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Stress Exposure and Physical, Mental, and Behavioral Health among American Indian Adults with Type 2 Diabetes

Authors:
  • University of Minnesota Medical School, Duluth campus

Abstract and Figures

American Indian (AI) communities experience disproportionate exposure to stressors and health inequities including type 2 diabetes. Yet, we know little about the role of psychosocial stressors for AI diabetes-related health outcomes. We investigated associations between a range of stressors and psychological, behavioral, and physical health for AIs with diabetes. This community-based participatory research with 5 AI tribes includes 192 AI adult type 2 diabetes patients recruited from clinical records at tribal clinics. Data are from computer-assisted interviews and medical charts. We found consistent bivariate relationships between chronic to discrete stressors and mental and behavioral health outcomes; several remained even after accounting for participant age, gender, and income. Fewer stressors were linked to physical health. We also document a dose–response relationship between stress accumulation and worse health. Findings underscore the importance of considering a broad range of stressors for comprehensive assessment of stress burden and diabetes. Policies and practices aimed at reducing stress exposure and promoting tools for stress management may be mechanisms for optimal health for AI diabetes patients.
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International Journal of
Environmental Research
and Public Health
Article
Stress Exposure and Physical, Mental, and Behavioral
Health among American Indian Adults with
Type 2 Diabetes
Melissa L. Walls 1,*, Kelley J. Sittner 2, Benjamin D. Aronson 3ID , Angie K. Forsberg 1,
Les B. Whitbeck 4and Mustafa al’Absi 1
1Department of Family Medicine and Biobehavioral Health ,University of Minnesota Medical School,
Duluth Campus, 1035 University Drive, 235 SMed, Duluth, MN 55812, USA; aforsber@d.umn.edu (A.F.);
malabsi@d.umn.edu (M.A.)
2
Department of Sociology, Oklahoma State University, Stillwater, OK 74078, USA; kelley.sittner@okstate.edu
3Department of Pharmacy Practice, Ohio Northern University, Ada, OH 45810, USA; b-aronson.1@onu.edu
4Department of Sociology, University of Nebraska, Lincoln, NE 68588, USA; lwhitbeck2@unl.edu
*Correspondence: mlwalls@d.umn.edu; Tel.: 218-726-8367; Fax: 218-726-7559
Received: 15 August 2017; Accepted: 13 September 2017; Published: 16 September 2017
Abstract:
American Indian (AI) communities experience disproportionate exposure to stressors and
health inequities including type 2 diabetes. Yet, we know little about the role of psychosocial stressors
for AI diabetes-related health outcomes. We investigated associations between a range of stressors
and psychological, behavioral, and physical health for AIs with diabetes. This community-based
participatory research with 5 AI tribes includes 192 AI adult type 2 diabetes patients recruited from
clinical records at tribal clinics. Data are from computer-assisted interviews and medical charts.
We found consistent bivariate relationships between chronic to discrete stressors and mental and
behavioral health outcomes; several remained even after accounting for participant age, gender,
and income. Fewer stressors were linked to physical health. We also document a dose–response
relationship between stress accumulation and worse health. Findings underscore the importance of
considering a broad range of stressors for comprehensive assessment of stress burden and diabetes.
Policies and practices aimed at reducing stress exposure and promoting tools for stress management
may be mechanisms for optimal health for AI diabetes patients.
Keywords: American Indian; diabetes; stress; Native American
1. Introduction
Stress process models of disease suggest that heightened exposure to stressors is associated
with worse health outcomes, reduced capacity for disease management, and increased risk for
disease-related complications and comorbidities [
1
,
2
]. Stress has been implicated in the etiology of type
2 diabetes mellitus for centuries [
3
], and stressors are known to compromise diabetes management,
metabolic control, treatment compliance and quality of life [
4
,
5
]. Further, the stress process is influenced
by social stratification systems whereby those occupying marginalized social and economic statuses
are more likely to experience higher levels of stress [6].
American Indian (AI) communities simultaneously experience disproportionate levels of
historical, social, and economic stressors and disparate rates of type 2 diabetes compared to the overall
United States population [
7
]. Diabetes prevalence rates are double for AI and Alaska Native (AN)
adults compared to the United States average (17.8% vs. 8.5%), representing the highest age-adjusted
prevalence of any racial or ethnic group [
8
]. In 2013, diabetes was the 5th leading cause of death
for AI/ANs of all ages and was a major contributor to the leading cause of death, cardiovascular
Int. J. Environ. Res. Public Health 2017,14, 1074; doi:10.3390/ijerph14091074 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2017,14, 1074 2 of 11
disease [
8
]. AIs living with diabetes also experience significantly more comorbidities and complications
like amputation, mental health disorders, and hypertension relative to the U.S. general population of
adults with diabetes [9].
