Abstract
Background:
Alcohol use disorder (AUD) is prevalent among Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn (OEF/OIF/OND) veterans. Pain and posttraumatic stress disorder (PTSD) are highly comorbid and increase risk of AUD. Prior studies linking pain or PTSD to AUD have not explored interactions between pain and PTSD symptoms.
Methods:
OEF/OIF/OND veterans (N=1,230) were recruited from social media websites as part of a cross-sectional study of veteran health behavior. Pain was assessed using the Pain Outcomes Questionnaire. PTSD symptoms and PTSD symptom clusters were assessed using the Posttraumatic Stress Disorder Checklist for DSM-5. AUD symptoms were assessed with the AUD Identification Test. Linear regression models were used to test for main and interaction effects in the full sample and separately by sex.
Results:
Both pain and PTSD symptoms were associated with increased AUD symptomology, though the relationship between pain and AUD was heighted at relatively low PTSD symptoms. With respect to PTSD symptom clusters, re-experiencing and negative cognitions and mood were associated with increased AUD symptomology. Interactions between pain and re-experiencing as well as pain and avoidance were revealed. Results for men mirrored the full sample, while an interaction between pain and negative cognitions and mood was associated with AUD in women.
Conclusions:
Results highlight associations between AUD, PTSD symptoms, and pain among veterans. While the relationship between pain and AUD appeared stronger in the context of low PTSD symptoms, both conditions were associated with increased AUD. Clinicians treating veterans with AUD should address the range of potential comorbidities.
Keywords: alcohol use disorder, posttraumatic stress disorder, pain, comorbidity, veterans
1. Introduction
American veterans from the conflicts in Iraq and Afghanistan (Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn [OEF/OIF/OND; hereafter referred to as OEF/OIF]) are at high risk of developing alcohol use disorder (AUD) (Straus et al., 2020). Many have comorbidities, with posttraumatic stress disorder (PTSD) and physical pain being especially prevalent. Nearly one-quarter of OEF/OIF veterans have PTSD (Fulton et al., 2015), with half reporting a pain-related medical diagnosis (Stecker et al., 2010). Unfortunately, many veterans with behavioral health problems are not engaged in care (Pedersen, Marshall, et al., 2017), citing limited access, stigma, and difficulty navigating benefits (Cheney et al., 2018). As most research involves veterans recruited from care settings, knowledge is limited regarding how comorbid pain and PTSD influence vulnerability for AUD among veterans outside of treatment (Gros et al., 2015).
AUD and PTSD are highly comorbid, with up to 63% of OEF/OIF veterans with AUD also meeting criteria for PTSD (Dworkin et al., 2018). Veterans with both conditions report poorer health and functioning than those with AUD alone (Norman et al., 2018). Also notable is the effect of different PTSD symptom presentations. After experiencing a traumatic event (e.g., combat), individuals with PTSD endorse some degree of re-experiencing (e.g., repeated memories, intrusive thoughts), avoidance (e.g., trying not to think about trauma, avoiding places that remind one of trauma), negative cognitions and mood (e.g., guilt, emotional numbness), and hyperarousal (e.g., being “on guard,” easily startled) (American Psychiatric Association, 2013). Those affected by PTSD symptoms might vary substantially their PTSD symptom cluster presentations, and different presentations may confer differential risk for AUD (Debell et al., 2014). One study (Capone et al., 2013) reported re-experiencing predicted alcohol use, whereas another (Jakupcak et al., 2010) identified emotional numbing and hyperarousal as predictors. Prior work also noted sex differences in PTSD and its influence on AUD. Veteran men report higher rates of PTSD (Fulton et al., 2015), although women may have more severe symptomology (Hourani et al., 2015). With respect to PTSD symptom clusters, one study (Scott et al., 2013) reported emotional numbing was associated with hazardous drinking among women OEF/OIF veterans, though no specific symptom cluster was associated with drinking in men. It remains unclear how PTSD symptoms might interact with additional health conditions to influence AUD.
Physical pain is among the most frequently endorsed symptoms among veterans (Stecker et al., 2010) and it is highly comorbid with PTSD, as two-thirds of veterans with PTSD also report chronic pain (Shipherd et al., 2007). The mutual maintenance model posits there are specific symptom pathways through which chronic pain and PTSD exacerbate one another (Sharp & Harvey, 2001), and this may carry implications for how they concurrently influence alcohol use. Indeed, veterans experiencing both pain and PTSD report worse psychological functioning than veterans with one condition, and they may be more likely to self-medicate with substances to relieve pain (Benedict et al., 2020). As both PTSD and pain are associated with alcohol misuse (Goebel et al., 2011), the sequelae of these conditions may synergistically raise propensity for AUD.
