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. Author manuscript; available in PMC: 2026 Mar 2.
Published in final edited form as: Transcult Psychiatry. 2025 Dec 16;63(1):56–69. doi: 10.1177/13634615251379451

Grief and Historical Trauma Among American Indian and Alaska Native Populations: A Cluster Analysis

Julie A Gameon 1, Monica C Skewes 2, Cory J Counts 2, Neha A John-Henderson 2
PMCID: PMC12950209  NIHMSID: NIHMS2143103  PMID: 41400964

Abstract

American Indian and Alaska Native (AI/AN) communities face significant social, economic, and health disparities rooted in historical traumas stemming from colonization. These disparities manifest in disproportionate rates of poverty, unemployment, and early mortality among AI/AN populations. High rates of premature deaths contribute to a persistent cycle of grief and loss, perpetuating mental and physical health problems. The current study explores the relationships between grief, historical trauma, and psychological and physical health outcomes among AI/AN individuals who have experienced the loss of a loved one. A cross-sectional survey was conducted with 576 AI adults, measuring complicated grief, adaptive grief, historical trauma, anxiety, depression, alcohol use problems, sleep health, and physical health. Cluster analysis revealed four distinct groups based on levels of complicated grief and historical trauma. Results indicated that higher levels of complicated grief and historical trauma were associated with worse mental and physical health outcomes. Moreover, individuals living on reservations reported higher levels of loss and historical trauma. Adaptive grief remained consistent across groups, suggesting a stable coping mechanism irrespective of grief severity. Findings underscore the need for interventions addressing grief and historical trauma to improve overall health outcomes in AI/AN communities.

Keywords: American Indian / Alaska Native, cluster analysis, grief, historical trauma

American Indian and Alaska Native (AI/AN) people experience social and economic inequities that result in significant health disparities and early mortality. For example, AI/ANs experience poverty at a disproportionate rate due to factors rooted in colonization (Davis et al., 2015), and poverty results in poorer health outcomes and greater early mortality (World Health Organization, 2003). Relative to the general population, AI/ANs are approximately twice as likely to experience poverty and unemployment (Castor et al., 2006). Life expectancy at birth for AI/ANs is lower than that of all other ethnic groups in the United States (Arias et al., 2021), with high mortality rates attributed to suicide, substance use, accidents and injuries, and chronic illnesses (Indian Health Service, 2018). One outcome of high rates of premature deaths among AI/ANs is a persistent cycle of grief and loss that translates into mental and physical health problems (Gameon, FireMoon, et al., 2024a). Among the many health concerns that may be caused or exacerbated by grief are anxiety, depression, substance use, sleep problems, and chronic illnesses (Kristensen et al., 2012; Masferrer et al., 2017; Stroebe et al., 2007). These prevalent health concerns represent important health disparities affecting AI/AN communities. Research is needed to understand the role of grief in health outcomes among AI/ANs.

Grief and Loss

Grief is defined as the emotional, psychological, behavioral, social, and physical reactions people experience when someone close to them dies (Boerner et al., 2016). Numerous factors influence a person’s experience with grieving, including their relationship with the deceased, the cause of death (e.g., prolonged illness, accidental death), cultural beliefs about death, and past experiences with loss (Arizmendi & O’Connor, 2015; Shear, 2015). While most people are able to cope with the loss of a loved one and eventually heal, some experience a prolonged or abnormal grieving process referred to as complicated grief (or Prolonged Grief Disorder or Persistent Complex Bereavement Disorder; Prigerson et al., 2009; Shear, 2015). Complicated grief represents a deviation from cultural and societal norms for grieving in either the length or intensity of the symptoms experienced (Arizmendi & O’Connor, 2015; Shear, 2015). Symptoms of complicated grief include a) intense sadness or distress that does not improve as time passes, b) continual yearning for the deceased person, c) digestive issues, d) continually ruminating on the death, e) emptiness, f) inability to perform daily activities, g) loss of interest in hobbies, h) fatigue, i) hallucinations of the deceased, j) loneliness, and k) suicidal ideation (American Psychiatric Association, 2022).

While grief research has focused on the maladaptive aspects of grief and its association with poor health outcomes, research has shifted to include adaptive grieving elements. Adaptive grief is similar to posttraumatic growth, which examines personal growth after a traumatic experience (Splevins et al., 2010; Tedeschi & Calhoun, 1996). Posttraumatic growth may involve spiritual growth or change, improved and more satisfying relationships, greater maturity, a changed philosophy of life, and a positive influence on life goals (Splevins et al., 2010; Tedeschi & Calhoun, 1996). Adaptive coping skills, such as utilizing social support, facilitate better outcomes after the loss of a loved one. Studies examining predictors of adaptive coping have found that people with a strong social support network of friends, family, and community members have a greater opportunity to rely on others for social support during the grieving process, resulting in better outcomes (Cacciatore et al., 2021; Howard Sharp et al., 2018).