There has been substantial research on the origins and extent of the diabetes epidemic, its
correlates, and complications among AI people [
10
13
]. Yet, there has been little empirical work on
the connections between stress and diabetes outcomes for AIs [
14
]. In one exception, a study of two
distinct tribal communities found that early-life trauma and neglect, environmental hassles, family
dysfunction, community economic distress and discrimination were significantly higher among adults
with diabetes than those without [
5
]. Notable limitations of that study included that these associations
were not uniform across communities, the reliance on self-reported diabetes diagnosis, and overall
impacts of stressors on diabetes-related outcomes were not examined.
Two important points regarding the empirical study of stress inform the current study. First,
early efforts to detail the impact of stressors on health relied heavily on discrete measures like
life events checklists [
15
]. Over time, critiques of the validity of checklist approaches propelled
researchers to consider the vast “realm of stressors” [
16
] and more adequately operationalize potential
stressful experiences (6). To conceptualize this approach, Wheaton’s [
16
,
17
] stress continuum promotes
consideration of a range of stressful experiences and typologies. At one end, acute stressful events with
discrete beginning and end points provide an anchor, with more chronic stressors (i.e., enduring, often
open-ended) situated at the opposite end of the spectrum. Thus, this continuum considers impacts of
stress exposure across a spectrum of stressor typologies and domains.
The second critical consideration lies in exclusion of diverse perspectives in the formation of stress
measurement, particularly those stressors most salient to racial/ethnic minority and marginalized
groups [
18
]. Studies relying solely on commonly used stress measures may thus underestimate the
impact of stressors on health and health behaviors. The sources of stress and consequences in AI
communities in particular are poorly understood in terms of the range and meaning of stressors
experienced [
19
]. To this end, our approach to measuring stress in American Indian communities for
the current study was heavily informed by qualitative community feedback (see Section 2).
The purpose of this study is to investigate linkages between a continuum of stressors and
diabetes-related health factors among AI adults living with type 2 diabetes. Specifically, we examine
the direct, cumulative and relative associations between stressors and behavioral, mental, and physical
health outcomes. Our selection of stressor variables was driven by our goals to (a) incorporate a
continuum of stressor possibilities ranging from discrete (e.g., life events) to chronic (e.g., distress
related to chronic disease) and (b) be responsive to tribal community members’ identification (via focus
groups) of stressors most salient to AIs living with type 2 diabetes [
20
]. In accord with previous research,
we expect that (Hypothesis 1) higher reports of stressor exposure will be significantly associated
with poorer health, and (Hypothesis 2) a “dose–response” relationship will occur between stressor
accumulation and health whereby progressively worse health outcomes are observed as number of
stressors reported increases. Further, we explore the relative associations between individual stressors
and health when accounting for demographic factors and a range of other stressors.
2. Materials and Methods
Data for this study are from the Maawaji’ idi-oog Mino-ayaawin (Gathering for Health) project, a
longitudinal community-based participatory research (CBPR) collaboration between the University
of Minnesota Medical School, Duluth campus and five Ojibwe communities in Minnesota and
Wisconsin. Community Research Councils (CRCs) comprised of an average of 6 members on each
reservation are active partners in the entire research process and have participated in all aspects of
study planning, protocol development, and implementation to ensure cultural and local acceptability
of study procedures. CRC members also serve as co-authors and co-presenters on various data
dissemination activities and ongoing work to translate findings into locally relevant programming
and services. Clinic-based staff members in each community assisted the team with participant
Int. J. Environ. Res. Public Health 2017,14, 1074 3 of 11
selection and medical chart reviews. Final protocol for the study was reviewed and approved by the
University of Minnesota Institutional Review Board (Study #1206S16361) and the National Indian
Health Service Institutional Review Board, and this manuscript was reviewed by all CRC members
prior to journal submission.
2.1. Sample
Clinical staff at each tribal site’s medical facility generated simple random samples for study
recruitment using clinic patient records. Inclusion criteria were a diagnosis of diabetes documented in
the medical record within 5 years of the sampling date, age 18 years or older, and self-identified as
American Indian. A total of 194 participants enrolled in the study, representing a baseline response
rate of 67%. Data for this report include responses from the 192 participants who completed a baseline
survey interview between November 2013 and November 2015.
