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. 2026 Sep 18;21:100481. doi: 10.1016/j.dadr.2026.100481

Alcohol use in adolescent chronic pain: Prevalence rates, perceptions of risk, and predictors of use

Camila Koike a,b,1, Nicole Tacugue a,1, Erin Sands a,1, Elissa R Weitzman c,d,e, Christine Greco a,b, Lydia A Shrier c,e, Bridget A Nestor a,b,f,2, Joe Kossowsky a,b,g,2,⁎
PMCID: PMC13634298  PMID: 42830988

Abstract

Introduction

Alcohol use (AU) in pediatric chronic pain may exacerbate pain and impact adult substance use behavior. This study characterizes prevalence rates and biopsychosocial correlates of AU in adolescents with chronic pain.

Methods

Cross-sectional survey study comprising adolescents aged 14–19 with chronic pain from a tertiary pediatric pain clinic.

Results

Among 243 adolescents, (68.70% female, Mage=16.9 [1.42] years), lifetime AU prevalence was 32.1% (78/243); past-year and past-month AU were 30.45% (74/243) and 16.46% (40/243), respectively. Adolescents endorsing AU were older, with higher depressive symptoms (ps<0.05). Age (OR=1.73, 95%CI [1.36–2.21], p < 0.001), depression (OR=2.18, 95%CI [1.28–3.74], p = 0.004), and behavioral approach (OR=1.18, 95%CI [1.02–1.37], p = 0.03) were significant predictors of lifetime AU. Adolescents who endorsed AU as less risky had higher odds of more frequent past-year AU compared to those who endorsed AU as more risky (OR ranged from 7.13 to 91.60, ps<0.02). Lifetime cannabis use (OR 5.71, 95%CI [2.17, 15.07], p < 0.001) and older age (OR 1.65, 95%CI [1.15, 2.38], p = 0.007) were associated with higher past-year AU frequencies.

Conclusion

Findings highlight depressed mood and low risk perception as potential intervention targets to reduce alcohol-related harm in adolescents with chronic pain.

Keywords: Alcohol use, Chronic pain, Adolescent, Mental health

Graphical Abstract

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Highlights

  • •

    Depression, behavioral approach, and age predicted lifetime alcohol use in pediatric pain.

  • •

    A permissive alcohol-risk threshold, lifetime cannabis use, and older age predicted more frequent past-year AU.

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    Adolescents with chronic pain and depression may be at higher risk for alcohol use.

1. Introduction

Alcohol is the most commonly used substance among adolescents, with 26.1% of US 10th graders and 41.7% of 12th graders reporting lifetime alcohol use (AU) (Miech et al., 2025, NIAAA, 2025). Ongoing neurodevelopmental changes during adolescence are associated with increased impulsivity and risk behaviors, which can increase the likelihood of substance use (Green et al., 2023). Substance use during this developmentally sensitive period can also have negative consequences that persist until adulthood (Steinberg, 2008, Shulman et al., 2016), and adolescent AU increases risk for problematic AU later in life (DuPont et al., 2018, Creswell et al., 2022). Adolescence, therefore, represents a critical developmental period for early identification to prevent AU escalation and harms (Chung et al., 2018, CDC, 2025).

In adolescents with chronic pain, rates of AU have been less studied. Globally, chronic pain affects approximately 21% of adolescents (Chambers et al., 2024) and commonly interferes with daily functioning. Chronic pain is associated with sleep disturbances (Valrie et al., 2013), psychological comorbidities (Noel et al., 2016), and impaired social engagement (Logan et al., 2008, Forgeron et al., 2010), all of which are linked to early AU in non-pain youth (Shadur et al., 2023). Further, pediatric pain is considered a risk factor for increased substance use (Rogers et al., 2025) because of the initial analgesic effect of AU for pain management. However, when consumed chronically, alcohol can exacerbate pain sensitivity (hyperalgesia), paradoxically increasing pain responses and reinforcing ongoing AU (Lee and Edwards, 2025).

