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. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: J Anxiety Disord. 2020 Jan 28;70:102195. doi: 10.1016/j.janxdis.2020.102195

Comparison of Latent Typologies of Posttraumatic Stress Disorder and Depression Symptoms across Military Personnel from India and the US

Ateka A Contractor 1, Nicole H Weiss 2, Melissa R Schick 3, Prathiba Natesan 4, Shannon Forkus 5, Rachita Sharma 6
PMCID: PMC7074844  NIHMSID: NIHMS1569127  PMID: 32035292

Abstract

Research has identified heterogeneous subgroups of individuals based on posttraumatic stress disorder (PTSD) and depression symptoms. Using data collected from veterans in India (N = 146) and U.S. (N = 194), we examined (1) best-fitting latent class solution; (2) multi-group invariance of the class solution; and (3) construct validity of optimal class solution. Results indicated that the optimal 4-class solution differed in severity and severity/type in the India and U.S. samples respectively. With similarity in the optimal number of classes across cultural samples, the meaning/nature of classes differed. In the India sample, anxiety severity predicted the Low Severity Class vs. all other classes, and the Moderately High Severity/High Severity Classes vs. the Moderately Low Severity Class; number of traumas predicted the High Severity vs. other classes; and resilience predicted the Moderately Low Severity vs. Moderately High Severity Classes. In the U.S. sample, alcohol use predicted the High Severity Class vs. all other classes, and the High Depression-Low PTSD Class vs. the Low Severity Class; rumination significantly predicted the High Severity and High Depression-Low PTSD Classes vs. each of the High PTSD-Low Depression and Low Severity Classes. Thus, meaning and nature of PTSD-depression subgroups may vary culturally; hence, culturally-sensitive interventions need to account for this heterogeneity.

Keywords: Posttraumatic stress disorder, depression, latent profile analyses, invariance, cross-cultural comparison

Introduction

The co-occurrence of posttraumatic stress disorder (PTSD) and depression symptoms (Bonde et al., 2016) is associated with functional impairment (Post, Zoellner, Youngstrom, & Feeny, 2012). Referencing comorbidity explanations, the “causality” viewpoint indicates that trauma/PTSD and depression are causal risk factors for each other; the “common factors” viewpoint highlights shared environmental and genetic risk factors for these disorders; and the “confounding factors” viewpoint suggests a coincidence in the PTSD-depression associations (Breslau, 2009; Flory & Yehuda, 2015; Strander, Thomsen, & Highfill-McRoy, 2014). Statistically, person-centered approaches accounting for population heterogeneity (Morin, Meyer, Creusier, & Biétry, 2016) are vital to examine the nature of and reasons for the PTSD-depression co-occurrence.

To elaborate, latent class analyses (LCA) and latent profile analyses (LPA), as examples of such person-centered statistical approaches, use categorical and continuous symptom indicators respectively in classifying meaningful subgroups of individuals (i.e., latent classes/typologies). The said subgroups are mutually exclusive and are not observed but rather latent. These approaches consider both the severity and type of endorsed symptoms in delineating and identifying subgroups of individuals; thereby, individuals in each subgroup share similar patterns of symptom endorsement (severity and type). In other words, the response to any particular item by a certain participant is conditionally dependent on the latent subgroup they belong to. Subsequently, these obtained meaningful subgroups of individuals can be compared qualitatively (e.g., symptom type) and quantitatively (e.g., symptom severity). Highlighting the significance of person-centered statistical approaches, research has shown that identified subgroups may moderate treatment effects (Zhang, Abarda, Contractor, Wang, & Dayton, 2018) and may differentially relate to mental health correlates (i.e., construct validity Dalenberg, Glaser, & Alhassoon, 2012). Relevant to the current study, subgroups of individuals with differing severity and/or types of PTSD and depression symptoms may have a different psychopathology profile and may require different and personalized treatment approaches; the nature of these subgroups may provide explanations about mechanisms underlying their co-occurrence patterns.

Most person-centered research indicates parallel severity across PTSD and depression symptoms represented as a 3- (Armour et al., 2015; Contractor et al., 2015) or a 4-class solution (Hruska, Irish, Pacella, Sledjeski, & Delahanty, 2014; Lai, Kelley, Harrison, Thompson, & Self-Brown, 2015). Such findings support that PTSD and depression symptoms may comprise of a common post-traumatic response (Ikin, Creamer, Sim, & McKenzie, 2010; Norman et al., 2011), perhaps sharing common distress (Kramer et al., 2016). Fewer studies have found classes differing in type, providing less support for a depressive-subtype of PTSD (Cao et al., 2016; Contractor, Roley-Roberts, Lagdon, & Armour, 2017; Flory & Yehuda, 2015), wherein individuals with comorbid PTSD and depression may differ from those with only PTSD or depression on biological and psychological indicators (Flory & Yehuda, 2015).

Beyond the mixed findings regarding the number/nature of PTSD-depression subgroups, such research is limited in Eastern cultures such as that of India. Importantly though, traumatic events are highly prevalent in India (Patel, 2000; Pathak, 2007), and are associated with significant post-trauma outcomes (Contractor et al., 2014; Kar, Jagadisha., Sharma, Murali, & Mehrotra, 2004; Venkateshan & Roy, 2016). Distinct characteristics of the Indian culture may influence the nature and/or severity of PTSD and depression symptoms. India, a predominantly collectivistic society (Hofstede, 2001; Triandis, 1993), focuses on interdependence and community needs/goals (Markus & Kitayama, 1991). These cultural values may enhance resiliency and coping (Bhushan & Kumar, 2007; Tummala-Narra, 2007), thereby, lowering symptom severity. Conversely, the Indian culture inculcates an external locus of control potentially promoting more passive coping and poorer mental health (Suar, Das, & Hota, 2010). Further, individuals in the Indian culture may express post-trauma distress via somatic symptoms (Marsella, Friedman, & Spain, 1996; Pole, Gone, & Kulkarni, 2008); individuals may tend to internalize emotional distress (Varma, Chandra, Thomas, & Carey, 2007), not express emotions, and not disclose mental health issues due to anticipated stigma (Pole et al., 2008).

