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Nonmedical Use of Prescription Stimulants and Nicotine among Community College Students

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Title: Nonmedical Use of Prescription Stimulants and Nicotine among Community College Students
Language: English
Authors: Hannah G. Truitt, Meredith K. Ginley, Kelly N. Foster, Rajkumar J. Sevak (ORCID 0000-0003-4354-5054), Nicholas E. Hagemeier
Source: Community College Review. 2024 52(2):193-209.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 17
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Two Year Colleges
Descriptors: Community College Students, Stimulants, Smoking, Substance Abuse, At Risk Persons, Alcohol Abuse, Mental Disorders, Student Characteristics, Access to Health Care, Age Differences, Gender Differences, Grade Point Average, Attention Deficit Hyperactivity Disorder
DOI: 10.1177/00915521231218208
ISSN: 0091-5521
1940-2325
Abstract: Objective: Despite community colleges accounting for 34% of all undergraduate enrollment, research on substance-use patterns among community college students is limited. Community college students may engage in substance use differently than their 4-year university counterparts due to differences in psychosocial factors and decreased availability of mental health services. The current study aimed to elucidate risk factors underlying non-medical use of prescription simulants (NMUS) and nicotine use by community college students. Methods: A web-based survey was administered to 10 of 13 community colleges within a southeastern state's Board of Regents school system. The survey included questions related to NMUS, nicotine use, alcohol use, mental health diagnosis, and demographics. Results: Overall, 9% of the participants reported NMUS, and 24.6% used nicotine. Multivariate analysis of variance and ?[subscript 2] tests revealed group differences among individuals using only nicotine, only NMUS, both nicotine and NMUS, and neither nicotine nor NMUS. Post-hoc 2 × 2 ?[subscript 2] tests indicated that individuals using both nicotine and NMUS had higher incidence of mental health diagnoses, were more likely to live in urban areas, reported higher weekly alcohol consumption, and were more likely to be male as compared to individuals using neither substance. Attention-deficit hyperactivity disorder (ADHD) symptoms were higher in individuals using only NMUS and both NMUS and nicotine as compared to those using only nicotine or neither substance. Conclusions: These findings provide insight into demographic and psychological variables associated with NMUS and nicotine use among community college students that could be benefitted by greater access to affordable mental health services.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1414133
Database: ERIC
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  Value: <anid>AN0175633372;ccr01apr.24;2024Feb27.05:28;v2.2.500</anid> <title id="AN0175633372-1">Nonmedical Use of Prescription Stimulants and Nicotine Among Community College Students </title> <p>Objective: Despite community colleges accounting for 34% of all undergraduate enrollment, research on substance-use patterns among community college students is limited. Community college students may engage in substance use differently than their 4-year university counterparts due to differences in psychosocial factors and decreased availability of mental health services. The current study aimed to elucidate risk factors underlying non-medical use of prescription simulants (NMUS) and nicotine use by community college students. Methods: A web-based survey was administered to 10 of 13 community colleges within a southeastern state's Board of Regents school system. The survey included questions related to NMUS, nicotine use, alcohol use, mental health diagnosis, and demographics. Results: Overall, 9% of the participants reported NMUS, and 24.6% used nicotine. Multivariate analysis of variance and χ<sup>2</sup> tests revealed group differences among individuals using only nicotine, only NMUS, both nicotine and NMUS, and neither nicotine nor NMUS. Post-hoc 2 × 2 χ<sup>2</sup> tests indicated that individuals using both nicotine and NMUS had higher incidence of mental health diagnoses, were more likely to live in urban areas, reported higher weekly alcohol consumption, and were more likely to be male as compared to individuals using neither substance. Attention-deficit hyperactivity disorder (ADHD) symptoms were higher in individuals using only NMUS and both NMUS and nicotine as compared to those using only nicotine or neither substance. Conclusions: These findings provide insight into demographic and psychological variables associated with NMUS and nicotine use among community college students that could be benefitted by greater access to affordable mental health services.</p> <p>Keywords: stimulant; nicotine; ADHD; substance misuse; smoking; NMUS</p> <hd id="AN0175633372-2">Introduction</hd> <p>Community colleges have successfully enabled millions of students to receive higher education in the United States (U.S.; [<reflink idref="bib2" id="ref1">2</reflink>]). There are presently 8.2 million community college students in the U.S., accounting for 34% of undergraduate enrollment ([<reflink idref="bib28" id="ref2">28</reflink>]). Community college enrollment rates have increased fivefold from 1970 to 2010, partially due to the relative accessibility and affordability of community colleges compared to traditional 4-year universities ([<reflink idref="bib2" id="ref3">2</reflink>]; [<reflink idref="bib10" id="ref4">10</reflink>]; [<reflink idref="bib28" id="ref5">28</reflink>]). Community college students are more likely than 4-year university students to earn incomes below the national poverty line, belong to racial or ethnic minority groups, be a parent, work a full-time or part-time job, and be older ([<reflink idref="bib2" id="ref6">2</reflink>]; [<reflink idref="bib11" id="ref7">11</reflink>]).</p> <p>The behavioral health needs of community college students remain underexamined and largely unaddressed by health policy ([<reflink idref="bib16" id="ref8">16</reflink>]). Institutionally, community colleges allocate few resources to addressing the behavioral health needs of their students ([<reflink idref="bib15" id="ref9">15</reflink>]; [<reflink idref="bib20" id="ref10">20</reflink>]; [<reflink idref="bib43" id="ref11">43</reflink>]). As compared to 4-year university students, community college students receive less psychoeducation on behavioral health topics and receive less support for behavioral health concerns ([<reflink idref="bib20" id="ref12">20</reflink>]). The lack of available health services combined with underlying psychosocial vulnerabilities may explain the disproportionally high rates of chronic illnesses, severe mental health concerns, and problematic substance use among community college students as compared to 4-year university students ([<reflink idref="bib7" id="ref13">7</reflink>]; [<reflink idref="bib20" id="ref14">20</reflink>]; [<reflink idref="bib33" id="ref15">33</reflink>]).</p> <p>Non-medical use of prescription drugs is a prevalent behavioral health issue among 4-year university students ([<reflink idref="bib21" id="ref16">21</reflink>]). Of the substances commonly misused by young adults, prescription stimulants are becoming increasingly recognized as problematic due to their relative popularity, harmful side effects, and addictive potential ([<reflink idref="bib37" id="ref17">37</reflink>]; [<reflink idref="bib47" id="ref18">47</reflink>]). Although prescription stimulants are highly efficacious for reducing symptoms of attention-deficit hyperactivity disorder (ADHD) when used as directed, they also have a high potential for misuse and addiction ([<reflink idref="bib40" id="ref19">40</reflink>]). Over 5 million adults report non-medically using prescription stimulants (NMUS) in the U.S. ([<reflink idref="bib31" id="ref20">31</reflink>]). High rates of NMUS are seen in 4-year university students, where an estimated 33% endorse NMUS in their lifetime and 17% report NMUS within the past year ([<reflink idref="bib6" id="ref21">6</reflink>]). However, the relative frequency of NMUS by community college students and relative risk factors for NMUS and co-occurring behavioral health concerns remains unknown.