Substance use and treatment utilization patterns of working-age American men who were not in employment, education, or training (NEET) during the COVID-19 pandemic

Substance use and treatment utilization patterns of working-age American men who were not in employment, education, or training (NEET) during the COVID-19 pandemic | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Substance use and treatment utilization patterns of working-age American men who were not in employment, education, or training (NEET) during the COVID-19 pandemic Wayne Kepner, Keith Humphreys This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7645273/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background A growing population of working-aged men are not in employment, education, or training (NEET). The COVID-19 pandemic increased rates of substance use disorders (SUDs) and affected treatment seeking in the general population, but the COVID era substance use patterns among NEET men are unknown. Methods We estimated the prevalence and correlates of NEET status among working-aged (18–64) men using data from the 2022 National Survey on Drug Use and Health, a nationally representative survey of non-institutionalized individuals in the United States. We developed logistic regression models to examine associations between NEET status and substance use behaviors and treatment engagement, adjusted for sociodemographic factors. Results An estimated 11.1% of working-aged men were NEET in 2022 representing 10.6 million individuals. NEET men were significantly more likely to be older, have lower income, be unmarried, and have lower educational attainment and be Non-Hispanic Black compared to non-NEET men. After adjusting for sociodemographic factors, NEET status was significantly associated with higher odds of prescription tranquilizer/sedative use disorder (aOR = 3.54, 1.97–6.37), methamphetamine use disorder (aOR = 3.10, 95% CI: 1.82–5.28), and prescription pain‑reliever use disorder (aOR = 2.88, 1.82–4.53), while being inversely associated with alcohol use disorder (aOR = 0.68, 0.54–0.85). Conclusion More than 1 in 10 working-aged men were NEET in 2022. Adjusted models showed higher odds of past-year SUDs but lower rates of alcohol use disorder. Targeted interventions should include age-appropriate, culturally tailored, and substance-specific treatment programs to improve public health. COVID-19 epidemiology NEET opioid use substance use disorder substance use treatment unemployment 1. Introduction The decline in American male workforce participation has created a growing population of men who are ‘not in employment, education or training (i.e., ‘NEET’); estimates suggest that 11% of prime-aged men (aged 25–54) were not participating in the labor force in 2019 ( 1 , 2 ). Traditional metrics of unemployment typically capture active jobseekers; it excludes individuals who are long-term unemployed, not looking for work, and at risk of permanent workforce exclusion. Thus, the NEET concept is the central focus of this study because it provides a broader framework that includes such individuals. The COVID-19 pandemic created disruptions to both employment and substance use patterns among American men. During the pandemic, labor force participations rates among prime-aged men ( 25 – 54 ) dropped to 86.4% (in April of 2020), meaning that 13.6% were not in the labor force ( 1 ). COVID-19 and measures associated with it (e.g., social distancing measures, business closures) exerted substantial effects both on substance use patterns and treatment seeking for substance use disorders (SUD) ( 3 ). These disruptions created complex, potentially reinforcing cycles, as SUD may perpetuate NEET status ( 3 ). Employment upheavals also varied by industry which led to significant disparities among vulnerable socio-economic populations who were disproportionately impacted by pandemic-related job losses and substance-related harms ( 4 , 5 ). Therefore, there is an urgent public health need to better understand the demographic profile, substance use behaviors, and SUD treatment utilization patterns of NEET men in the wake of the COVID-19 pandemic ( 6 ). These data can help policymakers and public health officials develop targeted interventions to address the complex relationship between NEET status and SUD while supporting workforce reintegration. Multiple studies have identified bi-directional links between substance use disorders and NEET status among men. In a national longitudinal cohort, Manhica et al., demonstrated that NEET status among young adults (ages 17–24) was associated with an increased risk of later SUD diagnosis among males 25 and older ( 7 , 8 ). In a study of 4,758 young Swiss men in their early 20s, previous cannabis use, and daily tobacco smoking predicted an increased likelihood of being NEET over 15 months ( 9 ). Rodwell demonstrated that adolescents ( 14 – 15 ) who used cannabis frequently (defined as at least weekly use) had a 74% higher likelihood of being NEET in young adulthood ( 24 – 25 ) compared to those