alone 96 (38) 39 (38) .900 Characteristic . Spectrum I only (n = 253) . Spectrum II (n = 102) . p . Average age in years, M (SD) 75.1 (5.8) 75.8 (6.4) .310 Female 195 (77) 75 (74) .530 African American 73 (29) 47 (46) .002 Married 96 (38) 45 (44) .320 Less than high school education 106 (42) 38 (37) .380 Center for Epidemiologic Studies–Depression scale score, M (SD) 14.9 (10.8) 14.3 (12.4) .690 Felt lonely in the week prior to interviewa (0 = no days, 3 = all days) 0.9 (1.2) 0.9 (1.2) .970 Lives alone 96 (38) 39 (38) .900 Note: SD = standard deviation. All values given are n (%) unless otherwise noted. aWhere 0 = no days, 3 = all days. Open in new tab Table 2. Characteristics of Spectrum I Participants Who Did and Did Not Participate in Spectrum II. Characteristic . Spectrum I only (n = 253) . Spectrum II (n = 102) . p . Average age in years, M (SD) 75.1 (5.8) 75.8 (6.4) .310 Female 195 (77) 75 (74) .530 African American 73 (29) 47 (46) .002 Married 96 (38) 45 (44) .320 Less than high school education 106 (42) 38 (37) .380 Center for Epidemiologic Studies–Depression scale score, M (SD) 14.9 (10.8) 14.3 (12.4) .690 Felt lonely in the week prior to interviewa (0 = no days, 3 = all days) 0.9 (1.2) 0.9 (1.2) .970 Lives alone 96 (38) 39 (38) .900 Characteristic . Spectrum I only (n = 253) . Spectrum II (n = 102) . p . Average age in years, M (SD) 75.1 (5.8) 75.8 (6.4) .310 Female 195 (77) 75 (74) .530 African American 73 (29) 47 (46) .002 Married 96 (38) 45 (44) .320 Less than high school education 106 (42) 38 (37) .380 Center for Epidemiologic Studies–Depression scale score, M (SD) 14.9 (10.8) 14.3 (12.4) .690 Felt lonely in the week prior to interviewa (0 = no days, 3 = all days) 0.9 (1.2) 0.9 (1.2) .970 Lives alone 96 (38) 39 (38) .900 Note: SD = standard deviation. All values given are n (%) unless otherwise noted. aWhere 0 = no days, 3 = all days. Open in new tab Table 3. Salient Words Obtained in the Freelisting Task: “What Words Would You Use to Describe a Depressed Person?”. Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Lonely 1 21 37 2.6 0.266 Lack of interest 2 20 35 4.2 0.188 Down 3 14 25 4.1 0.143 Sad 6 9 16 1.9 0.134 Not talkative 4 12 21 4.2 0.122 Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Lonely 1 21 37 2.6 0.266 Lack of interest 2 20 35 4.2 0.188 Down 3 14 25 4.1 0.143 Sad 6 9 16 1.9 0.134 Not talkative 4 12 21 4.2 0.122 Note: Data from Spectrum II (2002–2004). Open in new tab Table 3. Salient Words Obtained in the Freelisting Task: “What Words Would You Use to Describe a Depressed Person?”. Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Lonely 1 21 37 2.6 0.266 Lack of interest 2 20 35 4.2 0.188 Down 3 14 25 4.1 0.143 Sad 6 9 16 1.9 0.134 Not talkative 4 12 21 4.2 0.122 Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Lonely 1 21 37 2.6 0.266 Lack of interest 2 20 35 4.2 0.188 Down 3 14 25 4.1 0.143 Sad 6 9 16 1.9 0.134 Not talkative 4 12 21 4.2 0.122 Note: Data from Spectrum II (2002–2004). Open in new tab Table 4. Salient Words Obtained in the Freelisting Task: “What Words Would You Use to Describe Yourself When You Are Depressed, Down in the Dumps, or Feeling Blue?”. Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Sad 2 15 26 2.4 0.200 Lonely 1 17 30 3.4 0.193 Tired 3 13 23 3.3 0.161 Anxious 4 11 19 3.4 0.130 Depressed 6 10 18 2.9 0.119 Physical pain 16 6 11 1.5 0.094 Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Sad 2 15 26 2.4 0.200 Lonely 1 17 30 3.4 0.193 Tired 3 13 23 3.3 0.161 Anxious 4 11 19 3.4 0.130 Depressed 6 10 18 2.9 0.119 Physical pain 16 6 11 1.5 0.094 Note: Data from Spectrum II (2002–2004). Open in new tab Table 4. Salient Words Obtained in the Freelisting Task: “What Words Would You Use to Describe Yourself When You Are Depressed, Down in the Dumps, or Feeling Blue?”. Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Sad 2 15 26 2.4 0.200 Lonely 1 17 30 3.4 0.193 Tired 3 13 23 3.3 0.161 Anxious 4 11 19 3.4 0.130 Depressed 6 10 18 2.9 0.119 Physical pain 16 6 11 1.5 0.094 Word From Respondent . Unweighted Rank . Persons who Listed Word (n) . Respondents Who Listed Word (%) . Average Position in Freelists . Smith's Saliency score . Sad 2 15 26 2.4 0.200 Lonely 1 17 30 3.4 0.193 Tired 3 13 23 3.3 0.161 Anxious 4 11 19 3.4 0.130 Depressed 6 10 18 2.9 0.119 Physical pain 16 6 11 1.5 0.094 Note: Data from Spectrum II (2002–2004). Open in new tab Table 5. Relationship Among Loneliness, Personal Characteristics, Depression, and Functioning. Characteristic . Lonely in the Week Prior to Interview (n = 41) . Not Lonely in the Week Prior to Interview (n = 60) . p . Sociodemographic characteristics     Age in years 76.8 (6.4) 75.1 (6.3) .179     Female, n (%) 33 (80) 42 (70) .241     African American, n (%) 23 (56) 23 (38) .080     Less than high school education, n (%) 21 (51) 16 (27) .012     Married, n (%) 11 (27) 34 (57) .003 Psychological status     Center for Epidemiologic Studies–Depression scale modified 22.9 (12.1) 8.2 (8.3) <.001     Beck Anxiety Index 14.0 (10.2) 5.4 (5.3) <.001     Beck Hopelessness Scale 5.6 (4.5) 3.1 (2.3) <.001     Sadness, n (%) 30 (75) 19 (32) <.001     Anhedonia, n (%) 16 (39) 8 (13) .003 Functioning (Medical Outcomes Study Short Form-36)     General health score 41.8 (20.3) 53.8 (17.8) .002     Physical functioning score 48.9 (27.4) 69.6 (28.5) <.001     Social functioning score 55.2 (31.0) 79.8 (25.4) <.001     Body pain score 51.3 (23.6) 56.1 (24.1) .327     Role physical score 29.3 (35.8) 56.3 (39.8) .001     Role emotional score 53.7 (46.5) 88.6 (28.9) .001 Characteristic . Lonely in the Week Prior to Interview (n = 41) . Not Lonely in the Week Prior to Interview (n = 60) . p . Sociodemographic characteristics     Age in years 76.8 (6.4) 75.1 (6.3) .179     Female, n (%) 33 (80) 42 (70) .241     African American, n (%) 23 (56) 23 (38) .080     Less than high school education, n (%) 21 (51) 16 (27) .012     Married, n (%) 11 (27) 34 (57) .003 Psychological status     Center for Epidemiologic Studies–Depression scale modified 22.9 (12.1) 8.2 (8.3) <.001     Beck Anxiety Index 14.0 (10.2) 5.4 (5.3) <.001     Beck Hopelessness Scale 5.6 (4.5) 3.1 (2.3) <.001     Sadness, n (%) 30 (75) 19 (32) <.001     Anhedonia, n (%) 16 (39) 8 (13) .003 Functioning (Medical Outcomes Study Short Form-36)     General health score 41.8 (20.3) 53.8 (17.8) .002     Physical functioning score 48.9 (27.4) 69.6 (28.5) <.001     Social functioning score 55.2 (31.0) 79.8 (25.4) <.001     Body pain score 51.3 (23.6) 56.1 (24.1) .327     Role physical score 29.3 (35.8) 56.3 (39.8) .001     Role emotional score 53.7 (46.5) 88.6 (28.9) .001 Notes: All values given are M (SD) unless otherwise noted. Data from Spectrum I (2001–2003). SD = standard deviation. Open in new tab Table 5. Relationship Among Loneliness, Personal Characteristics, Depression, and Functioning. Characteristic . Lonely in the Week Prior to Interview (n = 41) . Not Lonely in the Week Prior to Interview (n = 60) . p . Sociodemographic characteristics     Age in years 76.8 (6.4) 75.1 (6.3) .179     Female, n (%) 33 (80) 42 (70) .241     African American, n (%) 23 (56) 23 (38) .080     Less than high school education, n (%) 21 (51) 16 (27) .012     Married, n (%) 11 (27) 34 (57) .003 Psychological status     Center for Epidemiologic Studies–Depression scale modified 22.9 (12.1) 8.2 (8.3) <.001     Beck Anxiety Index 14.0 (10.2) 5.4 (5.3) <.001     Beck Hopelessness Scale 5.6 (4.5) 3.1 (2.3) <.001     Sadness, n (%) 30 (75) 19 (32) <.001     Anhedonia, n (%) 16 (39) 8 (13) .003 Functioning (Medical Outcomes Study Short Form-36)     General health score 41.8 (20.3) 53.8 (17.8) .002     Physical functioning score 48.9 (27.4) 69.6 (28.5) <.001     Social functioning score 55.2 (31.0) 79.8 (25.4) <.001     Body pain score 51.3 (23.6) 56.1 (24.1) .327     Role physical score 29.3 (35.8) 56.3 (39.8) .001     Role emotional score 53.7 (46.5) 88.6 (28.9) .001 Characteristic . Lonely in the Week Prior to Interview (n = 41) . Not Lonely in the Week Prior to Interview (n = 60) . p . Sociodemographic characteristics     Age in years 76.8 (6.4) 75.1 (6.3) .179     Female, n (%) 33 (80) 42 (70) .241     African American, n (%) 23 (56) 23 (38) .080     Less than high school education, n (%) 21 (51) 16 (27) .012     Married, n (%) 11 (27) 34 (57) .003 Psychological status     Center for Epidemiologic Studies–Depression scale modified 22.9 (12.1) 8.2 (8.3) <.001     Beck Anxiety Index 14.0 (10.2) 5.4 (5.3) <.001     Beck Hopelessness Scale 5.6 (4.5) 3.1 (2.3) <.001     Sadness, n (%) 30 (75) 19 (32) <.001     Anhedonia, n (%) 16 (39) 8 (13) .003 Functioning (Medical Outcomes Study Short Form-36)     General health score 41.8 (20.3) 53.8 (17.8) .002     Physical functioning score 48.9 (27.4) 69.6 (28.5) <.001     Social functioning score 55.2 (31.0) 79.8 (25.4) <.001     Body pain score 51.3 (23.6) 56.1 (24.1) .327     Role physical score 29.3 (35.8) 56.3 (39.8) .001     Role emotional score 53.7 (46.5) 88.6 (28.9) .001 Notes: All values given are M (SD) unless otherwise noted. Data from Spectrum I (2001–2003). SD = standard deviation. Open in new tab The Spectrum Study was supported by Grants MH62210-01, MH62210-01S1, and MH67077-01 from the National Institute of Mental Health. Dr. Wittink was supported by a National Research Service Award (MH019931-08A1). Dr. Bogner is a Robert Wood Johnson Foundation Clinical Faculty Scholar (2004–2008). Drs. Gallo, Wittink, and Bogner were also supported by National Institute of Mental Health Awards K24 MH070407, K23 MH073658, and K23 MH67671. We would like to thank the anonymous reviewers for their careful reading and helpful suggestions for this article. References Adams, K. B., Sanders, S., Auth, E. A. ( 2004 ). Loneliness and depression in independent living retirement communities: Risk and resilience factors. Aging & Mental Health , 8 , 475 -485. Alpass, F. M., Neville, S. ( 2003 ). Loneliness, health and depression in older males. Aging & Mental Health , 7 , 212 -216. American Psychiatric Association. ( 1994 ). 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Journal

The Journals of Gerontology Series B: Psychological Sciences and Social SciencesOxford University Press

Published: Nov 1, 2006

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