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. Author manuscript; available in PMC: 2024 Mar 8.
Published in final edited form as: Subst Abus. 2021 Jun 4;43(1):212–221. doi: 10.1080/08897077.2021.1930629

Changes in young adult substance use during COVID-19 as a function of ACEs, depression, prior substance use and resilience

Katelyn F Romm 1, Brooke Patterson 2, Natalie D Crawford 3, Heather Posner 4, Carly D West 4, DeEnna Wedding 5, Kimberly Horn 6,7, Carla J Berg 1,8
PMCID: PMC10920401  NIHMSID: NIHMS1793043  PMID: 34086537

Abstract

Background:

Given the potential for increased substance use during COVID-19, we examined: 1) young adults’ changes in cigarette, e-cigarette, marijuana, and alcohol use from pre- to during COVID-19; and 2) related risk/protective factors. These findings could inform intervention efforts aimed at curbing increases in substance use during periods of societal stress.

Methods:

We analyzed Wave 3 (W3; Sept-Dec 2019) and Wave 4 (W4; March-May 2020) from the Vape shop Advertising, Place characteristics and Effects Surveillance (VAPES), a two-year, five-wave longitudinal study of young adults across 6 metropolitan areas. We examined risk/protective factors (i.e., adverse childhood experiences [ACEs], depressive symptoms, resilience) in relation to changes in past 30-day substance use frequency.

Results:

In this sample (N=1,084, Mage=24.76, SD=4.70; 51.8% female; 73.6% White; 12.5% Hispanic), W3/W4 past 30-day use prevalence was: 29.1% cigarettes (19.4% increased/26.4% decreased), 36.5% e-cigarettes (23.2% increased/28.6% decreased), 49.4% marijuana (27.2% increased/21.2% decreased), and 84.8% alcohol (32.9% increased/20.7% decreased). Multivariate regressions indicated that, greater increases were predicted by: for e-cigarettes, greater ACEs; and for alcohol, greater depression. Among those with low resilience, predictors included: for e-cigarettes, greater depression; and for marijuana, greater ACEs.

Conclusions:

Interventions to reduce substance use during societal stressors should target both risk and protective factors, particularly resilience.

Keywords: COVID-19, e-cigarettes, substance use, depression, ACEs, resilience

Introduction

COVID-19 was characterized as a global pandemic by the World Health Organization (WHO) on March 11, 2020. In the US, California became the first state to declare a state of emergency on March 18, 2020.1 Public health efforts to mitigate the spread of the disease (e.g., social distancing) led to societal stressors (e.g., social isolation, job loss, economic devastation) that are related to poorer mental health outcomes (e.g., depression, anxiety)2 and coping-related substance use.3

Such substance use is particularly concerning given its potential impact on COVID-19 related outcomes. Evidence suggests that cigarette and electronic cigarette (e-cigarette) use increases risk for COVID-19 and predicts worse disease outcomes (e.g., severity, mortality), perhaps due to lung exposure to toxic chemicals that may weaken the immune system and increase risk of infectious disease and respiratory infection.4–7 Similarly, marijuana use may increase risk for adverse outcomes due to the respiratory and pulmonary effects of COVID-19.8 Alcohol use also is associated with worse COVID-19 related-outcomes due to a weakened immune system.9 Although some individuals may attempt to reduce their substance use due to these COVID-19 related health risks,10,11 others may increase use, potentially due to psychosocial sequelae of COVID-19.12–15

Substance use is of particular concern among young adults (i.e., ages 18-early 30s),16 who have among the highest use rates of cigarettes, e-cigarettes, marijuana, and alcohol in the US (26.7% past-month tobacco use, 34.8% marijuana use, 58.3% alcohol use).17–19 An estimated 30% of US young adults reported increased use of cigarettes and e-cigarettes since COVID-19.10 Moreover, since COVID-19, alcohol use has increased by 0.74 days in the US, representing a 14% increase, with the greatest increases occurring among younger adults.20 Both alcohol and marijuana use have increased since before COVID-19 in US adolescents.21 Given potential confounding effects of substance use, particularly tobacco, on COVID-19 health outcomes, these findings are particularly concerning as the median age of COVID-19 cases dropped from age 46 to 27. The highest incidence for the disease from June-August, 2020 was for adults ages 20–29 (accounting for more than 20% of confirmed cases during that time).22

Although individuals likely vary in their risk for increased substance use during the COVID-19 pandemic,11,21 research is needed to document who is at greatest risk. Research suggests that risk for increased use of cigarettes and e-cigarettes was associated with lower COVID-19 risk perceptions,10,11 and increases in cigarette/other tobacco and alcohol use was associated with greater COVID-related stress.23 Given the recency of these circumstances, the research is currently limited regarding a broad range of risk and protective factors related to various substance use behaviors, particularly using longitudinal data. It is imperative to identify predictors of increased substance use among young adults during the COVID-19 pandemic. These data are needed to equip public health authorities and practitioners in their efforts to develop effective interventions and target important risk factors to curb increases in substance use during periods of societal stress.

The current study draws from a sociodevelopmental perspective (SDP) to examine the risk and protective factors for changes in substance use during COVID-19. The SDP suggests that individuals vary in their risk for engaging in substance use as a result of different risk factors that occur at the individual and social environmental levels.24,25 In this study, we draw on literature that indicates that substance use is associated with individual factors such as depressive symptoms26–29 and interpersonal factors such as experiencing adverse childhood events (i.e., ACEs).30–32 It is well established that exposure to stress, including experiencing depressive symptoms and ACEs, can lead to adverse consequences in young adulthood, including substance use.33–35 Furthermore, a growing body of evidence indicates that periods marked by significant brain maturation and plasticity, such as young adulthood, are especially characteristic of problematic outcomes, such as increased substance use, in response to stress.36 Thus, pre-existing vulnerabilities like experiencing depressive symptoms and ACEs may place young adults at even greater risk for increasing substance use during a period of societal stress involving state and national mitigation measures (e.g., social distancing) – COVID-19.

