Stressors, Coping Resources, and Depressive Symptoms

Stressors, Coping Resources, and Depressive Symptoms
among Rural American Indian Older Adults
Soonhee Roh
Department of Social Work, University of South Dakota, Sioux Falls, South Dakota, USA
Kathleen A. Brown-Rice
Division of Counseling and Psychology in Education, University of South Dakota, Vermilion,
South Dakota, USA
Kyoung Hag Lee
Wichita State University, Social Work, Wichita, Kansas, USA
Yeon-Shim Lee
San Francisco State University, Social Work, San Francisco, California, USA
Michael J. Lawler
University of South Dakota, School of Health Sciences, Vermilion, South Dakota, USA
James I. Martin
New York University, Social Work, New York, New York, USA
The purpose of this study was to examine the associations of physical health stressors and coping
resources with depressive symptoms among American Indian older adults age 50 years or older. The
study used a convenience sample of 227 rural American Indian older adults. A hierarchical multiple
regression tested three sets of predictors on depressive symptoms: (a) sociodemographics, (b) physical
health stressors (functional disability and chronic medical conditions), and (c) coping resources (social
support and spirituality). Most participants reported little difficulty in performing daily activities (e.g.,
eating, dressing, traveling, and managing money), while presenting over two types of chronic medical
conditions. Depressive symptoms were predicted by higher scores on perceived social support and lower
scores on functional disability; women and those having no health insurance also had higher levels of
depressive symptoms. Findings suggest that social work practitioners should engage family and
community support, advocate for access to adequate health care, and attend to women’s unique
circumstances and needs when working with American Indian older adults.
Keywords: Stressors, functional disability, social support, American Indian older adults, depressive
symptoms
345
Address correspondence to Soonhee Roh, PhD, Department of Social Work, University of South Dakota, 1400 West
22nd Street, Sioux Falls, South Dakota 57108, USA. E-mail: [email protected]
Social Work in Public Health, 30:345–359, 2015
Copyright q Taylor & Francis Group, LLC
ISSN: 1937-1918 print/1937-190X online
DOI: 10.1080/19371918.2015.1019174
INTRODUCTION
American Indian/Alaska Natives (AI/ANs) make up a growing population consisting of 566
federally recognized diverse tribes in the United States (Indian Health Service [IHS], 2009). The
number of AI/AN older adults age 60 years and older is projected to increase 280% between 2010
and 2050, from 629,000 to 1,766,000 (U.S. Census Bureau, 2012). Despite their continuous growth,
existing literature suggests that there are persistent health disparities among AI/AN older adults,
including chronic disease, diabetes, cancer, cardiovascular disease, and lower life expectancy
(Chapleski, Kaczynski, Gerbi, & Lichtenberg, 2004; Goins & Pilkerton, 2010; Weaver, 2005).
Moreover, AI/AN older adults are at a greater risk than any other racial group for experiencing
serious psychological distress, such as depression, post-traumatic stress disorder (PTSD), and mood
disorders (Barnes, Adams, & Powell-Griner, 2010; Dickerson & Johnson, 2012; Substance Abuse
and Mental Health Services Administration, 2013). Nonetheless, AI/AN adults are least likely to
receive preventive care and treatment services for mental health disorders (Agency for Healthcare
Research and Quality, 2013). Given the extent and effects of these mental health disparities, there
exists an urgent need to examine factors associated with psychological well-being in this population
to provide guidance for effective mental health preventions and interventions.
Depression is one of the most common psychological disorders among AI/AN older adults
(Singh et al., 2004). A substantial body of research shows that depression is a critical indicator of
psychological well-being, negatively affecting 15% to 20% of the general older adult population
(Gallo & Lebowitz, 1999). The prevalence of depression among AI/AN older adults is estimated to
range from 18% to 80% based on a variety of samples, measures of depression, age ranges, and
communities (Curyto et al., 1998; Manson, as cited in National Institute on Mental Health, 2001;
O’Nell, 1996).
These rates are commensurate with or much higher than those among the general population.