Although veteran men and women report pain at similar rates (Haskell et al., 2010), there are likely sex differences in the relationship between pain and PTSD symptomology (Unruh, 1996), with a study of veteran women linking re-experiencing and pain (Asmundson et al., 2004). A study with non-veterans indicated women with chronic pain were more likely to cope by using substances if they had also experienced trauma, while this was not so for men (Jamison et al., 2010). With respect to multiple comorbidities, a study of veterans seeking treatment for substance use and PTSD reported that, while pain was associated with PTSD symptoms, it did not predict substance use (Gros et al., 2015). A recent study of veteran primary care patients however reported strong associations among PTSD, pain, as well as AUD (Tiet & Moos, 2021). Further understanding the relationships between specific PTSD symptoms, pain, and AUD as well as sex differences in these relationships could provide intervention targets for clinicians treating men and women veterans with comorbidities.
Prior research has revealed associations between pain, PTSD, and AUD among veterans. However, studies have not explored interactions between pain and PTSD symptoms and most have not differentiated between PTSD symptom clusters. Further, studies typically involve veterans recruited from care settings. Considering the mutual maintenance model, we sought to explore among OEF/OIF veterans recruited outside of treatment settings whether comorbid pain and PTSD symptoms interact in their influence on AUD symptomology and whether specific PTSD symptom pathways are implicated. The present study has three aims. The first (Aim 1) assesses if PTSD symptoms moderate the association between pain and AUD symptomology, and we hypothesize that this association will be heightened for those with relatively high PTSD symptoms. Aims 2 and 3 are exploratory: the second (Aim 2), examines if specific PTSD symptom clusters moderate the association between pain and AUD symptomology. Finally, the third (Aim 3) assesses how interactions between pain and PTSD symptoms (as well as PTSD symptom clusters) differ between men and women veterans.
2. Methods
2.1. Participants and Procedures
In February 2020, advertisements were displayed on general social media websites (Facebook, Instagram) and military-specific social media websites (RallyPoint, We Are The Mighty). Participants were recruited for a larger cross-sectional survey study of young adult veteran health behavior which sought to inform future prevention work with young veterans before their health conditions became chronic. Participants were eligible if they were 18 to 40 years old and had separated from the United States Air Force, Army, Marine Corps, or Navy. There were no exclusion criteria besides not meeting eligibility criteria. Veterans were recruited outside of care settings but there were no criteria based on treatment receipt. Participants clicked on advertisements and were directed to a secure website with a consent form. If they consented to participate, they completed a 30-minute survey and were compensated with a $20 Amazon gift card. All procedures were approved by the local Institutional Review Board.
As the study was conducted online, a series of measures were implemented to ensure participants were actual veterans and were not completing the survey repeatedly to gain incentives. First, advertisements were displayed on military-specific social media websites that required proof of military service to enroll. We also displayed ads on general social media pages that allowed targeted adverting, such that ads were only displayed to individuals that had expressed interest in U.S. military-related content. Furthermore, we employed a series of internal validation checks within the survey. These sought to ensure participants were actual veterans (e.g., providing correct responses to “insider knowledge” items, endorsing consistent responses between items such as branch, rank, age, and pay grade at discharge), that they were not carelessly responding to the survey items (e.g., checking that they did not use all the same response values throughout the survey, checking time stamps to verify they completed the survey in a possible length of time, including “check items” to make sure participants were paying attention to item wording), and that they were not completing the survey more than once (e.g., similar response patterns from same IP address). We have used these checks in prior work to ensure we have a valid sample of eligible participants (Pedersen et al., 2015; Pedersen, Naranjo, et al., 2017).
During recruitment, 5,776 individuals clicked ads and reached the consent form. Of these, 2,750 (48%) did not pursue past the initial consent page (perhaps due to learning about their ineligibility) and 94 (2%) were screened and found to be ineligible. An additional 1,077 (19%) attempted to sign up for the study after the IRB-approved participant quota for the study was reached and the survey was closed. Thirty-two percent (1,855) clicked on the ads, consented to participate, and completed the survey. Of the 1,855 who completed the survey, 625 individuals failed validation checks and were dropped. The final sample was composed of 1,230 participants.
2.2. Measures
2.2.1. Demographics.
Participants self-reported sex, race/ethnicity, age, combat exposure, and behavioral health treatment receipt (i.e., care in the past three months for a mental health or substance use concern).
2.2.2. Pain.
Pain was assessed using the Pain Outcomes Questionnaire (POQ; Clark et al., 2003), a 19-item inventory that measures pain on the following dimensions: intensity, mobility, activities of daily living, vitality, negative affect, and fear. The POQ was developed and validated with veterans engaged in pain treatment. The POQ yields a summed score from 0 to 190 and had a reliability estimate of α = 0.91 in our sample. Since the POQ asks respondents to rate their pain in the last week and we did not collect additional information about diagnoses and/or the chronicity of pain symptoms, we will henceforth refer to “pain” (rather than “chronic pain,” “pain-related medical conditions,” etc.).