It is important to note, however, that not all individuals are uniformly affected by stress or stressful events. The SDP suggests that risk for engaging in substance use differs across individuals due to key protective factors,24,25 such as resilience. Resilience has been conceptualized in multiple ways within the literature, including as a set of traits,37 an outcome,38 or as a process of adaptation or success.38 Most often, and consistent with its conceptualization in the current study, resilience is operationalized as a trait that moderates the negative effect of stress and promotes adaptation, allowing the individual to successfully cope with change or misfortune.39 Indeed, resilience has been shown to moderate the effects of stress in general,40–42 and specifically with regard to substance use.43–45 Resilience has also been shown to buffer the effects of risk factors for other negative outcomes (e.g., depression, anxiety) during COVID-19.46 Thus, it is likely that resilience will moderate (i.e., buffer) associations among risk factors, including depressive symptoms and ACEs, with increases in substance use from pre- to during COVID-19.

Given the aforementioned literature and the need to expand our understanding of the impact of the COVID-19 pandemic on individual risk for negative substance use outcomes, this study examined: 1) changes in substance use behaviors (specifically number of days of cigarette smoking, e-cigarette use, marijuana use, and alcohol consumption in the past 30 days) from pre- COVID-19 to during COVID-19; 2) depressive symptoms and ACEs as risk factors for changes in use frequency of each substance; and 3) the extent to which resilience moderates the relationship between depressive symptoms or ACEs and changes in substance use, controlling for sociodemographic factors, COVID-19 related disruptions (i.e., changes in employment/living situation), and pre-pandemic substance use. It was hypothesized that both greater depressive symptoms and ACEs would be associated with greater increases in substance use from pre- to during COVID-19, but that these associations would be weaker for individuals with greater levels of resilience.

Materials & Methods

Study Design

The current study analyzed survey data among young adults (aged 18–34) participating in a two-year, five-wave longitudinal cohort study, the Vape shop Advertising, Place characteristics and Effects Surveillance (VAPES) study. VAPES examines the vape retail environment and its impact on e-cigarette use, drawing participants from six metropolitan statistical areas (MSAs: Atlanta, Boston, Minneapolis, Oklahoma City, San Diego, Seattle) with varied tobacco and marijuana legislative contexts.47 Bi-annual survey assessments began in Fall 2018. This study was approved by the George Washington University Institutional Review Board.

Participants & Recruitment

Participants were recruited via social media. Eligibility criteria were: 1) 18–34 years old; 2) residing in the six aforementioned MSAs; and 3) English speaking. Purposive, quota-based sampling was used to ensure sufficient proportions of the sample representing e-cigarette and cigarette users and to obtain roughly equal numbers of men and women and 40% racial/ethnic minority to explore use within subgroups. Ads posted on Facebook and Reddit targeted individuals: 1) using indicators reflecting those within the eligible age range and geographical locations (within 15 miles of their respective MSAs); 2) by identifying work groups or activities of interest that appeal to young adults (e.g., sports/athletics, entertainment, arts, lifestyle, technology), as well as tobacco-related interests (e.g., Marlboro, Juul, Swisher Sweets); and 3) by posting advertisements including images of young adults of diverse racial/ethnic backgrounds socializing in bars and/or outdoor spaces, young adult professionals in professional work settings, etc.

Once a potential participant clicked on an ad (e.g., “Help researchers learn more about what young adults in your city think about tobacco products!”), they were directed to a webpage with a study description and consent form. Once individuals consented, they were screened for eligibility. This screener also included questions regarding sex, race, ethnicity, and past 30-day use of e-cigarettes and cigarettes, which were used to facilitate reaching recruitment targets of subgroups in each MSA (i.e., limiting participation among specific subgroups once their target enrollment was reached). Enrollment varied for each MSA, and thus subgroup enrollment was capped by MSA. Eligible individuals allowed to advance were then routed to complete the online baseline (Wave 1) survey (administered via SurveyGizmo). Upon survey completion, participants were notified that, seven days after completing the baseline survey, they would be asked to confirm their participation by clicking a “confirm” button included in an email sent to them. Once participants clicked “confirm,” they were officially enrolled into the study and emailed their first incentive in the form of a $10 Amazon electronic gift card.

The participant flowchart, which followed consort guidance, is included in Figure 1 and briefly described here. The duration of the recruitment period ranged from 87 to 104 days across the six MSAs. Of the 10,433 Facebook and Reddit users who clicked on ads, 9,847 consented, of which 2,751 (27.9%) were not allowed to advance because they were either: a) ineligible (n=1,427) and/or b) excluded in order to reach subgroup target enrollment (n=1,279). Of those allowed to advance to the survey, the proportion of completers versus partial completers was 48.8% (3,460/7,096) versus 51.2% (3,635/7,096). Partial completers were deemed ineligible for the remainder of the study; the majority of partial completers (n=2,469, 67.9%) completed only the initial sociodemographic section of the survey. Of the 3,460 who completed the baseline survey, 3,006 (86.9%) confirmed participation at the seven-day follow-up (see our previous work for attrition analyses).48

Figure 1.

Figure 1.

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Participant recruitment flowchart

This study uses data from Wave 3 (W3; Fall 2019, 79.0% retention) and Wave 4 (W4; Spring 2020, 71.8% retention). W4 data collection was launched in late January 2020; we interrupted data collection in mid-March to add questions specific to COVID-19. Thus, roughly half of participants (n=1,559) were invited to complete the W4 assessment after these questions were added. Current analyses focus on 1,082 participants (69.4% of the 1,559) with complete data related to COVID-19 at W4 and factors from W3 included in these analyses.

Measures

Primary outcomes: Changes in substance use

At W3 and W4, participants were asked to report whether they had used the following products in their lifetime: cigarettes, e-cigarettes, marijuana, or alcohol.49 Those indicating lifetime use were asked to indicate number of days used in the past 30 days.49 Participants were able to “refuse” marijuana-related assessments. Use frequency change scores were operationalized as a continuous outcome and calculated by subtracting the number of days participants used each substance (e.g., cigarettes) at W4 from the number of days participants used that substance at W3 among those who reported any past 30-day use of each substance at W3 or W4 (i.e., cigarettes: n=315; e-cigarettes: n=395; marijuana: n=534; alcohol: n=917). Descriptive analyses characterized change as remained relatively stable (i.e., ±2 days in past 30), increased, or decreased.