who used cannabis infrequently or not at all. Although Rodwell’s study included both males and females, males had higher rates of frequent cannabis use during adolescence (19.3% of males versus 11.9% of females). Finally, Compton et al., showed that unemployed men and women aged 18 and older (defined as without a job in the past four weeks) had 40% higher rates of heavy alcohol use and 79% higher rates of illicit drug use than employed individuals ( 10 ). However, existing research on NEET men has typically focused on young adults and frequently lacked diverse samples, limiting generalizability to the context of the COVID-19 pandemic. Although the link between substance use and NEET status in men is robust, specific knowledge about substance use patterns among NEET men during this period remains limited. Pandemic-related social and economic disruptions reshaped both substance-use behaviors and pathways to treatment which may have lasting implications ( 3 ). The US experienced an average increase in alcohol use ( 11 ), drug overdose rates and SUD hospitalizations ( 12 , 13 ), and a decrease in tobacco use ( 14 ), with mixed evidence of increases in cannabis use ( 15 ). The COVID-19 pandemic created barriers to SUD treatment seeking through social distancing measures, but the relaxation of regulations on telehealth and medications for opioid use disorder may have helped counter forces that reduced treatment access ( 16 , 17 ). Evidence on changes in health services access for SUD are varied and fragmented across different study settings ( 18 ). Moreover, these changes may have had disproportionate impacts among vulnerable populations such as NEET men. Therefore, understanding their specific behavioral patterns during this period offers critical insights into responses to large-scale societal stressors. For example, changes in the type of substances used may signal increased need for workforce training and program development for specific treatment strategies (e.g. medications for opioid use disorder, contingency management). Therefore, characterizing substance use and treatment utilization patterns using national data from this timeframe is essential for developing targeted and effective interventions to enhance public health and enhance workforce integration. To help fill these knowledge gaps, we examined sociodemographic factors, substance use patterns, and SUD treatment utilization among working-age male participants in the 2022 National Survey on Drug Use and Health (NSDUH). Our primary objectives are to: ( 1 ) characterize substance-specific use patterns among working-age NEET vs non-NEET men, ( 2 ) examine their patterns of SUD treatment utilization, and ( 3 ) identify specific demographic variables for potential targeted interventions. These data can help develop policies and programs designed to help NEET men re-enter the workforce or education, mitigate the negative SUD-related consequences of NEET status, and increase national preparedness for future labor shocks ( 19 – 21 ). 2. Methods 2.1 Data source We analyzed data from the 2022 National Survey on Drug Use and Health (NSDUH), an annual cross-sectional federal government survey of non-institutionalized individuals in the United States. The NSDUH obtains a nationally representative probability sample through a complex survey design for all 50 states and the District of Columbia ( 22 ). Surveys are administered via computer-assisted interviewing and audio computer-assisted self-interviewing, though self-report data may be subject to recall and social desirability bias, particularly for sensitive substance use behaviors. The analytic sample included 27,047 men of working age (18–64 years) from the 2022 NSDUH dataset. The NSDUH’s weighted interview response rates are between 60.4% and 69.3%. To address potential nonresponse bias, the survey implements demographic-based weight adjustments, comprehensive nonresponse bias analyses, and targeted follow-up with nonrespondents ( 22 , 23 ). The study used publicly available and de-identified data and was exempt from IRB review. 2.2 Measures The primary outcome was NEET status, defined as : 1) not working in the past year, 2) not currently enrolled in school or training programs, and 3) not retired. This definition captures longer-term disengagement from work and education rather than short-term unemployment. NEET status was coded as a binary variable. Cases with missing data on employment, education, or retirement status were excluded from analyses. All other variables had complete data due to NSDUH’s imputation procedures. We considered the following demographic characteristics: age (18–25, 26–34, 35–49, and 50–64 years), race/ethnicity (Non-Hispanic (NH) Asian, NH Black/African-American, Hispanic, NH Native American/Alaskan Native, NH Native Hawaiian/Pacific Islander, NH Multiracial, and NH White), education (less than high school, high school graduate, some college, and college graduate), annual family income ( $ 75,000), relationship status (never married, married, and unmarried with the