2.2.3. Posttraumatic Stress Disorder Type Symptomology.
PTSD symptom severity was assessed using the 20-item Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5; Bovin et al., 2016), where participants indicated how often they were bothered by 20 symptoms of PTSD in the past month from not at all (0) to extremely (4). The PCL-5 yields a summed score from 0 to 80 and had a reliability estimate of α = 0.95 in our sample. The PCL-5 also yields scores for each of the four symptom clusters of re-experiencing (α = 0.87), avoidance (α = 0.74), negative cognitions and mood (α = 0.87), and hyperarousal (α = 0.85). Participants were asked whether they were considering a traumatic event (e.g., combat exposure, direct victimization, witnessing victimization, life-threatening illness, etc.) while responding to the PCL-5.
2.2.4. Alcohol Use Disorder.
Symptoms of AUD were assessed with the 10-item Alcohol Use Disorder Identification Test (AUDIT; Saunders et al., 1993). The AUDIT assesses frequency and quantity of alcohol use, binge drinking, and alcohol-related consequences and yields a summed score from 0 to 40 (α = 0.79).
2.3. Analytic Plan
A taxonomy of simple linear regression models was estimated to achieve the study aims. Specifically, to achieve Aim 1 we entered total PTSD score and pain as predictors of AUD symptomology, controlling for combat exposure and demographic covariates commonly associated with AUD (Alvanzo et al., 2011). To understand how differential PTSD scores and pain are associated with AUD symptomology, we then entered an interaction between total PTSD score and pain. To achieve Aim 2, we followed the same procedure but replaced the total PTSD score with PTSD symptom clusters. We estimated a series of models by entering interactions between pain and PTSD symptom clusters. Each symptom cluster was entered separately (sequentially starting with re-experiencing, then avoidance, negative cognitions and mood, and finally hyperarousal), and if an interaction was not significant it was removed for parsimony before entering the next. Finally, to assess how pain and PTSD symptom clusters are associated with AUD symptomology in men and women, we used a multi-group model in which main and interaction effects were entered, systematically, for men and women in the same model. In our final model, we used equality constraints to determine if regression coefficients were different between men and women. To understand interactions, prototypical plots were created with +/− 1 standard deviations (SD) above and below the mean. PTSD symptoms, PTSD subscales, pain, and AUD scores were treated as continuous variables, and all continuous variables were grand mean centered. Five participants with missing data were excluded from analyses. Our sample size provided ample power to detect effects at small and large effect sizes. All statistical analyses were completed in Mplus version 8.3 (Muthén & Muthén, 2017).
3. Results
Table 1 shows the demographic makeup of the sample and mean scores for study variables. The average age was 34.41 (SD = 3.67) years old. Veterans were mostly men, with women representing 11.23% of the sample. 9.4% reported any behavioral health treatment receipt over the past three months. The average AUDIT score was 14.92 (SD = 6.72), with 52.36% scoring 16 or higher indicating probable AUD (Babor et al., 2001). The average PTSD score was 22.77 (SD = 15.8), with 32.28% scoring 33 or higher indicating probable PTSD (Bovin et al., 2016); all but 1 participant endorsed exposure to a traumatic event, and 94.2% endorsed combat exposure specifically. The average pain score was 74 (SD = 26.44). Bivariate correlations were all in the expected directions (see Table 2).
Table 1.
Participant characteristics N=1,230
| M (SD) or n (%) | |
|---|---|
| Age | 34.41 (3.67) |
| Sex | |
| Men | 1,091 (88.77%) |
| Women | 138 (11.23%) |
| Race/ethnicity | |
| White | 975 (79.27%) |
| Black | 90 (7.32%) |
| Hispanic/Latino | 134 (10.89%) |
| Other/multiracial | 31 (2.52%) |
| Branch of service | |
| Air Force | 152 (12.36%) |
| Army | 802 (65.20%) |
| Marine Corps | 158 (12.85%) |
| Navy | 118 (9.59%) |
| Past 3 months’ behavioral health treatment | |
| Yes | 116 (9.4%) |
| No | 1,114 (90.6%) |
| Combat exposure | |
| Yes | 1,158 (94.15%) |
| No | 72 (5.85%) |
| Alcohol Use Disorder (AUDIT) | 14.92 (6.72) |
| Pain (POQ) | 74 (26.43) |
| PTSD symptoms (PCL-5 total score) | 22.77 (15.8) |
| PTSD symptom clusters (PCL-5 subscales) | |
| Re-experiencing | 5.82 (4.43) |
| Avoidance | 2.28 (1.92) |
| Negative cognitions and mood | 7.89 (5.75) |
| Hyperarousal | 6.78 (4.89) |
Note: Ranges for our study variables were as follows: AUDIT: 0–38, POQ: 1–162, PCL-5: 0–80, Re-experiencing: 0–20, Avoidance: 0–8, Negative cognitions and mood: 0–28, Hyperarousal: 0–24
Table 2.