2.2. Procedure
The survey for this study was heavily informed by community feedback derived from two sets
of focus groups at each of the five participating tribal sites. In the first groups, community members
were queried about sources of stress common in their tribal community; thematic results from Group 1
were used to identify and/or adapt stress measures that were presented to participants in Group 2 for
feedback on validity, comprehensiveness, and understandability.
For the survey component of the study, clinic staff sent study invitation letters and brochures
to residences of randomly selected patients. Non-refusing individuals were contacted by trained
community interviewers, screened for study eligibility, and formally invited to participate. Visits were
scheduled at a location of participants’ choosing, at which time interviewers gathered signed informed
consent and HIPAA authorization forms. Data from interviewer computers were electronically synced
via Internet connection to a secure server at the university, uploaded, and converted to SPSS data files.
Data for this manuscript include responses to baseline Computer-Assisted Personal Interviews (CAPI)
and clinical chart reviews, for which participants received a $50 incentive and a small, culturally
meaningful gift. Clinical staff completed medical chart reviews using data from patient health records.
2.3. Measures
Three control variables are included in these analyses: age (in years), gender (male = 0, female = 1),
and per capita household income were each assessed via self-report survey responses. Five dependent
variables are used: two physical health, one mental health, and two behavioral. One physical health
outcome, hemoglobin A1c (HbA1c), was retrieved from patient medical charts. Clinical staff recorded
the most recent HbA1c value available for each participant. Project interviewers trained in proper
anthropometric measurement and recording techniques collected an additional measure of physical
health, waist-to-hip ratio. Waist-to-hip ratios are widely used as a measure of health status and risk for
several diabetes-related comorbidities including cardiovascular events [
21
]. All remaining outcomes
were assessed via survey responses. As an indicator of mental health status, depressive symptoms
were measured using continuous responses to the Patient Health Questionnaire (PHQ-9) [
22
] with a
possible range of 0–27 and higher values representing more depressive symptoms. Medication adherence
questions were asked to a subsample of 166 participants indicating that they used oral or injectable
diabetes medications and were measured using the 4-item Morisky Medication Adherence Scale
(MMAS-4) [
23
]. Items from the MMAS-4 were summed, creating a possible score ranging from 0 to 4,
with higher scores indicating higher adherence. Average responses to two items from the Summary
of Diabetes Self-Care Activities (SDSCA) diet subscale was used to assess days/week adherence to a
healthy diet plan (possible range = 0–7 days) [24,25].
Six measures of psychosocial stressor exposure were included in these analyses; the sources,
means, standard deviations, and brief descriptions of measurement properties are described in
Figure 1[
26
30
]. Each stressor is mapped onto an adaptation of Wheaton’s continuum [
16
,
17
]
Int. J. Environ. Res. Public Health 2017,14, 1074 4 of 11
ranging from stressors more chronic/continuous in their operationalization on the left to those
more discrete/acute on the far right. Diabetes-Related Stress (Diabetes Distress) assesses feelings of
being overwhelmed by diabetes management. Family Criticism items gauge perceptions of family
disapproval and discouragement. Daily Hassles are indicated by everyday difficulties and nuisances
such as problems with work, vehicles, technology, and home repairs. Microaggressions represent a
range of racial insults including subtle but pervasive experiences like feelings of invisibility, portrayal
of Native people as mascots, and hearing that one doesn’t “look” Indian. Negative Financial Events
assess discrete adverse financial experiences occurring in the 6 months prior to interview date (e.g.,
being laid off or becoming unemployed, losing benefits, repossession, etc.). Stressful Live Events
are indicated by a checklist of possible stressful experiences such as moving, job changes, and role
transitions. Participants were asked to consider stressors experienced within the 6 months prior to
the interview for all of the scales with the exception of daily hassles, which was limited to the past
month. Using each of these stressor variables, we also calculated a cumulative score of above average
stress exposure. We recoded each of the 6 stressors so that individuals scoring above the mean for a
given stress variable = 1, and those scoring at or below the mean = 0. We then created a summation of
above-average stressors that revealed substantial outliers; namely, n = 9 participants scoring 5 and
n = 13 scoring 6; thus, we collapsed these two response categories for a final variable ranging in value
from 0 to 4 or more stressors.