</p> <p>In addition, NMUS is associated with many psychiatric and physical health problems ([<reflink idref="bib9" id="ref22">9</reflink>]; [<reflink idref="bib22" id="ref23">22</reflink>]). One correlate of NMUS is increased risk of nicotine use and dependence, which is associated with more severe and more treatment-resistant stimulant use, as well as more intense stimulant cravings ([<reflink idref="bib30" id="ref24">30</reflink>]; [<reflink idref="bib46" id="ref25">46</reflink>]). Concurrent nicotine and substance use, as compared to single substance use, increases the risk of adverse health events and financial strain ([<reflink idref="bib27" id="ref26">27</reflink>]; [<reflink idref="bib45" id="ref27">45</reflink>]). Therefore, particular concern for co-occurring NMUS and nicotine use is warranted.</p> <p>One reason individuals may engage in both NMUS and nicotine is to self-medicate for symptoms of ADHD ([<reflink idref="bib32" id="ref28">32</reflink>]). Both prescription stimulants and nicotine have positive effects on concentration, potentially contributing to the high rates of smoking and NMUS for individuals with ADHD ([<reflink idref="bib32" id="ref29">32</reflink>]; [<reflink idref="bib36" id="ref30">36</reflink>]; [<reflink idref="bib41" id="ref31">41</reflink>]). The cognitive hallmarks of ADHD, including impulsivity, inattention, and difficulty concentrating, also correspond with greater initiation of substance use, increased withdrawal symptoms, and lower rates of successful abstinence from substances ([<reflink idref="bib17" id="ref32">17</reflink>]; [<reflink idref="bib18" id="ref33">18</reflink>]; [<reflink idref="bib25" id="ref34">25</reflink>]). However, little is known about the relationship between ADHD symptoms, NMUS, and nicotine use among community college students.</p> <p>There are many shared demographic and psychosocial risk factors for misusing stimulants and using nicotine, including age, propensity toward sensation seeking, and high levels of impulsivity ([<reflink idref="bib6" id="ref35">6</reflink>]; [<reflink idref="bib19" id="ref36">19</reflink>]). However, definitive psychological and/or social mechanisms facilitating the co-occurring use of nicotine and prescription stimulants remain largely unknown among community college students. Comparing community college students who use both nicotine and NMUS to students only engaging in NMUS or nicotine use may elucidate risk factors and underlying mechanisms contributing to their use. The current study aims to explore the prevalence of NMUS and nicotine use among community college students and examine demographic and psychosocial differences among participants only engaging in NMUS, only engaging in nicotine use, students using both NMUS and nicotine, and students using neither.</p> <hd id="AN0175633372-3">Materials and Methods</hd> <p></p> <hd id="AN0175633372-4">Participants</hd> <p>The present study utilizes data collected from a cross-sectional study examining stimulant misuse in community college students. Potential participants were undergraduate students enrolled in 1 of 10 community colleges within a southeastern state's Board of Regents school system. The inclusion criteria were: (<reflink idref="bib1" id="ref37">1</reflink>) at least 18 years of age and (<reflink idref="bib2" id="ref38">2</reflink>) current enrollment (either part-time or full-time) in one of the state's community colleges. Informed consent was obtained from each participant at the beginning of the survey. The study university's Institutional Review Board (IRB) approved all study procedures.</p> <hd id="AN0175633372-5">Survey</hd> <p></p> <hd id="AN0175633372-6">Survey Instrument Development</hd> <p>The survey instrument was constructed by the research team based on constructs identified in the literature, other scales and questions that have been used in previous research, and questions of interest that were developed by the research team. Then, the survey was reviewed and assessed for content validity by the research team. The initial instrument was then pilot tested on the study university's campus for understanding, range measure, item order, and best practices for survey administration and construction. Twenty-five undergraduate college students were recruited to participate in pilot testing the instrument using Qualtrics web-based survey software. The research team selected an undergraduate class within the Sociology department (the home department for part of the research team) that was comprised of students in the approximate age range of students who would ultimately be included in the study. The faculty member administered the survey in class and then discussed the survey and methods with the students and gave an opportunity for feedback. After analysis of the pilot data, descriptive statistics were calculated for each variable and, where appropriate, distributions were checked for variability and normality. Items with little variability were modified or discontinued, and other measures of quality assurance were applied.</p> <hd id="AN0175633372-7">Survey Administration</hd> <p>With the support from the Assistant Vice Chancellor for Student Affairs, we collaborated with individual community colleges to administer the web-based survey. We communicated with community college institutional representatives, and with appropriate IRB approval, obtained databases of all current community college student email addresses and relevant demographic information that allowed us to tailor our email invitations. Ultimately, 10 of the total 13 community colleges within the state's school system provided databases of their enrolled students.</p> <p>The Tailored Design Method (TDM) was modified for web-based surveys to maximize response rate across four contacts to students ([<reflink idref="bib13" id="ref39">13</reflink>]). The methods outlined in TDM are considered best practices for conducting survey research and have been used successfully in multiple similar studies. An introductory email was sent to the pool of community college students mid-way through the spring semester. The introductory email indicated that in the next week they would receive a request to complete a web-based survey regarding NMUS. In the following week, an email with a link to the web-based survey software (i.e., Qualtrics) was sent. One week later, a reminder/thank you email was sent that also included a link to the survey. Finally, 1 week thereafter, a final email was sent, once again providing the students a link to the online questionnaire and notifying them that this will be the last time they were contacted. Participants were randomized into one of three conditions regarding compensation for study enrollment. These conditions were chosen based on debates in the extant literature surrounding the impact of incentives on research participation. The research team sought to address it by choosing three conditions most often available to academic researchers: no incentive at all, a small chance at a larger fiscal incentive, and a greater chance at a smaller branded incentive. Participants in the first condition were not given financial incentives for study completion. Participants in the second condition were offered the opportunity to enter a drawing for 1 of 10 $500 cash prizes to be delivered by university check. Participants in the third condition were offered the opportunity to enter a drawing for 1 of 500 $10 Starbucks gift cards. Winners of these drawings were selected randomly, and participants entered the drawing using a separate online survey that was linked to the end of the main survey but was not associated with their responses.