Predictors: Risk/protective factors

Depressive symptoms were assessed at W3 using the Patient Health Questionnaire – 2 item (PHQ-2), assessing symptoms in the past two weeks (0=not at all to 3=nearly every day; score range 0–6; α =.87).50 The PHQ-2 has demonstrated adequate reliability on general populations of college-aged individuals (α = .80-.84).51,52 ACEs were assessed at W3 using the ACEs-10 item, which was adapted from the CDC-Kaiser Permanente ACE Study, assessing maltreatment and household challenges before the age of 18 (0=no, 1=yes; score range 0–10; α=.81).53 This 10-item measure reflects the 10 most commonly endorsed items from this previous research.53 Resilience was assessed at W3 using the 6-item Brief Resilience Scale (i.e., bounce back quickly after hard time; hard time making it through stressful events; does not take long to recover from stressful event; hard to snap back when something bad happens; come through difficult times with no trouble; take long time to get over set-backs in life), which conceptualize resilience as a trait allowing individuals to adapt and cope with challenges (1=Strongly disagree to 5=Strongly agree; score range 1–5; α=.87).54

Covariates: Sociodemographics & COVID-19 related disruptions

Participants were asked to report their age, sex, sexual orientation, race, ethnicity, highest level of educational attainment, employment status, relationship status, and whether they had children (under age 18) in their home. At W4, participants were asked to indicate whether they were laid off from a job (yes vs. no) as a result of COVID-19 and whether they moved off campus (if in college) to live with parents/guardians or other relatives since COVID-19 (yes vs. no). Participants who were not currently enrolled in college were coded as “no” for moving off campus.

Data Analysis

Descriptive statistics were used to characterize the sample and change in substance use among users. Bivariate analyses were then conducted to characterize users (at either W3 or W4) versus non-users of the 4 substances. Then, bivariate analyses (i.e., t-tests, ANOVA, Pearson correlations) were used to examine predictors of interest and covariates in relation to the primary outcomes of use frequency changes. Next, multivariable regression models were built for each outcome including 1) covariates; 2) predictor variables; and 3) moderator effects of resilience on the risk factors (i.e., depressive symptoms, ACEs). Note that we did not include all sociodemographics in multivariable models due to multicollinearity (e.g., age correlated with education, employment, relationship status, and children in the home so only age was included). We conducted analyses in a stepwise manner, first including only covariates, then adding predictor variables controlling for covariates, and finally adding interaction terms while controlling for covariates and main effects. All analyses were conducted using SPSS version 26. Alpha was set at .05.

Results

Participant Characteristics

Analyses were first performed to assess outliers and the distribution of all continuous key study variables (i.e., depressive symptoms, ACEs, resilience, cigarette, e-cigarette, marijuana, alcohol use change). Based upon visual histogram inspection, skewness, and kurtosis values, all variables met the criteria for normal distribution.55

Among all participants (n=1,082), 6.7% resided in Atlanta, 18.3% in Boston, 23.1% in Minneapolis, 11.8% in Oklahoma City, 19.2% in San Diego, and 20.9% in Seattle. Participants were 24.77 years old on average (SD=4.68), 45.7% male, 32.1% sexual minority, 4.0% Black, 12.4% Asian, and 12.6% Hispanic (Table 1). Additionally, 17.1% of participants reported being laid off from their job and 12.1% reported moving home with their parents during COVID-19. Of the past 30 days at W3, average number of days used for each substance ranged from an average of 10.27 days for cigarettes to 12.85 days for e-cigarettes. On average, participants reported experiencing depressive symptoms between “several days” and “more than half the days” during the past 2 weeks and ~2–3 ACEs.

Table 1.

Participant charaacteristics and bivariate comparisons among past 30-day substance users and non-users at W3 or W4, N=1,082