Depression is reported to be the single most robust risk factor for suicide, and suicide rates for AI/
ANs are 3.2 times higher than the national average. Thus there is increased vulnerability to
depression and its detrimental impacts among AI/AN older adults (Centers for Disease Control and
Prevention [CDC], 2007). Existing literature in the AI/AN populations suggests that historical
oppression, trauma (e.g., war, genocide, land dispossession), chronic, multigenerational,
internalized experiences of subjugation, coupled with contemporary social/behavioral issues
(e.g., poverty, discrimination, educational inequities, violent victimization), are associated with
mental health inequity among older adults (Brave Heart, Chase, Elkins, & Altschul, 2011; Roh
et al., 2015).
A particular interest in this study was to examine protective factors that can buffer the
negative consequences of health constraints on depressive symptoms among AI older adults. The
stress and coping model (Lazarus & Folkman, 1984), which informed this study, postulates that
coping resources contribute to health by protecting individuals from the adverse effect of stress.
It acknowledges the importance of a range of personal and environmental stressors, including
chronic illness and functional disability, all of which influence the physical/mental health and
overall well-being of older adults (Aldwin, 1994; Kang, Basham, & Kim, 2013; Tann, Yabiku,
Okamoto, & Yanow, 2007). According to the model, stress precipitates a need to activate coping
mechanisms—the cognitive and behavioral efforts to manage specific external and internal
demands. Coping resources usually include a wide range of physical, psychological, spiritual,
and social supports (Lazarus & Folkman, 1984; Lee & Woo, 2013; Mui, 2001; Mui &
Burnette, 1994).
In studies of older adults, decline in physical health has been widely recognized as being among
the most common sources of stress, and a contributor to depressive symptoms (Jang, Borenstein,
Chiriboga, & Mortimer, 2005; Moussavi et al., 2007). Numerous studies demonstrate that chronic
physical conditions and depression are often comorbid, and functional disability is closely linked to
346 S. ROH ET AL.
future episodes of depression (Vink, Aartsen, & Schoevers, 2008). Yet relatively little is known
about positive coping resources or protective factors in response to stressful life conditions among
AI/AN older adults. Some research suggests that social support from others and spirituality may
enhance positive coping resources during stressful life conditions by alleviating negative emotional
reactions to stressors and mobilizing the use of more effective coping strategies and support
(Krause, 1999; Pearlin, Lieberman, Menaghan, & Mullan, 1981; Roh, Lee, Lee, & Martin, 2014;
Taylor, Chatters, & Levin, 2004).
The goal of this study was to examine determinants of depressive symptoms in AI older adults.
Based on the stress and coping model, we conceptualized functional disability and chronic medical
conditions as the major stressors, and social support and spirituality as psychosocial and spiritual
coping resources, to predict depressive symptoms. The effects of sociodemographic factors
(gender, education, health insurance, employment, and number of children) were included in the
estimation of these relationships. We hypothesized that among AI older adults, stressors and
depressive symptoms are positively associated, and coping resources and depressive symptoms are
inversely associated.
LITERATURE REVIEW
Older American Indians/Alaska Natives and Chronic Conditions
A growing body of literature highlights a glaring health disparity in chronic physical conditions
among AI/AN older adults. Compared to other racial groups, AI/AN older adults exhibit the highest
rates of heart disease, diabetes, asthma, and arthritis (Gallant, Spitze, & Grove, 2010; Kim, Bryant,
Goins, Worley, & Chiriboga, 2012). In another study of 505 AI/AN older adults age 55 years or
older who were enrolled tribal members and resided in the tribe’s service area, AI/AN elderly
participants reported higher rates of diabetes, hypertension, back pain, and vision loss than the
general older population (Goins & Pilkerton, 2010).
Chronic illness comorbidity was also significantly greater among AI/AN older adults (Kim,
Bryant, & Parmelee, 2012). When 1,039 rural community-resident AI/AN older adults were
surveyed, 57% reported three or more of 11 chronic conditions within a four-cluster comorbidity
structure that included cardiopulmonary, sensory-motor, depression, and arthritis (John, Kerby, &
Hennessy, 2003). Limited research also provides some initial evidence of a relationship between
chronic illness and depressive symptoms in this population. For example, in a study of 314 AI/AN
older adult participants residing in urban, reservation, and rural settings, depression was higher
among individuals with diabetes (Singh et al., 2004).