</p> <hd id="AN0175633372-8">Survey Questionnaire</hd> <p>Relevant descriptive information was obtained by asking participants to self-report their age, gender, and grade point average. Participants were asked to indicate whether they had been diagnosed by a health professional with a mental or behavioral illness/disorder while at their community college, with response options of "yes" and "no." Then, participants were asked a series of follow-up questions that asked if they had been diagnosed with specific mental or behavioral health disorders. Participants were also asked to report the average number of alcoholic beverages they consume each week. Each community college was classified as rural or urban based on county of location. Counties with <emph>≥</emph>50,000 residents were considered urban, and counties with <50,000 residents were considered rural ([<reflink idref="bib39" id="ref40">39</reflink>]). The county population for each community college was obtained through the Census Bureau's Population Estimates Program and then coded as rural or urban accordingly ([<reflink idref="bib39" id="ref41">39</reflink>]).</p> <p>To ensure reliable assessment of NMUS, the following definition was embedded within the survey: "NMUS" means "non-medical use of prescription stimulant medicines." NMUS is defined as: The use of a prescription stimulant by a person who does NOT have a legal prescription for that drug, OR the use of a prescription stimulant in a way other than prescribed (e.g., taking more doses of the drug than prescribed, taking the drug more frequently than prescribed, or taking the drug by a route other than prescribed, such as inhaling capsule contents for a more rapid effect). Sometimes these drugs (stimulants) are known as "uppers" or "speed." All of the following drugs are considered prescription stimulants for the purpose of this survey: Amphetamine and Dexamphetamine (Adderall), Armodafinil (Nuvigil), Dexamphetamine (Dexedrine), Dexmethylphenidate (Focalin), Lisdexampfetamine (Vyvanse), Methamphetamine (Desoxyn), Methylphenidate (Ritalin, Concerta, Metadate, and Methylin), Modafinil (Provigil), and Phentermine (Adipex, Suprenz, and Fastin). Here, NMUS does not refer to "over-the-counter" stimulants such as Dexatrim or No-Doz that can be bought without a doctor's prescription. This definition was considered in the context of those provided by the National Survey on Drug Use and Health ([<reflink idref="bib8" id="ref42">8</reflink>]) and other published articles on the topic ([<reflink idref="bib5" id="ref43">5</reflink>]; [<reflink idref="bib38" id="ref44">38</reflink>]; [<reflink idref="bib42" id="ref45">42</reflink>]).</p> <p>NMUS status was evaluated by asking participants if they have nonmedically used a prescription stimulant in the past 12 months. Nicotine usage status was determined by asking participants if they use any of the following tobacco products: chewable tobacco cans/pouches, cigarettes, cigars, e-cigarettes, vapors, or other tobacco products (please specify). Participants were also asked whether they felt the urge to use tobacco product(s) after non-medically using stimulants. ADHD symptoms were assessed using the 6-question Adult Self-Report Scale-Version 1.1 (ASRS) screener, which is a subset of the World Health Organization (WHO)'s 18-question Adult ADHD Self-Report Scale-Version 1.1 Symptom Checklist ([<reflink idref="bib23" id="ref46">23</reflink>]). The six questions within the ASRS correspond with symptoms of ADHD within the Diagnostic and Statistical Manual of Mental Disorders (4th ed; [<reflink idref="bib3" id="ref47">3</reflink>]). Although not independently used as a diagnostic assessment, results of the ASRS have been found to be predictive of ADHD diagnoses ([<reflink idref="bib23" id="ref48">23</reflink>]). Using the recommended ASRS scoring criteria, four or more endorsements of symptoms within the designated portion of the questionnaire is considered highly consistent with ADHD diagnosis ([<reflink idref="bib23" id="ref49">23</reflink>]). The ASRS demonstrates high classification accuracy and reliability ([<reflink idref="bib24" id="ref50">24</reflink>]) and internal consistency (Cronbach α =.84) in the current study.</p> <hd id="AN0175633372-9">Statistical Analyses</hd> <p>The IBM SPSS Statistics version 26 was used for conducting statistical analyses. Descriptive statistics were calculated to determine the demographic characteristics of the population and overall rates of NMUS and nicotine use. χ<sups>2</sups> were used to calculate group differences in nicotine use for individuals disclosing NMUS. χ<sups>2</sups> tests of independence were also calculated to examine differences in alcohol use, gender, status of attending school in a rural or urban area, and status of having been diagnosed with a mental or behavioral illness across four groups (neither NMUS nor nicotine use, only nicotine use, only NMUS, or both NMUS and nicotine use; hereafter referred to as neither, only nicotine, only NMUS, or both). Following a significant χ<sups>2</sups> value, partitioning was used post hoc to isolate the source of the significant interaction through 2 × 2 χ<sups>2</sups> testing ([<reflink idref="bib35" id="ref51">35</reflink>]). This required the four groups (e.g., neither, only nicotine, only NMUS, or both) to be collapsed into four dichotomous variables.</p> <p>A multivariate (one-way) analysis of variance (MANOVA) test was conducted to examine whether age, the number of alcoholic drinks consumed weekly, GPA, and ASRS total scores differed across four groups (neither, only nicotine, only NMUS, or both). The ASRS scores were not separated into hyperactive and inattentive scales because the strong correlation between the subscales (<emph>r</emph> (<reflink idref="bib3" id="ref52">3</reflink>,<reflink idref="bib101" id="ref53">101</reflink>) =.62, <emph>p</emph> <.001) indicated that little information could be gained by separating total ADHD symptoms into these components. Significant associations were examined further by post hoc non-parametric testing.</p> <p>Missing data were found to be missing completely at random, permitting the use of listwise deletion ([<reflink idref="bib14" id="ref54">14</reflink>]). Statistical significance was defined as <emph>p</emph><emph>≤</emph>.05, and Bonferroni corrections were applied at recommended levels to address the use of multiple comparisons ([<reflink idref="bib4" id="ref55">4</reflink>]).</p> <hd id="AN0175633372-10">Results</hd> <p>The questionnaire was sent to 53,096 community college students, 3,113 of whom completed the survey (5.86% response rate). Although low, this response rate is reasonable given the risk for non-response inherent within surveys of potentially sensitive information such as substance use ([<reflink idref="bib26" id="ref56">26</reflink>]; [<reflink idref="bib44" id="ref57">44</reflink>]). The sample was predominantly female (72.4%), white (81.7%), and young adult (<emph>M</emph> = 26.9, <emph>SD</emph> = 10.3). Demographic characteristics of the study population are presented in Table 1. Approximately 9% of respondents reported NMUS in the past year (<emph>n</emph> = 269), and 24.6% of participants reported using nicotine products (<emph>n</emph> = 733). Of participants who reported past year NMUS, 54.2% (<emph>n</emph> = 123) reported using nicotine. Results of the χ<sups>2</sups> tests of independence demonstrated a significant relation between nicotine use and NMUS χ<sups>2</sups> (<reflink idref="bib1" id="ref58">1</reflink>, _I_N_i_ = 2,<reflink idref="bib974" id="ref59">974</reflink>) = 115.5, <emph>p</emph> <.001, wherein participants who reported engaging in NMUS used nicotine at higher rates than participants denying NMUS. The urge to use a tobacco product after engaging in NMUS was indicated by 44.1% of participants reporting NMUS.</p> <p>Graph</p> <p>Table 1. Demographic and Psychosocial Characteristics of Participants Based on NMUS and Nicotine Use.