Total Cigarette users
 Cigarette non-users
 E-cigarette users
 E-cigarette non-users
 Marijuana users
 Marijuana non-users
 Alcohol users
 Alcohol non-users
N=1,082 (100%)
 N=315 (29.1%)
 N=767 (70.9%)
 N=395 (36.5%)
 N=687 (63.5%)
 N=534 (49.4%)
 N=548 (50.6%)
 N=917 (84.8%)
 N=165 (15.2%)
Variables N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) N (%) or M (SD)
Sociodemographics
MSA, N (%)
 Atlanta 72 (6.7) 23 (7.3) 49 (6.4) 36 (9.1) 36 (5.2) 27 (5.1) 45 (8.2) 59 (6.4) 13 (7.9)
 Boston 198 (18.3) 59 (18.7) 139 (18.1) 67 (17.0) 131 (19.1) 99 (18.5) 99 (18.1) 170 (18.5) 28 (17.0)
 Minneapolis 250 (23.1) 70 (22.2) 180 (23.5) 94 (23.8) 156 (22.7) 115 (21.5) 135 (24.6) 215 (23.4) 35 (21.2)
 Oklahoma City 128 (11.8) 29 (9.2) 99 (12.9) 40 (10.1) 88 (12.8) 50 (9.4) 78 (14.2) 105 (11.5) 23 (13.9)
 San Diego 208 (19.2) 51 (16.2) 157 (20.5) 66 (16.7) 142 (20.7) 106 (19.9) 102 (18.6) 176 (19.2) 32 (19.4)
 Seattle 226 (20.9) 83 (26.3) 143 (18.6) 92 (23.3) 134 (19.5) 137 (25.7) 89 (16.2) 192 (20.9) 34 (20.6)
Age, M (SD) 24.77 (4.68) 25.24 (4.81) 24.58 (4.61) 24.07 (4.70) 25.18 (4.62) 24.43 (4.59) 25.10 (4.74) 24.69 (4.64) 25.22 (4.87)
Sex, N (%)
 Male 494 (45.7) 155 (49.2) 339 (44.2) 188 (47.6) 306 (44.5) 226 (42.3) 268 (48.9) 402 (43.8) 92 (55.8)
 Female 556 (51.4) 150 (47.6) 406 (52.9) 193 (48.9) 363 (52.8) 293 (54.9) 263 (48.0) 488 (53.2) 68 (41.2)
 Other 32 (3.0) 10 (3.2) 22 (2.9) 14 (3.5) 18 (2.6) 15 (2.8) 17 (3.1) 27 (2.9) 5 (3.0)
Sexual minority, N (%) 347 (32.1) 112 (35.6) 235 (30.6) 143 (36.2) 204 (29.7) 208 (39.0) 139 (25.4) 304 (33.2) 43 (26.1)
Race, N (%)
 White 793 (73.3) 234 (74.3) 559 (72.9) 276 (69.9) 517 (75.3) 397 (74.3) 396 (72.3) 697 (76.0) 96 (58.2)
 Black 43 (4.0) 18 (5.7) 25 (3.3) 19 (4.8) 24 (3.5) 21 (3.9) 22 (4.0) 27 (2.9) 16 (9.7)
 Asian 134 (12.4) 29 (9.2) 105 (13.7) 51 (12.9) 83 (12.1) 57 (10.7) 77 (14.1) 103 (11.2) 31 (18.8)
 Other 112 (10.4) 34 (10.8) 78 (10.2) 49 (12.4) 63 (9.2) 59 (11.0) 53 (9.7) 90 (9.8) 22 (13.3)
Hispanic, N (%) 136 (12.6) 60 (19.0) 76 (9.9) 51 (12.9) 85 (12.4) 59 (11.0) 77 (14.1) 118 (12.9) 18 (10.9)
Education ≥bachelor’s degree, N (%) 822 (76.0) 208 (66.0) 614 (80.1) 265 (67.1) 557 (81.1) 382 (71.5) 440 (80.3) 713 (77.8) 109 (66.1)
Employment, N (%)
 Student 279 (25.8) 58 (18.4) 221 (28.8) 91 (23.0) 188 (27.4) 127 (23.8) 152 (27.7) 231 (25.2) 48 (29.1)
 Unemployed 97 (9.0) 49 (15.6) 48 (6.3) 43 (10.9) 54 (7.9) 45 (8.4) 52 (9.5) 70 (7.6) 27 (16.4)
 Full-time 448 (41.4) 119 (37.8) 329 (42.9) 138 (34.9) 310 (45.1) 210 (39.3) 238 (43.4) 393 (42.9) 55 (33.3)
 Part-time 258 (23.8) 89 (28.3) 169 (22.0) 123 (31.1) 135 (19.7) 152 (28.5) 106 (19.3) 223 (24.3) 35 (21.2)
Relationship status, N (%)
 Single 650 (60.1) 183 (58.1) 467 (60.9) 240 (60.8) 410 (59.7) 318 (59.6) 332 (60.6) 552 (60.2) 98 (59.4)
 Married/living with partner 422 (39.0) 130 (41.3) 292 (38.1) 151 (38.2) 271 (39.4) 210 (39.3) 212 (38.7) 359 (39.1) 63 (38.2)
 Other 10 (0.9) 2 (0.6) 8 (1.0) 4 (1.0) 6 (0.9) 6 (1.1) 4 (0.7) 6 (0.7) 4 (2.4)
Children in the home, N (%) 216 (20.0) 88 (27.9) 128 (16.7) 77 (19.5) 139 (20.2) 88 (16.5) 128 (23.4) 164 (17.9) 52 (31.5)
COVID-19 Factors
Laid off from job, N (%) 185 (17.1) 75 (23.8) 110 (14.3) 93 (23.5) 92 (13.4) 122 (22.8) 63 (11.5) 161 (17.6) 24 (14.5)
Moved in with parents, N (%) 132 (12.1) 23 (7.3) 109 (14.2) 50 (12.7) 82 (11.9) 61 (11.4) 71 (13.0) 109 (11.9) 23 (13.9)
W3 Substance Use
Past 30-day e-cigarette use, M (SD) 4.69 (9.74) 9.34 (11.97) 2.70 (7.80) 12.85 (12.45) -- 6.11 (10.40) 3.20 (8.72) 4.58 (9.56) 5.20 (10.61)
Past 30-day cigarette use, M (SD) 3.02 (7.74) 10.27 (11.36) -- 5.77 (9.70) 1.45 (5.78) 3.97 (8.55) 2.08 (6.71) 2.84 (7.40) 4.12 (9.47)
Past 30-day marijuana use, M (SD) 5.54 (9.94) 8.99 (11.62) 4.11 (8.77) 9.14 (11.61) 3.48 (8.16) 11.10 (11.67) -- 5.58 (9.81) 5.29 (10.75)
Past 30-day alcohol use, M (SD) 5.75 (6.29) 6.56 (6.65) 5.28 (6.02) 6.78 (7.27) 5.32 (5.78) 7.07 (6.60) 4.43 (5.67) 6.69 (6.30) --
W3 Risk/Protective Factors
Depressive symptoms, M (SD) 1.71 (1.72) 2.02 (1.84) 1.53 (1.63) 2.28 (1.84) 1.47 (1.61) 1.95 (1.80) 1.46 (1.60) 1.76 (1.74) 1.37 (1.54)
ACEs, M (SD) 2.22 (2.42) 2.81 (2.76) 1.89 (2.14) 3.06 (2.65) 1.88 (2.23) 2.59 (2.56) 1.86 (2.21) 2.23 (2.39) 2.19 (2.62)
Resilience, M (SD) 3.36 (0.91) 3.24 (0.93) 3.42 (0.89) 3.23 (0.95) 3.41 (0.89) 3.25 (0.90) 3.46 (0.91) 3.35 (0.91) 3.39 (0.89)
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Note. Bolded values denote statistical significance (per t-tests and Chi-square). The number of substance users at each wave is as follows: W3 cigarette use = 265 (26.1), W3 e-cigarette users = 328 (32.3), W3 marijuana users = 436 (43.0), W3 alcohol users = 789 (77.7); W4 cigarette users = 233 (21.5); W4 e-cigarette users = 287 (26.5), W4 marijuana users = 414 (38.3), W4 alcohol users = 818 (75.7).

Regarding outcome variables, the prevalence of past 30-day use of each product at either W3 or W4 was as follows: 29.1% for cigarettes, 36.5% for e-cigarettes, 49.4% for marijuana, and 84.8% for alcohol use. With regard to research question 1 (i.e., changes in substance use behaviors), users reported decreasing cigarette use frequency by 1.26 days (26.4% decreased, 19.4% increased, 54.2% stable [±2 days]), decreasing e-cigarette use by 0.58 days (28.6% decreased, 23.2% increased, 48.2% stable), increasing marijuana use by 0.82 days (21.2% decreased, 27.2% increased, 51.6% stable), and increasing alcohol use by 1.43 days (20.7% decreased, 32.9% increased, 46.4% stable) on average from W3 to W4.