Older American Indians/Alaska Natives and Functional Disability
AI/AN older adults are “permanently disabled at six times the rates of non-Latino whites ages 55 –
64” (Satter, Wallace, Garcia, & Smith, 2010, p. 20). Additionally, they report less physical activity
than older Whites (Denny, Holtzman, Goins, & Croft, 2005). In a comparison of white, African
American, and AI/AN older adults age 55 years and older, AI/AN older adults reported the highest
rates of functional limitation and self-care disability. Among AI/AN older adults, the risk factors of
having a disability include older age, female gender, lower educational achievement and household
income, lack of employment or a spouse, and nonurban residence (Goins, Moss, Buchwald, &
Guralnik, 2007). Furthermore, in a recent study, two thirds of AI/AN older adults reported some
degree of comorbidity of chronic diseases, low physical functioning, and a greater amount of
depressive symptoms (Goins & Pilkerton, 2010).
STRESSORS, COPING RESOURCES, AND DEPRESSIVE SYMPTOMS 347
Older American Indians/Alaska Natives and Coping Resources
Social Support
Social support—the emotional, instrumental, and financial assistance obtained from one’s social
network (Berkman, 1984)—is a significant determinant of psychological well-being and quality of
life among older adults (Kuo, Chong, & Joseph, 2008; Oxman & Hull, 2001). AI/ANs in traditional
communities believe the needs of the tribe take priority over those of the individual (Sue & Sue,
2012). Therefore, social support is seen in collectivist rather than individualist terms. Pyke and
Bengtson (1996) were the first to identify individualist and collectivist approaches to caring for an
elder family member. Individualistic families believe members should be self-reliant, and they have
minimal expectations for younger family members to care for elder family members; it is even seen
as an unwanted burden. Conversely, members of collectivist families are more likely to have a
strong commitment to their family and high levels of interdependence; care of elders is considered a
family responsibility and a way to reaffirm familial ties (Pyke, 1999). For AI/AN older adults, the
definition of family must also be expanded to include other stakeholders (e.g., friends, tribal
members, tribal elders, holy men). The individual is considered inseparable from the community;
therefore, an individual’s actions are understood to have a direct influence on the group (Hossain,
Skurky, Joe, & Hunt, 2011). Thus, social support among AI/AN older adults must be understood in
terms of a communal response.
Social support is the most widely studied coping resource; research consistently shows social
support to have strong, significant buffering effects on stress outcomes. However, only a few
studies have examined these relationships among AI/AN older adults. Using data collected from
1998 to 2010, Nelson, Noonan, Goldberg, and Buchwald (2013) found that the level of social
engagement was associated with mental status and self-reported health among AI/AN older adults
age 50 years or older, after adjusting for demographic variables. In particular, a higher level of
social engagement was associated with better physical and cognitive functioning and lower
depression scores. A sense of belonging has been found to partially mediate the relationship
between type of housing (e.g., own homes, nursing home, assisted living) and depressive symptoms
(McLaren, Turner, Gomez, McLachlan, & Gibbs, 2013). Although racial and ethnic demographics
were not reported for McLaren et al.’s (2013) sample, older adults living in a nursing home had
lower levels of belonging and higher levels of depressive symptoms; those residing in an assisted
living facility had average levels of belonging and lower depressive symptoms (McLaren et al.,
2013). In a more diverse sample (including 26% AI/ANs and 35% African Americans) of rural
older adults, social support and life satisfaction were positively correlated (Yoon, 2006).
Spirituality
Traditionally, AI/AN culture places a strong emphasis on spirituality and views illness as an
imbalance in an individual’s mental, spiritual, emotional, physical, and social functioning (Smyer
& Stenvig, 2007). In this view, wellness corresponds to equilibrium among these domains. Thus,
spirituality cannot be separated from the other domains of functioning, and it is an essential
component in the lives of AI/AN older adults. Traditional healers or shamans are considered
powerful facilitators for reducing physical/emotional pain or suffering. This understanding of
spirituality does “not match Western conceptions that are tied to particular religious beliefs or
institutions” (Kulis, Hodge, Ayers, Brown, & Marsiglia, 2012, p. 444).
Similar to findings that show close linkages between spirituality and psychological well-being in
the general population (Lawler-Row & Elliott, 2009), spirituality has been found to improve life
satisfaction and decrease depression symptoms among AI/AN older adults (Yoon & Lee, 2004).