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Variable</th><th align="center">Neither</th><th align="center">Nicotine only</th><th align="center">NMUS only</th><th align="center">Both</th><th align="center">χ<sup>2</sup> statistic</th></tr></thead><tbody><tr><td>Alcohol use</td><td /><td /><td /><td /><td>(3, <italic>n</italic> = 2,932) = 107.20<xref ref-type="table-fn" rid="tfn1">**</xref></td></tr><tr><td> No</td><td>69.4%</td><td>51.5%</td><td>42.2%</td><td>44.5%</td><td /></tr><tr><td> Yes</td><td>30.6%</td><td>48.5%</td><td>57.8%</td><td>55.5%</td><td /></tr><tr><td>Gender</td><td /><td /><td /><td /><td>(3, <italic>n</italic> = 2,959) = 65.95<xref ref-type="table-fn" rid="tfn1">**</xref></td></tr><tr><td> Male</td><td>23.70%</td><td>39.60%</td><td>25%</td><td>37.20%</td><td /></tr><tr><td> Female</td><td>76.30%</td><td>60.40%</td><td>75%</td><td>62.80%</td><td /></tr><tr><td>Rurality</td><td /><td /><td /><td /><td>(3, <italic>n</italic> = 2,891) = 10.25<xref ref-type="table-fn" rid="tfn1">*</xref></td></tr><tr><td> Rural</td><td>62.50%</td><td>67.50%</td><td>57.80%</td><td>54.50%</td><td /></tr><tr><td> Urban</td><td>37.50%</td><td>32.50%</td><td>42.20%</td><td>45.50%</td><td /></tr><tr><td>Diagnosed mental/behavioral health concern</td><td /><td /><td /><td /><td>(3, <italic>n</italic> = 2,972) = 75.81<xref ref-type="table-fn" rid="tfn1">**</xref></td></tr><tr><td> No</td><td>93.30%</td><td>72.90%</td><td>80.80%</td><td>76.40%</td><td /></tr><tr><td> Yes</td><td>7.70%</td><td>17.10%</td><td>19.20%</td><td>23.60%</td><td /></tr></tbody></table> </ephtml> </p> <p>1 <emph>p</emph> <.05. **<emph>p</emph> <.001.</p> <hd id="AN0175633372-11">χ2 Tests of Independence</hd> <p></p> <hd id="AN0175633372-12">Alcohol Use Between Groups</hd> <p>The χ<sups>2</sups> test of independence revealed significant differences in alcohol use among individuals by group (Table 1). Post hoc 2 × 2 χ<sups>2</sups> tests revealed that participants who used alcohol were significantly more likely to use only nicotine, only NMUS, or both, as compared to individuals denying alcohol use (Table 2).</p> <p>Graph</p> <p>Table 2. Outcomes From Post hoc 2 × 2 χ2 Tests of Independence.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="center" colspan="2">Alcohol use</th><th align="center">χ<sup>2</sup> statistic</th><th align="center" colspan="2">Gender</th><th align="center">χ<sup>2</sup> statistic</th><th align="center" colspan="2">Rurality</th><th align="center">χ<sup>2</sup> statistic</th><th align="center" colspan="2">Mental/behavioral diagnoses</th><th align="center">χ<sup>2</sup> statistic</th></tr></thead><tbody><tr><td>Neither</td><td>Yes</td><td>No</td><td>(1, <italic>n</italic> = 2,932) = 102.52<xref ref-type="table-fn" rid="tfn2">**</xref></td><td>Men</td><td>Women</td><td>(1, <italic>n</italic> = 2,959) = 56.42<xref ref-type="table-fn" rid="tfn2">**</xref></td><td>Rurality</td><td>Urban</td><td>(1, <italic>n</italic> = 2891) =.871</td><td>Yes</td><td>No</td><td>(1, <italic>n</italic> = 2,972) = 71.17<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr><tr><td>Yes</td><td>60.90%</td><td>78.30%</td><td /><td>62.00%</td><td>75.80%</td><td /><td>71.40%</td><td>73.00%</td><td /><td>51.70%</td><td>74.30%</td><td /></tr><tr><td>No</td><td>39.10%</td><td>21.70%</td><td /><td>38.00%</td><td>24.20%</td><td /><td>28.60%</td><td>27.00%</td><td /><td>48.30%</td><td>25.70%</td><td /></tr><tr><td>Nicotine only</td><td /><td /><td>(1, <italic>n</italic> = 2,932) = 48.94<xref ref-type="table-fn" rid="tfn2">**</xref></td><td /><td /><td>(1, <italic>n</italic> = 2,959) = 55.49<xref ref-type="table-fn" rid="tfn2">**</xref></td><td /><td /><td>(1, <italic>n</italic> = 2,891) = 6.39<xref ref-type="table-fn" rid="tfn2">**</xref></td><td /><td /><td>(1, <italic>n</italic> = 2,972) = 33.03<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr><tr><td> Yes</td><td>27.40%</td><td>16.40%</td><td /><td>29.30%</td><td>17.00%</td><td /><td>21.80%</td><td>17.90%</td><td /><td>32.80%</td><td>19.00%</td><td /></tr><tr><td> No</td><td>83.50%</td><td>72.60%</td><td /><td>70.70%</td><td>83.00%</td><td /><td>78.20%</td><td>82.10%</td><td /><td>67.20%</td><td>81.00%</td><td /></tr><tr><td>NMUS only</td><td /><td /><td>(1, <italic>n</italic> = 2,932) =21.31<xref ref-type="table-fn" rid="tfn2">**</xref></td><td /><td /><td>(1, <italic>n</italic> = 2,959) = 5.88<xref ref-type="table-fn" rid="tfn2">*</xref></td><td /><td /><td>(1, <italic>n</italic> = 2,891) = 1.22</td><td /><td /><td>(1, <italic>n</italic> = 2,972) = 8.29<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr><tr><td> Yes</td><td>5.60%</td><td>2.30%</td><td /><td>3.20%</td><td>3.60%</td><td /><td>3.20%</td><td>4.00%</td><td /><td>6.30%</td><td>3.20%</td><td /></tr><tr><td> No</td><td>94.40%</td><td>97.70%</td><td /><td>96.80%</td><td>96.40%</td><td /><td>96.80%</td><td>96.00%</td><td /><td>93.70%</td><td>96.80%</td><td /></tr><tr><td>Both</td><td /><td /><td>(1, <italic>n</italic> = 2,932) = 19.79<xref ref-type="table-fn" rid="tfn2">**</xref></td><td /><td /><td>(1, <italic>n</italic> = 2,959) =.35</td><td /><td /><td>(1, <italic>n</italic> = 2,891) = 3.89<xref ref-type="table-fn" rid="tfn2">*</xref></td><td /><td /><td>(1, <italic>n</italic> = 2,972) = 22.45<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr><tr><td> Yes</td><td>6.20%</td><td>2.80%</td><td /><td>5.50%</td><td>3.50%</td><td /><td>3.60%</td><td>5.10%</td><td /><td>9.10%</td><td>3.50%</td><td /></tr><tr><td> No</td><td>93.80%</td><td>97.20%</td><td /><td>94.50%</td><td>96.50%</td><td /><td>96.40%</td><td>94.90%</td><td /><td>90.90%</td><td>96.50%</td><td /></tr></tbody></table> </ephtml> </p> <p>2 <emph>p</emph> <.05. **<emph>p</emph> <.001.</p> <hd id="AN0175633372-13">Gender Differences Between Groups</hd> <p>The χ<sups>2</sups> test of independence found gender to significantly differ between groups (Table 1). Post hoc 2 × 2 χ<sups>2</sups> tests found men to be more likely to report using only nicotine or both (Table 2). Men and women were equally likely to report engaging in only NMUS.</p> <hd id="AN0175633372-14">Comparison of Attending School in Rural or Urban Areas by Group</hd> <p>Use of nicotine and NMUS differed between students attending rural and urban community colleges (Table 1). Post hoc 2 × 2 χ<sups>2</sups> tests found that participants attending schools in rural regions were more likely to use only nicotine and less likely to use both as compared to participants attending school in urban areas (Table 2). The use of neither and only NMUS did not differ between participants attending school in rural or urban areas.</p> <hd id="AN0175633372-15">Rates of Diagnosed Mental and Behavioral Health Concerns Between Groups</hd> <p>Use of NMUS and nicotine significantly differed between participants diagnosed with a mental or behavioral health concern and not diagnosed with a mental or behavioral health concern (Table 1). Post hoc 2 × 2 χ<sups>2</sups> tests found that participants with mental or behavioral health concerns were significantly more likely to report using only nicotine, only NMUS, and both, as compared to individuals without a diagnosed mental or behavioral health concern (Table 2).</p> <hd id="AN0175633372-16">MANOVA</hd> <p></p> <hd id="AN0175633372-17">Age, Alcoholic Drinks, GPA, and ASRS Scores by Group</hd> <p>The MANOVA omnibus test results demonstrated significant main effects of age, number of alcoholic drinks consumed, GPA, and ASRS scores across four groups (Table 3). Box's Test for Equivalence of Covariance Matrices was significant (Box <emph>M</emph> = 955.13, <emph>p</emph> <.001), indicating significant differences between the covariance matrices. Therefore, the Games-Howell post hoc test was used as a nonparametric approach that does not require equal sample sizes or equal variance among groups.</p> <p>Graph</p> <p>Table 3. Outcomes From MANOVA Statistics Based on NMUS and Nicotine Use of Participants.