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. Author manuscript; available in PMC: 2021 May 12.
Published in final edited form as: Subst Use Misuse. 2020 May 12;55(10):1702–1708. doi: 10.1080/10826084.2020.1756855

Electronic Cigarette Use and Associated Risk Factors in Pacific Islander Young Adults

Andrew M Subica a, Erick Guerrero b, Li-Tzy Wu c, Nia Aitaoto d, Derek Iwamoto e, Howard B Moss a
PMCID: PMC7521610  NIHMSID: NIHMS1625406  PMID: 32397921

Abstract

Background:

E-cigarette use is rapidly increasing among U.S. young adults, heightening their risk for vaping-related illnesses. Yet, little is known about e-cigarette use among young adult Native Hawaiians and Pacific Islanders (NHPI): an indigenous-colonized U.S. racial group rarely described in research literature. This exploratory study provides the first known data on e-cigarette use and potential risk factors in NHPI young adults.

Method:

Self-report data were collected from 143 NHPI young adults (age 18-30 years) living in two large NHPI communities: Samoans in urban Los Angeles County and Marshallese in rural Arkansas. We assessed rates of e-cigarette, cigarette, alcohol, and marijuana use, and positive and negative outcome expectancies from e-cigarettes, i.e., expected outcomes from e-cigarette use. To identify potential risk factors for NHPI e-cigarette use, regressions explored associations between participants’ current e-cigarette use with current cigarette, alcohol, and marijuana use, and e-cigarette outcome expectancies.

Results:

Among NHPI young adults, lifetime e-cigarette use rate was 53% and current use rate was 39%. Current rate of dual e-cigarette/cigarette, e-cigarette/alcohol, and e-cigarette/marijuana use were 38%, 35%, and 25%, respectively. In our regression models, current marijuana use and positive e-cigarette outcome expectancies significantly associated with current e-cigarette use.

Conclusions:

E-cigarette use is common among NHPI young adults, exceeding rates for other at-risk racial groups. Marijuana use and positive expectations about e-cigarette use may represent potential e-cigarette use risk factors. Collectively, findings underscore the need for additional research to further explore the scope and risk and protective factors for e-cigarette use in this understudied high-risk population.

INTRODUCTION

In the U.S., e-cigarette use has increased dramatically, particularly among young adults who possess the nation’s heaviest rates of e-cigarette use (Hu, 2016; Schulenberg, Johnston, O’Malley, Bachman, Miech, & Patrick, 2019). Although widely perceived as safer than cigarettes (Pearson, Richardson, Niaura, Vallone, & Abrams, 2012), emerging research suggests that e-cigarettes pose a significant threat to public health as they have been linked to the expression of numerous chronic diseases such as asthma, pulmonary disorder, heart disease, and cancer (Glantz & Bareham, 2018; Schweitzer, Wills, Tam, & Choi, 2017; Wills, Pagano, Williams, & Tam, 2019), as well as oxidative damage to DNA (Sakamaki-Ching et al., 2020). In addition to affecting users’ long-term health, recent evidence has associated e-cigarette use with severe immediate harms such as vaping-related lung injury/disease (Christiani, 2019; Layden et al., 2019) that have resulted in an increasing number of vaping-related deaths (CDC, 2019). Because young adults have primarily borne the brunt of outbreaks of vaping-related illness and death (CDC, 2019; Perrine et al., 2019), e-cigarettes’ increasing popularity and potentially lethal consequences have placed young adults—particularly those with specific risk profiles such as belonging to known at-risk racial groups such as Whites and Latina/os (USHHS, 2016)—at the forefront of this growing public health crisis.

Unfortunately, despite this crisis, little is known about e-cigarette use in young adults from indigenous/colonized U.S. racial populations. This is especially true for Native Hawaiians and Pacific Islanders (NHPIs): a rapidly growing U.S. racial group persistently understudied in extant literature. Due to their exposure to heavy U.S. colonization and historical trauma (Kaholokula, 2007; Yamada, 2004), NHPIs are affected by myriad health disparities (McElfish, Hallgren, & Yamada, 2015; Mishra, Luce-Aoelua, & Wilkens, 1996) including vulnerability to substance use; underscoring the need to investigate e-cigarette use and its relationships to other commonly used substances in NHPIs.

For instance, U.S. data from the National Surveys on Drug Use and Health revealed that in 2011, NHPIs aged ≥ 12 years had substantially greater prevalence vs. Whites aged ≥ 12 years of illicit/non-medical drug use (21.2% vs. 15.1%), particularly with respect to marijuana use (18.8% vs. 11.3%) (Wu, Blazer, Swartz, Burchett, Brady, & NIDA AAPI Workgroup, 2013). Similarly, a national study of 25 years of Centers for Disease Control and Prevention Youth Risk Behavior Survey data (Subica & Wu, 2018) indicated that NHPI adolescents reported significantly higher prevalence of lifetime substance use compared to White adolescents for many illicit substances including marijuana (42.1% vs. 38.5%), cocaine (11.0% vs. 7.0%), methamphetamines (9.0% vs. 6.1%), and heroin (8.2% vs. 2.2%). With regard to alcohol use, NHPI adolescents also reported the highest lifetime prevalence of pre-adolescent alcohol use (30.3%) and heavy episodic drinking (27.5%) of all U.S. racial groups (Subica & Wu, 2018). In addition, prior studies of Native Hawaiian adolescents uncovered the highest rates of alcohol use, chronic drinking, and heavy episodic drinking of all racial/ethnic groups in Hawai’i (Klingle & Miller, 1999; Nishimura, Goebert, Ramisetty-Mikler, & Caetano, 2005).