Correlations
| 1. | 2. | 3. | 4. | 5. | 6. | 7. | |
|---|---|---|---|---|---|---|---|
| 1. AUDIT | - | ||||||
| 2. Pain | .32 | - | |||||
| 3. Re-experiencing | .29 | .37 | - | ||||
| 4. Avoidance | .19 | .34 | .71 | - | |||
| 5. Negative cognitions and mood | .26 | .37 | .82 | .72 | - | ||
| 6. Hyperarousal | .23 | .38 | .85 | .76 | .86 | - | |
| 7. PCL-5 total score | .27 | .40 | .93 | .82 | .95 | .95 | - |
| 8. Sex (women) | −.34 | −.04 | .13 | .18 | .16 | .16 | .16 |
Note: all correlations except pain by sex were significant p < .05
Next, we present results in their unstandardized (β) and standardized (𝑏) forms. Table 3 displays results for Aim 1. In our main effects model, both PTSD symptoms (𝛽 = 0.10, 95% 𝐶𝐼 [0.08, 0.12]; 𝑏 = 0.24) and pain (𝛽 = 0.05, 95% 𝐶𝐼 [0.04, 0.06]; 𝑏 = 0.20) were associated with greater AUD symptomology. In model 2, the interaction between PTSD symptoms and pain was significant (𝛽 = −0.002, 95% 𝐶𝐼 [−0.003, 0.002]; 𝑏 = −0.16). Simple slopes analyses revealed pain was associated with increased AUD symptomology at both high (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.03, 𝑡 = 3.51, 𝑝 < .001) and low (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.09, 𝑡 = 9.16, 𝑝 < .001) PTSD symptoms, though the association was stronger at low PTSD symptoms (Figure 1).
Table 3.
Final model of effects between PTSD, PTSD symptom clusters, and pain predicting AUD symptomology. Estimates are displayed as β [95% Confidence Interval].
| Variable | Model 1 PTSD and pain main effects | Model 2 PTSD and pain interaction | Model 3 PTSD symptom cluster and pain main effects | Model 4 PTSD symptom cluster and pain interactions |
|---|---|---|---|---|
| Co-variates | ||||
| Intercept | 21.778 [18.08, 25.48] | 22.19 [18.55, 25.83] | 21.76 [18.08, 25.45] | 22.48 [18.85, 26.11] |
| Age | −0.10 [−0.19, −0.01] | −0.08 [−0.17, 0.01] | −0.10 [−0.19, −0.01] | −0.08 [−0.17, 0.01] |
| Sex | −7.07 [−8.13, −6.01] | −6.91 [−7.90, −5.98] | −6.84 [−8.00, −5.88] | −6.86 [−7.90, −5.81] |
| White (ref) | REF | REF | REF | REF |
| Hispanic | −0.11 [−1.24, 1.02] | −0.03 [−1.15, 1.08] | −0.25 [−1.38, 0.88] | −0.17 [−1.28, 0.95] |
| Black | 1.11 [−0.14, 2.15] | 1.32 [−0.09, 2.35] | 0.87 [−0.37, 2.12] | 0.89 [−0.33, 2.11] |
| Other race | −3.99 [−6.00, −1.98] | −3.61 [−5.59, −1.63] | −4.02 [−6.02, −2.02] | −3.61 [−5.58, −1.64] |
| Combat exposure | 4.70 [3.27, 6.12] | 3.84 [2.41, 5.27] | 4.59 [3.15, 6.02] | 3.54 [2.10, 4.99] |
| PCL-5 total score | 0.10 [0.08, 0.13] | 0.11 [0.09, 0.14] | ||
| Pain | 0.05 [0.04, 0.06] | 0.06 [0.05, 0.07] | 0.05 [0.04, 0.06] | 0.06 [0.05, 0.08] |
| PTSD symptom clusters | ||||
| Re-experiencing | 0.38 [0.23, 0.53] | 0.41 [0.30, 0.52] | ||
| Avoidance | −0.004 [−0.26, 0.26] | 0.04 [−0.20, 0.28] | ||
| Negative cognitions/mood | 0.18 [0.06, 0.30] | |||
| Hyperarousal | −0.19 [−0.35, −0.04] | |||
| Interactions | ||||
| PTSD* Pain | −0.002 [−0.003, −0.002] | |||
| Re-experiencing* Pain | −0.004 [−0.007, 0.000] | |||
| Avoidance* Pain | −0.01 [−0.02, −0.01] |
Note: Ref=reference group for self-reported race/ethnicity covariate (White is reference group). In model 1 we entered main effects of PTSD (PCL-5 scores) and pain (POQ scores) as well as all covariates. In model 2 we entered overall PTSD (PCL-5 scores) interacting with pain (POQ scores) as well as all covariates. In model 3 we entered main effects of PTSD symptom clusters (PCL-5 subscale scores) and pain (POQ scores). In model 4 we entered a taxonomy of interactions between PTSD symptom clusters (PCL-5 subscale scores) and pain (POQ scores). For parsimony, interactions that were not significant were removed. Values above represent our final model of PTSD symptom clusters and pain predicting alcohol use disorder (AUDIT scores).