Thus, the nature of PTSD-depression subgroups in the Indian culture may be different from those in Western samples. Cross-cultural studies need to empirically compare (vs. assume) if the number/nature of subgroups, means of symptom indicators within subgroups, variability of symptom indicators within subgroups, and relative sizes of subgroups are similar vs. different (Eid, Langeheine, & Diener, 2003; Finch, 2015; Morin et al., 2016). No study, to our knowledge, has cross-culturally compared PTSD-depression typological structures.

Addressing this significant limitation and using data from two culturally distinct samples (trauma-exposed military veterans from India and the U.S.), we examined (1) the best-fitting latent class solution in categorizing participants based on their responses to PTSD and depression items; (2) multi-group invariance (equivalence) of the latent class solution across samples; and (3) construct validity of the optimal latent class solution in each sample. We hypothesized finding an optimal 3- (Armour et al., 2015; Contractor et al., 2015) or 4-class solution (Hruska et al., 2014; Lai et al., 2015) differing primarily in severity in the U.S. sample. Further, we hypothesized finding an optimal 4-clas solution differing in severity and type in the India sample; this hypothesis is based on India’s similarities in the shared value of collectivism with the Eastern culture of China (Hofstede, 2001), where research has demonstrated that individuals can be meaningfully classified into four subgroups differing in severity and type based on their endorsement of PTSD and depression symptoms (Cao et al., 2016; Zhen, Quan, & Zhou, 2018). Additionally, covariates of number of trauma types, resilience, anxiety severity, rumination, alcohol use, and drug use were used to establish construct validity of the optimal class solution. Research indicates that number of traumas have differentiated subgroups of individuals with PTSD/depression only and co-morbid PTSD and depression (Choi, 2019); higher resilience has predicted lower PTSD and depression severity (Pietrzak, Johnson, Goldstein, Malley, & Southwick, 2009); anxiety severity co-occurs with PTSD and depression severity (Contractor et al., 2015); rumination is associated with PTSD and depression severity (Michael, Halligan, Clark, & Ehlers, 2007; Nolen-Hoeksema, 2000); risky alcohol use commonly co-occurs with PTSD and depression (Brière, Rohde, Seeley, Klein, & Lewinsohn, 2014; Goldstein et al., 2016); and co-morbid drug use with PTSD and depression is associated with worse outcomes (Driessen et al., 2008; Swendsen & Merikangas, 2000).

Method

Procedure and Participants

India sample.

The current study was funded by an internal grant awarded by University of North Texas (i.e., India Venture Fund); hence, sought and secured ethics approval by the University of North Texas Institutional Review Board (IRB). Although the concept of research-based Ethics Committees was introduced in India in 1980, it has not been implemented throughout the country because of a lack of requisite infrastructure (Kumar, 2006). To circumvent this, Schedule Y of the Indian Council of Medical Research (ICMR) Policy Statement for Ethics published in 1988 stated the following: “The trial site(s) may accept the approval granted to the protocol by the ethics committee of another trial site or the approval granted by an independent ethics committee, provided that the approving ethics committee(s) is/are willing to accept their responsibilities for the study at such trial site(s) and the trial site(s) is/are willing to accept such an arrangement and that the protocol version is same at all trial sites.” Thus, the IRB of University of North Texas approved data collection for this study in India.

The current study was described as an examination of the relation between stressful life events and emotional distress among ex-military veterans in India. Participants were 18 years or older, fluent in English/Hindi, computer literate, and ex-military veterans. Eligible and consenting participants completed the web-based survey via Qualtrics (Qualtrics, 2018). Since Hindi is the national language in India, all measures excluding the Connor-Davidson Resilience Scale (Davidson & Connor, 2017) were back-translated in the Hindi language using recommended procedures (Brislin, 1970; Villagran & Lucke, 2005). Participants were provided a choice to complete the study questionnaires in the Hindi or English language; no participant chose to complete the study questionnaires in Hindi. Consequently, all recorded data was in the English language.

Participants were recruited via several methods on a global level: (1) study information was circulated via electronic media to potential participants (e.g., emails, text messages, postings of study information on approved social media sites) by authorized study personnel; (2) recruitment was conducted by an authorized military psychologist in India who presented study information to military clients seeking mental health treatment; and (3) participants shared study information with other potential participants via electronic media (emails and text messages). As compensation, participants received either a pen (~ $5.00 value), a keychain (~ $5.00 value), a digital $5.00 gift card, or could request a donation of a $5.00 gift card to a predetermined charitable cause in India. From the original sample of 187 participants, we excluded 20 participants not meeting inclusionary criteria, 19 participants not endorsing any trauma, and two participants missing > 30% item-level data on primary measures. Detailed demographic information is provided in Table 1.

Table 1.