These elevated rates of substance use likely place NHPI young adults at heightened risk for e-cigarette use. Of particular concern are NHPIs’ elevated prevalence of conventional cigarette use as NHPI adults have been shown to have increased rates of cigarette smoking (Martell, 2016)—an established e-cigarette risk factor (Chapman & Wu, 2014)—compared to other U.S. racial groups. This heightened e-cigarette risk is most apparent in a recent dataset where NHPI adults reported higher prevalence of e-cigarette use (5.6%) vs. African Americans (2.1%) and Latinas/os (2.2%) (Narcisse, Dobbs, Long, Purvis, Kimminau, & McElfish, 2019).

Further compounding NHPIs’ heightened e-cigarette risk is their disproportionate exposure to aggressive tobacco marketing (Muggli, Pollay, Lew, & Joseph, 2002) with current data indicating e-cigarette marketing exposure positively predicted e-cigarette use among college students in Hawaii (Pokhrel et al., 2018a)—the state with the nation’s largest proportion of NHPIs. However, the few existing e-cigarette studies that have included NHPI young adults as part of their samples have primarily sampled college students (Pokhrel, Little, Fagan, Muranaka, & Herzog, 2014). This may potentially limit current knowledge of NHPI young adult e-cigarette use as only 47% of NHPI adults attend college vs. 55% of the general population (Teranishi et al., 2019) with 50-58% of NHPIs who attend college dropping out (Espinosa, Turk, & Taylor, 2017). Accordingly, only 19% of NHPIs complete four-year college (Ramakrishnan & Ahmad, 2014).

Thus, to capture a better understanding of e-cigarette use among NHPI young adults, we conducted the first known investigation of e-cigarette use among community-dwelling NHPI young adults. To identify potential e-cigarette risk factors affecting this population, we examined their (1) co-occurring use of conventional cigarettes, alcohol, and marijuana, the three most commonly used substances (Schulenberg et al., 2019); and (2) positive and negative e-cigarette outcome expectancies (i.e., expected outcomes from a behavior), as outcome expectancies are an important antecedent for substance use (Pokhrel et al., 2014) and have predicted e-cigarette use in prior studies, e.g., Brikmanis, Petersen, and Doran, 2017, Pohkrel et al., 2018a, and Pokhrel, Lam, Pagano, Kawamoto, and Herzog, 2018b.

Finally, because NHPIs compose a heterogeneous racial population consisting of numerous NHPI subgroups (Hixon, Hepler, & Kim, 2015), we examined NHPI young adults from two highly divergent NHPI subgroups living on the continental U.S. to enhance generalizability. These were urban Samoans living in Los Angeles County and rural Marshallese living in Northwest Arkansas, who represent the second-largest and fastest-growing U.S. NHPI subgroups, respectively (U.S. Census Bureau, 2010). We chose these two subgroups because their stark differences in settings (urban West Coast vs. the rural South), country of origin (U.S. territory vs. foreign nation), and language (Samoan vs. Marshallese) would greatly strengthen the generalizability of our study findings while simultaneously allowing us to explore the similarities and differences in e-cigarette use and e-cigarette risk factors between two disparate NHPI young adult populations.

METHODS

Participants and Recruitment

Study protocols received approval from the [redacted] IRB. Trained NHPI research staff collected self-administered survey data between May-November 2019 from NHPI young adults in the urban Samoan community in Los Angeles County, California and the rural Marshallese in Northwest Arkansas as these are the largest and fastest growing continental U.S. NHPI communities, respectively (U.S. Census, 2010). Inclusion criteria were age 18 to 30 years, full or part-Samoan or Marshallese heritage, and living in our target NHPI communities. The main exclusion criterion was lack of English reading and writing fluency (as determined by staff during the informed consent process). Participants were recruited using a non-probabilistic, respondent-driven sampling approach (Mays & Pope, 1995) used to capture representative community samples in earlier NHPI studies (Subica, Aitaoto, Link, Yamada, Henwood, & Sullivan, 2019a; Subica, Aitaoto, Sullivan, Henwood, Yamada, & Link, 2019b). This involved NHPI staff recruiting participants directly from diverse community settings where NHPI young adults congregate (e.g., recreational centers, community-based organizations), and also through social media outreach. Informed consent and surveys were conducted by staff in person as self-report survey or over the phone as an interview with participants receiving $20.

Measures

Demographics and E-Cigarette Use.