To date, Law and colleagues (2015) provide one of few reports investigating AU in pediatric pain, documenting lower AU rates compared to youth without pain. However, research findings overall appear mixed regarding associations between adolescent pain and AU (Fernández-León et al., 2024). Some studies report associations between increased pain intensity and greater AU frequency (McLaren et al., 2017, Horn-Hofmann et al., 2018), whereas others report the opposite pattern (Law et al., 2015, Rogers et al., 2025), perhaps due to physical and social limitations inherent to pain conditions. In related studies, youth with chronic medical conditions demonstrate higher AU rates compared to healthy counterparts (Wisk and Weitzman, 2016).

Several factors may be associated with AU in adolescents with pain. Adolescents with pain have poorer mental health than those without (McLaren et al., 2017), and depression is associated with more frequent AU and cannabis use in youth with pain (Law et al., 2015, Kossowsky et al., 2025, Rogers et al., 2025). Though not specific to only AU, Law and colleagues found older age predicted higher odds of substance use in adolescents with chronic pain, and functional disability predicted lower odds of use. Notably, unlike patterns observed in youth without pain, depression, loneliness, and pain intensity did not predict AU in this sample (Law et al., 2015). This contrasts with findings in cannabis use among adolescents with chronic pain, which indicate pain interference and depression are associated with cannabis use (Kossowsky et al., 2025).

Other potential correlates of AU in adolescents with pain remain underexplored: the extent to which AU risk perceptions influence drinking behavior (Horn-Hofmann et al., 2018), the extent to which the behavioral inhibition system predicts AU (Shao et al., 2025), and how parental monitoring impacts AU (Latendresse et al., 2017, Pelham et al., 2024). Prior findings suggest youth without chronic pain perceive alcohol-related consequences as less risky than youth with chronic pain (Fernández-León et al., 2024), and these risk perceptions may be linked to problematic AU (Horn-Hofmann et al., 2018) and general substance use (Jeanblanc, 2015, De Aquino et al., 2024). To the authors’ knowledge, these factors have not been examined across adolescents with chronic pain who do versus do not report AU.

The current study primarily aims to: 1) characterize prevalence rates and perceived risks and consequences of AU in adolescents with chronic pain, and 2) compare biopsychosocial factors between those with and without AU in this population. Characterizing AU rates in adolescents with chronic pain is critical, particularly since the COVID-19 pandemic, which led to disruptions that predicted increased substance use in this population (Nestor et al., 2025). Further, identifying relevant correlates of AU may help identify youth most vulnerable to ongoing AU and inform tailored treatment efforts. We hypothesized AU prevalence in adolescents with chronic pain would mirror national averages. We also hypothesized adolescents with chronic pain and AU would demonstrate more physical, behavioral and psychological comorbidities than their counterparts without AU.

1.1. Methods

1.1.1. Participants and procedures

In this cross-sectional study, participants were recruited from a pain treatment clinic at a pediatric hospital in the Northeast US between September 2021 to May 2024. Youth were eligible if they were: 14–19 years of age, receiving treatment for diagnosed chronic pain, and proficient in English. Participants who presented with severe cognitive impairment or were unable to assent were excluded. Based on an expected 25% prevalence of AU, a sample of 200 participants was estimated to detect small-to-moderate differences (d=0.4, α=0.05, power=0.8) between those with vs without AU. We approached 312 patients, 251 were enrolled, and 243 completed the survey.

Research staff identified eligible patients through screening weekly clinic schedules. The study team contacted eligible patients and provided study information via mail, email, or a secure patient portal message. The research team also contacted potential participants for recruitment via phone or during their office visit to the pain clinic. Participants and their parent/guardian provided informed assent/consent. Enrolled participants completed the survey via REDCap (Harris et al., 2009). Participants were compensated with a $20 gift card. Boston Children’s Hospital institutional review board approved this study (IRB-P00036526).

1.1.2. Measures

1.1.2.1. Demographics

Participants reported their age, gender, race, and ethnicity.

1.1.2.2. AU

To characterize AU, items were selected and combined from several validated self-report questionnaires (NIAAA, 2025; Monitoring the Future, 2025). Participants endorsed whether they had used alcohol in their lifetime, past year, and past month, and they specified their age at first use. Those who endorsed AU completed additional items regarding AU frequency, motives, perceived risks, beliefs, and intention for future use.