latter including widowed, divorced and separated individuals), and urbanicity (large metropolitan, small metropolitan, and non-metropolitan). Past-month substance use was assessed for each substance through self-report. The specific substances included alcohol, ecstasy/molly, LSD, cannabis, cocaine, heroin, fentanyl, methamphetamine, prescription opioids, prescription stimulants, prescription pain relievers and tranquilizers/sedatives. Misuse was defined as use of prescription drugs in any way a doctor did not direct ( 23 ). We assessed past-month nicotine dependence defined by NSDUH using the Nicotine Dependence Syndrome Scale ( 24 ). We further assessed substance use frequencies of cannabis, heroin, methamphetamine and alcohol. SUD outcomes (i.e., past year disorder) for alcohol and drugs were based on criteria from the DSM-5 ( 25 ). Substance use treatment was categorized as a binary variable that included receiving treatment for alcohol or drug use at any location, such as a hospital, rehabilitation facility, mental health center, emergency room, or doctor's office, including phone or video treatment. 2.3 Statistical analysis We estimated the prevalence of NEET status among working-age men and compared demographic characteristics, substance use behaviors, and SUD treatment utilization patterns between NEET and non-NEET men. Bivariable comparisons were conducted using Rao-Scott chi-square to determine differences for each of these conditions with respect to NEET status. To examine the association between NEET status and our outcomes of interest, we developed logistic regression models and adjusted for selected demographic characteristics (age, race/ethnicity, marital status, and urbanicity). To account for potential family-wise error resulting from multiple comparisons, we used familywise Bonferroni statistical correction for the 7 demographic variables (α = 0.007; 0.05/7) and the 11 outcomes variables (α = 0.0045; 0.05/11) to determine significance. We also ensured that multicollinearity was not present. Weights were included in all analyses and R studio version 4.3.2 was used to analyze data ( 26 ). The analysis incorporated NSDUH’s complex survey design using cluster weights, strata weights, and calibrated person-level weights ( 27 ). 3. Results 3.1 Sample characteristics The largest proportions of respondents were heterosexual (88.9%, 95% CI: 87.9–89.8), non-Hispanic White (58.6%, 95% CI: 56.7–61.0), aged 35–49 (31.5%, 95% CI: 30.5–32.5) or 50–64 (30.6%, 95% CI: 29.1–32.2), married, (45.1% CI: 43.6–46.6), and resided in large metropolitan areas (55.6%, 95% CI: 53.3–57.9). An estimated 11.1% (95% CI: 10.1–12.1) of working aged (18–64) males in the US were not in employment, education or training (i.e., NEET) representing an estimated 10.6 million individuals (95% CI: 9.7–11.6 million). Table 1 presents demographic characteristics according to NEET status. Significant racial/ethnic disparities were observed with Non-Hispanic Black men showing higher NEET rates (18.5% vs 11.5%) and Non-Hispanic Asian men showing lower rates (1.8% vs 6.9%). Individuals reporting gay sexual orientation had significantly higher NEET rates compared to individuals reporting heterosexual orientation (7.5% vs 3.7%). Compared to non-NEET men, NEET men were significantly more likely to be older (46.6% aged 50–64 vs 28.7%), have lower income (45.9% earning <$20,000 vs 10.4%), and less likely to be married (29.5% vs 47.3%). They were also less likely to be college graduates (11.0% vs 34.0%) and more likely to live in non-metropolitan areas (17.6% vs 11.4%). All demographic differences were statistically significant (ps < 0.007). 3.2 Primary analyses As shown in Table 2, NEET men had a distinctly different substance‑use and treatment profile than their non‑NEET counterparts. NEET men were more than twice as likely to have received any past-year substance use treatment (4.6% vs 1.8%; p = 0.008), and had higher rates of several substance use disorders including methamphetamine (3.5% vs 0.8%; p < 0.001), prescription opioid (5.5% vs 1.7%; p < 0.001) and tranquilizer/sedative use disorders (2.4% vs 0.7%; p < 0.001). NEET men were three times more likely to report opioid use disorder (heroin or prescription opioid) than non-NEET men (6.2% vs 2.0%; p < 0.001). Surprisingly, NEET men had significantly lower rates of alcohol use disorder compared to non-NEET men (11.9% vs 16.3%; p = 0.002). The overall prevalence of any past‑year SUD did not differ significantly between groups (27.2% vs 24.5%; p = 0.117). When looking at recent use, NEET men showed significant differences in nicotine dependence and measures of alcohol consumption but fewer differences regarding other substances. Specifically, NEET demonstrated more than twice the rate of nicotine dependence in the past 30 days compared to non-NEET men (10.1% vs. 24.4%, p < 0.001), but reported fewer binge drinking episodes (0.89 vs. 1.48 days, p < 0.001), fewer days drinking per week (0.61 vs. 1.81 days, p < 0.001), and fewer drinks per day (1.20 vs. 1.91, p < 0.001). Past 