BOLD = confidence interval does not include 0
Figure 1.
Interaction between PTSD symptoms and pain predicting AUDIT scores with 95% confidence intervals, full sample. PTSD symptoms were measured with the PCL-5, and pain was measured with the POQ. POQ scores plotted to +/− 1 standard deviations (SD) above and below the mean.
To address Aim 2, we sought to identify the association between PTSD symptom clusters and pain on AUD symptomology. Table 3, Model 3 displays results of our main effects model. Re-experiencing (𝛽 = 0.38, 95% 𝐶𝐼 [0.23, 0.52]; 𝑏 = 0.25) and negative cognitions and mood (𝛽 = 0.18, 95% 𝐶𝐼 [0.06, 0.28]; 𝑏 = 0.15) were positively associated with AUD symptomology, though hyperarousal (𝛽 = −0.19, 95% 𝐶𝐼 [−0.35, −0.04]; 𝑏 = −0.14) was negatively associated with AUD symptomology. Table 3, model 4 displays interactions between PTSD symptom clusters and pain. Interactions between pain and re-experiencing (𝛽 = −0.004, 95% 𝐶𝐼 [−0.007, 0.000]; 𝑏 = −0.07) as well as avoidance (𝛽 = −0.01, 95% 𝐶𝐼 [−0.02, −0.01]; 𝑏 = −0.12) were associated with AUD symptomology. Simple slopes analyses revealed that pain was associated with increased AUD symptomology at both high (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.04, 𝑡 = 4.31, 𝑝 < .001) and low (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.08, 𝑡 = 6.95, 𝑝 < .001) re-experiencing, though the association was stronger at low re-experiencing (Figure 2a). Similarly, pain was associated with increased AUD symptomology at both high (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.03, 𝑡 = 3.16, 𝑝 < .01) and low (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.09, 𝑡 = 7.90, 𝑝 < .001) avoidance, though the association was stronger at low avoidance (Figure 2b).
Figure 2a.
Interaction between re-experiencing and pain predicting AUDIT scores with 95% confidence intervals, full sample. Re-experiencing was measured with the PCL-5, and pain was measured with the POQ. POQ scores plotted to +/− 1 standard deviations (SD) above and below the mean.
Figure 2b.
Interaction between avoidance and pain predicting AUDIT scores with 95% confidence intervals, full sample. avoidance was measured with the PCL-5, and pain was measured with the POQ. POQ scores plotted to +/− 1 standard deviations (SD) above and below the mean.
To address Aim 3, we estimated models using participant sex as our grouping variable. In our main effects model (see Table 3, model 1), both PTSD symptoms (𝛽 = 0.12, 95% 𝐶𝐼 [0.10, 0.15]; 𝑏 = 0.30) and pain (𝛽 = 0.06, 95% 𝐶𝐼 [0.04, 0.07]; 𝑏 = 0.24) were associated with greater AUD symptomology for men, though neither pain nor PTSD symptoms were associated with AUD for women. Interactions between PTSD symptoms and pain (see Model 2) were significant for both men (𝛽 = −0.002, 95% 𝐶𝐼 [−0.003, −0.001]; 𝑏 = −0.15) and women (𝛽 = −0.002, 95% 𝐶𝐼 [−0.004, −0.001]; 𝑏 = −0.21). For men both high (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.04, 𝑡 = 4.84, 𝑝 < .001) and low (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.10, 𝑡 = 9.52, 𝑝 < .001) PTSD symptoms slopes were significant though the association between pain and AUD symptomology was stronger at low PTSD symptoms (Figure 3a). However, for women the slope was not significant at either high or low PTSD symptoms (Figure 3b).
Figure 3a.
Interaction between PTSD symptoms and pain predicting AUDIT scores with 95% confidence intervals, men-only sample. PTSD symptoms were measured with the PCL-5, and pain was measured with the POQ. POQ scores plotted to +/− 1 standard deviations (SD) above and below the mean.
Figure 3b.
Interaction between PTSD symptoms and pain predicting AUDIT scores with 95% confidence intervals, women-only sample. PTSD symptoms were measured with the PCL-5, and pain was measured with the POQ. POQ scores plotted to +/− 1 standard deviations (SD) above and below the mean.