Study demographic variables were age, gender, NHPI ethnicity (Samoan, Marshallese), education level, and marital status. As in earlier studies (Pokhrel et al., 2018a; 2018b), ever e-cigarette use was assessed by asking “Have you ever used any type of e-cigarette or similar vaping device (e.g., Juul)?” and current e-cigarette use (i.e., use in the past month) was determined by responses of “Daily,” “Less than daily, but at least once per week,” or “Less than weekly, but at least once per month” to “How often, if at all, do you currently use an e-cigarette or similar vaping device?” Number of days e-cigarettes were used in the past month were measured by asking, “during the past 30 days, on how many days did you use e-cigarettes or similar vaping device?” These three e-cigarette items (Pokhrel et al., 2018b) collectively produced excellent internal consistency with a Cronbach’s α of 0.92.

Current Cigarette, Alcohol, and Marijuana Use.

Current conventional cigarette use was determined by responses of “Some days” or “Every day” to “Do you now smoke cigarettes every day, some days, or not at all?” (Pokhrel et al., 2014). Current alcohol use was defined as any positive response (excluding “Never”) to the Alcohol Use Disorders Identification Test-Concise (AUDIT-C; Bush, Kivlahan, McDonell, Fihn, & Bradley, 1998) item querying “How often do you have a drink containing alcohol?” (Response options: “Never,” “Monthly or less,” “2 to 4 times a month,” “2 to 3 times per week,” “4 or more times a week”). Current marijuana use was operationalized as any days of reported use in the past month to the item “During the past 30 days, on how many days did you use marijuana, hashish, or another THC product?”

E-Cigarette Outcome Expectancies

E-cigarette outcome expectancies were assessed via validated 8-item Young Adult E-Cigarette Outcome Expectancy scale (Pokhrel et al., 2018b). This scale asked participants to rate on a 0-9 scale the likelihood (0=unlikely, 9=likely) that they would experience 4 positive outcomes (i.e., more popular, feel relaxed, enjoy vaping, smell good ) and 4 negative outcomes (i.e., hurt lungs, look awkward, become addicted, bad taste). Composite positive and negative expectancy scores were generated by taking the mean of the 4 positive and 4 negative outcome items, respectively (Pokhrel et al., 2018b). These 4 positive and 4 negative expectancy outcome items possessed strong internal consistencies with Cronbach’s α of 0.89 and 0.88, respectively.

Statistical Analyses

We analyzed data in SPSS v.24 (IBM, 2014) with descriptive statistics producing variable frequencies, means, standard deviations, and 95% confidence intervals. Chi-square and independent t-tests examined significant gender differences. We ran two hierarchical logistic regression (outcome variables=ever e-cigarette use and current e-cigarette use, respectively) and one linear regression model (outcome variable=number of days e-cigarettes used in past 30 days). Independent variables were demographics of age, gender, NHPI ethnicity, education, and marital status, and current use of cigarettes, alcohol, and marijuana entered in Step 1, and mean positive and negative e-cigarette outcome expectancies scores entered in Step 2 to determine whether outcome expectancies associated with e-cigarette use above and beyond Step 1 variables. To compare pseudo coefficients of determination in the logistic models, we used Nagelkerke’s R2 (Nagelkerke, 1991).

RESULTS

Participants Characteristics and Use of E-Cigarettes, Cigarettes, Alcohol, and Marijuana

Participants’ educational and marital status characteristics, as well as frequencies of e-cigarette and other relevant substance use by gender and NHPI ethnicity are presented in Table 1. Of the 143 total NHPI participants (Mage ± SD = 23.6 years ± 4.1 years; range = 18 to 30 years), 61.5% were men, 63.6% were of Marshallese ethnicity, and 80.5% reported single marital status. Consistent with prior research suggesting low levels of college attendance and completion among NHPI young adults, only 29.0% of participants reported attending college and only 8 total participants (5.6%) reported completing college with significant differences in educational status between Samoan and Marshallese participants (X2 (1, N=141) = 28.1, p < 0.01).

Table 1:

Sample characteristics and descriptive statistics

Variables Total Sample Men Women Samoan Marshallese
% n % n % n % n % n
Formal education
  < High school 16 22 18 16 11 6 4 2 23 20
  High school graduate 55 76 53** 46 56 30 45 23 61 53
  College and higher 29 40 25 22 33 18 51** 26 16 14
Marital status
  Single 80 107 84 69 75 38 67 32 88 75
  Married/Living as married 20 26 16 13 25 13 33 16 12 10
E-cigarette use
  Ever use e-cigarettes 53 76 53 47 53 29 71** 37 43 39
  Currently use e-cigarettes 39 55 41 46 35 19 40 21 37 34
Other substance use
  Currently use cigarettes 54 77 63** 55 40 22 62 32 49 45
  Currently use alcohol 79 113 85* 75 69 38 79 41 79 72
  Currently use marijuana 39 56 45 40 29 16 42 22 37 34
Mean SD Mean SD Mean SD Mean SD Mean SD
E-cigarette expectancies
  Positive expectancies 4.39 2.90 4.56 3.02 4.09 2.68 5.78** 1.71 3.57 3.14
  Popular 3.60 3.35 3.64 3.44 3.53 3.20 5.50** 2.60 2.51 3.25
  Relaxed 5.11 3.36 5.13 3.45 5.08 3.25 6.98** 1.87 4.05 3.56
  Enjoy vaping 4.58 3.38 4.83 3.44 4.15 3.25 5.92** 2.55 3.82 3.56
  Smells good 4.15 3.24 4.44 3.40 3.65 2.90 4.73 2.30 3.82 3.63
  Negative expectancies 4.10 2.89 3.90 3.04 4.46 2.58 6.01** 1.28 3.03 2.99
  Hurts lungs 4.44 3.43 4.13 3.57 5.00 3.13 6.21** 2.17 3.45 3.62
  Awkward 3.59 3.04 3.39 3.11 3.96 2.91 4.81** 2.18 2.90 3.25
  Become addicted 4.59 3.76 4.55 3.85 4.65 3.62 7.19** 2.65 3.12 3.49
  Bad taste 4.35 2.28 3.54 3.25 4.19 3.04 5.69** 1.94 2.69 3.24
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**