Descriptive information on demographics service-related information, and psychopathology constructs (India sample)

Full Sample (n = 146) Low Severity (n = 77) Moderately High Severity (n = 14) Moderately Low Severity (n = 27) High Severity (n = 28)
Mean (SD)
Age 43.65 (13.61) 45.73 (13.09) 37.07 (10.31) 53.27 (12.88) 31.93 (5.76)
Years in military service 16.94 (10.82) 16.91 (11.47) 10.83 (6.81) 24.15 (7.30) 9.47 (6.97)
PTSD intrusion severity 4.37 (4.12) 1.01 (0.99) 7.79 (1.85) 4.93 (1.59) 10.85 (1.49)
PTSD avoidance severity 1.79 (1.84) 0.38 (0.69) 3.21 (0.97) 2.33 (.73) 4.39 (1.34)
PTSD NACM severity 5.35 (5.94) 1.24 (1.30) 9 (3.42) 4.04 (2.56) 15.81 (2.00)
PTSD AAR severity 5.19 (4.80) 1.64 (1.40) 7.71 (2.23) 5.04 (2.09) 13.22 (2.12)
Somatic depression severity 3.06 (2.98) 1.10 (1.09) 5.50 (2.03) 2.34 (1.81) 7.61 (1.57)
Non-somatic depression severity 1.83 (2.39) 0.26 (0.64) 3.64 (1.50) 1.11 (1.28) 5.68 (1.31)
Number of traumas 9.09 (4.28) 7.63 (3.70) 9.14 (3.84) 7.78 (3.68) 14.32 (1.91)
n (% within column)*
Gender
Male 132 (91.70%) 71 (93.40%) 12 (85.70%) 25 (92.60%) 24 (88.90%)
Female 12 (8.30%) 5 (6.60%) 2 (14.30%) 2 (7.40%) 3 (11.10%)
Highest Educational Achievement
Graduation and below 51 (35.90%) 35 (46.10%) 6 (42.90%) 6 (23.10%) 4 (15.40%)
Post-graduation/professional degree 91 (64.10%) 41 (53.90%) 8 (57.10%) 20 (76.90%) 22 (84.60%)
Relationship Status
Not currently married 38 (26.60%) 20 (26.70%) 8 (57.10%) 5 (18.50%) 5 (18.50%)
Currently married 105 (73.40%) 55 (73.30%) 6 (42.90%) 22 (81.50%) 22 (81.50%)
Family Composition
Single 42 (29.60%) 23 (29.90%) 7 (53.80%) 3 (11.10%) 9 (36.00%)
Nuclear 63 (44.40%) 36 (46.80%) 3 (23.10%) 20 (74.10%) 4 (16.00%)
Joint 37 (26.10%) 18 (23.40%) 3 (23.10%) 4 (14.80%) 12 (48.00%)
Children
Yes 97 (67.80%) 50 (66.70%) 6 (42.90%) 22 (81.50%) 19 (70.40%)
No 46 (32.20%) 25 (33.30%) 8 (57.10%) 5 (18.50%) 8 (29.60%)
Religion
Hinduism 71 (49%) 39 (50.60%) 4 (28.60%) 13 (48.10%) 15 (55.60%)
Islam 5 (3.40%) 2 (2.60%) 0 0 3 (11.10%)
Christianity 25 (17.20%) 12 (15.60%) 4 (28.60%) 4 (14.80%) 5 (18.50%)
Sikhism 30 (20.70%) 16 (20.80%) 6 (42.90%) 8 (29.60%) 0
Buddhism 3 (2.10%) 0 0 0 3 (11.10%)
Jainism 1 (0.07%) 1 (1.30%) 0 0 0
Do not follow a religion 10 (6.90%) 7 (9.10%) 0 2 (7.40%) 1 (3.60%)
Current Household Income
< 18,00,000 (lac) Rupees 83 (57.20%) 38 (49.40%) 8 (57.10%) 12 (44.40%) 25 (92.60%)
≥18,00,000 (lac) Rupees 62 (42.80%) 39 (50.60%) 6 (42.90%) 15 (55.60%) 2 (7.40%)
Reprimanded/Received Demotion in Service
Yes 10 (7.00%) 0 0 0 10 (37.00%)
No 132 (93%) 76 (100%) 13 (100%) 26 (100%) 17 (63.00%)
Decorated for Service
Yes 60 (42.30%) 22 (29.30%) 4 (30.80%) 12 (44.40%) 22 (81.50%)
No 82 (57.70%) 53 (70.70%) 9 (69.20%) 15 (55.60%) 5 (18.50%)
Injured during Service
Yes 36 (24.70%) 11 (15.10%) 2 (20.00%) 5 (20.00%) 18 (69.20%)
No 98 (73.10%) 62 (84.90%) 8 (80.00%) 20 (80.00%) 8 (30.80%)
Battle Casualty
Yes 28 (32.30%) 6 (15.80%) 1 (10.00%) 3 (21.40%) 18 (72.00%)
No 59 (67.80%) 32 (84.20%) 9 (90.00%) 11 (78.60%) 7 (28.00%)
Branch of Service
Army 131 (94.90%) 71 (97.30%) 13 (100%) 26 (100%) 21 (80.80%)
Navy 3 (2.20%) 0 0 0 3 (11.50%)
Air-force 4 (2.90%) 2 (2.70%) 0 0 2 (7.70%)
Probable PTSD (>/= 31) 29 (19.90%) 0 4 (28.60%) 0 25 (89.30%)
Probable depression (>/= 10) 32 (21.90%) 0 6 (42.90%) 0 26 (92.90%)
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Note. PTSD is posttraumatic stress disorder; NACM is negative alterations in cognition and mood; AAR is alterations in arousal and reactivity;

*

All reported percentages are valid percentages to account for missing data

U.S. sample.

The current study was approved by the University of Rhode Island IRB. Participants were recruited via Amazon’s Mechanical Turk (MTurk); the study was described as a survey about military combat experiences and moral, emotional, and psychological well-being. Interested participants clicked the advertisement to learn more about the study, which directed them to an external web-based survey via Qualtrics. Participants were 18 years or older, were proficient in English, served in the US military, and were deployed to either Afghanistan or Iraq in support of recent operations. As compensation, participants received $2.00. From the original sample of 203 participants, we excluded six participants not endorsing any trauma, and three participants missing >30% item-level data on primary measures. Detailed demographic information is provided in Table 2.