Frequency: Participants reported number of AU occasions (0–40 +) over their lifetime, past year, and past month and number of occasions being drunk and being intoxicated (5 + drinks in one setting).

Negative Consequences: Participants with AU reported how frequently they experienced alcohol-related blackouts, injuries, or vomiting in the past year (1 =never, 2 =once or twice, 3 =sometimes, 4 =often). They also reported how often they went to the emergency room for AU-related reasons (1 =never, 2 =once, 3 =twice, 4 =3 or more occasions).

Perceived Risks: Participants reported how many standard drinks they considered risky or dangerous (1 = any number of drinks is risky/dangerous; 2 =more than 1 drink is risky/dangerous; 3 =more than 2 drinks is risky/dangerous; 4 =more than 3 drinks is risky/dangerous; 5 =more than 4 drinks is risky/dangerous; 6 =more than 5 drinks is risky/dangerous; 7 =don't consider alcohol risky/dangerous).

Intention of Future Use: Participants with AU reported intention for future AU in the next month and next year (1 =definitely will to 4 =definitely will not).

1.1.2.3. Pain, mental health, and behavioral health

The Numerical Rating Scale (NRS) (von Baeyer et al., 2009) assessed average pain intensity on a 0–10 scale (0 =no pain; 10 =worst pain imaginable).

The Functional Disability Inventory (FDI) (Walker and Greene, 1991) is a validated self-report instrument comprising 15 items measuring perceived challenges with physical activity in adolescents with chronic pain. Participants rated perceived challenges with activities at home, school, or recreational settings on a 5-item scale (0 =no trouble; 4 =impossible). Raw scores were summed to a total score (0−60). Higher scores indicate more severe pain-related functional disability. Cronbach’s alpha in the current study was 0.91, suggesting excellent reliability.

The Patient Reported Outcomes Measurement Information System (PROMIS) (Cella et al., 2010) is a validated series of self-report instruments investigating health-related domains. Participants completed PROMIS assessments for the following domains: pain interference, sleep disturbance, psychological stress, anxiety, and depression. Participants completed the pediatric (age 14–17) or adult (age 18–19) version of these measures depending on their age (Cella et al., 2010, Meltzer et al., 2020). Each PROMIS form consists of 8 items measuring frequency with which an action or feeling was experienced over the past week. Responses were scored using a 5-point scale (1 =never; 5 =almost always/always). Raw scores were summed and converted into standardized T-scores. Higher T-scores indicate more occurrences of the assessed domain. Cronbach’s alpha was between 0.85 and 0.96 across all assessments, suggesting excellent reliability.

1.1.2.4. Behavioral avoidance and inhibition

The Behavior Avoidance/Inhibition Scale (BIS/BAS) (Carver and White, 1994) is a validated self-report measure with 24-items rated on a 4-point scale (1 =very true for me, 2 =somewhat true for me; 3 =somewhat false for me; 4 =very false for me). This scale assesses individual differences in behavioral inhibition system (BIS) and behavioral activation system (BAS) sensitivity, comprising four domains: BAS Drive (4-item), BAS Fun Seeking (4-item), BAS Reward Responsiveness (5-item) and BIS (7-item). In accordance with published guidelines, items were reverse scored prior to summing, with the exception of two negatively worded BIS items, so that higher scores indicate greater BIS or BAS sensitivity. Cronbach’s alpha in the current study was 0.83, suggesting excellent reliability.

1.1.2.5. Parental supervision and involvement

Parental supervision and involvement were examined with the revised Supervision/Involvement Scale of the Pittsburgh Youth Study (Loeber and Stouthamer-Loeber, 1986). This is an 18-item validated survey assessing constructs of Parental Supervision, Involvement, Discussion of Daily Activities, and Curfew Time. Most questions used a 6-point likert scale (1 =almost never; 2 =once in a while; 3 =sometimes; 4 =often; 5 =almost always; 6 =not applicable); two questions allowed for open-ended responses describing whereabouts after school and on weekends. Higher scores indicate more of the measured parental construct. Cronbach’s alpha in the current study was 0.70, suggesting acceptable reliability.