Significant difference at .01 level between participants by gender or Pacific Islander ethnicity

*

Significant difference at .05 level between participants by gender or Pacific Islander ethnicity

Regarding e-cigarette use, 53.1% of NHPI participants reported ever e-cigarette use and 38.5% reported current e-cigarette use (i.e., use in the past month). On average, participants used e-cigarettes in 6.0 of the past 30 days. With regard to other substance use, 53.8% of participants reported current cigarette use, 79.0% reported current alcohol use, and 39.2% reported current marijuana use. Men were significantly more likely to report currently using cigarettes (X2 (1, N=132) = 10.31, p < 0.01) and alcohol (X2 (1, N=143) = 5.32, p < 0.05), but not e-cigarettes or marijuana. Similarly, Samoan participants were significantly more likely to report ever using e-cigarette (X2 (1, N=143) = 10.64, p < 0.01), but demonstrated no significant differences from Marshallese participants in rates of current e-cigarette, cigarette, alcohol, or marijuana use. Dual use of e-cigarettes among participants was: 28.0% (n=40) with conventional cigarettes; 35.0% with alcohol (n=50); and 25.2% with marijuana (n=36), accounting for 51.9%, 44.2%, and 64.3% of all current cigarette, alcohol, and marijuana users, respectively.

E-Cigarette Outcome Expectancies

Among participants, the most strongly endorsed positive outcomes were ‘feeling relaxed’ and ‘enjoying smoking’ with a mean positive outcome expectancy score of 4.4. The most strongly endorsed negative outcomes were ‘becoming addicted to e-cigarettes,’ ‘hurting lungs,’ and ‘having a bad taste’ with a mean negative outcome expectancy score of 4.1.

Although no significant gender differences were identified in mean positive and negative expectancies scores on any item, significant differences were noted between Samoan and Marshallese participants (see Table 1). Specifically, Samoan participants reported significantly higher mean scores than Marshallese participants (p<0.01) for every queried positive expectancy item (e.g., become popular, feel relaxed) and negative expectancy item (e.g., hurts lungs, become addicted) with the exception of the “smell good” positive expectancy item.

Regressions of Ever, Current, and Number of Days of E-Cigarette Use

For the ‘ever e-cigarette use’ logistic model, in Step 1, current marijuana use associated with 5.5 times (p<0.01) greater likelihood of ever using e-cigarettes. When composite positive and negative outcome expectancies were included in Step 2, only positive expectancies significantly associated with ever e-cigarette use with a 1-point increase in positive expectancies scores predicting 1.6 times (p<0.01) greater likelihood of ever using e-cigarettes.

In the ‘current e-cigarette use’ logistic model, in Step 1, current marijuana use associated with 8.8 times (p<0.01) greater likelihood of current e-cigarette use. In Step 2, current marijuana use associated with 6.2 times (p<0.01) greater likelihood for current e-cigarettes use while a 1-point increase in positive expectancies scores associated with 1.4 times (p<0.01) greater likelihood.

For the ‘number of days e-cigarettes used’ linear regression model, in Step 1, currently using marijuana significantly associated with greater number of days e-cigarettes were used (p<0.01) while in Step 2, current marijuana use and positive expectancies significantly associated with greater number of days e-cigarettes were used (p<0.01). Please see Table 2 for all model results.

Table 2:

Associations of e-cigarette ever use, current use, and days of use by current substance use and positive and negative e-cigarette outcome expectancies