Table 2.

Descriptive information on demographics, service-related information, and psychopathology constructs (U.S. Sample)

Full Sample (n = 194) Low Severity (n = 83) High PTSD-Low Depression (n = 26) High Depression-Low PTSD (n = 36) High Severity (n = 49)
Mean (SD)
Age 35.28 (8.11) 36.89 (8.75) 34.38 (7.44) 33.19 (6.12) 34.55 (8.27)
Years in military service 8.61 (6.30) 9.32 (6.86) 8.74 (5.67) 8.00 (5.67) 7.85 (6.14)
Number of deployments 3.44 (1.80) 3.52 (1.86) 3.58 (2.30) 3.22 (1.40) 3.39 (1.72)
PTSD intrusion severity 6.70 (6.08) 1.51 (2.25) 10.23 (3.79) 6.77 (3.88) 13.87 (4.07)
PTSD avoidance severity 3.02 (2.78) 0.69 (1.08) 5.60 (1.66) 2.47 (1.58) 6.24 (1.45)
PTSD NACM severity 9.20 (8.15) 1.71 (2.53) 10.19 (4.26) 12.34 (3.87) 20.25 (3.49)
PTSD AAR severity 9.54 (7.42) 2.67 (3.03) 12.62 (4.07) 11.55 (4.19) 18.57 (3.26)
Somatic depression severity 5.57 (4.46) 1.76 (2.16) 4.69 (1.96) 8.66 (2.65) 10.69 (2.52)
Non-somatic depression severity 3.68 (3.62) 0.93 (1.36) 1.69 (1.32) 5.79 (2.52) 8.22 (2.26)
Number of traumas 11.62 (4.95) 10.34 (5.32) 12.69 (4.28) 13.39 (4.29) 11.94 (4.63)
n (% within column)*
Gender
Male 150 (77.70%) 64 (78.00%) 22 (84.60%) 27 (75.00%) 37 (75.50%)
Female 43 (22.30%) 18 (22.00%) 4 (15.40%) 9 (25.00%) 12 (24.50%)
Race
White 135 (71.40%) 64 (78.00%) 19 (76.00%) 24 (68.60%) 28 (59.60%)
Black or African American 30 (15.90%) 14 (17.10%) 1 (4.00%) 7 (20.00%) 8 (17.00%)
Asian 16 (6.90%) 3 (3.70%) 3 (12.00%) 2 (5.70%) 5 (10.60%)
American Indian or Alaskan Native 11 (5.80%) 1 (1.20%) 2 (8.00%) 2 (5.70%) 6 (12.80%)
Ethnicity
Hispanic or Latino 26 (13.90%) 11 (13.60%) 4 (16.00%) 26 (13.90%) 6 (13.30%)
Not Hispanic or Latino 161 (86.10%) 70 (86.40%) 21 (84.00%) 161 (86.10%) 39 (86.70%)
Branch of Service
Army 101 (52.10%) 37 (44.60%) 13 (50.00%) 17 (47.20%) 34 (69.40%)
Navy 29 (14.90%) 14 (16.90%) 5 (19.20%) 7 (19.40%) 3 (6.10%)
Air Force 38 (19.60%) 22 (26.50%) 6 (23.10%) 7 (19.40%) 3 (6.10%)
Marines 26 (13.40%) 10 (12.00%) 2 (7.70%) 5 (13.90%) 9 (18.40%)
Type of Service
Active Duty 166 (85.60%) 70 (84.0%) 25 (96.20%) 32 (88.90% ) 39 (9.60%)
Reserves 14 (7.20%) 7 (8.40%) 1 (3.80%) 3 (8.30) 3 (6.10%)
National Guard 14 (7.20%) 6 (7.20%) 0 (0%) 1 (2.80%) 7 (14.30%)
Probable PTSD (>/= 31) 79 (43.60%) 0 19 (76.00%) 20 (60.60%) 40 (100.00%)
Probable Depression (>/= 10) 80 (43.20%) 4 (4.80%) 3 (11.50%) 31 (93.90%) 42 (97.70%)
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Note. PTSD is posttraumatic stress disorder; NACM is negative alterations in cognition and mood; AAR is alterations in arousal and reactivity;

*

All reported percentages are valid percentages to account for missing data

Measures

Life Events Checklist for DSM-5 (LEC-5; Weathers et al., 2013).

Administered to both samples, the LEC-5 is a 17-item self-report measure assessing lifetime traumatic events; the first 16 items assess specific types of trauma (e.g., transportation accident, assault) and the 17th item assesses exposure to other stressful life events not captured by the first 16 items. Participants responded to each item using a 6-point scale (Happened to me, Witnessed it, Learned about it, Part of my job, Not sure, Doesn’t apply). An 18th item was included in the India sample asking participants to indicate their most distressing trauma (the measure assessing PTSD symptoms was cued to the most distressing trauma reported for this item).

PTSD Checklist for DSM-5 (PCL-5; Weathers et al., 2013).

Administered to both samples, the PCL-5 is a 20-item self-report measure that assesses severity of PTSD symptoms referencing the previous month. Response options range from 0 (Not at all) to 4 (Extremely). In the India sample, participants completed the PCL-5 referencing the most distressing trauma on the LEC-5. In the U.S. sample, participants completed the PCL-5 referencing their most distressing deployment-related event. The PCL-5 demonstrates excellent psychometric properties (Wortmann et al., 2016). The ordinal Cronbach’s α for the PCL-5 intrusions, avoidance, NACM, and AAR subscales was .90, .79, .93, and .89 respectively in the India sample; and .95, .92, .92, and .94 respectively in the U.S. sample.