1.2. Data analysis

Statistical analyses were performed in Python version 3.11. Participants were categorized into two substance use groups (AU and No-AU) based on lifetime reports. To compare across age groups, adult and pediatric versions of the PROMIS measures (pain interference, sleep disturbance, sleep impairment, depressive symptoms, anxiety, and fatigue) were standardized using z-score transformations. Due to violations of normality and variance homogeneity assumptions based on Shapiro-Wilk and Levene's test, non-parametric tests were used. Group differences between AU and No-AU groups on standardized scores of demographic and health characteristics were analyzed using Mann-Whitney or Monte Carlo Chi-Squared tests and were corrected for multiple comparison (FDR). Raw means and standard deviations for each measure by age group are presented in Appendix 1. Spearman correlation analysis corrected by FDR was performed to examine the association between AU and behavioral, mental, social and physical health within the AU group. To facilitate interpretation of correlation results, we reversed scores of the "Intention of AU” variable so that higher scores indicated greater intention of use. Additional group-level Spearman correlations were conducted to examine associations between pain and affective variables across AU and No-AU groups. We applied Fisher’s r-to-z transformation to compare correlation coefficients between groups, shown in Appendix 3. Ordinal logistic regression model investigated whether perceived risks of AU predicted frequency of use in the past 12 months, controlling for lifetime cannabis use, age, and gender. Dummy variables were created with “any number of drinks is risky or dangerous” serving as a reference group. A hierarchical logistic regression examined predictors of lifetime AU. To address multicollinearity and reduce missing data, we created composite scores for conceptually related variables: (1) behavioral approach system (BAS; mean of reward responsiveness, drive, and fun seeking subscales), and (2) parental monitoring (mean of supervision involvement, discussing daily activities, and curfew time). Psychological stress was excluded due to high collinearity with depression (r = 0.76) and anxiety (r = 0.71). Predictors were entered in four hierarchical blocks: (1) demographics (age, gender, race, ethnicity), (2) pain variables (functional disability, pain interference, fatigue), (3) psychological symptoms (depression, anxiety, sleep disturbance, sleep impairment), (4) parenting and personality (parental monitoring, BAS, BIS). Statistical significance was defined as p < 0.05.

2. Results

A total sample of 243 adolescents (Mage = 16.9 (1.42) years; 68.70% (167/243) female) provided complete data. 312 patients were approached, 40 did not complete the survey, and 29 were ineligible. Within our entire sample, 32.1% (78/243) reported AU and 67.9% (165/243) No-AU. Further, 61.5% (48/78) participants in the AU group reported cannabis use and 8.5% (14/165) in the No-AU group. See Table 1 for demographics characteristics.

Table 1.

Demographics characteristics.