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="center" colspan="2">Neither</th><th align="center" colspan="2">Nicotine only</th><th align="center" colspan="2">NMUS only</th><th align="center" colspan="2">Both</th><th align="center" rowspan="2"><italic>F</italic></th></tr><tr><th /><th align="center"><italic>M</italic></th><th align="center"><italic>SD</italic></th><th align="center"><italic>M</italic></th><th align="center"><italic>SD</italic></th><th align="center"><italic>M</italic></th><th align="center"><italic>SD</italic></th><th align="center"><italic>M</italic></th><th align="center"><italic>SD</italic></th></tr></thead><tbody><tr><td>Age</td><td>26.54</td><td>10.28</td><td>27.89</td><td>10.36</td><td>25.48</td><td>8.02</td><td>24.07</td><td>7.73</td><td>5.98<xref ref-type="table-fn" rid="tfn3">**</xref></td></tr><tr><td>Weekly alcohol consumption</td><td>1.02</td><td>2.73</td><td>2.5</td><td>6.33</td><td>2.39</td><td>3.96</td><td>3.59</td><td>6.87</td><td>32.94<xref ref-type="table-fn" rid="tfn3">**</xref></td></tr><tr><td>GPA</td><td>3.29</td><td>0.66</td><td>3.09</td><td>0.73</td><td>3.26</td><td>0.56</td><td>3.1</td><td>0.73</td><td>14.01<xref ref-type="table-fn" rid="tfn3">**</xref></td></tr><tr><td>ASRS V1.1 total score</td><td>2.47</td><td>0.81</td><td>2.8</td><td>0.87</td><td>3.1</td><td>0.88</td><td>3.3</td><td>0.87</td><td>44.91<xref ref-type="table-fn" rid="tfn3">**</xref></td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>Note. M</emph> = mean; <emph>SD</emph> = standard deviation.</item> <item>4 <emph>p <.</emph>001.</item> </ulist> <p>The post hoc tests indicated that the individuals using neither were significantly older than those using both and the mean age of individuals only using nicotine was significantly higher than those using only NMUS. Individuals using only nicotine, only NMUS, or both consumed a significantly greater number of average alcoholic drinks per week than those using neither. The average number of alcoholic drinks per week was higher for individuals using both as compared to only nicotine or only NMUS; however, this finding did not meet statistical significance. The average GPA of students using neither or only NMUS was significantly higher than the GPA of students using only nicotine or using both.</p> <p>Lastly, the ASRS total scores differed significantly among the four groups (Table 3). Individuals using both had higher ASRS total scores than those using only nicotine, only NMUS, or neither. Participants using only NMUS reported higher average ASRS scores than individuals using only nicotine or neither. Participants using only nicotine had higher ASRS scores than those using neither.</p> <hd id="AN0175633372-18">Discussion</hd> <p>Of the community college students surveyed, 9% reported NMUS in the past year and 25% reported using nicotine products. Past research has indicated a positive association between NMUS and other forms of substance use, which is supported by the present study ([<reflink idref="bib29" id="ref60">29</reflink>]). Of the students who reported NMUS in the past year, most (54%) also reported using nicotine. Furthermore, rates of alcohol use were significantly higher for individuals using nicotine and/or NMUS compared to individuals denying nicotine use and/or NMUS. Despite previously established high rates of substance use among community college students, healthcare and psychiatric services are not consistently offered to community college students ([<reflink idref="bib16" id="ref61">16</reflink>]; [<reflink idref="bib33" id="ref62">33</reflink>]). Future research replicating services known to reduce problematic substance use in 4-year university students within community colleges is needed ([<reflink idref="bib7" id="ref63">7</reflink>]). For instance, screening and brief intervention is a quick and relatively inexpensive method for identifying problematic substance use that has been successfully implemented on 4-year university campuses. Such measures may be similarly efficacious on community college campuses ([<reflink idref="bib12" id="ref64">12</reflink>]). Future inquiry into the relation between other forms of substance use (e.g., marijuana, non-medical use of prescription opioids) and NMUS may elucidate other high-risk correlates of NMUS.</p> <p>Symptoms of ADHD differed between groups. Individuals reporting only NMUS or both also reported more highly endorsed symptoms of ADHD as compared to participants using only nicotine or neither. This finding is supported by existing research demonstrating an association between symptoms of ADHD and other risky health behaviors ([<reflink idref="bib36" id="ref65">36</reflink>]). There are many proposed hypotheses for the association between stimulant use and ADHD symptoms, including self-medication, behavioral response, underlying impulsivity, and the cluster of shared demographic risk factors ([<reflink idref="bib6" id="ref66">6</reflink>]; [<reflink idref="bib19" id="ref67">19</reflink>]; [<reflink idref="bib25" id="ref68">25</reflink>]; [<reflink idref="bib34" id="ref69">34</reflink>]). The prevalence of concurrent NMUS and nicotine use among individuals with high levels of self-reported symptoms of ADHD supports the self-medication hypothesis ([<reflink idref="bib41" id="ref70">41</reflink>]). To the extent that the ASRS scores estimate ADHD symptoms, the present findings indicate that subclinical levels of inattention and hyperactivity symptoms could pose a risk for nicotine use and/or NMUS. However, the ASRS is not an independent diagnostic tool for ADHD, and the current findings should be replicated in individuals diagnosed with ADHD.</p> <p>Interestingly, GPA did not differ between participants using only NMUS and participants using neither NMUS nor nicotine. Since NMUS is often used with the intent of study enhancement for individuals with symptoms of ADHD, it may be that awareness of and access to alternative methods of strengthening academic performance mitigates the desire for NMUS (e.g., tutoring services, academic accommodations when warranted). Given the present finding that NMUS was associated with symptoms of ADHD, undertreated and/or undiagnosed ADHD may be a risk factor for NMUS. Given the often prohibitive cost of psychological assessments and the tendency for community college students to earn an annual income below the federal poverty guidelines, the provision of low-cost psychological assessment by student health services may be particularly beneficial for the detection and treatment of ADHD, thereby reducing NMUS ([<reflink idref="bib15" id="ref71">15</reflink>]).</p> <hd id="AN0175633372-19">Limitations</hd> <p>These results should be considered with certain limitations. Participants were community college students in the southeastern U.S., predominantly white and female, which may limit generalizability to other populations. Therefore, future research should attempt to replicate these findings in participants with broader geographic and demographic diversity. Although low survey response rates are not necessarily indicative of non-response bias, this is of concern for the present study and may limit the generalizability of these findings. Participants were asked if they had been diagnosed with mental or behavioral illnesses/disorders since beginning college with the intent of capturing individuals with active symptoms of behavioral health concerns. However, this item may have failed to identify participants who were previously diagnosed with a mental or behavioral health concern who experience ongoing symptoms. Future research examining historical diagnoses may provide further insight into these relations. Finally, the study relied on self-report questionnaires which may not reflect true psychological tendencies and substance use, due to social desirability bias and recall bias ([<reflink idref="bib1" id="ref72">1</reflink>]). However, the use of validated, anonymous questionnaires and assessment of substance use only in the past year may have mitigated their impact on the study results.