30-day alcohol consumption was significantly lower in NEET men (61.3% vs 34.1%, p < 0.001). Frequency distributions suggest that when NEET men used methamphetamine, they are less likely to be either casual users (1–2 days: 5.3% vs 16.9%) or daily users (20–30 days: 23.3% vs 63.9%) and show higher use rates at intermediate frequencies (6–19 days: 64.6% vs 9.9%; p < 0.001)." In our adjusted models (Table 3), with all else being equal, working-aged NEET men were at lower odds of past-year alcohol‑use disorder but substantially increased odds of several SUDs. After applying the Bonferroni‑corrected α = 0.0045, NEET status was associated with lower odds of alcohol‑use disorder (aOR = 0.68, 95% CI = 0.54–0.85), and higher odds of cannabis use disorder (aOR = 1.40, 95% CI = 1.13–1.74), methamphetamine use disorder (aOR = 3.10, 95% CI = 1.82–5.28), prescription pain reliever use disorder (aOR = 2.88, 95% CI = 1.82–4.53), any opioid use disorder (heroin or prescription; aOR = 2.68, 95% CI = 1.71–4.20, and prescription tranquilizer/sedative use disorder (aOR = 3.54, 95% CI = 1.97–6.37). No significant differences emerged for cocaine, heroin, prescription‑stimulant, or any drug and alcohol use disorder, and the association with past‑year substance use treatment engagement fell below the Bonferroni-adjusted threshold for significance. Overall, the adjusted findings highlight a distinct substance use pattern in which NEET men carry disproportionate risks for high‑harm prescription and illicit drug disorders while exhibiting comparatively lower alcohol involvement. 4. Discussion 4.1 Principal findings In this nationally representative study, NEET men had substantially higher odds of prescription tranquilizer/sedative use disorder, methamphetamine use disorder, and opioid use disorder compared to their counterparts. We estimated that 11.1% of working-aged men (18–64) were not engaged in employment, education or training in 2022 during the COVID-19 pandemic. This is remarkable given that the U.S. enjoyed historically low rates of unemployment (less than 4%) at the time of data collection ( 28 ). Our study adds to the literature by providing a more comprehensive picture of the NEET population that shows an overrepresentation of middle-aged and older adults ( 29 ). Consistent with past research, we found a high prevalence of past-year SUDs among NEET men, but past 30-day use of illicit substances was similar among both groups. Importantly, NEET men showed strikingly lower rates of alcohol use and higher rates of nicotine dependence. Despite NEET men being disproportionately in rural areas where SUD treatment services tend to in short supply, they had slightly higher prevalence of SUD treatment engagement. These findings indicate that public health interventions should be tailored by substance type (e.g., prescription drugs, methamphetamine) and designed with age- and culturally-appropriate components for this vulnerable population. Our estimate that 11.1% of men (18–64) were NEET in 2022 closely aligns with previous research indicating that 11% of prime-aged men (aged 25–54) were not participating in the labor force in 2019 (Bureau of Labor Statistics, 2023; Eberstadt, 2022). NEET men also had significantly higher past-year prevalence of several SUDs which is consistent with previous work that has established independent associations between NEET status and substance use ( 7 , 19 , 30 ). In contrast with previous literature, NEET men had significantly lower alcohol use among several key measures including fewer binge drinking episodes, fewer drinking days per week, fewer days with any alcohol consumption in the past month, and fewer drinks per day. This finding contrasts with an earlier study using the same NSDUH dataset which found significantly higher rates of heavy alcohol use and alcohol use disorders among unemployed individuals between 2002 and 2010 ( 10 ). However, Compton et al. defined unemployed as those without a job in the last four weeks whereas our study looked at longer-term unemployment which was defined as at 52 weeks. Although alcohol consumption frequency increased in the general population during the COVID-19 pandemic ( 11 , 31 ), this trend varied among different demographic groups ( 32 , 33 ). Our finding of lower alcohol use among NEET men aligns with a "polarization effect" in alcohol consumption patterns during the COVID-19 pandemic. A study of Swiss men demonstrated that those reporting higher COVID-19-related health concerns and/or financial anxieties reported a decrease in their alcohol consumption, while those with fewer such concerns reported an increase ( 34 ). A large study of over 30,000 adults in the UK found a similar polarization based on socioeconomic position; individuals with low income were more likely to reduce their drinking whereas those with high income were more likely to drink more during the pandemic ( 35 ). Given that nearly half of the NEET men in our study earned less than $ 20,000 annually and were over the age of 50, it is likely they experienced the