We also examined frequent use at both waves (i.e., => 25 day users) and found that 16.1% of cigarette, 24.8% of e-cigarette, 21.0% of marijuana, and 2.1% of alcohol users were classified as frequent users at each wave. Among frequent users, participants reported decreasing cigarette use by 0.16 days (28.6% decreased, 13.0% increased, 58.4% stable), decreasing e-cigarette use by 0.57 days (20.4% decreased, 19.0% increased, 60.6% stable), increasing marijuana use by 0.16 days (15.1% decreased, 31.3% increased, 53.6% stable), and increasing alcohol use by 0.26 days (22.6% decreased, 48.4% increased, 29% stable).

Bivariate Comparisons of Users vs. Nonusers

Cigarette users (vs. non-users) were older, more likely to be non-Hispanic, less educated, in a household with children, laid off from a job, and to have maintained residence independent from parents; they also reported greater e-cigarette, marijuana, and alcohol use at W3, and reported greater depressive symptoms, greater ACEs, and lower levels of resilience (Table 1). E-cigarette users were younger, more likely to be sexual minorities, less educated, more likely to be employed full-or part-time, and laid-off; they also reported greater past 30-day cigarette, marijuana, and alcohol use at W3, as well as greater depressive symptoms, greater ACEs, and lower levels of resilience. Marijuana users were younger, more likely to be sexual minority, less educated, employed part-time, in a home without children, and laid off; they also reported greater e-cigarette, cigarette, and alcohol use at W3, as well as greater depressive symptoms, greater ACEs, and lower levels of resilience. Finally, alcohol users were more likely to be female, white, more educated, employed part-time, in a home without children, and report greater depressive symptoms.

Multivariable Regression Results

Because our findings did not vary across steps of model building, we reported coefficients from our final models. Regarding research question 2 (i.e., depressive symptoms and ACEs as risk factors for changes in substance use), multivariable regression (Table 2) indicated that predictors of greater increases in cigarette use frequency included less frequent W3 e-cigarette and cigarette use. Predictors of greater increases in e-cigarette use included less frequent W3 e-cigarette use and greater ACEs. Predictors of greater increases in marijuana use were being laid off from a job due to COVID-19, not moving in with parents due to COVID-19, and less frequent W3 marijuana use. Finally, predictors of greater increases in alcohol use included being older, being male, being Hispanic, lower levels of past 30-day alcohol use at W3, and greater depressive symptoms.

Table 2.

Multivariable regressions examining correlates of change in substance use

Change in Cigarette Use (M=−1.26, SD=9.11)
 Change in E-cigarette Use (M=−0.58, SD=9.83)
 Change in Marijuana Use (M=0.82, SD=7.93)
 Change in Alcohol Use (M=1.43, SD=6.27)
Variables B B (SE) B B (SE) B B (SE) B B (SE)
Sociodemographics
Age 0.06 0.12 (0.12) 0.07 0.13 (0.12) −0.03 −0.05 (0.08) 0.09 0.12 (0.05)
Male (ref=female) −0.04 −0.74 (1.13) 0.07 1.31 (1.06) −0.04 −0.65 (0.73) 0.09 1.12 (0.44)
Sexual minority (ref=heterosexual) 0.03 0.60 (1.22) 0.06 1.13 (1.07) −0.01 −0.19 (0.75) −0.01 −0.17 (0.48)
 Black 0.03 1.05 (2.30) −0.03 −1.25 (2.38) −04 −1.44 (1.85) 0.01 0.11 (1.21)
 Asian 0.03 0.81 (1.77) 0.01 0.24 (1.46) 0.05 1.20 (1.15) −0.09 −1.67 (0.68)
 Other 0.04 1.29 (1.76) −0.08 −2.47 (1.52) −0.02 −0.41 (1.16) 0.06 1.38 (0.74)
Hispanic (ref=non-Hispanic) 0.01 0.05 (1.34) −0.01 −0.40 (1.47) 0.02 0.40 (1.15) 0..02 0.39 (0.65)
COVID-19 Factors
Laid off from job −0.02 −0.36 (1.29) −0.04 −0.82 (1.24) 0.16 3.04 (0.85) 0.04 0.59 (0.57)
Moved in with parents −1.58 −3.28 (2.07) −0.03 −0.85 (1.56) −0.12 −2.30 (1.21) −0.08 −1.47 (0.70)
Adjusted R 2 .175 .116 .087 .089
W3 Substance Use
Past 30-day e-cigarette use −0.13 −0.10 (0.04) −0.35 −0.27 (0.04) 0.06 0.05 (0.04) 0.04 0.03 (0.02)
Past 30-day cigarette use −0.45 −0.37 (0.05) 0.03 0.03 (0.05) 0.05 0.05 (0.04) −0.05 −0.04 (0.03)
Past 30-day marijuana use 0.11 0.09 (0.05) −0.02 −0.02 (0.04) −0.27 −0.18 (0.03) 0.01 0.01 (0.02)
Past 30-day alcohol use −0.05 −0.06 (0.07) −0.04 −0.05 (0.08) −0.02 −0.02 (0.06) −0.28 −0.28 (0.03)
W3 Risk/Protective Factors
Depressive symptoms 0.05 0.26 (0.32) 0.01 0.02 (0.32) −0.04 −0.17 (0.23) 0.09 0.34 (0.15)
ACEs −0.09 −0.31 (0.23) 0.15 0.52 (0.20) −0.02 −0.05 (0.16) −0.05 −0.13 (0.10)
Resilience 0.04 0.36 (0.60) −0.06 −0.61 (0.59) −0.01 −0.03 (0.44) 0.06 0.43 (0.26)
Adjusted R 2 .178 .125 .089 .093
Interactions among Risk/Protective Factors
Depressive symptoms X Resilience 0.02 0.08 (0.27) 0.11 0.51 (0.26) −0.06 −0.22 (0.21) 0.05 0.15 (0.12)
ACEs X Resilience −0.08 −0.30 (0.23) 0.10 0.35 (0.21) 0.09 0.28 (0.16) 0.02 0.05 (0.10)
Adjusted R 2 .178 .149 .091 .093
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Note. Bolded values denote statistical significance (p<.05).