DV = Ever used e-cigarettes
 DV = Current e-cigarette use
 DV = # Days of e-cigarette use
Variables AOR 95% CI Pseudo
R2 (ΔR2)		 AOR 95% CI Pseudo
R2 (ΔR2)		 β R2 (ΔR2)
Step 1
Demographic
  Age 1.02 0.90-1.17 0.38 0.99 0.87-1.13 0.35 −0.04 0.29
  Gender: Men 1.19 0.32-4.38 2.58 0.73-9.10 0.22
  PI ethnicity: Samoan 2.03 0.55-7.53 0.31 0.08-1.17 0.21
  Formal education: Less than high school
 High school graduate 1.63 0.41-6.56 1.40 0.35-5.58 −0.05
 College or higher 2.26 0.38-13.37 0.96 0.17-5.31 −0.10
  Marital status: Single 3.71 0.67-20.57 1.93 0.49-7.64 0.01
Substance use
  Currently use cigarettes 1.96 0.62-6.18 1.06 0.33-3.41 0.03
  Currently use alcohol 2.96 0.63-13.92 0.93 0.17-5.18 0.03
  Currently use marijuana 5.54** 1.84-16.73 8.79** 2.89-26.75 0.38**
Step 2
Demographic
  Age 1.01 0.87-1.18 0.55 (0.17) 0.97 0.85-1.12 0.45 (0.10) −0.02 0.42 (0.13)
  Gender: Men 0.84 0.17-4.12 2.50 0.63-9.92 0.16
  PI ethnicity: Samoan 0.77 0.16-3.85 0.18** 0.04-0.77 0.14
  Formal education: Less than high school
 High school graduate 1.57 0.31-7.95 1.18 0.25-5.55 −0.09
 College or higher 2.08 0.28-15.70 0.65 0.10-4.17 −0.19
  Marital status: Single 4.65 0.69-31.25 2.13 0.51-8.79 0.02
Substance use
  Currently use cigarettes 1.35 0.35-5.15 0.79 0.21-2.95 −0.04
  Currently use alcohol 3.06 0.48-19.44 0.81 0.11-5.94 −0.08
  Currently use marijuana 2.99 0.76-11.80 6.21** 1.74-22.18 0.34**
E-Cigarette expectancies
  Positive expectancies 1.57** 1.18-2.11 1.43** 1.10-1.87 0.50**
  Negative expectancies 1.07 0.81-1.43 0.99 0.75-1.30 −0.17
Open in a new tab
**

p < .01;

*

p < .05

DV = Dependent Variable; AOR = Adjusted Odds Ratio; CI = Confidence Interval; β = Standardized Regression Coefficient; Pseudo R2 = Nagelkerke R2; ΔR2 = Change in R2 between Step 1 and Step 2

DISCUSSION

To our knowledge, this is the first study to directly investigate e-cigarette use in NHPI communities, generating novel insights regarding the scope of e-cigarette use in this understudied indigenous/colonized racial population. Study findings indicated that over 50% of NHPI young adults (18-30 years) had ever used e-cigarettes, exceeding the young adult lifetime e-cigarette use rates reported for other high-risk U.S racial groups. These reported rates include 37% for Latina/o young adults, 40% for White young adults, and 44% for Asian American young adults, with African American young adults reporting the lowest rates of any racial group at 23% (USHHS, 2016; Maglalang, Brown-Johnson, & Prochaska, 2016). Approximately 4 in 10 NHPI young adults reported currently using e-cigarettes, nearly three times the 13.6% rate of current e-cigarette use for U.S. young adults aged 18-24 years, and over six times the 5.8% rate for U.S. young adults aged 25-30 years (USHHS, 2016). Therefore, relative to their peers, our results revealed substantial e-cigarette use disparities in NHPI young adults, increasing their risk for vaping-related illness and harm.

Consistent with earlier research finding strong associations between the use of conventional cigarettes and e-cigarettes (Chapman & Wu, 2014; Dutra & Glantz, 2014), dual use was common among NHPI young adults, with over half of current cigarette users concurrently using e-cigarettes. But when current cigarette use was examined as a potential e-cigarette use risk factor in our regression models, after adjusting for current alcohol and marijuana use, current cigarette use did not associate with any of our e-cigarette use variables. Instead, current marijuana use showed the strongest substance-related association with e-cigarette use in our sample, associating with six times greater likelihood for current e-cigarette use after accounting for participants’ cigarette and alcohol use, and e-cigarette outcome expectancies. Accordingly, using marijuana may be a more potent e-cigarette risk factor in NHPI young adults than conventional cigarettes; a theory bolstered by our added finding that 64% of current marijuana users also used e-cigarettes. Further research is now needed to elucidate the reasons for this marijuana/e-cigarette relationship, such as NHPIs potentially using e-cigarettes to vape marijuana (Giroud, De Cesare, Berthet, Varlet, Concha-Lozano, & Favrat, 2015).

In particular, our finding of a strong marijuana-e-cigarette relationship among NHPI young adults may have significant implications for understanding and thus reducing the elevated e-cigarette use found in our study. In extant data, NHPIs use marijuana at significantly higher rates than non-NHPIs (Wu et al., 2013; Wu & Blazer, 2015), particularly at younger ages such as pre-adolescence and adolescence (Subica & Wu, 2018) when individuals appear to be most vulnerable to initiating e-cigarette use (Chapman & Wu, 2014). Therefore, to limit e-cigarette use and related harms in NHPI young adults, researchers should consider studying and monitoring NHPIs’ marijuana and e-cigarette use starting in pre-adolescence to better discern the source of this relationship, and its implications for promoting and sustaining NHPIs’ e-cigarette use over time.