In Model 3, we explored the main effect of PTSD symptom clusters by sex. Among men pain (𝛽 = 0.06, 95% 𝐶𝐼 [0.04, 0.07]; 𝑏 = 0.25), re-experiencing (𝛽 = 0.42, 95% 𝐶𝐼 [0.27, 0.55]; 𝑏 = 0.30), and negative cognitions and mood (𝛽 = 0.21, 95% 𝐶𝐼 [0.09, 0.34]; 𝑏 = 0.19) were associated with greater AUD symptomology, while hyperarousal (𝛽 = −0.22, 95% 𝐶𝐼 [−0.38, −0.09]; 𝑏 = −0.17) was associated with less AUD symptomology. Among women, neither pain nor any of the PTSD symptom clusters were significant. In Model 4, among men interactions between pain and re-experiencing (𝛽 = −0.004, 95% 𝐶𝐼 [−0.008, 0.000]; 𝑏 = −0.09) as well as avoidance (𝛽 = −0.01, 95% 𝐶𝐼 [−0.02, −0.003]; 𝑏 = −0.17) were associated with AUD symptomology. Pain was associated with higher AUD symptomology at both high (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.08, 𝑡 = 4.27, 𝑝 < .001) and low (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.11, 𝑡 = 10.15, 𝑝 < .001) re-experiencing, though the association was stronger at low re-experiencing (Figure 4a). Similarly, pain was associated with increased AUD symptomology at both high (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.07, 𝑡 = 9.82, 𝑝 < .001) and low (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.12, 𝑡 = 5.90, 𝑝 < .001) avoidance, though the association was stronger at low avoidance (Figure 4b). Among women, a significant interaction between pain and negative cognitions and mood (𝛽 = −0.007, 95% 𝐶𝐼 [−0.012, −0.002]; 𝑏 = −0.17) was associated with AUD symptomology. However only the slope at low (𝑠𝑙𝑜𝑝𝑒 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡 = 0.06, 𝑡 = 2.01, 𝑝 = .047) negative cognitions and mood was significant, indicating that increased pain is associated with increased drinking only at low negative cognitions and mood (Figure 4c).
Figure 4a.
Interaction between re-experiencing and pain predicting AUDIT scores with 95% confidence intervals, men-only sample. Re-experiencing was measured with the PCL-5, and pain was measured with the POQ. POQ scores plotted to +/− 1 standard deviations (SD) above and below the mean.
Figure 4b.
Interaction between avoidance and pain predicting AUDIT scores with 95% confidence intervals, men-only sample. Avoidance was measured with the PCL-5, and pain was measured with the POQ. POQ scores plotted to +/− 1 standard deviations (SD) above and below the mean.
Figure 4c.
Interaction between negative cognitions and mood and pain predicting AUDIT scores with 95% confidence intervals, women-only sample. Negative cognitions and mood was measured with the PCL-5, and pain was measured with the POQ. POQ scores plotted to +/− 1 standard deviations (SD) above and below the mean.
4. Discussion
Military culture is associated with heavy drinking, and many veterans report problematic drinking hindering readjustment after discharge (Ames & Cunradi, 2005; Norman et al., 2014). Thus, understanding the determinants of veteran drinking is important. Our results indicating both pain and PTSD symptoms were positively associated with AUD symptomology are in line with previous findings involving either PTSD (Dworkin et al., 2018) or pain (Zale et al., 2015). Our interaction models expand on past work and suggest veterans with both pain and PTSD symptoms might have greater symptomology than those with a single condition. However, while we hypothesized the association between pain and AUD symptomology would be stronger among those with relatively high PTSD symptoms, we report the opposite result: the association between pain and AUD symptomology appeared stronger among those with low PTSD symptoms. This appears contrary to the mutual maintenance model which suggests comorbid PTSD symptoms and pain exacerbate one another and might synergistically increase risk (Sharp & Harvey, 2001). Our result may be partially due to a ceiling effect as our sample had high AUD symptomology, with over half endorsing AUDIT scores above the cutoff for likely AUD. Perhaps those with pain had such high AUDIT scores on average that additional symptomology (i.e., PTSD symptoms) exerted a milder influence on drinking than on those without pain. There is also neuroimaging and experimental evidence suggesting individuals with PTSD may have reduced pain sensitivity (Geuze et al., 2007; Mostoufi et al., 2014), which may also explain the weaker relationship between pain and AUD symptomology at heightened PTSD symptoms.
With respect to PTSD symptom clusters, the positive association between AUD symptomology and negative cognitions and mood in line with prior work (Jakupcak et al., 2010) and conceptions of alcohol use as self-medication for difficult emotions (Khantzian, 1997). In line with other work (Capone et al., 2013) re-experiencing was also positively associated with AUD symptomology; as veterans with re-experiencing symptoms drink to cope with emotional distress, this may again point to the role of emotion (McDevitt-Murphy et al., 2017). Contrary to prior work (Jakupcak et al., 2010) however, hyperarousal and AUD symptomology were negatively associated, suggesting perhaps veterans recruited in treatment settings might describe or cope with symptoms differently than those outside such settings. Interaction effects between pain and PTSD symptom clusters mirrored those with total PTSD scores: the association between pain and AUD symptomology appeared stronger at low levels of both re-experiencing and avoidance. This is intriguing as avoidant coping has been linked to both drinking and worse pain outcomes (Hasking et al., 2011; Possemato et al., 2015; Ramírez-Maestre et al., 2014) and thus one might expect that avoidance may be a pathway through which pain and PTSD symptoms exacerbate one another and motivate drinking. Perhaps differences in how avoidance is conceptualized in the context of different conditions may partly explain this result. Still, significant slopes suggest avoidance and re-experiencing might be salient intervention targets for those with both high and low pain.