With regard to research question 3 (i.e., resilience as a moderator of the associations among risk factors and changes in substance use), a significant interaction among resilience and depressive symptoms emerged for changes in e-cigarette use, such that greater depressive symptoms were associated with greater increases in e-cigarette use for individuals with low (simple slope=.92, p<.05), but not high levels of resilience (simple slope=.64, ns). Additionally, an interaction effect of resilience by ACEs emerged for marijuana use, such that ACEs were associated with greater increases in marijuana use for individuals with low (simple slope=.90, p<.05), but not high levels of resilience (simple slope=−.25, ns). Findings for changes in cigarette and alcohol use did not vary by resilience.

Discussion

This study leveraged the SDP to examine risk and protective factors in relation to substance use outcomes during COVID-19. Among young adults who used these substances at either W3 or W4, roughly half reported relatively consistent frequency of past-month use (within 2 days) across the four substances. Notably, a larger proportion of users decreased rather than increased their cigarette and e-cigarette use, whereas a larger proportion of users increased rather than decreased their marijuana and alcohol use.

Consistent with the SDP,24,25 both individual (i.e., initial substance use, depressive symptoms) and interpersonal (i.e., ACEs) factors predicted changes in substance use from pre- to during COVID-19. Less frequent pre-pandemic use of marijuana and alcohol predicted greater increases in their respective use. This may be indicative of a ceiling effect, such that individuals who were using at high levels prior to COVID-19 had limited potential for use increases. Interestingly, less frequent initial e-cigarette use predicted greater increases in cigarette use. This finding could have several explanations, including limitations in access to vape products versus cigarettes, differential costs of products to achieve the nicotine exposure desired,18 and/or less frequently being in public places where cigarette smoking would otherwise be restricted. Additionally, prior research suggests that escalation in cigarette use is common among individuals who have ever used e-cigarettes.56

In addition to prior substance use, experiencing more ACEs predicted greater increases in e-cigarette use, aligning with prior studies of other substance use in the college years,32 but extending the literature to indicate that e-cigarette use may have similar underlying mechanisms and risk factors. Furthermore, experiencing more depressive symptoms predicted increases in alcohol use, perhaps pointing to ineffective coping skills and alcohol use as a compensatory behavior, particularly relevant when access to mental health resources may have been restricted.

As prior research suggests,43,46,57 resilience moderated associations among ACEs, depressive symptoms, and substance use escalation during COVID-19. As in prior research,40–42 those with greater levels of resilience did not show associations between depressive symptoms, ACEs, and substance use escalation; however, among individuals with lower levels of resilience, more depressive symptoms predicted increases in e-cigarette use, and more ACEs predicted increases in marijuana use. Individuals with greater resilience may turn to healthier coping mechanisms during periods of stress, ultimately reducing their risk for increasing their substance use as a compensatory behavior. In terms of sociodemographics, being male, Hispanic, and older in age were associated with greater increases in alcohol use, which is consistent with previous findings.21,58–60

Current findings suggest that intervention efforts should be informed by risk and protective factors for substance use. While several factors placed all individuals at greater risk for increasing their substance use during COVID-19, some risk factors were only present among individuals with lower levels of resilience, suggesting that resilience may be an important individual characteristic to screen for prior to intervention and to target in intervention. Findings stress the importance for future researchers to continue to examine the differential associations among psychosocial risk factors and the use of a broad range of substances, as current findings demonstrate unique associations among risk factors, protective factors, and changes in specific substances.

Limitations

The study has some limitations, including limited generalizability to other young adults in the included MSAs or across the US. Additionally, rates of substance use should not be interpreted as prevalence rates, as our sampling design aimed to achieve a sample with roughly a third being past 30-day e-cigarette and cigarette users, respectively. Also note that, although the subsamples recruited across MSAs were different with regard to sociodemographic characteristics and tobacco/marijuana use, the ICCs across all multilevel models were low, indicating that MSAs accounted for little effect on the outcomes in this study. Self-reported data also has the potential for bias. However, while other studies conducted at a cross-section during COVID-19 relied on self-reported retrospective data regarding one’s perceived change in substance use,10,21 the current study utilized longitudinal data across a six-month period during COVID-19 to assess substance use change. The current study controlled for only two COVID-19 related disruptions (i.e., loss of employment, moving back in with parents), although preliminary analyses examining other COVID-19 related factors indicated limited variability (e.g., experiencing COVID-19 symptoms) or multicollinearity with variables included (e.g., loss of employment and financial impact). However, previous research has demonstrated the importance of other COVID-19 factors, such as perceived risk of COVID-19, in predicting substance use;10,11 thus, future research should control for a range of COVID-19 related disruptions. Finally, although guided by the SDP, current analyses did not examine variables at the macro-level. Future research should examine the influence of multiple levels (i.e., individual, interpersonal, environmental) on changes in substance use from pre- to during COVID-19.

Conclusion

As public health authorities look for ways to prevent substance use escalation during COVID-19, it is crucial to understand the factors that influence individuals’ likelihood for increasing their use of substances. Current findings indicate that not all substance use patterns changed similarly from pre- to during COVID-19 and that young adults demonstrated unique patterns of change across substances that were distinctly predicted by psychosocial factors. Thus, interventions might target those at highest risk (i.e., those with higher levels of ACEs, depressive symptoms, and prior substance use) and target modifiable risk factors, such as resilience, depressive symptoms, and substance use related cognitions and behaviors in order to address substance use vulnerability during periods of societal stress.

Statement 1: Role of Funding Source

This publication was supported by the US National Cancer Institute (R01CA215155–01A1; PI: Berg). Dr. Berg is also supported by other US National Cancer Institute funding (R01CA179422–01; PI: Berg; R01CA239178–01A1; MPIs: Berg, Levine), the US National Institutes of Health/Fogarty International Center (1R01TW010664–01; MPIs: Berg, Kegler), and the US National Institute of Environmental Health Sciences/Fogarty International Center (D43ES030927–01; MPIs: Berg, Marsit, Sturua).

Footnotes

Statement 3: Conflicts of Interest

The authors declare no conflicts of interests.