Arcury et al. (2007) found lower levels of depressive symptoms to be associated with receiving
religious support (i.e., feel that people in their congregation love and care for them) in a diverse
348 S. ROH ET AL.
sample (including 26% AI/AN older adults) of community-dwelling rural older adults with
diabetes. Moreover, spirituality and depression were inversely correlated in a sample of rural older
adults with significant minority representation (including 26% AI/ANs and 35% African
Americans) (Yoon, 2006).
METHOD
Sample and Data Collection
After receiving approval by the University of South Dakota Institutional Review Board, data were
collected from rural AI older adults age 50 years or older between January and May 2013. Being
drawn from nonmetropolitan areas with fewer than 50,000 residents (The White House Office of
Management and Budget, 2012), the sample could be classified as rural and composed of offreservation AI older adults. The lower age limit of 50 was selected because of the lower life
expectancy of AI older adults compared to other Americans (IHS, 2014). Participants were
recruited through a variety of off-reservation locations including AI churches, social service
centers, other religious organizations, senior housing facilities, senior centers, and three powwows
in South Dakota and Minnesota. A total of 235 AI older adults participated in the study. Eight
participants who failed to complete the study questionnaire were excluded, resulting in a final
sample of 227 AI older adults. Although the study used a self-administered questionnaire, trained
interviewers were available for anyone who might need assistance; two participants did require
such assistance. Prior to collecting data, the first author explained the purpose and procedures of the
study, the kinds of questions that would be asked, confidentiality of data, and participants’ benefits
and risks, and participants gave informed written consent. The questionnaire took about 30 minutes
to complete, and participants were offered $10 for their time.
Measures
Outcome Variable: Depressive Symptoms
The Geriatric Depression Scale –Short Form (GDS-SF; Sheikh & Yesavage, 1986) was used to
measure depressive symptoms. Participants respond to the 15 items according to a yes/no format.
Items include “Do you feel your life is empty?” and “Are you in good spirits mostly?” Scores from
0 to 5 indicate no depression, 6 to 10 probable depression, and 11 to 15 severe depression (Sheikh &
Yesavage, 1986). Internal consistency was .81 in this study.
Major Health Stressors
Chronic Medical Conditions
Participants were asked to report existing medical conditions using a nine-item list of chronic
diseases and conditions (e.g., arthritis, stroke, heart problems, diabetes, and cancer) commonly
found among older populations. A yes/no response format was used for this measure, and a
summated score was calculated. Higher scores represent multiple chronic conditions.
Functional Disability
Functional disability was assessed with a 20-item composite measure of the physical activities of
daily living, instrumental activities of daily living, the Physical Performance Scale (Nagi, 1976),
STRESSORS, COPING RESOURCES, AND DEPRESSIVE SYMPTOMS 349
and the Functional Health Scale (Fillenbaum, 1988; Rosow & Breslau, 1966). Items covered a wide
range of activities, such as eating, dressing, traveling, managing money, carrying a bag of groceries,
and ability to reach above the head with one’s arms. Participants were asked about the extent to
which they could perform each activity, with the available responses 0 (without help), 1 (with some
help), and 2 (unable to do). Internal consistency was .94 in this study.
Coping Resources
Social Support
The study used the Multidimensional Scale of Perceived Social Support (MSPSS; Zimet, Davlem,
Zimet, & Farley, 1988), a 12-item instrument that measures perceived social support from family,
friends, and significant others with a 4-point response format ranging from 1 (strongly disagree) to
4 (strongly agree). It has been used extensively with diverse populations including homeless people
and cancer patients (Gilbar & Refaeli, 2000; Wu & Serper, 1999). A total score is obtained by
summing the items; higher scores represent higher levels of perceived social support. Internal
consistency was .94 in this study.
Spirituality
Spirituality was measured with the Duke University Religion Index, a five-item measure of
religiosity (DUREL; Koenig & Bu¨ssing, 2010). The DUREL is made up of three dimensions of
religiosity: organizational (one item), nonorganizational (one item), and intrinsic (three items).