Variable Total Sample
N = 243 		No-AU
n = 165 		AU
n = 78 		Cohen’s d Adjusted p-value
Demographics
Age, mean (SD) 16.90 (1.42) 16.63 (1.45) 17.45 (1.18) -0.59 < 0.001⁎
Age of alcohol use onset, mean (SD) 15.12 (1.58) NA 15.12 (1.58) NA NA
Hispanic, n (%) 19 (7.80) 13 (7.90) 6 (7.70) NA 0.17
Race, n (%)
American Indian or Alaska Native 1 (0.40) 1 (0.60) 0 (0.00) NA 0.85
White 200 (82.30) 136 (82.40) 64 (82.10) NA
Black or African American 3 (1.20) 2 (1.20) 1 (1.30) NA
Asian 3 (1.20) 3 (1.80) 0 (0.00) NA
Other Race/Multiracial 36 (14.80) 23 (13.90) 13 (16.70) NA
Gender, n (%)
Female 167 (68.70) 113 (68.50) 54 (69.20) NA 0.79
Male 36 (14.80) 24 (14.50) 12 (15.40) NA
Non-binary 24 (9.90) 15 (9.10) 9 (11.50) NA
Other 16 (6.60) 13 (7.80) 3 (3.90) NA
Pain and Fatigue, n (SD)
Pain Intensity 5.54 (1.68) 5.61 (1.72) 5.40 (1.60) 0.13 0.61
Functional Disability 23.97 (11.58) 24.58(11.81) 22.69 (11.05) 0.16 0.50
Pain Interference Adults 59.97 (8.17) 59.04 (9.03) 61.08 (7.00) -0.25 0.82
Pain Interference Pediatric 60.26 (7.00) 60.20 (7.12) 60.42 (6.72) -0.03 0.99
Fatigue Adults 56.94 (10.72) 56.45 (11.58) 57.52 (9.79) -0.10 0.89
Fatigue Pediatric 59.75 (12.21) 59.30 (12.88) 60.94 (10.23) -0.13 0.642
Sleep, n (SD)
Sleep Impairment Adults 58.45 (9.52) 57.17 (10.29) 59.99 (8.41) -0.30 0.82
Sleep Impairment Pediatric 62.93 (8.15) 62.38 (8.33) 64.41 (7.56) -0.25 0.34
Sleep Disturbance Adults 56.22 (8.96) 55.21(9.43) 57.43 (8.35) -0.25 0.64
Sleep Disturbance Pediatric 65.10 (10.13) 64.67 (9.81) 66.22 (10.95) -0.15 0.50
Psychosocial, n (SD)
Depressive Symptoms Adult 55.42 (9.17) 54.15 (9.67) 56.95 (8.43) -0.31 0.53
Depressive Symptoms Pediatric 56.89 (11.64) 55.31 (12.44) 61.07 (7.88) -0.51 0.03⁎
Anxiety Adult 57.38 (10.42) 54.99 (10.84) 60.16 (9.34) -0.51 0.50
Anxiety Pediatric 56.53 (11.52) 56.08 (12.15) 57.72 (9.64) -0.14 0.508
Psychological Stress 62.20 (9.59) 61.27 (10.05) 64.17 (8.28) -0.31 0.33
Behavioral Avoidance and Inhibition, n (SD)
BAS Drive 10.19 (2.64) 9.84 (2.64) 10.93 (2.51) -0.42 0.03⁎
BAS Fun Seeking 11.20 (2.46) 10.88 (2.58) 11.86 (2.06) -0.40 0.05⁎
BAS Reward Responsiveness 16.73 (2.54) 16.76 (2.54) 16.67 (2.56) 0.03 0.83
BIS 19.97 (3.27) 19.85 (3.45) 20.23 (2.88) -0.11 0.82
Parental Supervision, n (SD)
Parental Supervision and Involvement 4.15 (0.71) 4.20 (0.69) 4.04 (0.76) 0.23 0.39
Discussion of Daily Activities 4.13 (1.01) 4.12 (1.02) 4.14 (1.01) -0.02 0.88
Curfew Time 2.76 (1.49) 2.79 (1.46) 2.71 (1.53) 0.05 0.82
Total Parental Supervision 10.13 (2.73) 9.98 (2.76) 10.44 (2.64) -0.17 0.50
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Note: AU: alcohol use; No-AU: no alcohol use.

Adjusted p-value: FDR correlated p-values

⁎Comparisons between AU and No-AU groups reflect significant differences

2.1. Group differences of participant characteristics

Participants in the AU group were older (d=-0.59, p < 0.001) and reported more pediatric depressive symptoms (d=-0.51, p = 0.03) compared to No-AU. The AU group also reported higher BAS drive (d=-0.42, p = 0.03) and fun seeking (d=-0.40, p = 0.05) scores than No-AU. No other significant differences emerged between groups. See Table 1 for group differences and Appendix 1 for combined z-scores. Adjusting for age, BAS drive (b=1.09, 95% CI 0.38–1.81, p < 0.02) and fun seeking (b=1.1, 95% CI 0.48–1.71, p < 0.02) subscales and pediatric depressive symptoms (b=5.20, 95% CI 1.95–8.45, p < 0.02) continued to differ significantly between groups, and psychological stress emerged as significantly different (b=3.36, 95% CI 0.89–5.83, p < 0.05).