Figure 1.
Stress measurement sources, scoring, means, standard deviations (S.D.), and position on
a continuum.
2.4. Analytic Approach
We generated Pearson’s correlation coefficients to describe focal relationships between individual
stressors and health outcomes. Means comparisons were used to determine the associations between
accumulation of above-average stressors and health. Lastly, we included all stressor variables in
a single ordinary least squares regression analysis for each health outcome to identify the relative
influence of stressors. Because of possible statistical power limitations and intercorrelations among
some of the stressors, exact p-values are displayed alongside multivariate results and alpha was set at
p< 0.10.
3. Results
A little over one-half (56%) of the study participants were female, with a mean age of 46.3 years
(SD = 12.21). Average per capita household income was $9767 (SD = 8901). The sample mean HbA1c
was 7.7% (61.1 mmol/mol) (SD 2.2%; SD 24.2 mmol/mol), the average waist-to-hip ratio was 1.03
(SD = 0.29), and the mean PHQ-9 depression score was 5.27 (SD = 5.6). The average medication
Int. J. Environ. Res. Public Health 2017,14, 1074 5 of 11
adherence score was 2.53 (SD = 1.3), and participants reported following a healthy diet plan an average
of 2.9 days per week (SD = 1.6).
Bivariate relationships between individual stressors and outcomes are displayed in Table 1. HbA1c
levels were positively and significantly associated with diabetes distress. Two stressors, negative
financial events, and negative life events were related to larger waist-to-hip ratios. All six of the
stressors were significantly and positively correlated with depressive symptoms; the strength of the
correlation was strongest for family stress followed closely by microaggressions and daily hassles.
Five of the stressors included in this study were significantly related to worse medication adherence
with the largest correlation coefficient observed for diabetes distress. Four stressors were linked to
worse diet adherence: diabetes distress, family criticism, daily hassles, and negative financial events;
the largest correlation coefficient of these was financial events.
Table 1. Pearson’s correlation coefficients for major study variables.
1 2 3 4 5 6 7 8 9 10 11
1. Diabetes Distress 1
2. Family Criticism 0.11
3. Daily Hassles 0.44 *** 0.23 ** 1
4. Microaggressions 0.22 ** 0.01 0.14 * 1
5. Financial Events 0.15 * 0.12 0.17 * 0.21 ** 1
6. Negative Life Events 0.16 * 0.06 0.13 0.43 *** 0.38 *** 1
7. HbA1c 0.18 * 0.12 0.04 0.04 0.04 0.03 1
8. Waist-to-Hip Ratio 0.12 0.08 0.07 0.09 0.21 ** 0.15 *
0.04
1
9. Depressive
Symptoms 0.20 ** 0.28 *** 0.26 *** 0.27 *** 0.20 ** 0.22 ** 0.03
0.01
1
10. Medication
Adherence
0.31 ***
0.16 *
0.30 ***
0.14 0.22 ** 0.18 *
0.04
0.01
0.29 ***
1
11. Adherence to Diet
Plan
0.25 ***
0.20 **
0.23 ***
0.03
0.27 ***
0.13
0.08
0.12
0.24 ***
0.34 *** 1
Notes: *** Correlation is significant at the 0.001 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Our next step was to investigate the potential cumulative impact of stressor exposure on health
behaviors and outcomes. Means comparisons results are displayed in Figure 2, with mean values for
each health outcome displayed on the y-axis and the cumulative above-average stressor values plotted
across the x-axis. With the exception of HbA1c, exposure to above-average stressors accumulation
was paired with worsening health/health behaviors. These trends were statistically significant for
depressive symptoms (F(4, 184) = 9.23; p< 0.001), medication adherence (F(4, 184) = 8.82; p< 0.001),
and diet plan adherence (F(4, 184) = 3.03; p< 0.05).
Our final set of analyses explore the relative influence of individual stressors on health when
taking into account possible effects of gender, age, per capita household income, and exposure levels
to all other stressors on the continuum. Looking first at the control variables, older participants
had significantly lower HbA1c values and better medication and diet plan adherence. Females had
significantly lower waist-to-hip ratios and higher depressive symptoms than did males. Per capita
household income was unrelated to any of the outcomes. In Model 1, after accounting for controls
and other stressors, diabetes distress remained a significant predictor of higher HbA1c levels. Those
reporting more negative financial events had greater waist-to-hip ratios in Model 2. Both family
criticism and microaggressions were positively and significantly related to depressive symptoms
(Model 3) and had similar effect sizes (i.e., standardized coefficients). Diabetes distress and daily
hassles were negatively associated with medication adherence with comparable effect sizes as shown in
Model 4. In Model 5, diabetes distress and financial events were negatively associated with adherence
to a diet plan, again with similar effect sizes observed.