Organizational and nonorganizational religiosity are scored on 6-point scales, whereas the intrinsic
religiosity items use a 5-point Likert-type scale. A total score is calculated by summing the scores
for all items; and higher scores reflect higher levels of spirituality. Internal consistency was .84 in
this study.
Demographics
Five additional items measured participants’ gender, health insurance, employment status,
education, and number of children.
Data Analysis
Descriptives and correlations were calculated to understand the sample’s demographic
characteristics and to examine associations among the key variables. Hierarchical multiple
regression was used to test the two hypotheses. To do so, the continuous dependent variable,
depressive symptoms, was regressed on (a) demographic variables, (b) functional disability and
chronic medical conditions, and (c) social support and spirituality in three steps to understand the
roles of health stressors and coping resources in each step. The regression analysis identified the
amount of variance in depressive symptoms that was accounted for by each step (George &
Mallery, 2014). Because depressive symptoms were not normally distributed, a square root
transformation was used for this variable. The tolerance scores for all predictors were greater than
.81, indicating no problems with multicollinearity (Mertler & Vannatta, 2009). IBM SPSS version
21 was used to conduct the analyses.
350 S. ROH ET AL.
RESULTS
Characteristics of the Sample
Table 1 presents the demographic characteristics of the sample of 227 older AIs. The sample was
54% female; most participants had at least a high school degree or Graduate Equivalency Diploma
(GED). About one half were employed, and more than 60% had health insurance. However, nearly
one half of the respondents made less than $20,000 annually. About three fourths of them lived with
a spouse, their children, or someone else. On average, respondents had three to four children. The
sample’s age, marital status, income, and living arrangements were similar to those reported by a
previous national survey of older AIs (Goins & Pilkerton, 2010).
Description of the Main Variables
Table 2 depicts the ranges, means, and standard deviations of the main variables, as well as the
correlations of depressive symptoms with each of the other variables. The mean score on depressive
symptoms was a low 2.28 (SD ¼ 2.77), which is lower than the mean score found among older
adults in assisted living facilities (Lee, Besthorn, Bolin, & Jun, 2012), but higher than the mean
score reported for a general sample of older African American women (Pedraza, Dotson, Willis,
Graff-Radford, & Lucas, 2009). Based on their scores, most respondents (86.5 %) did not have
depressive symptoms, whereas 9.3 % had mild depressive symptoms, 1.4 % had moderate
depressive symptoms, and 2.3 % had severe depressive symptoms.
Although many respondents (56.2%) had more than two types of chronic medical conditions,
most (71.7%) had no physical functioning problems for daily activities. The mean score for social
support (38.7) was higher than the mean reported for a general sample of older adults in rural areas
TABLE 1
Demographic Characteristics (N ¼ 227)
Range or Category % or Mean (M)
Age 50–95 60.7 (M; SD ¼ 8.42)
Gender Female 54.3%
Marital statusa Married 36.4%
Divorced 22.5%
Widowed 12.1%
Never married 17.7%
Education Less than high school (HS) diploma/Graduate
Equivalent Diploma (GED)
8.3%
HS diploma/GED 42.3%
More than HS diploma/GED 49.4%
Health insurance Yes 62.8%
Employed Yes 49.1%
Annual household income Less than $20,000 46.7%
$20,000–$40,000 23.3%
$40,001–$60,000 15.4%
More than $60,000 14.5%
Living arrangement Living alone 26.0%
Living with spouse 41.1%
Living with children 20.3%
Living with someone else 12.6%
Number of children 0–16 3.7 (M; SD ¼ 2.32)
a Some participants reported marital statuses other than those listed.
STRESSORS, COPING RESOURCES, AND DEPRESSIVE SYMPTOMS 351
(Yoon & Lee, 2007). The mean score of spirituality (M ¼ 20.47, SD ¼ 5.17) was comparable to
that reported for cancer patients in Indiana (Kristeller, Rhodes, Cripe, & Sheets, 2005). Depressive
symptoms correlated in a positive direction with functional disability, and in a negative direction
with social support and spirituality. No significant correlation was found between depressive
symptoms and chronic medical conditions.
Because of the low scores on depression in this sample, we examined correlations between
depression symptoms and the predictors after removing participants who had symptom scores of 5
or higher. As expected, all of the correlations decreased, but they did not decrease to zero. Thus,
small differences among depressive symptom scores could still be impacted by the predictors in the
regression model.