References

  • 1.Elassar A. This is where each state is during its phased reopening. http://www.cnn.com/interactive/2020/us/states-reopen-coronavirus-trnd/ Published 2020. Accessed.
  • 2.Marroquin B, Vine V, & Morgan R. Mental health during the COVID-19 pandemic: Effects of stay-at-home policies, social distancing behavior, and social resources. Psychiatry Research 2020;293. [DOI] [PMC free article] [PubMed]
  • 3.Mota P Avoiding a new epidemic during a pandemic: The importance of assessing the risk of substance use disorders in the COVID-19 era. Psychiatry Res 2020;290:113142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.National Center for Chronic Disease P, Health Promotion Office on S, Health. Reports of the Surgeon General. In: The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General Atlanta (GA): Centers for Disease Control and Prevention (US); 2014. [Google Scholar]
  • 5.NIDA. COVID-19: Potential Implications for Individuals with Substance Use Disorders 2020.
  • 6.Patanavanich R, Glantz SA. Smoking Is Associated With COVID-19 Progression: A Meta-analysis. Nicotine Tob Res 2020;22(9):1653–1656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Vardavas CI, Nikitara K. COVID-19 and smoking: A systematic review of the evidence. Tob Induc Dis 2020;18:20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Berlin I, Thomas D, Le Faou AL, Cornuz J. COVID-19 and Smoking. Nicotine Tob Res 2020;22(9):1650–1652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Sarkar D, Jung MK, & Wang J. Alcohol and the Immune System. Alcohol Research 2015;37(2):153–155. [Google Scholar]
  • 10.Klemperer EM, West JC, Peasley-Miklus C, Villanti AC. Change in Tobacco and Electronic Cigarette Use and Motivation to Quit in Response to COVID-19. Nicotine Tob Res 2020;22(9):1662–1663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Kowitt SD, Cornacchione Ross J, Jarman KL, et al. Tobacco Quit Intentions and Behaviors among Cigar Smokers in the United States in Response to COVID-19. Int J Environ Res Public Health 2020;17(15). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.McKee SA, Sinha R, Weinberger AH, et al. Stress decreases the ability to resist smoking and potentiates smoking intensity and reward. J Psychopharmacol 2011;25(4):490–502. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Slopen N, Kontos EZ, Ryff CD, Ayanian JZ, Albert MA, Williams DR. Psychosocial stress and cigarette smoking persistence, cessation, and relapse over 9–10 years: a prospective study of middle-aged adults in the United States. Cancer Causes Control 2013;24(10):1849–1863. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Tsourtos G, Ward PR, Miller ER, et al. Does Resilience Moderate the Relationship Between Stress and Smoking Status? Subst Use Misuse 2019;54(3):412–425. [DOI] [PubMed] [Google Scholar]
  • 15.Wills TA, Sandy JM, Yaeger AM. Stress and smoking in adolescence: a test of directional hypotheses. Health Psychol 2002;21(2):122–130. [PubMed] [Google Scholar]
  • 16.Arnett JJ. Presidential address: The emergence of emerging adulthood: A personal history. Emerging Adulthood 2014;2:155–162. [Google Scholar]
  • 17.Hedden SL KJ, Lipari R, Medley G, Tice P. Behavioral Trends in the United States: Results from the 2014 National Survey on Drug Use and Health 2015.
  • 18.National Academies of Sciences E, Medicine, Health, et al. In: Eaton DL, Kwan LY, Stratton K, eds. Public Health Consequences of E-Cigarettes Washington (DC): National Academies Press (US) Copyright 2018 by the National Academy of Sciences. All rights reserved.; 2018. [PubMed] [Google Scholar]
  • 19.Schauer GL, King BA, Bunnell RE, Promoff G, McAfee TA. Toking, Vaping, and Eating for Health or Fun: Marijuana Use Patterns in Adults, U.S., 2014. Am J Prev Med 2016;50(1):1–8. [DOI] [PubMed] [Google Scholar]
  • 20.Pollard MS, Tucker JS, Green HD, Jr. Changes in Adult Alcohol Use and Consequences During the COVID-19 Pandemic in the US. JAMA Netw Open 2020;3(9):e2022942. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Dumas TM, Ellis W, Litt DM. What Does Adolescent Substance Use Look Like During the COVID-19 Pandemic? Examining Changes in Frequency, Social Contexts, and Pandemic-Related Predictors. J Adolesc Health 2020;67(3):354–361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Boehmer TK, DeVies J, Caruso E, et al. Changing Age Distribution of the COVID-19 Pandemic - United States, May-August 2020. MMWR Morb Mortal Wkly Rep 2020;69(39):1404–1409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Stanton R, To QG, Khalesi S, et al. Depression, Anxiety and Stress during COVID-19: Associations with Changes in Physical Activity, Sleep, Tobacco and Alcohol Use in Australian Adults. Int J Environ Res Public Health 2020;17(11). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Hawkins JD, Weis JG. The social development model: An integrated approach to delinquency prevention. J Prim Prev 1985;6(2):73–97. [DOI] [PubMed] [Google Scholar]
  • 25.Catalano RF, Kosterman R, Hawkins JD, Newcomb MD, Abbott RD. Modeling the Etiology of Adolescent Substance Use: A Test of the Social Development Model. J Drug Issues 1996;26(2):429–455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Berg CJ, Wen H, Cummings JR, Ahluwalia JS, Druss BG. Depression and substance abuse and dependency in relation to current smoking status and frequency of smoking among nondaily and daily smokers. Am J Addict 2013;22(6):581–589. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Crane NA, Langenecker SA, Mermelstein RJ. Gender differences in the associations among marijuana use, cigarette use, and symptoms of depression during adolescence and young adulthood. Addict Behav 2015;49:33–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Danielsson AK, Lundin A, Agardh E, Allebeck P, Forsell Y. Cannabis use, depression and anxiety: A 3-year prospective population-based study. J Affect Disord 2016;193:103–108. [DOI] [PubMed] [Google Scholar]