Int. J. Environ. Res. Public Health 2017,14, 1074 6 of 11
Int. J. Environ. Res. Public Health 2017, 14, 1074; doi:10.3390/ijerph14091074 6 of 11
Figure 2. Accumulation of stressors by health outcomes.
4. Discussion
Type 2 diabetes mellitus has been labeled an epidemic in many AI communities and has
motivated research to promote a deeper understanding of processes influencing diabetes outcomes
for AI people. While stressors have long been known to influence the onset and course of diabetes,
we move beyond existing literature to investigate associations between a continuum of stressors
[17,18] and health outcomes within five AI communities with consideration of family, chronic
disease, financial, and minority (microaggressions) stress contexts.
In support of Hypothesis 1, our findings reveal linkages between stress exposure and worse
behavioral, psychological, and physical health for AI people. A majority of the stressors from across
the continuum (Figure 1) were associated with worse behavioral (medication and diet plan
adherence) and mental (distress) health in our bivariate models. Diabetes distress was significantly
related to poorer physical health in terms of higher HbA1c values. In addition, the two most discrete
measures stressors, negative financial and stressful life events, were positively associated with
waist-to-hip ratios in bivariate analyses. Thus, while we found at least one significant association
between stress and every type of outcome in this study, the stressors were most consistently
correlated with worse behavioral and mental health conditions (as opposed to physical health).
Depression and nonadherence to medication and diet regimes have been linked to poorer
diabetes-related health including microvascular and macrovascular complications, hospitalizations,
and Emergency Department visits [32–34], so it is possible that the influences of stressful experiences
on mental and behavioral health may lead to worse physical health later on.
1
2
3
4
5
01234+
Diet (days/week)
Stressors
1
2
3
4
01234+
MMAS-4
Stressors
5
6
7
8
9
10
01234+
HbA1c
Stressors
0.5
0.75
1
1.25
1.5
01234+
Hip-to-waist ratio
Stressors
0
2
4
6
8
10
01234+
PHQ-9
Stressors
A
C
B
D
E
Note: X-axis displays the count in above
average exposure to stressors. Box A: A1c. Box
B: Waist-to-hip ratio. Box C: Patient Health
Questionnaire 9-item depressive symptoms
scale. Box D: 4-item Morisky Medication
Adherence Scale. Box E: Average days/week
following a healthy diet plan.
Figure 2. Accumulation of stressors by health outcomes.
4. Discussion
Type 2 diabetes mellitus has been labeled an epidemic in many AI communities and has motivated
research to promote a deeper understanding of processes influencing diabetes outcomes for AI people.
While stressors have long been known to influence the onset and course of diabetes, we move beyond
existing literature to investigate associations between a continuum of stressors [
16
,
17
] and health
outcomes within five AI communities with consideration of family, chronic disease, financial, and
minority (microaggressions) stress contexts.
In support of Hypothesis 1, our findings reveal linkages between stress exposure and worse
behavioral, psychological, and physical health for AI people. A majority of the stressors from across
the continuum (Figure 1) were associated with worse behavioral (medication and diet plan adherence)
and mental (distress) health in our bivariate models. Diabetes distress was significantly related to
poorer physical health in terms of higher HbA1c values. In addition, the two most discrete measures
stressors, negative financial and stressful life events, were positively associated with waist-to-hip ratios
in bivariate analyses. Thus, while we found at least one significant association between stress and every
type of outcome in this study, the stressors were most consistently correlated with worse behavioral and
mental health conditions (as opposed to physical health). Depression and nonadherence to medication
and diet regimes have been linked to poorer diabetes-related health including microvascular and
macrovascular complications, hospitalizations, and Emergency Department visits [
31
33
], so it is
possible that the influences of stressful experiences on mental and behavioral health may lead to worse
physical health later on.
Int. J. Environ. Res. Public Health 2017,14, 1074 7 of 11
In partial support of Hypothesis 2, exposure to multiple stressors appeared to have an
accumulating, negative effect that was statistically significant for diet plan adherence, medication
adherence, and depressive symptoms (Figure 2). Thus, those participants reporting the highest
levels of stress across the continuum were most at risk for treatment non-adherence and depression.