Hierarchical Multiple Regression Models of Depressive Symptoms
Table 3 displays the hierarchical regression results on the roles of functional disability, chronic
medical conditions, social support, and spirituality in explaining depressive symptoms among AI
older adults. In Step 1, demographic variables explained 9.9% of the variance in depressive
symptoms. In Step 2, functional disability and chronic medical conditions added 14.5% to the
TABLE 2
Range, Mean, and Standard Deviation of Main Variables and
Correlation with Depressive Symptoms (N ¼ 227)
Range M SD r
Depressive symptoms 0–13 2.28 2.77
Functional disability 0–25 1.92 4.64 .458***
Chronic medical conditions 0–7 2.10 1.72 .090
Social support 12–48 38.69 6.89 2.346***
Spirituality 5–27 20.47 5.17 2.213***
*** p , .001.
TABLE 3
Prediction of Depressive Symptoms among Older American Indians: Standardized
Regression Coefficients and Standard Errors (N ¼ 227)
Predictor
Step 1 Step 2 Step 3
ß SE ß SE ß SE
Gender (female) .604 .452 .517 .417 .858* .397
Education 2.544 .299 2.515 .276 2.391 .259
Health insurance 2.633 .467 2.701 .439 2.885* .411
Employment 21.022* .465 2.348 .446 2.420 .416
Number of children .087 .094 .065 .090 .046 .084
Functional disability .238*** .045 .209*** .042
Chronic medical conditions .063 .122 .048 .114
Social support 2.132*** .030
Spirituality 2.045 .039
F 3.507** 7.227*** 9.296***
R 2 change .099 .145 .107
R 2 .099 .244 .351
Adjusted R 2 .071 .210 .313
*p , .05, **p , .01, ***p , .001.
352 S. ROH ET AL.
explained variance. In the final step, social support and spirituality added another 10.7% to the
explanation, for a total explained variance of 31.3%.
In the full model, functional disability and social support were significant predictors of
depressive symptoms, along with the covariates gender and health insurance. When controlling for
the other variables, higher social support predicted fewer depressive symptoms (b ¼ 2.132,
p # .001), whereas higher functional disability predicted more depressive symptoms (b ¼ .209,
p # .001). Female gender also predicted higher scores on depressive symptoms (b ¼ .858,
p # .05), but having health insurance predicted lower scores on depressive symptoms (b ¼ 2.885,
p # .05). Education, employment status, and number of children were not significant predictors of
depressive symptoms.
DISCUSSION
The purpose of this study was to examine determinants of depressive symptoms among AI older
adults. Guided by the stress and coping model (Aldwin, 1994; Lazarus & Folkman, 1984), we
conceptualized functional disability and chronic medical conditions as the major stressors, social
support and spirituality as coping resources, and depressive symptoms as the outcome. Our focus
was on positive coping mechanisms, or resilience, in response to life adversity, which has rarely
been studied among older AIs.
The results showed that greater functional disability is an important health-related stressor,
as indicated by its significant positive association with depressive symptoms. Among the coping
resources, social support is an important protective factor, as indicated by its significant
negative association with depressive symptoms. However, we did not find evidence of a
relationship between depressive symptoms and either chronic medical conditions or spirituality.
In addition to stressors and coping, female gender and the availability of health insurance
contributed to the explanation of depressive symptom variance. As the full model explained
about one third of the variance in depressive symptoms among AI older adults, the findings
support the continued use of the stress and coping model for future research on depression
among older AIs.
Functional disability was positively associated with depressive symptoms in a previous study of
AI older adults (Chapleski et al., 2004), so the current findings reinforce the salience of this variable
as a predictor of depressive symptoms in this population. Given the findings on functional disability
and the higher rates of AI older adult who are permanently disabled (Satter et al., 2010), it is
important for mental health providers to assess for signs and symptoms of depression in this
population using culturally appropriate methods. One possibility is the Diagnostic and Statistical
Manual of Mental Disorders (DSM-5) Cultural Formulation Interview (American Psychiatric
Association, 2013), which utilizes 16 questions to assess the cultural definition of the problem;
cultural perceptions of cause, context, and support; cultural factors affecting self-coping and past
help seeking; and cultural factors affecting current help seeking.