Grief in AI/AN Populations

The DSM diagnostic criteria for complicated grief do not capture the cultural factors that may influence how AI/AN people experience grief. Cultural traditions influence how people understand death, mourning practices, and how people cope with death, suggesting that existing diagnostic criteria for complicated grief may not be appropriate for some individuals from ethnic/racial minority groups (Smid et al., 2018). Moreover, AI/AN people may not express complicated grief-related psychological distress in an expected pattern (Smid et al., 2018). Cultural differences in mental health diagnosis are not limited to complicated grief, as Indigenous researchers have advocated for more culturally informed diagnostic criteria for AI/AN people (Gone, 2023; Gone & Kirmayer, 2020; Langa & Gone, 2020). Complicated grief diagnostic criteria should account for important aspects of lived experiences (e.g., historical trauma, social networks, cultural norms) that influence how AI/AN people cope with the loss of a loved one.

Grief in Native communities is often experienced collectively, reflecting the strong interconnections within these groups. AI/AN communities often have a collectivist cultural orientation, with a long history of mutual care and support, fostering strong kinship-based relationships (Mohatt, Hazel et al., 2004a, 2004b). These relationships extend beyond biological ties to include communal and cultural bonds, providing a broader social network for support in times of distress (Roh et al., 2015; Stumblingbear-Riddle & Romans, 2012). However, a larger kinship network also increases the likelihood of experiencing more frequent losses. In close-knit AI/AN communities, the grief caused by the death of someone outside a person’s biological family can ripple across the entire community (Skewes et al., 2020). In fact, AI/AN people experience more frequent significant losses when compared to non-Native people, with an estimated one to two losses per year, compared to one to two deaths every decade in non-Native populations (Dankovchik et al., 2015).

While much research on AI/AN grief focuses on health disparities and causes of death, fewer studies address culturally specific aspects of grieving. One qualitative study with Lakota tribal members found that grief practices focusing on connection to family and community were seen as most helpful in processing loss (Stone, 1998). This study also highlighted the role of spirituality in the grieving process, with participants preferring either traditional cultural or Christian practices depending on their cultural connection (Stone, 1998). Other studies found that Native spiritual beliefs and traditional practices were crucial for coping with grief (Randall, 2018). Importantly, the death of a tribal Elder was viewed as having a profound impact on both the individual and the community due to the loss of cultural knowledge and leadership the Elder provided (Shunkamolah, 2009).

Existing evidence-based practices for complicated grief have not been tested in AI/AN populations. Moreover, they may not reflect the cultural norms and traditions of AI/AN people. Therefore, interventions developed to address mental health among AI/ANs need to be culturally grounded to effectively treat psychological distress while embracing cultural traditions and spiritual practices (Gone, 2022; Manson, 2020; Walters et al., 2020). AI/AN communities hold the knowledge of how to support their people following the loss of a loved one. Researchers must respect this knowledge and work with communities to find ways to meld cultural knowledge and evidence-based practices to maximize the impact of efforts to help people struggling with grief (Joseph, 2024; Walters et al., 2009).

Health Disparities

There is evidence of greater rates of mental and physical health problems associated with grief among AI/AN populations, including anxiety, depression, alcohol problems, sleep problems, and physical illnesses. Anxiety and depression are comorbid disorders experienced disproportionately by AI/ANs in the United States (Beals et al., 2005; Brave Heart et al., 2016; McKinley et al., 2021) and are tied to suicide (Kalin, 2021; Thompson et al., 2005). Suicide represents a critical health concern in Indian Country, with suicide rates 20% higher among AI/ANs than among Whites (US Department of Health and Human Services, 2019). Moreover, epidemiological data show an increase of 20% in suicide rates among non-Hispanic AI/ANs between 2015 and 2020, an increase markedly greater than the <1% increase seen in the overall US population (Stephenson, 2022; Stone, 2022). Grief has been shown to be linked to a higher risk of suicidal ideation (Prigerson et al., 1999) and, among ANs, to suicide attempts (Gregory, 1994).

Alcohol use disorder (AUD) is also disproportionately represented among AI/ANs despite greater rates of lifetime and past-year abstinence relative to other ethnic and racial groups in the United States (Cunningham et al., 2016). That is, while many AI/AN people do not drink alcohol at all, there is evidence of greater rates of alcohol problems and AUD among those who do (Grant et al., 2015, 2016). Despite widespread notions that greater alcohol problems among AI/ANs are attributable to biological or genetic factors, there is little evidence that biogenetic predispositions are responsible for alcohol-related health disparities (Ehlers & Gizer, 2013; Enoch & Albaugh, 2017). Rather, inequities in the social determinants of health and historical factors are important predictors of alcohol problems in Native communities (Evans-Campbell et al., 2012; Gameon & Skewes, 2021; Skewes & Blume, 2019; Warne & Frizzell, 2014). Notably, alcohol problems are comorbid with anxiety and depression (Klimkiewicz et al., 2015; Sullivan et al., 2005; Swendsen et al., 1998; Swendsen & Merikangas, 2000), suicide (Boenisch et al., 2010; Norström & Rossow, 2016; Sher, 2006; Sullivan et al., 2005), and also linked to grief and loss (Parisi et al., 2019; Zuckoff et al., 2006), including in research with AI/ANs (Gameon, 2021; Gameon, FireMoon et al., 2024a, 2024b).