heightened health and financial vulnerabilities that led to a reduction in alcohol consumption as a protective or cost-saving response to pandemic-related stressors. NEET men had substantially higher rates of past-year SUDs for multiple substances, but measures of past 30-day substance use were not significantly different between the two groups. This might suggest that some individuals reduced or ceased their substance use after becoming NEET due to financial constraints. This discrepancy might also indicate that comparable use of illicit substances might lead to more severe levels of functional impairment in the context of long-term unemployment, socioeconomic differences and the COVID-19 pandemic ( 36 ). Social distancing measures, telehealth prescribing of prescription medications, lower prices for synthetic drugs and mental health co-morbidity may partially explain this specific substance use profile ( 13 , 37 – 39 ). For example, NEET status was shown to be associated with severe symptoms of anxiety and moderate/severe symptoms of depression and concurrent mental health and substance problems ( 30 , 40 ). Our population's older age and over-representation of Non-Hispanic Black males may also partially explain our findings due to increased intersectional vulnerability ( 41 , 42 ). In our sample, NEET status was disproportionately concentrated among older working age adults, which contrasts with much of the existing literature that predominantly focuses on adolescent and young adult NEET populations (Mawn et al., 2017; O'Dea et al., 2014; Rodwell et al., 2018; Stea et al., 2024). This suggests that COVID-19 may have uniquely affected older adult workers in terms of employment challenges compared to previous economic downturns (Li and Mutchler, 2021). It could also be that older men have been using substances for more years, which may have increased the severity of their disorder and simultaneously entrenched their NEET status (Kuerbis, 2020; Silver et al., 2022). The significant overrepresentation of men who identify as Non-Hispanic Black in the NEET population underscores the need for culturally tailored interventions. NEET men were significantly more likely to lack a high school diploma and far less likely to hold college degrees. These demographic patterns suggest disparities around the impact of COVID-19 on employment sectors especially among those requiring in-person work and those employing men without college credentials (Moen et al., 2020). The concentration of NEET status among older working age adult men represents a significant shift from the traditional view of NEET as a youth-centered problem. Although NEET men showed higher rates of treatment seeking, this difference attenuated after adjustment which suggests that interventions should prioritize enhancing treatment effectiveness and retention as well as substance-specific programs. Previous studies show mixed results, with some indicating a treatment gap among long-term unemployed individuals, while others suggest unemployment rates significantly correlate with substance use treatment admissions ( 43 , 44 ). Potential mechanisms for the attenuation include differential treatment access by rurality, demographic profile (older vs younger), and the availability of substance specific treatment options (e.g. methamphetamines). The high prevalence of illicit substance use among the NEET population is cause for concern due to the potential for serious harms related to hospitalizations and fatal overdose especially among vulnerable populations ( 45 ). For example, hospitalizations for stimulant use increased by 42.6% for adults aged 55–64 in California from 2017–2021 ( 46 ). Beginning in 2020, over 50% of all drug overdose deaths among adults aged 25–64 are estimated to have occurred among males with at most a high school-level education ( 47 ). During the COVID-19 pandemic, Black individuals experienced the largest percentage increase in drug overdose mortality (48.8%), surpassing overdose rates among White individuals for the first time since 1999 ( 48 ). These data suggest that a high proportion of NEET men in our sample are at increased risk of mortality due to their demographic and substance use profiles and likely account for a disproportionate number of overdose fatalities. 4.2 Implications NEET men during the COVID-19 pandemic display unique substance use behaviors that have higher risk profiles which warrant immediate attention. Our findings highlight the urgent need for an integrated model of treatment for NEET men that addresses substance use, employment, and socio-economic barriers to workforce reentry and risk mitigation. For example, increasing educational attainment and improving economic conditions could have the potential to decrease unemployment rates and reduce substance use disorders simultaneously. Promising approaches include substance-specific programs targeting prevalent drugs among NEET men (e.g. methamphetamine, opioids), contingency management interventions that simultaneously address employment and substance