Patient Health Questionnaire-9 (PHQ-9; Kroenke & Spitzer, 2002).

Administered to both samples, the PHQ-9 is a 9-item self-report measure assessing depression severity referencing the past 2 weeks. Response options range from 0 (Not at all) to 3 (Nearly every day). We used the non-somatic (1, 2, 6, and 9) and somatic (3, 4, 5, 7, and 8) depression subscales (Elhai et al., 2012; Krause, Reed, & McArdle, 2010). The PHQ-9 demonstrates excellent psychometrics (Kroenke & Spitzer, 2002; Kroenke, Spitzer, & Williams, 2001). The ordinal Cronbach’s α for the somatic and non-somatic depression subscales was .89 and .86 respectively in the India sample; and .91 and .59 respectively in the U.S. sample.

Generalized Anxiety Disorder-7 (GAD-7; Spitzer, Kroenke, Williams, & Löwe, 2006).

Administered to the India sample, the GAD-7 is a 7-item self-report measure of anxiety symptoms over the past two weeks. The GAD-7 demonstrates acceptable psychometrics (Löwe et al., 2008; Spitzer et al., 2006); ordinal Cronbach’s α was .92 in the current study.

Connor-Davidson Resilience Scale (CD-RISC; Davidson & Connor, 2017).

Administered to the India sample, the 25-item CD-RISC examines resilience referencing the past month. The CD-RISC-25 demonstrates good psychometrics (Connor & Davidson, 2003; Singh & Yu, 2010); ordinal Cronbach’s α was .95 in the current study.

Alcohol Use Disorder Identification Test (AUDIT; Saunders, Aasland, Babor, De la Fuente, & Grant, 1993).

Administered to the U.S. sample, the AUDIT is a 10-item self-report measure that assesses alcohol consumption, drinking behaviors, adverse reactions to drinking, and alcohol-related problems referencing the past year. The AUDIT demonstrates good psychometrics (Saunders et al., 1993); ordinal Cronbach’s α was .93 in the current study.

Drug Abuse Screening Test (DAST-10; Skinner, 1982).

Administered to the U.S. sample, the DAST-10 is a 10-item self-report measure that assesses the presence of problems related to drug use (e.g., occupational/relational problems, illegal activities, regret) referencing the past year. The DAST has adequate psychometrics (Yudko, Lozhkina, & Fouts, 2007); ordinal Cronbach’s α was .95 in the current study.

Ruminative Response Scale–Brooding (RRS-B; Treynor, Gonzalez, & Nolen-Hoeksema, 2003).

Administered to the U.S. sample, the RRS-B is a 5-item self-report measure assessing tendency towards a passive focus on negative emotions (no reference to any specific timeframe). The RRS-B has adequate psychometrics (Erdur-Baker & Bugay, 2010); ordinal Cronbach’s α was .91 in the current study.

Data Analyses

First, we conducted latent profile analyses (LPA) with Mplus 8 in each sample using the PCL-5 subscales (intrusions, avoidance, NACM, and AAR) and PHQ-9 subscales (somatic and non-somatic depression) as indicators. The estimator was Maximum Likelihood estimation with robust standard errors (MLR), and 1-5 class models were analyzed. The optimal class solution had lower Bayesian Information Criterion (BIC) and sample-size adjusted BIC (SSABIC) values; an Adjusted Lo–Mendell–Rubin (LMR) Likelihood Ratio Test p<.05, a Bootstrapped Likelihood Ratio Test (BLRT) p<.05, higher entropy values, parsimony, and conceptual meaning (DiStefano & Kamphaus, 2006; Nylund, Asparouhov, & Muthén, 2007; Nylund, Bellmore, Nishina, & Graham, 2007).

Second, we compared the typological structures across the samples using Multiple Groups (MG) LPA (Eid et al., 2003; Finch, 2015; Kankaraš, Moors, & Vermunt, 2010). The models are examined in a progressively restrictive manner; in other words, higher levels of invariance are examined contingent on finding invariance at the prior step. The restricted model is compared to the unrestricted model by using fit indices of Akaike Information Criterion (AIC), BIC, and SSABIC values where lower values indicate a better-fitting model (Chung & Breslau, 2008; Kankaraš et al., 2010; Morin et al., 2016). For configural profile similarity, we assessed if the optimal number of classes was the same across compared sample. For structural profile similarity, we imposed equality constraints on the within-profile means (i.e., indicator levels) across the samples to assess if they had similar levels of indicators. For dispersion profile similarity, we equated indicators’ variability (within-profile inter-individual differences) to identify if the samples were comparably homogenous and internally consistent. Lastly, distributional profile similarity was examined by equating latent class sizes (i.e. relative frequencies) across samples (Eid et al., 2003; Morin et al., 2016).

Third, we tested the effects of covariates (India sample – number of trauma types, anxiety severity, and resilience; U.S. sample – number of trauma types, rumination, alcohol use, and drug use) on latent class membership of the optimal model in each group (construct validity). We used the three-step approach to account for misspecification bias (Vermunt, 2010).

Results

LPA (see Table 3)

Table 3.

Fit indices of the latent class models and invariance testing models

Model AIC BIC SSABIC Entropy Lo–Mendell–Rubin Likelihood Ratio Test (p) Bootstrapped Likelihood Ratio Test (p)
India Sample
1 class 4444.42 4480.28 4442.25
2 class 3580.73 3637.42 3577.29 .99 853.24 (p < .001) p < .001
3 class 3369.7 3447.36 3365.09 .98 218.67 (p = .006) p < .001
4 class 3278.96 3377.42 3273.00 .98 101.91 (p = .02) p < .001
U.S. Sample
1 class 6907.46 6946.67 6908.66
2 class 6092.87 6154.96 6094.77 .94 806.71 (p < .001) p < .001
3 class 5870.53 5955.50 5873.13 .94 230.10 (p = .001) p < .001
4 class 5790.76 5898.60 5794.06 .94 91.30 (p = .069) p < .001
5 class 5731.07 5861.78 5735.07 .92 71.75 (p = .417) p < .001
Invariance Models
Configural Profile Similarity 9536.261 9792.800 9580.264 .97
Structural Profile Similarity 9654.352 9818.997 9682.593 .95
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Note. AIC is Akaike Information Criterion; BIC is Bayesian Information Criterion; SSABIC is sample-size adjusted BIC; bolded values represent the optimal class solution for the current study.