The lack of findings on chronic medical stressors contradicts previous studies that found
evidence of a relationship between these two variables among older AIs (Calhoun et al., 2010;
Goins & Pilkerton, 2010; Singh et al., 2004). The difference could be related to the fact that
previous studies had medical providers verify the presence of a chronic medical condition (Calhoun
et al., 2010; Singh et al., 2004) or the presence of a medical stressor was determined by a
comorbidity scale administered in an interview format (Goins & Pilkerton, 2010). When looking at
the present findings it appears that functional disability is more predictive of depressive
symptomatology than having a chronic medical condition, among AI older adults. However, among
the sample few participants reported depressive symptoms, which might account for this result. It is
also important to consider the collectivist viewpoint in traditional AI/AN culture. Individuals may
STRESSORS, COPING RESOURCES, AND DEPRESSIVE SYMPTOMS 353
perceive that having a functional disability is more of a strain on the community than having a
chronic medical condition, and this could lead to higher levels of depressive symptoms.
Because higher social support predicted fewer depressive symptoms in this study, social support
should be seen as an essential coping strategy for AI older adults. This finding corresponds with
previous research that found a negative correlation between depressive symptoms and social
support (Arcury et al., 2007; Yoon, 2006). Given that AI/AN culture embraces a collective support
system, providers should implement health care strategies to encourage community support and
employ the knowledge and assistance of culture keepers (e.g., tribal elders, traditional healers)
(Hartmann & Gone, 2012). Additionally, AI/ANs conceptualize social functioning as part of a
holistic view of wellness (i.e., balance among an individual’s mental, spiritual, emotional, physical,
and social functioning) (Smyer & Stenvig, 2007). Thus, social work providers should use a broad
definition of family and communal support when working with older AIs.
Because spirituality is traditionally an integral part of the AI older adult view of wellness, it is
surprising that this study found no relationship between spirituality and depressive
symptomatology. One possible explanation has to do with the way spirituality was measured
in this study. Although the DUREL had strong internal consistency, its validity among AI older
adults is unknown. In addition, assessing spirituality among AI older adults is complicated by the
disruption of indigenous practices due to historical losses suffered by the AI/AN people (Kulis
et al., 2012). Therefore, these findings on spirituality must be interpreted with caution.
Developing reliable and valid measures of spirituality among older AIs should be a priority for
future research.
The finding of an inverse relationship between health insurance and depressive symptoms is also
important. The majority of this study’s participants reported having insurance. However, about 60%
of AI/AN obtain behavioral and medical health care from the IHS, which spends $2,741 per
beneficiary in comparison to $7,239 spent by privately and publicly funded personal health care
services for the general U.S. population (IHS, 2013b). Notably, less than 10% of the IHS funds are
allocated for mental health and substance abuse treatment even though the rates of mental health
and substance abuse problems among AI/ANs are substantial. This disparity in receiving adequate
services is predominately due to “inadequate education, disproportionate poverty, discrimination in
the delivery of health services, and cultural differences” (IHS, 2013a), as well as the services being
geographically inaccessible (Satter et al., 2010). Thus social work practitioners should advocate for
their AI older adults clients to ensure that access to adequate health care is available.
Because women in this sample reported higher depressive symptoms than men, future research
should examine gender-specific risk factors for depression within this population. Previous research
has found depression among AI/AN women to be associated a history of interpersonal violence
(Evans-Campbell, Lindhorst, Huang, & Walters, 2006). Notably, in one study 34% of AI/AN
women reported being raped, and 61% reported being physical assaulted (Tjaden & Toennes,
2000). Even though this study found support for the stress and coping model among women and
men, providers and researchers should attend to the unique circumstances and needs of AI older
women. In one such effort, Walters and Simoni (2002) developed an indigenous stress and coping
model for AI/AN women in which life stressors include historical trauma and discrimination
suffered by AI/ANs, and coping resources include identity attitudes, enculturation, spiritual coping,
and traditional health practices. Future research should continue to examine these constructs in
order to assist providers in addressing the specific cultural needs of AI older women.