use, re-education programs that target low-income communities or justice-involved individuals, and policy changes that extend health coverage for individuals transitioning out of employment to maintain treatment continuity ( 49 ). Focusing on enhancing work-related recovery capital for NEET men engaged in treatment may have indirect benefits for SUD outcomes ( 50 ); professional treatment programs (e.g. physician’s health programs) have high rates of success ( 51 ). Evidence-based interventions such as overdose rescue medications and medications for opioid use disorder are critical tools for treatment and should be and made easily available for low-income and uninsured populations and accessible in a wide variety of settings ( 52 , 53 ). The widespread use of synthetic drugs in the post-COVID era, and the new epidemic of stimulant and opioid co-use make these interventions critically important ( 54 ). Early prevention strategies aimed at reducing harmful substance use among unemployed men is warranted, especially when considering that stimulant use disorders have no FDA-approved medication-based treatment options. Future studies should consider adapting substance use prevention programs tailored to unemployed youth to middle-aged and older adults of different cultural backgrounds ( 55 ). Because the COVID-19 pandemic likely influenced our findings related to the NEET population, follow-up studies are warranted. 4.3 Limitations Our study is limited due to the self-report nature of the survey which is therefore susceptible to recall and social desirability bias. Additionally, self-report of drug class used may lead to misidentification of specific substances. Multiple testing could be a limitation, but test results (ps < 0.0045) remained significant in light of a Bonferroni correction (alpha = 0.0045). In addition, our study is limited to community-living NEET men aged 18–64 and may not be representative of certain vulnerable subpopulations such as individuals experiencing homelessness who do not use shelters or those in institutional settings. Also, the NSDUH does not include a diagnostic interview; responses to survey questions are not a direct clinical diagnosis. However, proxy diagnoses in the NSDUH are used extensively in the literature and have been validated in previous studies. Additionally, while our study examines potential COVID-19 impacts on NEET men's substance use patterns, the cross-sectional nature of our data limits causal inference regarding pandemic-related effects. 5. Conclusion More than one in ten U.S. working-aged men were NEET in 2022, and they showed elevated risk of tranquilizer/sedative, methamphetamine, and prescription-opioid and use disorders while showing unexpectedly lower alcohol use. These findings call for multi-level evidence-based interventions that integrate substance-specific treatments (e.g., MOUD, contingency management for stimulants) with vocational assistance, and healthcare policies that maintain insurance coverage during periods of long-term unemployment; targeted interventions should be age-appropriate and culturally tailored. Declarations Declarations Competing Interests Keith Humphreys declared a personal financial interest as a non-executive director for Indivior PLC, a company that makes buprenorphine. This role began in November 2023.Wayne Kepner declared that he has no known competing financial interests or personal relationships that could be perceived as influencing the work reported in this paper. Keith Humphreys Conceptualization, Investigation, Writing - Original Draft, Writing - Review & Editing, Supervision, and Funding acquisition. Wayne Kepner Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Project administration, and Funding acquisition. Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable. Funding Source: This work was supported by the National Institute on Drug Abuse of the National Institutes of Health under Award Number T32DA035165, and the William and Katharine Duhamel Addiction Medicine Fund. Dr. Humphreys was supported by a Senior Research Career Scientist award from the Veterans Health Administration. The content is solely the responsibility of the authors and does not represent the official position of the funders. Author Contribution Keith Humphreys: Conceptualization, Investigation, Writing - Original Draft, Writing - Review & Editing, Supervision, and Funding acquisition.Wayne Kepner: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Project administration, and Funding acquisition. Acknowledgements Not applicable. Data Availability The data used in this study came from publicly available datasets from the National Survey on Drug Use and Health (NSDUH). These datasets can be accessed through the Substance Abuse and Mental Health Data Archive (SAMHDA) at https://datafiles.samhsa.gov/ after a brief application process. Additional data from these analyses are available from the authors upon reasonable request References Bureau of Labor Statistics USDoL. Labor force participation rate for people ages 25 to 54 in May 2023 highest since January 2007 [The Economics Daily]. 