Our study further adds to the literature on comorbidity among OEF/OIF veterans by exploring sex differences in the relationship between pain, PTSD symptoms and AUD. In men, re-experiencing, and negative cognitions and mood remained positively associated with AUD symptomology, though in women, we did not find a main effect for PTSD symptoms in general or any specific symptom cluster. These results diverge from prior work linking PTSD symptoms to drinking in women but not men OEF/OIF veterans (Scott et al., 2013). Pain also remained a correlate of drinking for men, especially at low PTSD symptoms. However, for women pain was associated with similar AUDIT scores across both low and high PTSD symptoms. This diverges from work with a non-veteran sample suggesting that women with pain may be particularly likely to self-medicate if they also endorse a history of psychological trauma (Jamison et al., 2010). With respect to interactions between pain and PTSD symptom clusters, results for men paralleled those of the full sample, though for women we report an interaction between pain and negative cognitions and mood: pain was associated with heightened AUD symptomology only in the context of low negative cognitions and mood. Our results highlight the need to further understand sex differences in pain experience (Berkley, 1997), the ways in which PTSD symptoms may alter pain processing (Geuze et al., 2007), and how these factors differentially impact alcohol use in men and women.
Our study has several limitations. Compared to prior findings with young adult veterans (Lee, 2019; Straus et al., 2020), our sample had comparable rates of PTSD but was high in heavy drinkers, perhaps because recruitment materials noted alcohol assessments. Our results may not generalize to lighter drinking samples, to veterans who do not use social media, or to those recruited from treatment settings. Our measure of pain (POQ) was validated with veterans in chronic pain treatment (Clark et al., 2003), but we did not collect information on involvement in chronic pain treatment nor on the type or chronicity of pain in our sample. Further, prior work does not provide guidance on how to interpret severity for the full scale; thus, our sample’s POQ scores should be interpreted with caution. Further, although we were powered to find effects, the relatively low percentage of women may have limited our ability to detect some sex differences. However, many studies of veterans lack adequate numbers of women to examine sex differences, and it is important to present such findings given the often-unmet behavioral health needs of women veterans (U.S. Department of Veteran’s Affairs, 2015). Also, our data are cross-sectional and do not allow us to establish causal relationships. Notably, although there are strong correlations between study variables, variance inflation factor statistics for all predictors in our symptom cluster main effects model were under 5, suggesting our estimates are likely not affected by multicollinearity (Alin, 2010).
The current study has broad clinical implications. While results generally suggest the relationship between pain and AUD is stronger for those with relatively low PTSD symptoms, both conditions were associated with increased AUD and as such clinicians treating veterans with AUD should screen for and address the range of potential comorbidities. Particularly, clinicians who associate pain with opioid misuse should consider that pain is also a determinant of problematic drinking in veterans. With respect to PTSD symptom clusters, our study builds on past work suggesting they are differentially associated with drinking, and clinicians treating veterans with comorbidities should be attuned to the emotionally salient aspects of patients’ clinical presentations. Furthermore, considering our sample recruited outside of treatment settings endorsed high rates of these conditions, we emphasize the need to improve access to medical and behavioral health care. This might involve efforts to reduce stigma regarding care or engaging veterans in digital interventions. Finally, our results point to preliminary sex-specific effects of pain and PTSD symptoms in their association with AUD, suggesting services for veteran men with AUD particularly should attend to comorbidity. As some results were discrepant with prior findings with women, these associations should continue to be explored among larger samples of women veterans so clinicians can adequately meet their behavioral health needs.
Table 4.
Final model of effects between PTSD, PTSD symptom clusters, and pain predicting AUD symptomology between men and women. Estimates are displayed as β [95% CI].