Given the past atrocities experienced by AI/AN women and men, future studies should examine
the relevance of historical losses to depressive symptoms to AI older adults. The theory of historical
trauma proposes that decades of historical losses (e.g., loss of people, land, culture) suffered by
AI/ANs at the hands of the dominant European culture (Brave Heart et al., 2011; Brave Heart &
DeBruyn, 1998) has resulted in a legacy of psychological and social-environmental problems that
continue today (Whitbeck, Adams, Hoyt, & Chen, 2004). Future researchers can utilize the
354 S. ROH ET AL.
Historical Loss Scale to assess whether depression among AI older adults is related to these
historical losses (Whitbeck et al., 2004).
Finally, it is important to consider the low level of depression in the sample. Two possible
explanations have to do with the heterogeneity of AI/AN populations and the validity of the
measure of depressive symptoms. First, other research reflects heterogeneity in the prevalence of
depression across AI/AN populations (Gone & Trimble, 2012). This heterogeneity has been
typically depicted in relationship to differences across distinct tribes. However, within population
differences related to age are also possible. To this point, however, AI older adults have received
scant attention in studies on behavioral health (Miller-Cribbs, Byers, & Moxley, 2009). Second,
because AI/AN populations may perceive mental health and depression more holistically and in
qualitatively distinct ways than the general population (O’Nell, 1996), the validity of the measure
of depression used in this study might not have been adequate for AI older adults.
Limitations and Implications
Several limitations of this study should be noted. First, the study used a convenience sample
of AI/AN older adults from two midwestern states, which is not likely to be representative of all
AI/AN older adults. Those who chose to participate in the study might be different from other AI/
AN older adults in several ways, including their willingness to participate in research, and the level
of depressive symptoms and stressors among them. Also, data on tribal membership was not
collected. Studies with more representative samples of AI/AN older adults and generally (also
across different tribes and rural/urban contexts) will provide a fuller picture of physical and mental
health effects that could inform geriatric social work practice. Second, the study’s cross-sectional
design prevents any causal attributions concerning depressive symptoms. Although functional
disability might cause AI/AN older adults to have depressive symptoms, it is also possible that
feeling depressed might worsen functional limitation and disability. Likewise, this study is unable
to determine the direction of influence between social support and depressive symptoms. Third,
though the scales used in this study had strong internal consistency, they had not been used
previously with AI/AN older adults. Thus the validity of these measures when used with an AI/AN
population is unknown. A particular concern has to do with cultural norms for expressing emotional
status and health conditions among AI/ANs as compared to other populations. Fourth, though great
effort was made to collect data in a manner that was respectful of AI culture, it is possible that
participants were reluctant to provide sensitive information to the researchers, who were not AIs.
Despite this study’s limitations, it contributes to the scarce literature regarding AI/AN older
adults by providing insights into appropriate approaches to depressive symptom management and
health promotion among AI/AN older adults. The stress and coping model (Aldwin, 1994; Lazarus
& Folkman, 1984) is useful for understanding depression in this population, but researchers and
providers should also consider alternative models that address the impact of historical trauma and
other culturally-specific influences on the mental health of AI/AN older adults. Findings from this
study and the lack of literature specific to this population suggest a strong need for future research
with AI/AN older adults to ascertain culture-specific stressors and coping resources, and to inform
current and future models of mental health care practice with this population.
In conclusion, the importance of social support as a predictor of depressive symptoms among
AI/AN older adults must be underlined. A review of journal articles between the years 1980 to 2000
found family and community involvement in treatment and the role of traditional healing to be
salient factors in mental health services for AI/AN populations (Manson, 2000). Informal supports
may provide an important alternative rather than a barrier to professional mental health services
(Freedenthal & Stiffman, 2007). With many AI/AN populations preferring traditional healing and
the incorporation of friends and family members (Manson, 2000) into mental health interventions,
these existing strengths within AI/AN communities are important areas to inform treatment
STRESSORS, COPING RESOURCES, AND DEPRESSIVE SYMPTOMS 355
development. Given the importance of social support and AI/AN preferences, family-focused
interventions are viable alternatives to individually focused interventions. Additionally, social work
practitioners working with AI/AN older adults should inquire about their social support resources
and encourage involvement to increase their social networks and supportive relationships and
alleviate social isolation and depressive symptoms.
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