2023 [Available from: https://www.bls.gov/opub/ted/2023/labor-force-participation-rate-for-people-ages-25-to-54-in-may-2023-highest-since-january-2007.htm Eberstadt N. Men Without Work: Post-Pandemic Edition (2022): Templeton Foundation Press; 2022. Marsden J, Brown J, Clark L, Cousijn J, Hall W, Hickman M et al. The impact of the COVID-19 pandemic on addictive disorders-an update. Addiction. 2022. Li Y, Mutchler JE. Older adults and the economic impact of the COVID-19 pandemic. Older adults and COVID-19. Routledge; 2021. pp. 201–11. Friedman JR, Nguemeni Tiako MJ, Hansen H. Understanding and addressing widening racial inequalities in drug overdose. Am J Psychiatry. 2024;181(5):381–90. Volkow ND. Collision of the COVID-19 and addiction epidemics. American College of Physicians; 2020. pp. 61–2. Manhica H, Lundin A, Danielsson A-K. Not in education, employment, or training (NEET) and risk of alcohol use disorder: a nationwide register-linkage study with 485 839 Swedish youths. BMJ open. 2019;9(10):e032888. Manhica H, Yacaman-Mendez D, Sjöqvist H, Lundin A, Agardh E, Danielsson A-K. Trajectories of NEET (Not in Education, Employment, and Training) in emerging adulthood, and later drug use disorder-a national cohort study. Drug Alcohol Depend. 2022;233:109350. Baggio S, Iglesias K, Deline S, Studer J, Henchoz Y, Mohler-Kuo M, et al. Not in education, employment, or training status among young Swiss men. Longitudinal associations with mental health and substance use. J Adolesc Health. 2015;56(2):238–43. Compton WM, Gfroerer J, Conway KP, Finger MS. Unemployment and substance outcomes in the United States 2002–2010. Drug Alcohol Depend. 2014;142:350–3. Sohi I, Chrystoja BR, Rehm J, Wells S, Monteiro M, Ali S, et al. Changes in alcohol use during the COVID-19 pandemic and previous pandemics: A systematic review. Alcoholism: Clin experimental Res. 2022;46(4):498–513. Imtiaz S, Nafeh F, Russell C, Ali F, Elton-Marshall T, Rehm J. The impact of the novel coronavirus disease (COVID-19) pandemic on drug overdose-related deaths in the United States and Canada: a systematic review of observational studies and analysis of public health surveillance data. Substance Abuse Treatment, Prevention, and Policy. 2021;16:1–14. Venkatesh AK, Janke AT, Kinsman J, Rothenberg C, Goyal P, Malicki C, et al. Emergency department utilization for substance use disorders and mental health conditions during COVID-19. PLoS ONE. 2022;17(1):e0262136. Almeda N, Gómez-Gómez I. The impact of the COVID-19 pandemic on smoking consumption: a systematic review of longitudinal studies. Front Psychiatry. 2022;13:941575. Mehra K, Rup J, Wiese JL, Watson TM, Bonato S, Rueda S. Changes in self-reported cannabis use during the COVID-19 pandemic: a scoping review. BMC Public Health. 2023;23(1):2139. Jones CM, Diallo MM, Vythilingam M, Schier JG, Eisenstat M, Compton WM. Characteristics and correlates of US clinicians prescribing buprenorphine for opioid use disorder treatment using expanded authorities during the COVID-19 pandemic. Drug Alcohol Depend. 2021;225:108783. Krawczyk N, Rivera BD, King C, Dooling BC. Pandemic telehealth flexibilities for buprenorphine treatment: a synthesis of evidence and policy implications for expanding opioid use disorder care in the United States. Health Affairs Scholar. 2023;1(1):qxad013. Vo AT, Patton T, Peacock A, Larney S, Borquez A. Illicit substance use and the COVID-19 pandemic in the United States: a scoping review and characterization of research evidence in unprecedented times. Int J Environ Res Public Health. 2022;19(14):8883. Chandler RF, Lozada ARS. Health status among NEET adolescents and young adults in the United States, 2016–2018. SSM-population health. 2021;14:100814. Jemberie WB, Stewart Williams J, Eriksson M, Grönlund A-S, Ng N, Blom Nilsson M, et al. Substance use disorders and COVID-19: multi-faceted problems which require multi-pronged solutions. Front Psychiatry. 2020;11:714. Mawn L, Oliver EJ, Akhter N, Bambra CL, Torgerson C, Bridle C, et al. Are we failing young people not in employment, education or training (NEETs)? A systematic review and meta-analysis of re-engagement interventions. Syst reviews. 2017;6:1–17. Substance Abuse and Mental Health Services Administration. 2022 National Survey on Drug Use and Health (NSDUH): Methodological Resource Book (MRB). Rockville, MD; 2023. Center for Behavioral Health Statistics and Quality. 2022 National Survey on Drug Use and Health (NSDUH): Methodological summary and definitions. Rockville, MD; 2023. Shiffman S, Waters AJ, Hickcox M. The nicotine dependence syndrome scale: a multidimensional measure of nicotine dependence. Nicotine Tob Res. 2004;6(2):327–48. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Fifth ed. Washington, DC: American Psychiatric Publishing; 2013. Allaire J, Boston. MA. 2012;770(394):165–71. Heeringa SG, West BT, Berglund PA. Applied survey data analysis. chapman and hall/CRC; 2017. Essien LSL, Owens MD. Greg. Unemployment rate returned to its prepandemic level in 2022. 