India sample.

Based on established guidelines, we selected the 4-class solution as optimal. There were model convergence problems with the 5-class solution possibly attributable to small sample size in one of the classes (Hipp & Bauer, 2006). See Figure 1 for a graphical depiction of the 4-class solution. Class 1 with overall lowest severity was labelled “Low Severity;” in contrast, Class 4 with overall highest severity was labelled “High Severity.” Class 2 and Class 3 were characterized by moderately high and moderately low severity, thus labelled as “Moderately High Severity” and “Moderately Low Severity,” respectively.

Figure 1.

Figure 1.

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Latent classes of participants based on PTSD and depression subscale scores (India sample)

Note. PCL-5 is PTSD Checklist for DSM-5; PHQ-9 is Patient Health Questionnaire-9; NACM is negative alterations in cognition and mood; AAR is alterations in arousal and reactivity; Class 1 is the Low Severity Class; Class 2 is the Moderately High Severity Class; Class 3 is the Moderately Low Severity Class; Class 4 is the High Severity Class.

U.S. sample.

Based on established guidelines, we selected the 4-class solution as optimal. According to the LMR value guidelines, the 3-class solution was optimal; however, LMR is not considered as the most robust indicator in selecting the optimal class solution (Nylund, Asparouhov, et al., 2007). We gave more importance to the BIC and SSABIC values; the SSABIC is considered as a more robust index when considering all possible combinations of number of indicators and sample sizes (Nylund, Asparouhov, et al., 2007). Although the BIC and SSABIC values continued to decrease beyond the 4-class solution, the difference in values began to plateau between the 3- and 4-class solutions (DiStefano & Kamphaus, 2006; Raftery, 1995). Additionally, there was no difference in the entropy values across the 3- and 4-class solutions and prior research has supported a 4-class solution (Au, Dickstein, Comer, Salters-Pedneault, & Litz, 2013; Hruska et al., 2014; Lai et al., 2015).

See Figure 2 for a graphical depiction of the 4-class solution. Overall, Class 2 with overall lowest severity was labelled “Low Severity;” in contrast, Class 3 with overall highest severity was labelled “High Severity.” Class 1 was characterized by relatively higher PTSD subscale severity and lower depression subscale severity compared to Class 4; hence, Classes 1 and 4 were labelled as “High PTSD-Low Depression” and “High Depression-Low PTSD,” respectively.

Figure 2.

Figure 2.

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Latent classes of participants based on PTSD and depression subscale scores (U.S sample)

Note. PCL-5 is PTSD Checklist for DSM-5; PHQ-9 is Patient Health Questionnaire-9; NACM is negative alterations in cognition and mood; AAR is alterations in arousal and reactivity; Class 1 is the High PTSD-Low Depression Class; Class 2 is the Low Severity Class; Class 3 is the High Severity Class; Class 4 is the High Depression-Low PTSD Class.

MGLPA (See Table 3)

Results indicated configural profile similarity of PTSD-depression typologies across the cultural groups, wherein each cultural group had an optimal 4-class solution. However, results indicated lack of structural profile similarity across the cultural groups based on increasing AIC, BIC, and SSABIC values from the configural profile simialarity model. Based on these results, further invariance testing (i.e., dispersion and distributional profile similarity) was not conducted.

Construct Validity of the Optimal Class Solution (see Table 4)

Table 4.

Results of multinomial logistic regression analyses

India Sample (OR; 95% CI)
Class 2 vs. 1# Class 3 vs. 1# Class 4 vs. 1# Class 3 vs. 2# Class 4 vs. 2# Class 4 vs. 3#
Anxiety Severity 6.03 (2.78 – 13.10)p = .04 3.27 (1.83–5.87)p = .02 11.69 (3.37 – 40.56)p = .15 0.54 (0.33-0.89)p = .001 1.94 (0.77–4.87)p = .30 3.57 (1.19–10.72)p = .20
Resilience 0.95 (0.87–1.04)p = .25 1.03 (0.97–1.10)p = .32 0.96 (0.86–1.09)p = .53 1.09 (1.01–1.17)p = .03 1.01 (0.91–1.13)p = .81 .93 (0.84–1.04)p = .18
Number of trauma types 1.30 (0.86–1.97)p = .28 1.01 (0.84–1.22)p = .90 3.49 (1.79–6.77)p = .04 0.78 (0.53–1.15)p = .16 2.68 (1.52–4.72)p = .03 3.45 (1.79–6.64)p = .03
U.S. Sample (OR; 95% CI)
Class 2 vs. 1# Class 3 vs. 1# Class 4 vs. 1# Class 3 vs. 2# Class 4 vs. 2# Class 4 vs. 3#
Number of trauma types 0.93 (0.85-1.03)p = .17 0.92 (0.78-1.07)p = .26 1.00 (0.85-1.19)p = .98 0.98 (0.86-1.24)p = .11 1.07 (0.93-1.24)p = .35 1.09 (0.96-1.25)p = .21
Alcohol use 1.00 (0.92-1.08)p = .92 1.15 (1.04-1.27)p = .01 1.07 (0.98-1.16)p = .14 1.15 (1.06-1.15)p = .003 1.07 (1.00-1.15)p = .06 .93 (0.86-1.00)p = .05
Drug use 1.18 (0.77-1.82)p = .48 1.36 (0.89-2.07)p = .22 1.37 (0.90-2.09)p = .21 1.15 (0.87-1.53)p = .37 1.16 (0.88-1.53)p = .33 1.01 (0.79-1.29)p = .93
Rumination 1.03 (0.91-1.16)p = .66 1.34 (1.13-1.58)p = .003 1.33 (1.14-1.56)p = .002 1.30 (1.11-1.53)p = .01 1.30 (1.10-1.52)p = .01 1.00 (0.86-1.15)p = .96
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Note. India Sample: Class 1 is the Low Severity Class; Class 2 is the Moderately High Severity Class; Class 3 is the Moderately Low Severity Class; Class 4 is the High Severity Class; U.S. Sample: Class 1 is the High PTSD-Low Depression Class; Class 2 is the Low Severity Class; Class 3 is the High Severity Class; Class 4 is the High Depression-Low PTSD Class; # indicates the reference class; bolded values represent significant odd ratio estimates.