</p> <hd id="AN0175633372-20">Conclusions</hd> <p>The present study offers novel insights into variables affecting NMUS and nicotine use among community college students. The findings indicate that community college students engage in both NMUS and nicotine use at non-negligible rates. Individuals using both nicotine and NMUS had higher rates of diagnosed mental or behavioral health concerns, alcohol consumed weekly, and symptoms of ADHD, as compared to participants using only nicotine, only NMUS, or neither substance. Individuals engaging in concurrent NMUS and nicotine use were also more likely to attend school in an urban area and be male. Increasing accessibility of affordable, evidence-based pharmacological and psychological interventions for behavioral health concerns could be useful for decreasing the rates of NMUS and nicotine use among community college students.</p> <ref id="AN0175633372-21"> <title> References </title> <blist> <bibl id="bib1" idref="ref37" type="bt">1</bibl> <bibtext> Althubaiti A. (2016). Information bias in health research: Definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare, 9, 211–217. https://doi.org/10.2147/JMDH.S104807</bibtext> </blist> <blist> <bibl id="bib2" idref="ref1" type="bt">2</bibl> <bibtext> American Association of Community Colleges. (2020). Fast facts. Retrieved January 31, 2021, from https://<ulink href="http://www.aacc.nche.edu/research-trends/fast-facts/">www.aacc.nche.edu/research-trends/fast-facts/</ulink></bibtext> </blist> <blist> <bibl id="bib3" idref="ref47" type="bt">3</bibl> <bibtext> American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). American Psychiatric Publishing, Inc.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref55" type="bt">4</bibl> <bibtext> Armstrong R. A. (2014). When to use the Bonferroni correction. Ophthalmic & Physiological Optics: The Journal of the British College of Ophthalmic Opticians, 34(5), 502–508. https://doi.org/10.1111/opo.12131</bibtext> </blist> <blist> <bibl id="bib5" idref="ref43" type="bt">5</bibl> <bibtext> Bavarian N., Flay B. R., Ketcham P. L., Smit E. (2013). Development and psychometric properties of a theory-guided prescription stimulant misuse questionnaire for college students. Substance Use & Misuse, 48, 457–469.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref21" type="bt">6</bibl> <bibtext> Benson K., Flory K., Humphreys K. L., Lee S. S. (2015). Misuse of stimulant medication among college students: A comprehensive review and meta-analysis. Clinical Child and Family Psychology Review, 18, 50–76.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref13" type="bt">7</bibl> <bibtext> Cadigan J. M., Lee C. M. (2019). Identifying barriers to mental health service utilization among heavy drinking community college students. Community College Journal of Research and Practice, 43(8), 585–594. https://doi.org/10.1080/10668926.2018.1520659</bibtext> </blist> <blist> <bibl id="bib8" idref="ref42" type="bt">8</bibl> <bibtext> Center for Behavioral Health Statistics and Quality. (2015). 2014 National Survey on Drug Use and Health: Methodological summary and definitions. Substance Abuse and Mental Health Services Administration.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref22" type="bt">9</bibl> <bibtext> Chen L. Y., Crum R. M., Strain E. C., Alexander G. C., Kaufmann C., Mojtabai R. (2016). Prescriptions, non-medical use, and emergency department visits involving prescription stimulants. The Journal of Clinical Psychiatry, 77(3), e297–e304. https://doi.org/10.4088/JCP.14m09291</bibtext> </blist> <blist> <bibtext> Chiauzzi E., Donovan E., Black R., Cooney E., Buechner A., Wood M. (2011). A survey of 100 community colleges on student substance use, programming, and collaborations. Journal of American College Health, 59(6), 563–573. https://doi.org/10.1080/07448481.2010.534214</bibtext> </blist> <blist> <bibtext> Cremeens-Matthews J., Chaney B. (2016). Patterns of alcohol use: A two-year college and four-year university comparison case study. Community College Journal of Research and Practice, 40(1), 23–33. https://doi.org/10.1080/10668926.2014.95204</bibtext> </blist> <blist> <bibtext> Denering L. L., Spear S. E. (2012). Routine use of screening and brief intervention for college students in a university counseling center. Journal of Psychoactive Drugs, 44(4), 318–324. https://doi.org/10.1080/02791072.2012.718647</bibtext> </blist> <blist> <bibtext> Dillman D. A., Phelps G., Tortora R., Swift K., Kohrell J., Berck J., Messer B. L. (2009). Response rate and measurement differences in mixed-mode surveys using mail, telephone, interactive voice response (IVR) and the Internet. Social Science Research, 38(1), 1–18. https://doi.org/10.1016/j.ssresearch.2008.03.007</bibtext> </blist> <blist> <bibtext> Downey R. G., King C. (1998). Missing data in Likert ratings: A comparison of replacement methods. The Journal of General Psychology, 125(2), 175–191. https://doi.org/10.1080/00221309809595542</bibtext> </blist> <blist> <bibtext> Dykes-Anderson M. (2013). The case for comprehensive counseling centers at community colleges. Community College Journal of Research and Practice, 37(10), 742–749. https://doi.org/10.1080/10668921003723235</bibtext> </blist> <blist> <bibtext> Fortney J. C., Curran G. M., Hunt J. B., Lu L., Eisenberg D., Valenstein M. (2017). Mental health treatment seeking among veteran and civilian community college students. Psychiatric Services, 68(8), 851–855. https://doi.org/10.1176/appi.ps.201600240</bibtext> </blist> <blist> <bibtext> Gudmundsdottir B. G., Weyandt L., Ernudottir G. B. (2020). Prescription stimulant misuse and ADHD symptomatology among college students in Iceland. Journal of Attention Disorders, 24(3), 384–401. https://doi.org/10.1177/108705471668437</bibtext> </blist> <blist> <bibtext> Hakulinen C., Hintsanen M., Munafo M. R., Virtanen M., Kivimaki M., Batty G. D., Jokela M. (2015). Personality and smoking: Individual-participant meta-analysis of nine cohort studies. Addiction, 110, 1844–1852.</bibtext> </blist> <blist> <bibtext> Harvanko A. M., Strickland J. C., Slone S. A., Shelton B. J., Reynolds B. A. (2019). Dimensions of impulsive behavior: Predicting contingency management treatment outcomes for adolescent smokers. Addictive Behaviors, 90, 334–340. https://doi.org/10.1016/j.addbeh.2018.11.031</bibtext> </blist> <blist> <bibtext> Katz D. S., Davison K. (2014). Community college student mental health: A comparative analysis. Community College Review, 42(4), 307–326. https://doi.org/10.1177/0091552114535466</bibtext> </blist> <blist> <bibtext> Kennedy S. (2018). Raising awareness about prescription and stimulant abuse in college students through on-campus community involvement projects. Journal of Undergraduate Neuroscience Education, 17(1), A50–A53.</bibtext> </blist> <blist> <bibtext> Kennedy J. N., Bebarta V. S., Varney S. M., Zarzabal L. A., Ganem V. J. (2015). Prescription stimulant misuse in a military population. Military Medicine, 180(Suppl. 3), 191–194. https://doi.org/10.7205/MILMED-D-14-00375</bibtext> </blist> <blist> <bibtext> Kessler R. C., Adler L., Ames M., Demler O., Faraone S., Hiripi E., Howes M. J., Jin R., Scnik K., Spencer T., Ustun T. B., Walters E. E. (2005). The World Health Organization adult ADHD self-report scale (ASRS): A short screening scale for use in the general population. Psychological Medicine, 35(2), 245–256. 10.1017/S0033291704002892</bibtext> </blist> <blist> <bibtext> Kessler R. C., Adler L. A., Gruber M. J., Sarawate C. A., Spencer T., Van Brunt D. L. (2007). Validity of the World Health Organization Adult ADHD Self-Report Scale (ASRS) screener in a representative sample of health plan members. International Journal of Methods in Psychiatric Research, 16(2), 52–65. https://doi.org/10.1002/mpr.208</bibtext> </blist> <blist> <bibtext> Kvaavik E., Rise J. (2012). How do impulsivity and education relate to smoking initiation and cessation among young adults? Journal of Studies on Alcohol and Drugs, 73, 804–810.