Sleep problems represent another important health concern affecting AI/AN populations (Ehlers et al., 2017). The estimated prevalence of insufficient sleep among AI/ANs ranges from 15% to 40% and is associated with cardiometabolic risk, poor physical health, chronic pain, depression and anxiety, psychological distress, alcohol use, and substance use disorders (Arnold et al., 2013; Ehlers et al., 2017; Lombardero et al., 2019). Prior work in AI adults indicates that sleep is linked to social processes and psychological factors, including loneliness and a sense of control over external stressors (John-Henderson, Palmer & Thomas, 2019; John-Henderson et al., 2021a, 2021b). Sleep problems are so detrimental to mental health that they are now considered a risk factor for suicide (Goldstein et al., 2008; Liu, 2004; Liu & Buysse, 2006; McCall, 2011). Poor sleep quality also has been shown to be associated with grief (Milic et al., 2019). Bereaved people have greater rates of insomnia than non-bereaved people, and people with insomnia reported greater disruptive symptoms of grief relative to those without insomnia (Hardison et al., 2005). Complicated grief, in particular, is associated with sleep problems even after controlling for depression and posttraumatic stress disorder symptoms (Szuhany et al., 2020).

In addition to the health problems discussed above, AI/ANs experience greater rates of physical illness across a spectrum of diseases and disorders relative to other groups in the United States (e.g., Espey et al., 2014; Kim et al., 2012). Among the most striking health disparities are those related to cardiovascular health and associated risk factors (Hutchinson & Shin, 2014), diabetes (O’Connell et al., 2010), cancer (Liddell et al., 2018; Roubidoux et al., 2022), chronic liver disease (Suryaprasad et al., 2014), chronic kidney disease (Narva & Sequist, 2010), and chronic respiratory disease (Laffey et al., 2021) in AI/AN populations. In addition, self-reported health is poorer among AI/ANs relative to other racial and ethnic groups (Adakai et al., 2018). These health problems are exacerbated by inadequate access and other barriers to appropriate health care (Cromer et al., 2019; Espey et al., 2014; Kim et al., 2012; Liddell et al., 2018), likely contributing to greater mortality rates.

Grief and Health

There is evidence that grief is a risk factor for the mental and physical health problems discussed above. Research has found that complicated grief symptoms among bereaved participants prospectively predicted incidences of cancer, indicators of heart disease, smoking, poor diet, impaired sleep, depression, anxiety, suicidal ideation, and alcohol problems (Prigerson et al., 1997). One study with soldiers found that there was a dose-dependent relationship between difficulty coping with grief and physical health, with increased difficulty predicting worse health outcomes (Toblin et al., 2012). This relationship held after controlling for other risk factors, such as combat experiences, injuries, depression, and PTSD. Among parents who had lost a child, difficulty with grief was associated with worse self-reported psychological and physical health, sleep difficulties, use of medical services, and use of sick leave (Lannen et al., 2008). Another study with 20,453 participants found that severe grief was associated with poor self-reported health, greater mental health symptoms, and greater use of health services (Thimm et al., 2020). Due to high rates of premature deaths from various causes among AI/ANs, it is likely that grief plays a role in the various health disparities affecting Native communities, creating a cycle of loss, grief, health problems, and more loss.

Historical Trauma

Research on grief and health among AI/AN people also must consider the role of collective grief resulting from losses accumulated throughout history. Historical trauma refers to the cumulative effects of mass trauma experienced across generations of AI/ANs (Brave Heart & DeBruyn, 1998; Evans-Campbell, 2008; Whitbeck et al., 2004). Native people have endured numerous losses throughout history stemming from genocidal government policies and violent acts of colonialism, and grief from these historical losses continues to affect health outcomes today (Evans-Campbell, 2008; Smallwood et al., 2021; Warne & Frizzell, 2014; Warne & Lajimodiere, 2015). A review of historical trauma research with AI/ANs reported significant associations between historical trauma and negative health outcomes (Gone et al., 2019). In particular, historical trauma has been shown to be associated with mental health symptoms such as depression, anger, and anxiety (Duran et al., 1998; Walls & Whitbeck, 2011; Whitbeck et al., 2004), substance use (Brockie et al., 2015; Pokhrel & Herzog, 2014; Whitbeck et al., 2004), and risk for cardiovascular disease and poor mental health (John-Henderson et al., 2022; John-Henderson & Ginty, 2023). Of note, underlying historical trauma predicted greater psychological stress in response to a new negative life event (John-Henderson & Ginty, 2020), suggesting that historical trauma may increase the likelihood of complicated grief in response to a new loss.