Additionally, study data revealed that holding positive expectations about the outcomes of e-cigarette use (e.g., using e-cigarettes will increase relaxation) significantly associated with all e-cigarette use variables. These findings closely align with prior research indicating that positive expectations about e-cigarette use represent a significant e-cigarette risk factor in young adults (Brikmanis et al., 2017; Pokhrel et al., 2014; 2018a; 2018b). Consequently, one potential avenue for reducing NHPI young adults’ e-cigarette risk may be through challenging their positive expectations surrounding e-cigarette use using evidence-based approaches. Or, if current approaches prove ineffective, by designing and implementing culturally adapted/grounded intervention approaches for NHPIs (Okamoto, Kulis, Marsiglia, Steiker, & Dustman, 2014).

In comparing our two NHPI subgroups, Samoan and Marshallese participants were similar in terms of demographics (with the exception of Samoan young adults having higher rates of college or higher education) and current use of all examined substances (e.g., e-cigarettes, marijuana). However, Samoan participants reported higher rates of ever using e-cigarettes than Marshallese participants (71% vs. 43%), although this difference disappeared when examining rates of current e-cigarette use. Thus, Marshallese individuals may be less likely to try e-cigarettes but more likely to persist in using e-cigarettes into young adulthood than their Samoan counterparts.

Lastly, Samoan participants reported higher levels for nearly all positive and negative e-cigarette outcome expectancies vs. Marshallese participants, which may indicate greater awareness/exposure among Samoan young adults to e-cigarettes and/or e-cigarette marketing (Pokhrel et al., 2018a). Possible explanations for this pattern that could be explored in future research include potentially (1) greater access to, or penetration of, e-cigarettes in urban Los Angeles County NHPI vs. rural Arkansas NHPI communities; (2) greater susceptibility of Samoan young adults to e-cigarette messaging/marketing (Mantey, Cooper, Clendennen, Pasch, & Perry, 2016); or (3) greater U.S. acculturation among Samoans vs. Marshallese, as shown in prior research (Subica et al., 2019b).

Several study limitations are noted. First, employing a non-probabilistic community sampling approach, while empirically supported for assessing hard-to-reach populations (Alvarez, Vasquez, Mayorga, Feaster, & Mitrani, 2006), may have impacted study generalizability. In addition, although we assessed several important demographic variables as controls for our regressions, we did not assess all possible demographic variables such as income, employment status, and parental educational level. Future research should consider further assessing the influence of these and other potentially relevant demographic variables on NHPI young adults’ e-cigarette use. Also, because substance use underreporting is common in minority samples (Johnson & Bowman, 2003), utilizing self-report data may have introduced biasing into results. However, given the extreme scarcity of NHPI e-cigarette use data, even with some biasing our exploratory results are notable and contribute important information that enhances our knowledge of NHPI e-cigarette use. Finally, as this study focused on quantifying e-cigarette use, follow-up qualitative research should be conducted with diverse NHPI communities to illuminate the specific underlying factors driving NHPI young adults’ elevated use.

Collectively, our novel findings are among the first to describe a concerning pattern of e-cigarette use among NHPI young adults, and highlights potential pathways for reducing their e-cigarette risk. While further research is required, curtailing NHPI e-cigarette use may require developing culturally tailored prevention interventions to address their unique cultural risk and protective factors (Castro & Alarcon, 2002) by applying culturally grounded participatory approaches to bolster intervention feasibility and effectiveness (Okamoto et al., 2014) for this overlooked and underserved racial population.

Acknowledgements:

We would like to acknowledge the hard work and close partnership of the Office of Samoan Affairs and Arkansas Coalition of Marshallese, and the interest and support of our Pacific Islander communities, who made this study possible.

Funding: This work was supported by the National Institutes of Health/National Institute of Alcohol Abuse and Alcoholism (R21 AA026689). LW has also received research support from the National Institutes of Health (UG1DA040317, R01MD007658), Patient-Centered Outcomes Research Institute, and Centers for Disease Control and Prevention. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Alcohol Abuse and Alcoholism or the National Institutes of Health.

Footnotes

Disclosure of Interest: The authors have no interests to disclose.