</bibtext> </blist> <blist> <bibtext> Maclennan B., Kypri K., Langley J., Room R. (2012). Non-response bias in a community survey of drinking, alcohol-related experiences and public opinion on alcohol policy. Drug and Alcohol Dependence, 126(1–2), 189–194. https://doi.org/10.1016/j.drugalcdep.2012.05.014</bibtext> </blist> <blist> <bibtext> McKee S. A., Weinberger A. H. (2013). How can we use our knowledge of alcohol-tobacco interactions to reduce alcohol use? Annual Review of Clinical Psychology, 9, 649–674. https://doi.org/10.1146/annurev-clinpsy-050212-185549</bibtext> </blist> <blist> <bibtext> National Center for Educational Statistics. (2020). Fast facts: Back to school statistics. Retrieved January 31, 2021, from https://nces.ed.gov/fastfacts/display.asp?id=372</bibtext> </blist> <blist> <bibtext> Novak S. P., Kroutil L. A., Williams R. L., Van Brunt D. L. (2007). The non-medical use of prescription ADHD medications: Results from a national internet panel. Substance Abuse Treatment, Prevention, and Policy, 2, 32. https://doi.org/10.1186/1747-597X-2-32</bibtext> </blist> <blist> <bibtext> Ouellet-Plamondon C., Mohamed N. S., Sharif-Razi M., Simpkin E., George T. P. (2014). Treatment of comorbid tobacco addiction in substance use and psychiatric disorders. Current Addiction Reports, 1, 61–68.</bibtext> </blist> <blist> <bibtext> Piper B. J., Ogden C. L., Simoyan O. M., Chung D. Y., Caggiano J. F., Nichols S. D., McCall K. L. (2018). Trends in use of prescription stimulants in the United States and Territories, 2006 to 2016. PloS one, 13(11), e0206100. https://doi.org/10.1371/journal.pone.0206100</bibtext> </blist> <blist> <bibtext> Roberts W., Peters J. R., Adams Z. W., Lynam D. R., Milich R. (2014). Identifying the facets of impulsivity that explain the relation between ADHD symptoms and substance use in a nonclinical sample. Addictive Behaviors, 39(8), 1272–1277. https://doi.org/10.1016/j.addbeh.2014.04.005</bibtext> </blist> <blist> <bibtext> Salgado García F., Bursac Z., Derefinko K. J. (2020). Cumulative risk of substance use in community college students. The American Journal on Addictions, 29(2), 97–104. https://doi.org/10.1111/ajad.12983</bibtext> </blist> <blist> <bibtext> Sevak R. J., Stoops W. W., Rush C. R. (2010). Behavioral effects of d-amphetamine in humans: Influence of subclinical levels of inattention and hyperactivity. American Journal of Drug and Alcohol Abuse, 36(4), 220–227. https://doi.org/10.3109/00952990.2010.494213</bibtext> </blist> <blist> <bibtext> Sharpe D. (2015). Chi-Square test is statistically significant: Now what? Practical Assessment, Research, and Evaluation, 20, 8. https://doi.org/10.7275/tbfa-x148</bibtext> </blist> <blist> <bibtext> Sibley M. H., Pelham W. E., Molina B. S. G., Coxe S., Kipp H., Gnagy E. M., Meinzer M., Ross J. M., Lahey B. B. (2014). The role of early childhood ADHD and subsequent CD in the initiation and escalation of adolescent cigarette, alcohol, and marijuana use. Journal of Abnormal Psychology, 123, 362–374.</bibtext> </blist> <blist> <bibtext> Smith T. E., Martel M. M., DeSantis A. D. (2017). Subjective report of side effects of prescribed and nonprescribed psychostimulant use in young adults. Substance Use & Misuse, 52(4), 548–552. https://doi.org/10.1080/10826084.2016.1240694</bibtext> </blist> <blist> <bibtext> Substance Abuse and Mental Health Services Administration. (2014). Receipt of services for behavioral health problems: Results from the 2014 National Survey on Drug Use and Health. https://<ulink href="http://www.samhsa.gov/data/sites/default/files/NSDUH-DR-FRR3-2014/NSDUH-DR-FRR3-2014/NSDUH-DR-FRR3-2014.pdf">www.samhsa.gov/data/sites/default/files/NSDUH-DR-FRR3-2014/NSDUH-DR-FRR3-2014/NSDUH-DR-FRR3-2014.pdf</ulink></bibtext> </blist> <blist> <bibtext> U.S. Census Bureau. (2019). 2010 Census urban and rural classification and urban area criteria. Retrieved October 5, 2021, from https://<ulink href="http://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural/2010-urban-rural.html">www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural/2010-urban-rural.html</ulink></bibtext> </blist> <blist> <bibtext> U.S. Food and Drug Administration. (2015). Medication guides. <ulink href="http://www.fda.gov/drugs/drugsafety/ucm085729.htm">http://www.fda.gov/drugs/drugsafety/ucm085729.htm</ulink></bibtext> </blist> <blist> <bibtext> Van Amsterdam J., van der Velde B., Schulte M., van den Brink W. (2018). Causal factors of increased smoking in ADHD: A systematic review. Substance Use & Misuse, 53(3), 432–445. 10.1080/10826084.2017.1334066</bibtext> </blist> <blist> <bibtext> Volger E. J., McLendon A. N., Fuller S. H., Herring C. T. (2014). Prevalence of self- reported nonmedical use of prescription stimulants in North Carolina Doctor of Pharmacy students. Journal of Pharmacy Practice, 27(2), 158–168.</bibtext> </blist> <blist> <bibtext> Wall A. F., BaileyShea C., McIntosh S. (2012). Community college student alcohol use: Developing context-specific evidence and prevention approaches. Community College Review, 40(1), 25–45. https://doi.org/10.1177/0091552112437757</bibtext> </blist> <blist> <bibtext> Wallander L., Tikkanen R. H., Mannheimer L. N., Östergren P. O., Plantin L. (2015). The problem of non-response in population surveys on the topic of HIV and sexuality: A comparative study. European Journal of Public Health, 25(1), 172–177. https://doi.org/10.1093/eurpub/cku154</bibtext> </blist> <blist> <bibtext> Weinberger A. H., Funk A. P., Goodwin R. D. (2016). A review of epidemiologic research on smoking behavior among persons with alcohol and illicit substance use disorders. Preventive Medicine, 92, 148–159. https://doi.org/10.1016/j.ypmed.2016.05.011</bibtext> </blist> <blist> <bibtext> Weinberger A. H., Sofuoglu M. (2009). The impact of cigarette smoking on stimulant addiction. The American Journal of Drug and Alcohol Abuse, 35(1), 12–17. https://doi.org/10.1080/00952990802326280</bibtext> </blist> <blist> <bibtext> Weyandt L. L., Oster D. R., Marraccini M. E., Gudmundsdottir B. G., Munro B. A., Rathkey E. S., McCallum A. (2016). Prescription stimulant medication misuse: Where are we and where do we go from here? Experimental and Clinical Psychopharmacology, 24(5), 400–414. https://doi.org/10.1037/pha0000093</bibtext> </blist> </ref> <ref id="AN0175633372-22"> <title> Footnotes </title> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the Tennessee Board of Regents under Grant TBR 1508 to Dr. Sevak and Dr. Hagemeier.</bibtext> </blist> <blist> <bibtext> Rajkumar J. Sevak</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0003-4354-5054</bibtext> </blist> </ref> <aug> <p>By Hannah G. Truitt; Meredith K. Ginley; Kelly N. Foster; Rajkumar J. Sevak and Nicholas E. Hagemeier</p> <p>Reported by Author; Author; Author; Author; Author</p> <p></p> <p>Hannah G. Truitt, MA, is a 5th-year Ph.D. candidate in clinical psychology at East Tennessee State University and a neuropsychology intern at Brigham and Women's Hospital. Her research primarily investigates the relation between substance use and neuropsychological conditions among traditionally underserved populations.</p> <p>Meredith K. Ginley, PhD, is an assistant professor in the Department of Psychology at East Tennessee State University. She received her doctorate from The University of Memphis. She completed a clinical internship at the University of Mississippi Medical Center and at postdoctoral fellowship in addiction at the University of Connecticut School of Medicine/UConn Health. Her research and professional interests include psychological assessment and the prevention and treatment of addictive disorders.</p> <p>Kelly N. Foster, PhD, is an associate professor of sociology and director of the Applied Social Research Lab (ASRL) at East Tennessee State University (ETSU). She received her PhD in Educational Psychology in the Research, Evaluation, Measurement, and Statistics concentration from the University of Georgia. Her research interests include survey research methodology, applied sociology, health-related survey methodology, new technologies in research methodology, and statistical analysis.</p> <p>Rajkumar J. Sevak, PhD, RPh, is an assistant professor of Pharmacy Practice at University of the Pacific School of Pharmacy. He received his doctorate from The University of Texas Health Science Center at San Antonio. He completed a clinical pharmacy residency at the Auburn University and a postdoctoral fellowship at the East Tennessee State University. His research interests include elucidation of factors affecting illicit use of substances.