Current Study

A cross-sectional survey was conducted with AI participants to examine relationships between complicated grief, historical trauma, and mental and physical health. The current research is part of a larger project on various forms of trauma and health in American Indians, with a focus on resilience factors. All measures included in the current work were reviewed by a long-standing Community Advisory Board (CAB) comprised of community members of an American Indian tribe in Montana to ensure that measures were appropriate and relevant.

Methods

Participants

To be eligible for the study, participants had to self-identify as AI, currently live within the United States, and be at least 18 years old. The sample consisted of 576 AI adults (M = 39 years, SD = 15 years). 59.4% (n= 342) identified as male, and 40.6% (n= 234) identified as female. The majority of the sample did not live on a tribal reservation (84%; n = 484). Participants reported losing about two important people two years before data collection. Table 1 presents the mean, standard deviation, observed range values, and internal consistency for the main variables of interest.

Table 1.

Descriptive Statistics (N = 576).

Variable Observed Range Mean SD α
Age 18–95 39.19 15.59
Complicated grief  0–76 26.83 20.23 0.97
Adaptive grief  0–28 11.78  7.03 0.89
Historical trauma 12–72 34.00 16.53 0.96
Important people lost in past 2 years  0–10  2.60  2.41
Depression  0–20  7.33  4.28 0.73
Anxiety  0–21  9.98  5.20 0.71
Alcohol use problems  0–40  8.79  7.95 0.83
Sleep  0–12  6.23  2.89 0.75
Physical health  0–29  5.69  5.15 0.92
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Procedure

The University Institutional Review Board approved the current study. Qualtrics recruited participants and provided electronic informed consent before they were granted access to the web-based survey. Qualtrics draws participants from managed research panels for groups that are harder to reach, including AIs, through niche panels developed through targeted recruiting. Qualtrics collected and screened all data for quality. All de-identified data was sent to the principal investigator in an Excel file and subsequently transferred to SPSS (IBM V 29) for statistical analyses.

Measures

When available, measures that were developed and validated for use in AI/AN communities were used in the current study. This includes the Indigenous Grief Inventory and the Historical Loss Scale. Other measures were validated in non-Native populations but have been used in other studies with AI/AN people.

Grief..

The 26-item Indigenous Grief Inventory (IGI; Gameon, 2021) was used to assess complicated grief reactions associated with losing a loved one and experiences with healing from grief and loss. The IGI is comprised of two subscales assessing complicated grief (e.g., “I feel that life is meaningless without the person who passed”) and adaptive grief (e.g., “Since my loss, I have found new opportunities to help others and serve my community”). When completing the measure, participants are prompted to think of a loved one whose loss has greatly affected them and to select a response that best describes their experience. Response options ranged from 0 (never) to 4 (always).

Historical Trauma.

Historical trauma thoughts were measured using the 12-item Historical Loss Scale (HLS; Whitbeck et al., 2004). Participants were asked to report the frequency with which they think about specific historical losses experienced by AI/AN people on a scale from 6 (several times a day) to 1 (never). Item responses are summed to yield a total historical loss score, with higher scores indicating greater historical trauma.

Anxiety and Depression.

The Hospital Anxiety and Depression Scale (HADS; Snaith, 2003) is a 14-item measure used to assess current symptoms of depression and anxiety. The depression and anxiety subscales are comprised of seven items each, and participants were asked to rate the extent to which they had experienced each symptom in the previous week on a four-point response scale (0–3). Responses are summed to yield a total score for each subscale ranging between 0 and 21. This scale has been used with AI/AN people in other studies (e.g., John-Henderson & Ginty, 2023).

Alcohol Use Problems.

The Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993) is a 10-item self-report AUDIT screening tool for hazardous drinking. Participants were asked to review a description of a standard drink (i.e., 12 oz. of regular beer or hard seltzer, 8–9 oz. of craft beer, 4–5 oz. of wine, or 1.5 oz. of hard liquor) as well as images depicting a standard drink. Items are scored from 0 to 6, and responses are summed to yield a total score ranging from 0 to 60. This measure was previously used to assess alcohol use problems in AI/AN populations (e.g., John-Henderson & Ginty, 2023).

Sleep Health.

The RU-SATED (Buysse, 2014) measure is used to assess sleep health across six dimensions: regularity (how often the participant goes to bed and gets out of bed at the same time each day), satisfaction (how often participants are satisfied with their sleep), alertness (how often the participant stays awake all day without dozing), timing (how often they are asleep or in bed between hours of 2:00 am and 4:00 am), efficiency (how often they are awake for 30 min of less during the night), and duration (how often they sleep between 7 and 9 h a day. Response options are 0 (rarely/never), 1 (sometimes), and 2 (usually/always). Responses are summed across the six dimensions; higher scores reflect better sleep health. This measure has been used in previous studies with AI/AN populations (e.g., John-Henderson et al., 2021a).