India sample.

Anxiety severity was significant in predicting membership in the Moderately High Severity (B = 1.78, p < .001), and the Moderately Low Severity (B = 1.19, p < .001) Classes compared to the Low Severity Class. Comparing the Low and High Severity Classes, results indicated significant covariates of anxiety severity (B = 2.46, p < .001), and number of trauma types (B = 1.25, p < .001). Comparing the Moderately High and Moderately Low Severity Classes, results indicated significant covariates of anxiety severity (B = −.61, p = .02) and resilience (B = .08, p = .02). Further, number of trauma types (B = .99, p = .001) significantly predicted the High Severity vs. Moderately High Severity Class membership. Lastly, comparing the Moderately Low Severity and High Severity Classes, results indicated significant covariates of anxiety severity (B = 1.27, p = .02), and number of trauma types (B = 1.24, p < .001).

U.S. sample.

Compared to the High PTSD-Low Depression Class, alcohol use (B = .14, p = .01) and rumination (B = .29, p = .001) significantly predicted membership in the High Severity Class. Also, rumination (B = .29, p < .001) was significant in predicting the High Depression-Low PTSD vs. High PTSD-Low Depression Class membership. No covariates significantly predicted membership in the Low Severity Class compared to the High PTSD-Low Depression Class. Compared to the Low Severity Class, alcohol use (B = .14, p = .001) and rumination (B = .26, p =.002) were significant predictors of the High Severity Class. Further, compared to the High Depression-Low PTSD Class, alcohol use (B = .07, p = .05) and rumination (B = .26, p = .002) were significant predictors of the Low Severity Class. Finally, compared to the High Severity Class, alcohol use (B = −.07, p = .05) was a significant predictor of the High Depression-Low PTSD Class.

Discussion

Study results indicated optimal 4-class solutions in the India and U.S. samples, providing support for configural profile similarity. However, these classes lacked structural profile similarity, differing in meaning and nature across the compared cultural samples. Specifically, subgroups in the India sample were characterized by (1) lower severity of PTSD and depression symptoms, (2) moderately higher severity of PTSD and depression symptoms, (3) moderately lower severity of PTSD and depression symptoms, and (4) higher severity of PTSD and depression symptoms. Conversely, subgroups in the U.S. sample were characterzied by (1) higher severity of PTSD symptoms and lower severity of depression symptoms, (2) lower severity of PTSD and depression symptoms, (3) higher severity of PTSD and depression symptoms, and (4) lower severity of PTSD symptoms and higher severity of depression symptoms. Thus, classes in the India sample differed only in severity (i.e., quantitatively), whereas those in the U.S. sample differed in both severity and type (i.e., qualitatively), providing tentative support for a depressive subtype of PTSD in this sample.

Several factors may explain differences in typological structures across the compared cultural samples. From a methodological perspective, the types of Criterion A traumas participants referenced when completing the PCL-5 differed across the samples. The India sample completed the PCL-5 in reference to their most traumatic event (suggesting potential variability in trauma types across classes), whereas participants in the U.S. sample referenced their most distressing deployment-related event (limiting variability in trauma types across classes). From a mechanism-based perspective, different biological, psychological, and social factors may have influenced the nature of the PTSD-depression classes in the India and U.S. samples. For example, classes may have differed in the extent of activation of the approach and withdrawal systems stemming from neuroanatomical variations (Kemp et al., 2010); this may have resulted in differing levels of positive and negative affect in the India vs. U.S. samples (Shankman & Klein, 2003). Lastly, from a cultural perspective, differences in emotional experiences (Eid & Diener, 2009) may have accounted for the inter-sample differences given the central role of emotions in PTSD/depression’s diagnostic criteria (American Psychiatric Association, 2013) alongside empirical evidence for deficits in emotional functioning in PTSD and depression (Litz, Orsillo, Kaloupek, & Weathers, 2000; Thompson et al., 2012). For instance, individuals from collectivistic cultures (e.g., India) may view intense emotions as threatening, irrelevant, or illness-causing, and thus value their moderation or suppression, resulting in less frequent/intense emotions than individuals from individualistic cultures (e.g., U.S.; Eid & Diener, 2009). Alternatively, evidence supports cross-cultural differences in the clinical expression of PTSD and depression. Regarding depression, individuals from non-Western countries have found to report more somatic and fewer psychological symptoms (Ryder et al., 2008). Cross-cultural variability also has been documented for PTSD, specifically for avoidance and numbing symptoms, distressing dreams, and somatic symptoms (Hinton & Lewis-Fernández, 2011). Additional research is needed to further examine these explanations in accounting for differences in the PTSD-depression typologies.