| Variable | Model 1 PTSD and pain main effect | Model 2 PTSD and pain interaction | Model 3 PTSD symptom cluster and pain main effects | Model 4 PTSD symptom cluster and pain interactions |
|---|---|---|---|---|
| Men | ||||
| Covariates | ||||
| Intercept | 13.87 [10.21, 16.93] | 14.99 [11.37, 18.62] | 14.23 [10.60, 17.87] | 15.57 [11.94, 19.20] |
| Age | −0.07 [−0.17, 0.02] | −0.07 [−0.16, 0.02] | −0.07 [−0.16, 0.02] | −0.07 [−0.16, 0.02] |
| Sex | ||||
| White (ref) | REF | REF | REF | REF |
| Hispanic | −0.004 [−1.25, 1.23] | −0.12 [−1.34, 1.09] | −0.16 [−1.38, 1.07] | −0.25 [−1.46, 0.96] |
| Black | 0.02 [−1.26, 1.30] | 0.07 [−1.19, 1.33] | −0.20 [−1.48, 1.07] | −0.12 [−1.38, 1.14] |
| Other race | −4.84 [−6.90, −2.78] | −4.57 [−6.61, −2.54] | −4.76 [−6.80, −2.72] | −4.43 [−6.45, −2.42] |
| Combat exposure | 4.67 [2.91, 6.44] | 3.74 [1.96, 5.51] | 4.25 [2.47, 6.03] | 3.17 [1.38, 4.97] |
| PCL-5 total score | 0.12 [0.10, 0.14] | 0.13 [0.11, 0.15] | ||
| Pain | 0.06 [0.04, 0.07] | 0.07 [0.05, 0.08] | 0.06 [0.04, 0.07] | 0.10 [0.07, 0.12] |
| PTSD symptom clusters | ||||
| Re-experiencing | 0.42 [0.27, 0.58] | 0.47 [0.36, 0.58] | ||
| Avoidance | −0.01 [−0.28, 0.27] | 0.05 [−0.20, 0.29] | ||
| Negative cognitions/mood | 0.21 [0.09, 0.32] | |||
| Hyperarousal | −0.22 [−0.38, −0.06] | |||
| Interactions | ||||
| PTSD* Pain | −0.002 [−0.003, −0.001] | |||
| Re-experiencing* Pain | −0.004 [−0.01, 0.000] | |||
| Avoidance* Pain | −0.01 [−0.02, −0.003] | |||
| Women | ||||
| Covariates | ||||
| Intercept | 12.04 [3.38, 19.31] | 11.03 [2.55, 19.51] | 12.29 [3.46, 21.13] | 10.68 [2.07, 19.30] |
| Age | −0.25 [−0.51, 0.01] | −0.18 [−0.44, 0.08] | −0.25 [−0.52, 0.01] | −0.18 [−0.44, 0.08] |
| Sex | ||||
| White (ref) | REF | REF | REF | REF |
| Hispanic | −0.84 [−3.71, 2.03] | −0.16 [−3.01, 2.68] | −0.67 [−3.64, 2.30] | 0.01 [−2.83, 2.85] |
| Black | 6.31 [2.42, 10.18] | 6.13 [2.35, 9.92] | 6.50 [2.51, 10.48] | 6.28 [2.51, 10.06] |
| Other race | 1.89 [−4.47, 8.25] | 2.66 [−3.57, 8.88] | −0.20 [−4.37, 8.45] | 2.91 [−3.32, 9.13] |
| Combat exposure | 5.50 [2.66, 7.87] | 4.76 [1.95, 7.58] | 5.39 [2.52, 8.26] | 4.99 [2.21, 7.77] |
| PCL-5 total score | 0.01 [−0.07, 0.10] | 0.03 [−0.06, 0.12] | ||
| Pain | 0.01 [−0.04, 0.06] | 0.01 [−0.04, 0.06] | 0.01 [−0.04, 0.06] | 0.01 [−0.04, 0.05] |
| PTSD symptom clusters | ||||
| Re-experiencing | −0.07 [−0.52, 0.37] | |||
| Avoidance | 0.11 [−0.61, 0.83] | |||
| Negative cognitions/mood | −0.001 [−0.31, 0.31] | 0.06 [−0.15, 0.27] | ||
| Hyperarousal | 0.06 [−0.38, 0.50] | |||
| Interactions | ||||
| PTSD* Pain | −0.002 [−0.004, −0.001] | |||
| Neg cognitions/mood* Pain | −0.01 [−0.01, −0.002] |
Note: Ref = reference group for self-reported race/ethnicity covariate (White is reference group). We ran each model with stratified samples of men and women separately. In model 1 we entered main effects of PTSD (PCL-5 scores) and pain (POQ scores) as well as all covariates. In model 2 we entered overall PTSD (PCL-5 scores) interacting with pain (POQ scores) as well as all covariates. In model 3 we entered main effects of PTSD symptom clusters (PCL-5 subscale scores) and pain (POQ scores). In model 4 we entered a taxonomy of interactions between PTSD symptom clusters (PCL-5 subscale scores) and pain (POQ scores). For parsimony, interactions that were not significant were removed. Values above represent our final model of PTSD symptom clusters and pain predicting alcohol use disorder (AUDIT scores) for men and women.
BOLD = confidence interval does not include 0
Highlights.
Pain and PTSD symptoms were positively associated with AUD symptomology
The association between pain and AUD symptomology was stronger among those with relatively low PTSD symptoms
Re-experiencing, and negative cognitions and mood were associated with alcohol use
We report evidence for sex differences in these relationships
Funding:
This work was supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (R01AA026575) awarded to Eric R. Pedersen.
Role of funding source:
This work was supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (R01AA026575) awarded to Eric R. Pedersen. NIAAA had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
Footnotes
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Declarations of Interest: None
Conflict of Interest No conflict declared
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