2.2. Alcohol use (AU)

Age of first AU ranged from 12 to 18 years (M=15.12; SD=1.6) years. Past year and past month AU prevalence rates were 30.45% (74/243) and 16.46% (40/243), respectively. Occasions of lifetime, past month and year AU, drunkenness, and intoxication are described in Fig. 1 and Fig. 2. Appendix 3 presents correlations between AU and behavioral, mental, social and physical health variables.

Fig. 1.

Fig. 1

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Distribution of Alcohol Use Occasions Across Lifetime, Past-Year, and Past-Month. Note: The percentage of responses is displayed above each bar.

Fig. 2.

Fig. 2

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Frequency of Drunkenness and Alcohol Intoxication in the Past-Year and Past-Month. Note: The percentage of responses is displayed above each bar. Alcohol intoxication is defined as 5 + drinks in one setting.

2.2.1. Reported likelihood of AU

Participants reported varying intention for future AU. Regarding AU in the next year, 47.4% (37/78) indicated they probably will, while 29.5% (23/78) reported definitely will. A smaller portion expressed lower intention of use: 17.8% (14/78) reported they probably will not, and 5.1% (4/78) reported definitely will not.

In contrast, for AU intention in the next month, 12.8% (10/78) reported they definitely will use alcohol, and 29.5% (23/78) reported probably will. A larger proportion expressed lower intention of use, with 34.6% (27/78) indicating they probably will not and 23.0% (18/78) stating they definitely will not.

2.2.2. Adverse experiences

Participants reported how often they experienced blackouts from AU in the past year: 80.8% (63/78) never, 14.1% (11/78) once or twice, 5.1% (4/78) sometimes, and 0% (0/78) often. Participants also reported frequency of AU-related injury in the past year: 87.2% (68/78) never, 11.5% (9/78) once or twice, 1.3% (1/78) sometimes, and 0% (0/78) often. Participant report of past year vomiting due to AU was 69.2% (54/78) never, 28.2% (22/78) once or twice, 2.6% (2/78) sometimes, and 0% (0/78) often. No participants reported going to the emergency room due to AU-related issues.

2.2.3. Perceived risk of alcohol consumption

Overall, 14.1% of participants (11/78) endorsed that any quantity of alcohol was risky or dangerous, 11.4% (9/78) considered more than one drink consumed on a single occasion as risky or dangerous, 23.1% (18/78) viewed more than two drinks as risky, 24.4% of participants (19/78) perceived more than three drinks as risky, 15.4% (12/78) identified more than four drinks as risky, and 11.4% (9/78) considered more than five drinks to be risky or dangerous. No participants endorsed alcohol as categorically non-risky.

In an ordinal logistic model controlling for lifetime cannabis use, age, and gender, adolescents who endorsed more permissive thresholds had higher odds of more frequent AU in the past year compared to those who consider any number of drinks risky or dangerous, with odds ratios increasing across thresholds (more than 2 drinks: OR 9.03, 95%CI [1.99, 41.10], p = 0.004; more than 3 drinks: OR 15.60, 95%CI [3.30, 73.74], p < 0.001; more than 5 drinks: OR 91.60, 95%CI [12.97, 647.17], p < 0.001). Lifetime cannabis use (OR 5.71; 95%CI 2.17, 15.07, p < 0.001) and older age (OR 1.65, 95%CI [1.15, 2.38], p = 0.007) were associated with higher AU frequency in the past year. Gender was not significantly associated with AU frequency. Full model results are described in Appendix 4.

2.3. Hierarchical regression analysis: predictors of AU

In a hierarchical logistic regression examining predictors of lifetime AU, model fit improved significantly across models (Model 1 R²=0.088; Model 2 R²=0.108, ΔR²=0.019; Model 3 R²=0.161, ΔR²=0.053; Model 4 R²=0.177, ΔR²=0.016). The addition of psychological variables produced the largest improvement in model fit (ΔR²=0.053). In the final model, four predictors were significant; see Fig. 3 for the forest plot of odds ratios. Age was the strongest predictor (OR 1.73, 95%CI [1.36–2.21], p < 0.001), with each additional year associated with 73% higher odds of lifetime AU. Depression was also a significant predictor (OR 2.18, 95%CI [1.28–3.74], p = 0.004), with higher depression scores associated with more than double the odds of AU. Functional disability showed a marginally protective effect (OR 0.95, 95%CI [0.90–1.00], p = 0.06), with higher disability associated with slightly lower odds of AU. BAS total score was also significant (OR 1.18, 95%CI [1.02–1.37], p = 0.03), with a one-point increase in behavioral approach associated with 18% higher odds of AU.