Notably, the four PTSD-depression subgroups for the India sample, although similar in number, were different in type compared to subgroups identified by research in other Eastern cultures such as China (Cao et al., 2016; Zhen et al., 2018). Specifically, the 4-class solution in the India sample did not differ qualitatively, which is in contrast to findings from research conducted in China. Sample and methodological differences may be contributing factors for discrepant results; for instance, the two studies conducted in China used data from victims of a natural disaster in contrast to the current study that utilized data from military veterans. Additionally, although China and India share collectivistic values, other cultural differences may have contributed to discrepant findings. For instance, India and China, respectively, (1) have cultural roots of Hinduism and Confucianism, (2) analytical and holistic modes of thinking, and (3) are relatively vocal/direct and subtle/indirect in dealing with concerns (Jin, Gavin, & Kang, 2012).

Current study findings potentially highlight differences in the etiology and treatment of the PTSD-depression co-occurrence. For both samples, PTSD and depression symptoms fluctuated in relation to one another; hence, the development and maintenance of symptoms of PTSD and depression may be closely tied to one another (Ying, Wu, & Lin, 2012). Thus, treatment providers need to assess for depression symptoms among individuals identified by PTSD symptoms and PTSD symptoms among individuals identified by depression symptoms. Further, findings suggest the utility of interventions mitigating both PTSD and depression symptoms concurrently (e.g., Trauma-focused Cognitive Behavioral Therapy; Lenz & Hollenbaugh, 2015), further emphasized by the fact that co-occurring depression symptoms may complicate PTSD treatment (McLean, Morris, Conklin, Jayawickreme, & Foa, 2014), and increase risk for behavioral problems (e.g., substance use; Gil-Rivas, Prause, & Grella, 2009). Notably, differences in the nature of subgroups (e.g., depressive subtype of PTSD) also suggest that PTSD-depression treatments may need to be individualized for effectiveness. For instance, if classes are influenced by physiological differences and differ in symptom type, different physiological/pharmacological treatments (e.g., targeting approach/withdrawal) may be effective for different classes. Relatedly, study findings underscore the need for culturally-sensitive practices in treating co-occurring PTSD and depression. Our findings provide initial evidence for culturally-distinct subgroups; if replicated, these subgroups may inform risk and recovery post-trauma profiles for India and U.S. veterans, and highlight the need for tailoring preventive and remedial interventions to account for the cultural heterogeneity in PTSD and depression symptoms.

Generally speaking, across both the India and U.S. samples, individuals in classes characterized by greater PTSD and depression severity exhibited the highest levels of clinically-relevant covariates, with a few notable exceptions. First, in the India sample, levels of resilience differed only among individuals in the Moderately Low Severity vs. Moderately High Severity Classes, with those in the latter class having lower levels of resilience. Perhaps, resilience does not protect against developing PTSD and depression symptoms, but instead that it may reduce the likelihood of a more severe presentation of PTSD and depression symptoms. Indeed, among individuals experiencing chronic and repeated trauma (e.g., veterans), research demonstrates that resiliency resources can become overburdened, leading to a sizable proportion of the population being symptomatic, albeit less so than those who demonstrate less resilience (Hobfoll, Mancini, Hall, Canetti, & Bonanno, 2011). Second, in the U.S. sample, individuals in the High Depression-Low PTSD Class endorsed greater rumination vs. the Low Severity and High PTSD-Low Depression Classes. Conversely, compared to the High PTSD-Low Depression Class, no covariates significantly predicted membership in the Low Severity Class in the U.S. sample. These findings indicate that depression vs. PTSD symptoms may be particularly salient to some clinically-relevant outcomes such as rumination post-trauma. Lastly, number of traumas predicted class membership in the India but not in the U.S. sample possibly attributed to the lack of structural profile similarity across these cultural samples. Specifically, number of traumas may demonstrate a relation with PTSD-depression classes predominantly characterized by similar symptom severity (India sample) vs. type (U.S. sample).

In terms of study limitations, the cross-sectional and correlational nature of the data precludes determination of the precise nature and direction of the examined relationships; prospective and longitudinal investigations are needed. Second, this study relied on individuals’ self-report, which may have been influenced by willingness and/or ability to report accurately. Lastly, although our focus on military veterans is a strength of this study, our findings require replication across larger, more diverse samples. Despite these limitations, this study extends existing research by utilizing a person-centered approach to understand the PTSD-depression co-occurrence and their relation with covariates among distinct cultural samples. Overall, results suggest tailoring interventions to account for the PTSD-depression heterogeneity across identified groups.

Highlights.

  • We examined and compared PTSD-depression typologies across India and U.S. veterans.

  • Optimal 4-class solution differed in severity and severity/type in India and U.S. samples respectively.

  • Across cultural samples, classes were similar in number but different in meaning/nature.

  • Anxiety, number of traumas, and resilience predicted class membership in India sample.

  • Alcohol use and rumination predicted class membership in U.S. sample.

Acknowledgements

We acknowledge the contribution of Dr. Samir Rawat in helping with the data collection from India.

Funding Sources

This work was supported by the University of North Texas India Venture Fund (A.A.C., and R.S.); and the National Institute on Drug Abuse (N.H.W., K23DA039327). The funding sources had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Footnotes

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Declaration of Interests

None

Contributor Information

Ateka A. Contractor, Department of Psychology, University of North Texas

Nicole H. Weiss, Department of Psychology, University of Rhode Island

Melissa R. Schick, Department of Psychology, University of Rhode Island

Prathiba Natesan, Department of Educational Psychology, University of North Texas.

Shannon Forkus, Department of Psychology, University of Rhode Island.

Rachita Sharma, Department of Rehabilitation and Health Services, University of North Texas.

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