Physical Health.

Physical health was assessed using Midlife in the USA national survey items (Ryff & Almeida, 2009). Participants were provided a list of 30 items related to chronic health problems (e.g., diabetes, asthma, arthritis). They were asked to select “yes” or “no” if they had experienced a health problem in the past 12 months. The measure was previously used to assess chronic health conditions in adult AI samples (e.g., John-Henderson & Ginty, 2023).

Data Analysis

Statistical analyses were conducted using SPSS 29.0 statistical software. Prior to data analysis, variables were screened for skewness and kurtosis. All variables resulted within the acceptable range of −2 and +2 (Podsakoff et al., 2003). Cluster analyses were used to identify subgroups related to grief and historical trauma in the sample (Clatworthy et al., 2005; Murtagh & Pierre, 2014). The analysis utilized Ward’s method of sample clustering (Murtagh & Pierre, 2014). Ward’s method is advantageous in that it generates clusters that are as equal as possible in proximity of mean values by calculating the mean of each cluster and determining the distance value between each item in a given cluster. Next, the number of clusters was determined using a dendrogram, and K-means, or an iterative partitioning cluster method, was used to find similarities and dissimilarities within the clusters. Once the cluster structure was determined, analyses of variance (ANOVAs) were performed with other study variables to create profiles for each cluster.

Results

Cluster Analysis

A hierarchal cluster analysis was conducted to identify a significant grouping of participants based on reports of historical trauma and loss of individuals in one’s life. In the current sample, Ward’s cluster method generated four distinct clusters based upon responses, resulting in the smallest sum of squares producing distinct cluster values within the data range. Visual inspection of the dendrogram and agglomeration coefficients obtained with Ward’s method indicated a five-cluster solution. Cluster memberships were then determined through non-hierarchical K-means cluster analysis computed to identify an optimal five-factor solution. The five-cluster solution was not supported, as clusters were not significantly different. A four-cluster solution was supported by analyses indicating a difference in fit between Ward’s method and K-means clustering. The four-cluster solution is depicted in Figure 1. Independent one-way ANOVAs and post hoc analyses showed that the four clusters differed on the CG and HT variables (p < .05). On the AG variable, cluster 1 was significantly lower than other clusters. However, AG was not significantly different between clusters 2, 3, and 4 (Table 2).

Figure 1.

Figure 1.

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Complicated Grief, Adaptive Grief, and Historical Trauma Means. Note. CG and HT Means Were Significantly Different Across All Clusters. AG Means Were Significantly Different at Cluster 1 Compared to Clusters 2, 3, and 4.

Table 2.

Cluster Analysis (N = 576).

Cluster 1 (N = 196) Cluster 2 (N = 102) Cluster 3 (N = 174) Cluster 4 (N = 104)
Age 43.30 (17.62)bcd 33.29 (11.47)a 35.80 (14.06)a 37.42 (14.96)a
Sex (% female) 32.7 43.1 44.2 47.1
Reservation statuse (% on reservation)  8.7 30.4 19  8.7
Complicated grief  7.03 (5.23)bcd 54.01 (10.65)acd 39.45 (8.42)abd 16.38 (9.93)abc
Adaptive grief  9.52 (8.26)bcd 12.34 (7.72)a 12.90 (5.51)a 11.67 (6.76)a
Important people lost in past 2 years  1.87 (1.99)bc  3.71 (2.70)acd  2.90 (2.37)ab  2.59 (2.43)b
Historical trauma 18.10 (6.59)bcd 53.63 (10.21)acd 32.43 (10.28)abd 47.37 (10.78)abc
Depression  4.87 (3.95)bcd  9.23 (3.48)ad  9.35 (3.44)ad  6.75 (4.31)abc
Anxiety  6.31 (4.64)bcd 13.82 (3.56)acd 12.03 (3.81)abd  9.67 (5.09)abc
Alcohol use problems  6.02 (5.15)bc 13.47 (10.50)ad 11.04 (8.57)ad  6.73 (5.88)bc
Sleep  7.05 (3.22)bc  5.71 (2.95)a  5.55 (2.32)a  6.37 (2.67)
Physical health  2.47 (1.39)bcd  8.44 (6.81)ad  6.70 (5.43)ad  4.42 (3.88)abc
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Note. Values are reported as mean (s.d.), Differences denote p < 0.05.

a

Different from Cluster 1.

b

Different from Cluster 2.

c

Different from Cluster 3.

d

Different from Cluster 4.

e

Denotes significant chi-square (p < 0.05).

Group Comparisons

Analysis of other study variables revealed several significant differences between the clusters. Table 2 shows descriptive statistics for each cluster and ANOVAs, including the results of post hoc analyses.