Because most of the stressors in this study focus on recent experiences, the accumulation of life-course
adversity by way of earlier or childhood stress exposure is not captured in these analyses and warrants
future study.
Our community-informed process of stress measurement and the culturally specific focus of
the study is an important contribution given historical omission of Indigenous voices in stress
measurement. Several of our findings are consistent with previous research in samples not including AI
participants. For instance, our general conclusion that stress is associated with worse diabetes-related
outcomes is on par with prior work in diverse settings, as is the finding that women exhibited
higher levels of depressive symptoms than did men [
34
,
35
]. Still, the cultural context of our study
allows for a deeper interpretation of general findings. For instance, being older was associated
with lower HbA1c levels and better medication and diet plan adherence. From an Indigenous
perspective, this “elder effect” bolsters community calls for intergenerational programming and
cultural reverence for elders, suggesting their important role in diabetes prevention, intervention, and
education within AI communities. As another example, microaggressions represent subtle, everyday
forms of discrimination [
36
] and were significantly correlated with depressive symptoms in this sample.
Our measure directly specified microaggressive experiences on the basis of AI group membership,
and this points to the importance of culturally safe care and consideration of privilege dynamics in
diabetes treatment for non-majority cultural group members. This may be particularly critical for AI
communities where legacies of medical mistreatment are coupled with some of the highest rates of
type 2 diabetes in the nation. Family stress is an arguably ubiquitous form of stress cross-culturally,
but the close-knit nature of the small, rural AI communities included in this study, the relevance of
large extended family kinship networks, and the Indigenous cultural notions of belongingness may
create special salience and/or impact of family-related stressors for health [37].
While many researchers consider diabetes distress as an outcome of living with type 2 diabetes,
we conceptualized it here as a disease-specific stressor and an indication of patient struggles with
diabetes management. The results in Table 1show that diabetes distress was associated with all
outcomes with the exception of waist-to-hip ratio. Further, the bivariate correlation between diabetes
distress and depressive symptoms was modest in strength (r= 0.20; p< 0.01). These findings are in
line with the work of Fisher and colleagues who have distinguished diabetes-specific distress from
psychopathology, thereby highlighting the need for consideration of contextual influences on distress
for diabetes patients [
38
]. Other studies have shown that diabetes distress is associated with poorer
glycemic control and worse diabetes outcomes [
33
,
39
]; however, these studies have not included
other stressors. In the present study, diabetes distress was significantly related to HbA1c, medication
adherence, and diet plan adherence net the effects of other stressors and control variables.
Survey-based health research has historically focused primarily on discrete forms of stress (e.g.,
stressful life event checklists). Our examination of the relative influence of stressors vis-à-vis one
another (Table 2) showed that chronic/continuous strains more frequently remained significant
predictors of diabetes outcomes than did discrete forms of stress; in fact, negative life events (perhaps
the most discrete form of stress in this study) were no longer associated with health outcomes in these
models. Previous authors have argued that enduring, chronic stress may be more likely to generate
allostatic load (i.e., wear and tear on the body that can amass with repeated exposure to stress) and
therefore create vulnerability to disease and health problems [
40
], and our findings fall in line with
this argument.
Int. J. Environ. Res. Public Health 2017,14, 1074 8 of 11
Table 2. Ordinary least squares regression analyses of the relative relationships between stressors and health outcomes.