References

  1. Alvarez RA, Vasquez E, Mayorga CC, Feaster DJ, & Mitrani VB (2006). Increasing minority research participation through community organization outreach. Western Journal of Nursing Research, 28(5), 541–560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Brikmanis K, Petersen A, & Doran N (2017). E-cigarette use, perceptions, and cigarette smoking intentions in a community sample of young adult nondaily cigarette smokers. Psychology of Addictive Behaviors, 31(3), 336–342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bush K, Kivlahan DR, McDonell MB, Fihn SD, & Bradley KA (1998). The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Archives of Internal Medicine, 158(16), 1789–1795. [DOI] [PubMed] [Google Scholar]
  4. Castro FG, Alarcon EH. Integrating Cultural Variables into Drug Abuse Prevention and Treatment with Racial/Ethnic Minorities. J Drug Issues. 2002;32(3):783–810. [Google Scholar]
  5. Centers for Disease Control and Prevention. Outbreak of severe pulmonary disease associated with using e-cigarette products: investigation notice. August 30, 2019. (https://www.cdc.gov/tobacco/basic_information/e-cigarettes/severe-lung-disease.html).
  6. Chapman SLC, & Wu LT (2014). E-cigarette prevalence and correlates of use among adolescents versus adults: a review and comparison. Journal of psychiatric research, 54, 43–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Christiani DC (2019). Vaping-induced lung injury. N Engl J Med, 6(10.1164). [DOI] [PubMed] [Google Scholar]
  8. Cullen KA, Ambrose BK, Gentzke AS, Apelberg BJ, Jamal A, & King BA (2018). Notes from the field: Use of electronic cigarettes and any tobacco product among middle and high school students—United States, 2011–2018. Morbidity and Mortality Weekly Report, 67(45), 1276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dutra LM, & Glantz SA (2014). Electronic cigarettes and conventional cigarette use among US adolescents: a cross-sectional study. JAMA Pediatrics, 168(7), 610–617. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Espinosa LL, Turk JM, & Taylor M (2017). Pulling back the curtain: Enrollment and outcomes at minority serving institutions. Available at: https://www.acenet.edu/Documents/Pulling-Back-the-Curtain-Enrollment-and-Outcomes-at-MSIs.pdf [Accessed on January 1, 2020]. [Google Scholar]
  11. Giroud C, de Cesare M, Berthet A, Varlet V, Concha-Lozano N, & Favrat B (2015). E-cigarettes: A review of new trends in cannabis use. International Journal of Environmental Research and Public Health, 12(8), 9988–10008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Glantz SA, & Bareham DW (2018). E-cigarettes: use, effects on smoking, risks, and policy implications. Annual Review of Public Health, 39, 215–235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Goniewicz ML, Knysak J, Gawron M, Kosmider L, Sobczak A, Kurek J, … & Jacob P (2014). Levels of selected carcinogens and toxicants in vapour from electronic cigarettes. Tobacco Control, 23(2), 133–139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hixson L, Hepler BB, & Kim MO (2015). The Native Hawaiian and Other Pacific Islander Population. United States Census Bureau: Washington D.C. [Google Scholar]
  15. Hu SS (2016). Tobacco product use among adults—United States, 2013–2014. MMWR. Morbidity and Mortality Weekly Report, 65. [DOI] [PubMed] [Google Scholar]
  16. Kaholokula JK (2007). Colonialism, acculturation and depression among Kānaka Maoli of Hawai’i Penina Uliuli: Confronting challenges in mental health for Pacific Peoples. University of Hawaii Press: Honolulu, HI. [Google Scholar]
  17. Johnson TP, & Bowman PJ (2003). Cross-cultural sources of measurement error in substance use surveys. Substance Use & Misuse, 38(10), 1447–1490. [DOI] [PubMed] [Google Scholar]
  18. Klingle RS, & Miller MD (1999). Hawaii Student Alcohol and Drug Use Study. State of Hawaii Department of Health, Alcohol, and Drug Abuse Division: Honolulu, HI. [Google Scholar]
  19. Layden JE, Ghinai I, Pray I, Kimball A, Layer M, Tenforde M, … & Haupt T (2019). Pulmonary illness related to e-cigarette use in Illinois and Wisconsin—preliminary report. New England Journal of Medicine. [DOI] [PubMed] [Google Scholar]
  20. Maglalang DD, Brown-Johnson C, & Prochaska JJ (2016). Associations with E-cigarette use among Asian American and Pacific Islander young adults in California. Preventive Medicine Reports, 4, 29–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Mantey DS, Cooper MR, Clendennen SL, Pasch KE, & Perry CL (2016). E-cigarette marketing exposure is associated with e-cigarette use among US youth. Journal of Adolescent Health, 58(6), 686–690. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Martell BN (2016). Disparities in adult cigarette smoking—United States, 2002–2005 and 2010–2013. MMWR. Morbidity and Mortality Weekly Report, 65. [DOI] [PubMed] [Google Scholar]
  23. Mays N, & Pope C (1995). Qualitative research: Observational methods in health care settings. BMJ, 311(6998):182–184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. McElfish PA, Hallgren E, & Yamada S (2015). Effect of U.S. health policies on health care access for Marshallese migrants. American Journal of Public Health, 105, 637–643. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Mishra SI, Luce-Aoelua PH, Wilkens LR. Cancer among Indigenous Populations. The Experience of American Samoans. Cancer. 1996;78(7 Suppl):1553–1557. [PubMed] [Google Scholar]
  26. Muggli ME, Pollay RW, Lew R, & Joseph AM (2002). Targeting of Asian Americans and Pacific Islanders by the tobacco industry: Results from the Minnesota Tobacco Document Depository. Tobacco Control, 11(3), 201–209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Nagelkerke NJD 1991. A note on the general definition of the coefficient of determination. Biometrika, 78:3, 691–692. [Google Scholar]