Cluster 1 (Low CG, Low HT).

Cluster 1 had lost significantly fewer individuals in the past two years compared to Cluster 2 (p=<.001, 95% CI = −38.53, −32.53), Cluster 3 (p=<.001, 95% CI= −16.89, −11.77), and Cluster 4 (p = <.001, 95% CI= −32.25, −26.29). Cluster 1 was significantly lower in anxiety compared to Cluster 2 (p = <.001, 95% CI = −8.9, −6.12), Cluster 3 (p=<.001, 95% CI= −6.9, −4.53), and Cluster 4 (p=<.001, 95% CI = −4.74, −1.97). Cluster 1 was significantly lower in depression compared to Cluster 2 (p = <.001, 95% CI= −5.56, −3.11), Cluster 3 (p=<.001, 95% CI = −5.52, −3.43), and Cluster 4 (p=<.001, 95% CI = −3.1, −.66).

Cluster 1 had significantly lower alcohol use problems compared to Cluster 2 (p = <.001, 95% CI = −10.02, −4.89) and Cluster 3 (p = <.001, 95% CI = −7.17, −2.88). Cluster 1 had significantly better self-reported sleep compared to Cluster 2 (p = <.001, 95% CI = .43, 2.25) and Cluster 3 (p = <.001, 95% CI =.73, 2.28). Cluster 1 had significantly better self-reported physical health outcomes in the past 12 months compared to Cluster 2 (p = <.001, 95% CI = −7.65, −4.27), Cluster 3 (p=<.001, 95% CI= −5.66, −2.77), and Cluster 4 (p=<.001, 95% CI = −3.62, −.27). Cluster 1 was significantly older compared to Cluster 2 (p=<.001, 95% CI = 5.11, 14.89), Cluster 3 (p = <.001, 95% CI = 3.32, 11.67), and Cluster 4 (p =.009, 95% CI = 1.01, 10.74).

Cluster 2 (High CG, High HT).

Cluster 2 had lost significantly more individuals in the past two years compared to Cluster 1 (p = <.001, 95% CI = 32.53, 38.52), Cluster 3 (p=<.001, 95% CI = 18.14, 24.27), and Cluster 4 (p=<.001, 95% CI = 2.84, 9.68). Cluster 2 was significantly higher in anxiety compared to Cluster 1 (p = <.001, 95% CI= −8.9, −6.12), Cluster 3 (p=<.001, 95% CI = −6.9, −4.53), and Cluster 4 (p=<.001, 95% CI = −4.74, −1.97). Cluster 2 was significantly higher in depression compared to Cluster 1 (p = <.001, 95% CI = −5.56, −3.11) and Cluster 4 (p=<.001, 95% CI = −3.1, −.66).

Cluster 2 had significantly higher alcohol use problems compared to Cluster 1 (p = <.001, 95% CI = −10.02, −4.89) and Cluster 4 (p=<.001, 95% CI= −7.17, −2.88). Cluster 2 had significantly worse self-reported sleep compared to Cluster 1 (p = <.001, 95% CI = .43, 2.25) but was not significantly different than Cluster 3 and Cluster 4 (p < .05). Cluster 2 had significantly worse self-reported physical health outcomes in the past 12 months compared to Cluster 1 (p=<.001, 95% CI = −7.65, −4.27) and Cluster 4 (p = <.001, 95% CI = −3.62, −.27). Cluster 2 had the highest percentage of individuals living within a reservation ((p = .013, 95% CI = −.35, −.03).

Cluster 3 (Moderate CG, Moderate HT).

Cluster 3 had lost significantly more individuals in the past 12 months compared to Cluster 1 (p=<.001, 95% CI= 11.06, −1.39), but significantly fewer than Cluster 2 (p = <.001, 95% CI = −24.27, −18.13). Cluster 3 was significantly higher in anxiety compared to Cluster 1 (p = <.001, 95% CI = 4.53, 6.9) and Cluster 4 (p=<.001, 95% CI =.93, 3.77) but significantly lower compared to Cluster 2 (p = .005, 95% CI= −3.22, −.37). Cluster 3 was significantly higher in depression compared to Cluster 1 (p = <.001, 95% CI = 3.43, 5.52) and Cluster 4 (p=<.001, 95% CI= 1.35, 3.84).

Cluster 3 had significantly higher alcohol use problems compared to Cluster 1 (p = <.001, 95% CI = 2.88, 7.17) and Cluster 4 (p=<.001, 95% CI = 1.78, 6.83). Cluster 3 had significantly worse self-reported sleep compared to Cluster 1 (p=<.001, 95% CI = −2.28, −.73). Cluster 3 had significantly worse self-reported physical health outcomes in the past 12 months compared to Cluster 1 (p = <.001, 95% CI = 2.77, 5.66) and Cluster 4 (p=<.001, 95% CI =.56, 3.99).