Model 1 Model 2 Model 3 Model 4 Model 5
HbA1c Waist-to-Hip Ratio Depressive Symptoms Medication Adherence Adherence to Diet Plan
B (SD) βpValue B (SD) βpValue B (SD) βpValue B (SD) βpValue B (SD) βpValue
(Constant) 9.23 0.00 0.96 0.00 0.71 0.73 2.73 0.00 2.88 0.00
Age (years) 0.04 0.21 0.01 0.00 0.05 0.50 0.03 0.07 0.29 0.02 0.24 0.00 0.02 0.18 0.01
(0.01) (0.00) (0.03) (0.01) (0.01)
Gender (Female = 1) 0.52 0.12 0.12 0.08 0.14 0.07 2.26 0.20 0.00 0.31 0.12 0.10 0.32 0.10 0.16
(0.33) (0.04) (0.77) (0.18) (0.22)
Household Income 0.02 0.06 0.40 0.00 0.04 0.65 0.06 0.09 0.18 0.01 0.04 0.62 0.02 0.12 0.10
(0.02) (0.00) (0.04) (0.01) (0.01)
Diabetes Distress 0.36 0.22 0.01 0.02 0.11 0.21 0.00 0.00 1.00 0.14 0.15 0.08 0.19 0.16 0.03
(0.13) (0.02) (0.03) (0.08) (0.09)
Family Criticism 0.45 0.09 0.23 0.02 0.03 0.68 3.07 0.24 0.00 0.22 0.08 0.28 0.40 0.11 0.11
(0.37) (0.05) (0.86) (0.20) (0.25)
Daily Hassles 0.02 0.04 0.65 0.00 0.02 0.85 0.17 0.10 0.17 0.05 0.14 0.09 0.06 0.12 0.12
(0.05) (0.01) (0.12) (0.03) (0.04)
Microaggressions 0.22 0.05 0.55 0.00 0.00 1.00 2.62 0.23 0.00 0.09 0.04 0.65 0.20 0.06 0.42
(0.37) (0.05) (0.87) (0.21) (0.25)
Financial Events 0.16 0.11 0.18 0.03 0.15 0.07 0.20 0.05 0.48 0.06 0.07 0.36 0.18 0.17 0.03
(0.12) (0.02) (0.28) (0.07) (0.08)
Negative Life Events
0.01 0.02 0.83 0.01 0.06 0.46 0.12 0.07 0.39 0.03 0.07 0.38 0.00 0.01 0.92
(0.06) (0.01) (0.14) (0.03) (0.04)
Note: Two-tailed tests of significance. Bold indicates those coefficients that are statistically significant (p<0.10).
Int. J. Environ. Res. Public Health 2017,14, 1074 9 of 11
We recognize some important limitations exist in this investigation including the cross-sectional
data, which yield findings that are correlational rather than causal in nature. Because of the multitude
of significant bivariate associations among the independent variables and a limited sample size, the
multivariate results shown in Table 2may be underpowered for detecting meaningful associations
between individual stressors and outcomes relative to all other stressors in the model. We thus chose
0.10 as an alpha value for determining statistical significance which increases possibilities of type 1
errors (i.e., false positives). Our attempt to map stressors onto a continuum ranging from discrete to
chronic in nature is an imperfect approximation of lived experience. For example, while we measure
financial events as singular instances with clear beginning and end points and thus position this
measure on the discrete end of the continuum, such events may signal chronic experiences with
financial strain. In addition, our measure of stressor accumulation does not account for possible
differential effects (e.g., weighting) of specific stress measures. These conceptual limitations should be
noted when interpreting findings.
5. Conclusions
The results of this study suggest that consideration of a range of stressor typologies is necessary
for a complete understanding of processes influencing mental, behavioral, and physical health for AI
patients living with type 2 diabetes. A logical next step is to identify coping resources and responses
that may mitigate or offset the negative impacts of stress on health. In addition, longitudinal analyses
will permit examination of stress proliferation, or the processes through which primary stressors give
rise to additional stressful experiences [
41
]. For example, pervasive chronic strains like poverty may
contribute to more discrete/acute stressful experiences. Of further importance is identifying how we
can intervene on the proliferation process to reduce stress burden for patients living with chronic
diseases. From a clinical standpoint and given these connections between stress and diabetes health,
in-depth social histories may help diabetes care providers understand patient experiences with stress
in order to activate coping tools and open doors for coping resources. In addition, policies aimed at
ameliorating chronic exposure to stressors like financial strain (poverty) and educational programing
to increase awareness of the nature and impact of microaggressive experiences may address root
determinants of stress exposure for AI patients.
Acknowledgments:
We gratefully acknowledge the dedicated Community Research Council and Clinical Project
Members of the Gathering for Health team: Sidnee Kellar, Rose Barber, Robert Miller, Tweed Shuman, Lorraine
Smith, Sandy Zeznanski, Patty Subera, Tracy Martin, Geraldine Whiteman, Lisa Perry, Trisha Prentice, Alexis
Mason, Charity Prentice-Pemberton, Kathy Dudley, Romona Nelson, Eileen Miller, Geraldine Brun, Murphy
Thomas, Mary Sikora-Petersen, Tina Handeland, GayeAnn Allen, Frances Whitfield, Phillip Chapman, Sr.,
Hope Williams, Betty Jo Graveen, Daniel Chapman, Jr., Doris Isham, Stan Day, Jane Villebrun, Beverly Steel,
Muriel Deegan, Peggy Connor, Michael Connor, Ray E. Villebrun, Sr., Pam Hughes, Cindy McDougall, Melanie
McMichael, Robert Thompson, and Sandra Kier. Research reported in this manuscript is supported by the National
Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Number
DK091250 (M. Walls, PI). The content is solely the responsibility of the authors and does not necessarily represent
the official views of the National Institutes of Health.