  • 29.Marmorstein NR. Longitudinal associations between alcohol problems and depressive symptoms: early adolescence through early adulthood. Alcohol Clin Exp Res 2009;33(1):49–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Alcalá HE, von Ehrenstein OS, Tomiyama AJ. Adverse Childhood Experiences and Use of Cigarettes and Smokeless Tobacco Products. J Community Health 2016;41(5):969–976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Shin SH, Conley D, Ksinan Jiskrova G, Wills TA. Adverse Childhood Experiences and E-Cigarette Use During Young Adulthood. Am J Addict 2019;28(4):303–310. [DOI] [PubMed] [Google Scholar]
  • 32.Windle M, Haardörfer R, Getachew B, et al. A multivariate analysis of adverse childhood experiences and health behaviors and outcomes among college students. J Am Coll Health 2018;66(4):246–251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.McEwen BS. Glucocorticoids, depression, and mood disorders: structural remodeling in the brain. Metabolism 2005;54(5 Suppl 1):20–23. [DOI] [PubMed] [Google Scholar]
  • 34.McEwen BS. Physiology and neurobiology of stress and adaptation: central role of the brain. Physiol Rev 2007;87(3):873–904. [DOI] [PubMed] [Google Scholar]
  • 35.De Kloet ER, Vreugdenhil E, Oitzl MS, Joëls M. Brain corticosteroid receptor balance in health and disease. Endocr Rev 1998;19(3):269–301. [DOI] [PubMed] [Google Scholar]
  • 36.Eiland L, Romeo RD. Stress and the developing adolescent brain. Neuroscience 2013;249:162–171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Jacelon CS. The trait and process of resilience. J Adv Nurs 1997;25(1):123–129. [DOI] [PubMed] [Google Scholar]
  • 38.Olsson CA, Bond L, Burns JM, Vella-Brodrick DA, Sawyer SM. Adolescent resilience: a concept analysis. J Adolesc 2003;26(1):1–11. [DOI] [PubMed] [Google Scholar]
  • 39.Wagnild GM, Young HM. Development and psychometric evaluation of the Resilience Scale. J Nurs Meas 1993;1(2):165–178. [PubMed] [Google Scholar]
  • 40.Collishaw S, Pickles A, Messer J, Rutter M, Shearer C, Maughan B. Resilience to adult psychopathology following childhood maltreatment: evidence from a community sample. Child Abuse Negl 2007;31(3):211–229. [DOI] [PubMed] [Google Scholar]
  • 41.Masten AS. Ordinary magic. Resilience processes in development. Am Psychol 2001;56(3):227–238. [DOI] [PubMed] [Google Scholar]
  • 42.Bonanno GA. Loss, trauma, and human resilience: have we underestimated the human capacity to thrive after extremely aversive events? Am Psychol 2004;59(1):20–28. [DOI] [PubMed] [Google Scholar]
  • 43.Goldstein AL, Faulkner B, Wekerle C. The relationship among internal resilience, smoking, alcohol use, and depression symptoms in emerging adults transitioning out of child welfare. Child Abuse Negl 2013;37(1):22–32. [DOI] [PubMed] [Google Scholar]
  • 44.Kurtz SP, Pagano ME, Buttram ME, Ungar M. Brief interventions for young adults who use drugs: The moderating effects of resilience and trauma. J Subst Abuse Treat 2019;101:18–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Hodder RK, Daly J, Freund M, Bowman J, Hazell T, Wiggers J. A school-based resilience intervention to decrease tobacco, alcohol and marijuana use in high school students. BMC Public Health 2011;11:722. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Zhang J, Yang Z, Wang X, et al. The relationship between resilience, anxiety and depression among patients with mild symptoms of COVID-19 in China: A cross-sectional study. J Clin Nurs 2020. [DOI] [PMC free article] [PubMed]
  • 47.Center PHL. Commercial Tobacco and Marijuana 2020.
  • 48.Berg C, Duan X, Getachew B, Pulvers K, Crawford N, Sussman S, Ma Y, Jones-Harrell C, & Henriksen L. Young adult e-cigarette use and retail exposure in 6 US metropolitan areas. Tobacco Regulatory Science 2021;7(1):59–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Hyland A, Ambrose BK, Conway KP, et al. Design and methods of the Population Assessment of Tobacco and Health (PATH) Study. Tob Control 2017;26(4):371–378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care 2003;41(11):1284–1292. [DOI] [PubMed] [Google Scholar]
  • 51.Zhang YL, Liang W, Chen ZM, et al. Validity and reliability of Patient Health Questionnaire-9 and Patient Health Questionnaire-2 to screen for depression among college students in China. Asia Pac Psychiatry 2013;5(4):268–275. [DOI] [PubMed] [Google Scholar]
  • 52.Tsai FJ, Huang YH, Liu HC, Huang KY, Huang YH, Liu SI. Patient health questionnaire for school-based depression screening among Chinese adolescents. Pediatrics 2014;133(2):e402–409. [DOI] [PubMed] [Google Scholar]
  • 53.Felitti VJ, Anda RF, Nordenberg D, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am J Prev Med 1998;14(4):245–258. [DOI] [PubMed] [Google Scholar]
  • 54.Smith BW, Dalen J, Wiggins K, Tooley E, Christopher P, Bernard J. The brief resilience scale: assessing the ability to bounce back. Int J Behav Med 2008;15(3):194–200. [DOI] [PubMed] [Google Scholar]
  • 55.Mishra P, Pandey CM, Singh U, Gupta A, Sahu C, Keshri A. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth 2019;22(1):67–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Doran N, Brikmanis K, Petersen A, et al. Does e-cigarette use predict cigarette escalation? A longitudinal study of young adult non-daily smokers. Prev Med 2017;100:279–284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Berg CJ, Haardorfer R, McBride CM, et al. Resilience and biomarkers of health risk in Black smokers and nonsmokers. Health Psychol 2017;36(11):1047–1058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Esper LF EF. Gender differences and association between psychological stress and alcohol consumption: A systematic review. Journal of Alcoholism & Drug Dependence 2013;1.
  • 59.Goldbach JT, Berger Cardoso J, Cervantes RC, Duan L. The relation between stress and alcohol use among Hispanic adolescents. Psychol Addict Behav 2015;29(4):960–968. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Blumenthal H, Leen-Feldner EW, Frala JL, Badour CL, Ham LS. Social anxiety and motives for alcohol use among adolescents. Psychol Addict Behav 2010;24(3):529–534. [DOI] [PMC free article] [PubMed] [Google Scholar]

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