Cluster 4 (Low CG, High HT).

Cluster 4 had lost significantly fewer individuals in the past two years compared to Cluster 2 (p=<.001, 95% CI = −9.68, −2.83). Cluster 4 was significantly higher in anxiety compared to Cluster 1 (p = <.001, 95% CI = 1.98, 4.75), and significantly lower in anxiety than Cluster 2 (p = <.001, 95% CI = −5.74, −2.56) and Cluster 3 (p = <.001, 95% CI = −3.77, −.94). Cluster 4 was significantly higher in depression compared to Cluster 1 (p = <.001, 95% CI = 1.35, 3.84), but significantly lower in depression compared to Cluster 2 (p = <.001, 95% CI= −3.86, −1.35) and Cluster 3 (p=<.001, 95% CI = −3.84, −1.35).

Cluster 4 had significantly lower alcohol use problems compared to Cluster 2 (p = <.001, 95% CI = −9.63, −3.85) and Cluster 3 (p=<.001, 95% CI= −6.83, −1.78). Cluster 4 was not significantly different in self-reported sleep compared to all other Clusters (ps> .05). Cluster 4 had significantly worse self-reported physical health outcomes in the past 12 months compared to Cluster 1 (p = <.001, 95% CI= .265, 3.62), but significantly better self-reported symptoms compared to Cluster 2 (p = <.001, 95% CI = −5.95, −2.09) and Cluster 3 (p = <.001, 95% CI = −3.99, −.56).

Discussion

The current study aimed to use a data-driven approach to identify profiles of historical trauma and grief among AI/AN people. To accomplish this, a cluster analysis was performed to discover how complicated grief, adaptive grief, and historical trauma group together and are associated with health outcomes for AI people who have experienced the loss of a loved one.

Complicated Grief and Historical Trauma

When looking at the cluster of AI adults with high CG and HT, it appears that grief and historical trauma have an additive effect leading to worse outcomes. This supports past studies (e.g., Gameon, 2021; Gameon, FireMoon et al., 2024a, 2024b) in which participants reported that historical and current experiences of grief are connected and collectively make it harder to cope with loss. In the present study, participants with high CG and HT reported poorer mental and physical health than the other clusters. They also reported more losses in the past two years, higher depression and anxiety scores, greater alcohol use, poorer sleep, and poorer physical health scores. These findings support the need for interventions to help people cope with grief after a loss and suggest the need to augment individual-level grief interventions with historical trauma or collective grief interventions. Native cultural practices may be beneficial in this regard. In addition, interventions aimed at addressing substance use, mental health problems, and physical health problems in Native communities may be more effective if they involve intervention content designed to address HT and CG in tandem with the targeted health concern. Treating complicated grief may improve other health outcomes. Interventions tailored to help people with grief would benefit from existing historical trauma interventions, such as Oyate Ptayela (Brave Heart, 1999; Brave Heart & DeBruyn, 1998). This would be important in helping people who are coping with distress from current and historical loss.

AI/AN People Living on Reservations

Interestingly, participants in Cluster 2 (high CG and high HT) and Cluster 3 (moderate CG and moderate HT) were more likely to live on an AI reservation than those in other clusters. Of the 92 people in the sample who reported living on a reservation, 64 were in Clusters 2 and 3 (69.57%). This finding suggests that participants who live on reservations experience greater complicated grief and greater historical trauma. Expanded kinship networks in reservation communities may explain this (Mohatt et al., 2004a, 2024b). People who live on reservations are more closely connected to community members, with losses exerting broader effects on CG. It also may reflect inequities in the social determinants of health in reservation communities that contribute to greater rates of early mortality, which then affect CG. Participants in Cluster 2 reported the greatest number of losses in the previous two years, and the greatest percentage of respondents living on reservations was relative to all other clusters.

Moreover, greater cultural embeddedness in reservation communities may contribute to greater awareness of historically traumatic events, producing greater HT scores. There is a clear need to improve conditions on AI reservations through community organizing, revitalization, and advocacy while simultaneously developing or strengthening interventions to address CG in tandem with physical and mental health promotion activities. It may be that people living on AI reservations may need culturally grounded, tailored approaches to such interventions. Interventions should integrate cultural traditions and spiritual practices, respecting community knowledge, to effectively address mental health and grief in AI/AN communities (Gone, 2022; Manson, 2020; Walters et al., 2020).

Adaptive Grief

Adaptive grief scores were similar across groups. In these analyses, it appeared that adaptive grief and complicated grief were orthogonal constructs, suggesting that interventions to decrease CG may not affect AG. If individuals suffering from loss can learn or strengthen skills to cope with grief or are given support that allows them to expand or more effectively utilize their social support networks, it may reduce CG without having an effect on AG. Further research on AG and its correlates is needed. Furthermore, CG interventions may need to be ongoing or long term in populations that experience numerous repeated losses without adequate time to heal. It is unknown whether individual-level interventions would effectively decrease CG and increase AG, but this is a promising line of future inquiry.