Author Contributions:
Melissa L. Walls served as principal investigator for the project, led the design of the work
and supervised acquisition of data. Melissa L. Walls analyzed data for the current manuscript and led the first
written draft. Kelley J. Sittner assisted with the conception of the manuscript focus, drafted specific sections of
the manuscript, assisted with interpretation of the data, and provided editing and revision support for working
drafts. Benjamin D. Aronson contributed to the interpretation of the data and provided revisions for drafts of the
work. Angie K. Forsberg offered substantial contributions to the acquisition of the data and revised and edited the
final manuscript. Les B. Whitbeck originally conceived of the design for the study, contributed to interpretation of
the data, and critically revised drafts. Mustafa al’Absi contributed to the original conception of the project design,
assisted with interpretation of findings, and critically revised drafts. All authors approved the final version to be
published and agree to be accountable for all aspects of the work.
Conflicts of Interest: The authors report no conflicts of interest.
Int. J. Environ. Res. Public Health 2017,14, 1074 10 of 11
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©
2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... A large body of research implicates the repeated daily experiences of discrimination as both acute and chronic stressors, which increase allostatic load and have long-term health consequences [183]. Among IP around the world, discrimination and microaggressions have been associated with a higher likelihood of self-reported diabetes among southwest AIs [56], increased diabetes distress and higher blood pressure in AI from the northern Midwest [184][185][186], higher self-reported CVD (including heart attack and stroke) among Maori in New Zealand [187] and CVD diagnosis among the Indigenous Sami population in Norway [188]. ...
... Additionally, negative emotional states, including depression, anxiety, anger, and hostility, have also been proposed to act as a mechanism for discrimination's negative effect on health [189]. The experience of discrimination among Indigenous people is associated with depression [186,190,191], anxiety [192], anger and aggression [193,194] and more general mental health concerns [187,195]. Mental health appears to function as a mediator between negative life experience and CMD risk, possibly due to severity of life experience or lowered coping mechanisms that lead to reduced positive health behaviors or significant damage to physiological functioning. ...
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Errors in Byline, Author Affiliations, and Acknowledgment. In the Original Article titled “Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication,” published in the June issue of the ARCHIVES (2005;62:617-627), an author’s name was inadvertently omitted from the byline on page 617. The byline should have appeared as follows: “Ronald C. Kessler, PhD; Wai Tat Chiu, AM; Olga Demler, MA, MS; Kathleen R. Merikangas, PhD; Ellen E. Walters, MS.” Also on that page, the affiliations paragraph should have appeared as follows: Department of Health Care Policy, Harvard Medical School, Boston, Mass (Drs Kessler, Chiu, Demler, and Walters); Section on Developmental Genetic Epidemiology, National Institute of Mental Health, Bethesda, Md (Dr Merikangas). On page 626, the acknowledgment paragraph should have appeared as follows: We thank Jerry Garcia, BA, Sara Belopavlovich, BA, Eric Bourke, BA, and Todd Strauss, MAT, for assistance with manuscript preparation and the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on the data analysis. We appreciate the helpful comments of William Eaton, PhD, Michael Von Korff, ScD, and Hans-Ulrich Wittchen, PhD, on earlier manuscripts. Online versions of this article on the Archives of General Psychiatry Web site were corrected on June 10, 2005.
Chapter
The word stress has many connotations. There are two quite distinct areas of ambiguity surrounding this term. One has to do with the stage of the stress process at which stress occurs. Some use stress to refer to the problems people face (the stimulus), others to refer to the generalized response to these problems (as in “psychological stress”), and still others to refer to a mediating state of the organism in response to threat that may or may not generalize (the black box between stimulus and generalized response). It may be helpful, therefore, to distinguish at the outset among Stressors, stress, and distress—the stimulus problem, the processing state of the organism that remains unmapped in the psychosocial approach, and the generalized behavioral response. The term strain is also sometimes used to refer to Stressors, but I use it, following its original meaning, to refer to the response side of the model.