</p> <p>Nicholas E. Hagemeier, PharmD, PhD, is professor of Pharmacy Practice and Vice Provost for Research at East Tennessee State University. His degrees were earned from Purdue University. Dr. Hagemeier's research focuses on pharmacists' and prescribers' roles in the prevention and treatment of substance use disorder.</p> </aug> <nolink nlid="nl1" bibid="bib28" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib10" firstref="ref4"></nolink> <nolink nlid="nl3" bibid="bib11" firstref="ref7"></nolink> <nolink nlid="nl4" bibid="bib16" firstref="ref8"></nolink> <nolink nlid="nl5" bibid="bib15" firstref="ref9"></nolink> <nolink nlid="nl6" bibid="bib20" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib43" firstref="ref11"></nolink> <nolink nlid="nl8" bibid="bib33" firstref="ref15"></nolink> <nolink nlid="nl9" bibid="bib21" firstref="ref16"></nolink> <nolink nlid="nl10" bibid="bib37" firstref="ref17"></nolink> <nolink nlid="nl11" bibid="bib47" firstref="ref18"></nolink> <nolink nlid="nl12" bibid="bib40" firstref="ref19"></nolink> <nolink nlid="nl13" bibid="bib31" firstref="ref20"></nolink> <nolink nlid="nl14" bibid="bib22" firstref="ref23"></nolink> <nolink nlid="nl15" bibid="bib30" firstref="ref24"></nolink> <nolink nlid="nl16" bibid="bib46" firstref="ref25"></nolink> <nolink nlid="nl17" bibid="bib27" firstref="ref26"></nolink> <nolink nlid="nl18" bibid="bib45" firstref="ref27"></nolink> <nolink nlid="nl19" bibid="bib32" firstref="ref28"></nolink> <nolink nlid="nl20" bibid="bib36" firstref="ref30"></nolink> <nolink nlid="nl21" bibid="bib41" firstref="ref31"></nolink> <nolink nlid="nl22" bibid="bib17" firstref="ref32"></nolink> <nolink nlid="nl23" bibid="bib18" firstref="ref33"></nolink> <nolink nlid="nl24" bibid="bib25" firstref="ref34"></nolink> <nolink nlid="nl25" bibid="bib19" firstref="ref36"></nolink> <nolink nlid="nl26" bibid="bib13" firstref="ref39"></nolink> <nolink nlid="nl27" bibid="bib39" firstref="ref40"></nolink> <nolink nlid="nl28" bibid="bib38" firstref="ref44"></nolink> <nolink nlid="nl29" bibid="bib42" firstref="ref45"></nolink> <nolink nlid="nl30" bibid="bib23" firstref="ref46"></nolink> <nolink nlid="nl31" bibid="bib24" firstref="ref50"></nolink> <nolink nlid="nl32" bibid="bib35" firstref="ref51"></nolink> <nolink nlid="nl33" bibid="bib101" firstref="ref53"></nolink> <nolink nlid="nl34" bibid="bib14" firstref="ref54"></nolink> <nolink nlid="nl35" bibid="bib26" firstref="ref56"></nolink> <nolink nlid="nl36" bibid="bib44" firstref="ref57"></nolink> <nolink nlid="nl37" bibid="bib974" firstref="ref59"></nolink> <nolink nlid="nl38" bibid="bib29" firstref="ref60"></nolink> <nolink nlid="nl39" bibid="bib12" firstref="ref64"></nolink> <nolink nlid="nl40" bibid="bib34" firstref="ref69"></nolink>
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  Label: Title
  Group: Ti
  Data: Nonmedical Use of Prescription Stimulants and Nicotine among Community College Students
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hannah+G%2E+Truitt%22">Hannah G. Truitt</searchLink><br /><searchLink fieldCode="AR" term="%22Meredith+K%2E+Ginley%22">Meredith K. Ginley</searchLink><br /><searchLink fieldCode="AR" term="%22Kelly+N%2E+Foster%22">Kelly N. Foster</searchLink><br /><searchLink fieldCode="AR" term="%22Rajkumar+J%2E+Sevak%22">Rajkumar J. Sevak</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4354-5054">0000-0003-4354-5054</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nicholas+E%2E+Hagemeier%22">Nicholas E. Hagemeier</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Community+College+Review%22"><i>Community College Review</i></searchLink>. 2024 52(2):193-209.
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  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 17
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
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  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
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  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Two+Year+Colleges%22">Two Year Colleges</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Community+College+Students%22">Community College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Stimulants%22">Stimulants</searchLink><br /><searchLink fieldCode="DE" term="%22Smoking%22">Smoking</searchLink><br /><searchLink fieldCode="DE" term="%22Substance+Abuse%22">Substance Abuse</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Persons%22">At Risk Persons</searchLink><br /><searchLink fieldCode="DE" term="%22Alcohol+Abuse%22">Alcohol Abuse</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+Disorders%22">Mental Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+Health+Care%22">Access to Health Care</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+Point+Average%22">Grade Point Average</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/00915521231218208
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0091-5521<br />1940-2325
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: Despite community colleges accounting for 34% of all undergraduate enrollment, research on substance-use patterns among community college students is limited. Community college students may engage in substance use differently than their 4-year university counterparts due to differences in psychosocial factors and decreased availability of mental health services. The current study aimed to elucidate risk factors underlying non-medical use of prescription simulants (NMUS) and nicotine use by community college students. Methods: A web-based survey was administered to 10 of 13 community colleges within a southeastern state's Board of Regents school system. The survey included questions related to NMUS, nicotine use, alcohol use, mental health diagnosis, and demographics. Results: Overall, 9% of the participants reported NMUS, and 24.6% used nicotine. Multivariate analysis of variance and ?[subscript 2] tests revealed group differences among individuals using only nicotine, only NMUS, both nicotine and NMUS, and neither nicotine nor NMUS. Post-hoc 2 × 2 ?[subscript 2] tests indicated that individuals using both nicotine and NMUS had higher incidence of mental health diagnoses, were more likely to live in urban areas, reported higher weekly alcohol consumption, and were more likely to be male as compared to individuals using neither substance. Attention-deficit hyperactivity disorder (ADHD) symptoms were higher in individuals using only NMUS and both NMUS and nicotine as compared to those using only nicotine or neither substance. Conclusions: These findings provide insight into demographic and psychological variables associated with NMUS and nicotine use among community college students that could be benefitted by greater access to affordable mental health services.
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  Data: 2024
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  Data: EJ1414133
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        Value: 10.1177/00915521231218208
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 193
    Subjects:
      – SubjectFull: Community College Students
        Type: general
      – SubjectFull: Stimulants
        Type: general
      – SubjectFull: Smoking
        Type: general
      – SubjectFull: Substance Abuse
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      – SubjectFull: At Risk Persons
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      – SubjectFull: Gender Differences
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      – SubjectFull: Grade Point Average
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      – SubjectFull: Attention Deficit Hyperactivity Disorder
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      – TitleFull: Nonmedical Use of Prescription Stimulants and Nicotine among Community College Students
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