Limitations and Future Directions

The current work has important limitations. First, the survey is cross-sectional which limits our ability to investigate the directionality of effects in observed relationships. Based on theoretical links and prior work linking grief and trauma to our outcomes (Gameon, 2021; Gameon et al., 2024a, 2024b; Lannen et al., 2008; Prigerson et al., 1997; Thimm et al., 2020; Toblin et al., 2012), we posit that grief and historical trauma affect our outcomes. However, it is possible that poor mental health, poor sleep, physical health, and alcohol problems may affect experiences of grief and historical trauma. Prospective research on these relationships would further elucidate the directionality of effects in the relationship between grief trauma and the outcomes included in the current work.

The measures used in the current study also pose an important limitation. When available, we used measures that were developed and validated for use in AI/AN populations; however, this only included the IGI and HLS. Few measures have been validated for use in AI/AN populations. Therefore, they may not be assessing nuanced aspects of the construct that are unique to AI/AN people. The measures we selected for the study had been used in other AI/AN research projects and were approved by the CAB. In the future, more work is needed to validate measures in AI/AN samples.

The current sample includes AI adults from states and communities across the United States who reported losing a loved one. The relationship between measured constructs could be different as a function of living environments and tribal affiliation, and this warrants investigation. Future research efforts in this area should measure these constructs in daily life to better understand how complicated and adaptive grief and thoughts about historical trauma present in real time and to understand the dynamic relationship between these constructs and health-relevant outcomes, including the ones measured in the current study.

Finally, the observed relationships may change with the amount of loss individuals experience. Future research should include more nuanced consideration of the number of loved ones individuals have lost to understand whether the quantity of loss affects observed relationships between grief, historical trauma, and health-relevant outcomes.

Conclusion

Overall, the current work makes an important initial contribution to understanding how profiles of grief and historical trauma may inform health-relevant outcomes in AI adults living across the United States. Current losses may trigger reminders of previous and historical losses, compounding the effects of grief and loss and contributing to maladaptive coping, which in turn may contribute to health disparities. The present findings highlight the need for measuring both constructs to provide a comprehensive picture of how different forms of grief, trauma, and loss may contribute to or protect against poor health.

Acknowledgments

Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Numbers P20GM103474, U55GM115371, and P20GM104417 and by the National Institute on Minority Health and Health Disparities of the National Institute of Health under Award Number R01MD015894. The content is solely the authors’ responsibility and does not necessarily represent the official views of the National Institutes of Health.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Minority Health and Health Disparities of the National Institute of Health, National Institute of General Medical Sciences of the National Institutes of Health, (grant number R01MD015894, P20GM103474, P20GM104417, U55GM115371).

Biographies

Julie A. Gameon, PhD, is an Assistant Professor in the Psychology department at Montana State University. She is dedicated to addressing health disparities among American Indian and Alaska Native populations. Her research program focuses on employing a community-based participatory research (CBPR) approach to collaborate with partners in American Indian communities in order to adapt, develop, and implement culturally competent interventions that promote health equity. Specific interests include health research focused on grief, suicide, historical trauma, interpersonal trauma, and substance use.

Monica C. Skewes, PhD, is a Professor of Psychology at Montana State University with over 20 years of experience in health disparities research. Since 2014, she has worked in partnership with Native communities in Montana to develop and evaluate culturally grounded mental health interventions. She is currently the Principal Investigator of a NIDA-funded R01 to test the efficacy of a culturally adapted intervention for substance use disorders. Dr. Skewes’s research program uses a community-based participatory research (CBPR) framework to ensure that research is respectful, equitable, and benefits the community.

Cory J. Counts, PhD, is a former professor of psychology in the Department of Psychology at Montana State University. He studies the biopsychosocial mechanisms of stress in at-risk populations, such as trauma and poverty. His published works focus on mental health, physical health, biomarkers of health, and cognitive or environmental resiliency factors that may help buffer the deleterious effects of physiological stress.

Neha A. John-Henderson, PhD, is the co-Director of the Sleep Lab at Montana State University and a professor of psychology in the Department of Psychology at Montana State University. Dr. John-Henderson’s research is focused on understanding biopsychosocial health risk and resilience factors in health disparity populations. She is currently a Principal Investigator of a grant funded by the National Institute on Minority Health and Health Disparities (RO1MD015894) which examines longitudinal relationships between social connectedness and health in Blackfeet American Indian adults, and a Co-Principal Investigator of a grant funded by the National Heart Lung and Blood Institute (R01HL63237) which is focused on the relationship between psychological and physiological responses to acute stress and health in Blackfeet American Indian adults. Her current work focuses on the potential for social connectedness to inform health behaviors, reactions to stress, and physiological outcomes that shape and influence mental and physical health in American Indian adults.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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