Fig. 3.

Fig. 3

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Predictors of Lifetime Alcohol Use in Final Model (Model 4).

3. Discussion

This study aimed to characterize prevalence rates, patterns of use and perceived risks and consequences of AU in adolescents with chronic pain and identify predictors of AU within adolescents with chronic pain. The prevalence of AU in our study aligns with national averages (Miech et al., 2025), and consistent with our hypotheses, adolescents with AU exhibited elevated depression and greater behavioral approach sensitivity compared to the No-AU group. Additionally, adolescents who endorsed more permissive thresholds for alcohol-related risk also reported more frequent AU in the past year relative to those who considered any amount of alcohol risky. Our findings also indicated age, depression and behavioral approach were significant predictors of lifetime AU. Overall, our findings highlight key psychological and behavioral targets for interventions in adolescents with AU and chronic pain.

Approximately 32% of adolescents with chronic pain reported at least one occasion of AU in their lifetime, aligning with national prevalence rates that range from 26% to 42% among high schoolers (Miech et al., 2025, NIAAA, 2025). In our sample, mean age of AU onset was 15 years, consistent with national trends and typically associated with more frequent alcohol consumption and elevated psychological stress (DuPont et al., 2018, Creswell et al., 2022). Although AU rates in the current sample were not elevated compared to national samples, adolescents with chronic pain face heightened risk for psychological comorbidities (Wrona et al., 2021), which may increase their vulnerability to substance use (Jeanblanc, 2015, De Aquino et al., 2024). Consequently, this interplay may contribute to a reinforcing cycle of psychological stress, substance use, and persistent pain symptoms, although in the current study, no differences in pain or fatigue emerged between AU and No-AU groups. Our findings also reveal lifetime cannabis use was associated with higher AU frequency, consistent with prior work examining increased AU in co-use of alcohol and cannabis in the same adolescent pain sample (Koike et al., 2026). Together, findings further underscore the necessity of examining AU in adolescents with chronic pain, particularly in the context of polysubstance use and psychological vulnerability.

Adolescents with chronic pain also may rely on multiple medications that can adversely interact with alcohol, potentially exacerbating pain symptoms and increasing the risk of mental health comorbidities in a population already experiencing significant psychological distress (Bagge and Borges, 2017, Wrona and Melnyk, 2021, De Aquino et al., 2024, Secci et al., 2024). We found adolescents with AU exhibited elevated depression and psychological stress compared to the No-AU group. This aligns with other work demonstrating adolescents with AU have reported more stress and depressive symptoms compared to their non-drinking peers (McLaren et al., 2017). Of note, AU was not associated with pain symptoms in our cohort, however associations were found among pain symptoms, depression, and anxiety. Magnitudes and directions of correlations between pain and affective variables were consistent with those reported in an adult pain population (Fitzpatrick-Schmidt et al., 2025).

Our regression results underscore factors associated with lifetime AU among adolescents with chronic pain. Consistent with prior work, age and depression were both significant predictors of AU in the current study (Monitoring the Future, 2025). Behavioral approach sensitivity emerged as a significant predictor, with higher BAS scores associated with greater odds of lifetime AU, which is consistent with the findings linking reward sensitivity to substance use initiation in healthy adolescents (Urošević et al., 2015, Shao et al., 2025), indicating that the reward-driven pathway to adolescent drinking may operate similarly in youth with chronic pain. This pattern was mainly driven by the higher scores in the drive and fun-seeking subscales in adolescents with AU compared to the No-AU group; no difference was found in reward responsiveness. Further, functional disability showed a marginal protective association, which may be explained by adolescents with more functional disability 1) not being able to attend social events in which alcohol is consumed (e.g. parties) and 2) being more closely monitored by parents/guardians. Both possibilities may reduce opportunity for AU (Law et al., 2015). Given participants with AU and No-AU reported similar levels of parental supervision, interpretations of findings are limited in identifying to what extent parental monitoring influences AU.