  28. Narcisse MR, Dobbs P, Long CR, Purvis RS, Kimminau KS, & McElfish PA (2019). Electronic cigarette use and psychological distress in the Native Hawaiian and Pacific Islander adults compared with other racial/ethnic groups: Data from the National Health Interview Survey, 2014. Journal of Community Psychology. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Nishimura ST, Goebert DA, Ramisetty-Mikler S, & Caetano R (2005). Adolescent alcohol use and suicide indicators among adolescents in Hawaii. Cultural Diversity and Ethnic Minority Psychology, 11(4), 309–320. [DOI] [PubMed] [Google Scholar]
  30. Okamoto SK, Kulis S, Marsiglia FF, Steiker LKH, & Dustman P (2014). A continuum of approaches toward developing culturally focused prevention interventions: From adaptation to grounding. The journal of primary prevention, 35(2), 103–112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Pearson JL, Richardson A, Niaura RS, Vallone DM, & Abrams DB (2012). e-Cigarette awareness, use, and harm perceptions in US adults. American Journal of Public Health, 102(9), 1758–1766. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Perrine CG, Pickens CM, Boehmer TK, King BA, Jones CM, DeSisto CL, … & Landen MG (2019). Characteristics of a multistate outbreak of lung injury associated with e-cigarette use, or vaping—United States, 2019. Morbidity and Mortality Weekly Report, 68(39), 860. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Pokhrel P, Little MA, Fagan P, Muranaka N, & Herzog TA (2014). Electronic cigarette use outcome expectancies among college students. Addictive Behaviors, 39(6), 1062–1065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Pokhrel P, Fagan P, Herzog TA, Laestadius L, Buente W, Kawamoto CT, … & Unger JB (2018a). Social media e-cigarette exposure and e-cigarette expectancies and use among young adults. Addictive Behaviors, 78, 51–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Pokhrel P, Lam TH, Pagano I, Kawamoto CT, & Herzog TA (2018b). Young adult e-cigarette use outcome expectancies: Validity of a revised scale and a short scale. Addictive Behaviors, 78, 193–199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Ramakrishnan K, & Ahmad FZ (2014). State of Asian Americans and Pacific Islanders Series: A multifaceted portrait of a growing population. Washington, DC: Center for American Progress. [Google Scholar]
  37. Sakamaki-Ching S, Williams M, Hua M, Li J, Bates SM, Robinson AN, Lyons TW, Goniewicz ML, Talbot P (2020). Correlation between biomarkers of exposure, effect and potential harm in the urine of electronic cigarette users. BMJ Open Respiratory Research, 7(1), 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Schulenberg JE, Johnston LD, O’Malley PM, Bachman JG, Miech RA, & Patrick ME (2019). Monitoring the Future national survey results on drug use, 1975-2018: Volume II, college students and adults ages 19-60. Ann Arbor: Institute for Social Research, The University of Michigan, 482 pp. [Google Scholar]
  39. Schweitzer RJ, Wills TA, Tam E, Pagano I, & Choi K (2017). E-cigarette use and asthma in a multiethnic sample of adolescents. Preventive Medicine, 105, 226–231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. SPSS, Version 24. Armonk, NY, IBM Corp, 2016 [Google Scholar]
  41. Subica AM, Aitaoto N, Link BG, Yamada AM, Henwood BF, & Sullivan G (2019a). Mental health status, need, and unmet need for mental health services among US Pacific Islanders. Psychiatric Services, . [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Subica AM, Aitaoto N, Sullivan JG, Henwood BF, Yamada AM, & Link BG (2019b). Mental illness stigma among Pacific Islanders. Psychiatry Research, 273, 578–585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Subica AM, & Wu LT (2018). Substance use and suicide in Pacific Islander, American Indian, and multiracial youth. American Journal of Preventive Medicine, 54(6), 795–805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Teranishi RT, Le A, Gutierrez RAE, Venturanza R, Hafoka I, Gogue DT-L, & Uluave L (2019). Native Hawaiians and Pacific Islanders in higher education: A call to action. APIA Scholars: Los Angeles, CA: Retrieved on 2/27/20 from: https://apiascholars.org/wp-content/uploads/2019/12/NHPI_Report.pdf [Google Scholar]
  45. U.S. Census Bureau. (2010). American Factfinder. Available from: http://factfinder.census.gov
  46. U.S. Department of Health and Human Services. (2016). E-cigarette use among youth and young adults. A report of the Surgeon General; Atlanta, GA. [Google Scholar]
  47. Wills TA, Pagano I, Williams RJ, & Tam EK (2019). E-cigarette use and respiratory disorder in an adult sample. Drug and Alcohol Dependence, 194, 363–370. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wu LT, Blazer DG, Swartz MS, Burchett B, Brady KT, & Workgroup NA (2013). Illicit and nonmedical drug use among Asian Americans, Native Hawaiians/Pacific Islanders, and mixed-race individuals. Drug and Alcohol Dependence, 133(2), 360–367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Wu LT, & Blazer DG (2015). Substance use disorders and co-morbidities among Asian Americans and Native Hawaiians/Pacific Islanders. Psychological Medicine, 45(3), 481–494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Yamada S (2004). Cancer, Reproductive Abnormalities, and Diabetes in Micronesia: The Effect of Nuclear Testing. Pacific Health Dialogue, 1(2), 216–221. [PubMed] [Google Scholar]

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