Finally, our findings showed adolescents who endorsed higher thresholds for alcohol-related risk reported more frequent use in the past year relative to those who considered any amount of alcohol risky. This result expands upon prior studies demonstrating how coping-related drinking motives moderate positive associations between pain severity and drinking frequency in adolescents (Horn-Hofmann et al., 2018); our findings suggest risk perception is another potential factor influencing the susceptibility of adolescents with chronic pain to engage in AU. Participants’ perceptions of the minimum quantity of alcohol considered risky and dangerous demonstrated variability in risk thresholds, with the modal response identifying more than three drinks as risky, and 86% of participants endorsing a nonzero threshold. This is concerning given any amount of alcohol consumption increases risk of physical, mental, and academic consequences in healthy adolescents (Alinsky and Hipp, 2024). Adolescents with chronic pain may be even more susceptible to problematic AU based on risk perception thresholds for alcohol consumption. Clinically, these findings highlight psychoeducation and risk perception as critical targets for intervention.

Limitations of this study guide directions for future research. First, due to its cross-sectional design, temporal and causal relationships cannot be established; longitudinal investigations comparing AU and No-AU groups may clarify the direction of these associations. Second, the U.S. Dietary Guidelines (U.S. Department of Health and Human Services USDHHS, 2025) define heavy drinking as consuming five or more drinks on one occasion for men and four or more for women. Because most participants in our sample were female, applying a uniform threshold of five or more drinks may have underestimated the prevalence of heavy drinking and associated intoxication. Third, the current sample was recruited from a single pediatric pain treatment clinic in the Northeastern US, so variability in clinic-based pain-management approaches and participants’ use of licit or illicit analgesic substances may represent potential confounding factors. Information on prescribed medications or current medication use at the time of baseline assessment was available for only a subset of participants, and medication indications were not systematically assessed. Moreover, as the medication information reflected treatment around the time of study enrollment, the temporal relationship between medication exposure and alcohol use could not be established. Therefore, we could not reliably assess the potential influence of prescribed medications on alcohol-use patterns or their associations with pain-related and psychosocial factors. Fourth, although the demographic characteristics of the current sample resemble those of larger cohorts of pediatric pain patients (Rodriguez-Restrepo and AuBuchon, 2024), future research should aim to recruit more diverse and geographically distributed samples.

Adolescents with chronic pain appear to engage in AU at comparable rates to healthy peers (Miech et al., 2025, NIAAA, 2025). Given this clinical cohort’s baseline predispositions to psychological comorbidities (Wrona et al., 2021), AU in adolescents with chronic pain poses a risk of exacerbating mental health symptoms and contributing to poorer clinical outcomes. Our results emphasize the importance of screenings for mental health and AU as part of multidisciplinary intervention for adolescents with chronic pain.

Author disclosures

All authors certify that:

The manuscript is original and not under consideration elsewhere.

All authors have approved the final version.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This study was supported by the NIH grant K01DA057374.

CRediT authorship contribution statement

Erin Sands: Writing – review & editing, Writing – original draft, Project administration, Formal analysis, Data curation. Nicole Tacugue: Writing – review & editing, Writing – original draft, Visualization, Project administration, Investigation, Formal analysis, Data curation. Camila Koike: Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Bridget A. Nestor: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. Lydia A. Shrier: Writing – review & editing, Supervision, Conceptualization. Christine Greco: Writing – review & editing, Conceptualization. Elissa R. Weitzman: Writing – review & editing, Supervision, Methodology, Conceptualization. Joe Kossowsky: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization.

Ethics declaration

Written informed consent to take part in the study and to publish the article has been obtained from all participants or their legal representatives. The privacy rights of participants have been observed.

This study was performed in accordance with relevant laws, regulatory frameworks and guidelines where the research took place. This study was approved by the Boston Children's Hospital. (Approval No. IRB-P00036526)

Declaration of Competing Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Appendix A

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dadr.2026.100481.

Appendix A. Supplementary material

Supplementary material

mmc1.docx (2.5MB, docx)

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