2023. Stea TH, Bertelsen TB, Fegran L, Sejersted E, Kleppang AL, Fyhn T. Interventions targeting young people not in employment, education or training (NEET) for increased likelihood of return to school or employment—A systematic review. PLoS ONE. 2024;19(6):e0306285. Basta M, Karakonstantis S, Koutra K, Dafermos V, Papargiris A, Drakaki M, et al. NEET status among young Greeks: association with mental health and substance use. J Affect Disord. 2019;253:210–7. Gonçalves PD, Moura HF, Do Amaral RA, Castaldelli-Maia JM, Malbergier A. Alcohol use and COVID-19: can we predict the impact of the pandemic on alcohol use based on the previous crises in the 21st century? A brief review. Front Psychiatry. 2020;11:581113. Nolte-Troha C, Roser P, Henkel D, Scherbaum N, Koller G, Franke A. Unemployment and Substance Use: An Updated Review of Studies from North America and Europe. Healthcare. 2023;11(8):1182. Jeong S, Fox AM. Enhanced unemployment benefits, mental health, and substance use among low-income households during the COVID-19 pandemic. Soc Sci Med. 2023;328:115973. Labhart F, Gmel G. A polarisation rather than just an increase or a decrease-Exploring different approaches to measure the impact of the COVID-19 pandemic on alcohol consumption after one year. Int J Alcohol Drug Res. 2023;11(2):70–81. Garnett C, Jackson S, Oldham M, Brown J, Steptoe A, Fancourt D. Factors associated with drinking behaviour during COVID-19 social distancing and lockdown among adults in the UK. Drug Alcohol Depend. 2021;219:108461. Norvell Gustavsson I, Jonsson F. Exploring the experiences of NEET-situated young people within the context of the COVID-19 pandemic using resonance theory. J Appl Youth Stud. 2024;7(3):253–72. Keller MS, Kiefer E, Campbell S, Bradley K, Mashburn R, Bawa M, et al. Sustained increase of sedative-hypnotic prescribing during the COVID-19 pandemic in a large urban health system: an observational study. J Gen Intern Med. 2021;36(11):3618–20. Taylor S, Paluszek MM, Rachor GS, McKay D, Asmundson GJ. Substance use and abuse, COVID-19-related distress, and disregard for social distancing: A network analysis. Addict Behav. 2021;114:106754. de Dios C, Fernandes BS, Whalen K, Bandewar S, Suchting R, Weaver MF, et al. Prescription fill patterns for benzodiazepine and opioid drugs during the COVID-19 pandemic in the United States. Drug Alcohol Depend. 2021;229:109176. Goldman-Mellor S, Caspi A, Arseneault L, Ajala N, Ambler A, Danese A, et al. Committed to work but vulnerable: Self‐perceptions and mental health in NEET 18‐year olds from a contemporary British cohort. J Child Psychol Psychiatry. 2016;57(2):196–203. Zapolski TC, Pedersen SL, McCarthy DM, Smith GT. Less drinking, yet more problems: understanding African American drinking and related problems. Psychol Bull. 2014;140(1):188. Silver SR, Li J, Quay B. Employment status, unemployment duration, and health-related metrics among US adults of prime working age: behavioral risk factor surveillance system, 2018–2019. Am J Ind Med. 2022;65(1):59–71. Azagba S, Shan L, Qeadan F, Wolfson M. Unemployment rate, opioids misuse and other substance abuse: quasi-experimental evidence from treatment admissions data. BMC Psychiatry. 2021;21:1–9. Scherbaum N, Mikoteit T, Witkowski L, Bonnet U, Specka M, Schifano F, et al. New Access Routes to Undertreated Populations; How Do Problem Substance Users Recruited from an Unemployment Office Differ from Detoxification Treatment Inpatients? Int J Environ Res Public Health. 2021;18(24):13014. Kepner W, Courchesne-Krak NS, Satybaldiyeva N, Narasimhan R, Marienfeld CB. Emergency care utilization in persons with substance related diagnoses. Addict Behav Rep. 2024:100573. Han BH, Brennan JJ, Kepner WE, Chen S, Lin S, Carley JA, et al. Trends in stimulant-related emergency department visits among adults in California, 2017–2021. Am J Emerg Med. 2025;93:94–8. Xu JJ, Seamans MJ, Friedman JR. Drug overdose mortality rates by educational attainment and sex for adults aged 25–64 in the United States before and during the COVID-19 pandemic, 2015–2021. Drug Alcohol Depend. 2024;255:111014. Friedman JR, Hansen H. Evaluation of increases in drug overdose mortality rates in the US by race and ethnicity before and during the COVID-19 pandemic. JAMA psychiatry. 2022;79(4):379–81. Huang Y, Rumrill S, Chun J, Osak R. Demographic and Vocational Rehabilitation Service Correlates of Employment Outcomes in People With Substance Use Disorders During COVID-19. Rehabilitation Couns Bull. 2024;0(0):00343552241236870. Frone MR, Chosewood LC, Osborne JC, Howard JJ. Workplace supported recovery from substance use disorders: defining the construct, developing a model, and proposing an agenda for future research. Occup health Sci. 2022;6(4):475–511. DuPont RL, McLellan AT, Carr G, Gendel M, Skipper GE. How are addicted physicians treated? A national survey of physician health programs. J Subst Abuse Treat. 2009;37(1):1–7. Frank RG, Fry CE. The impact of expanded Medicaid eligibility on access to naloxone. Addiction. 2019;114(9):1567–74. Wen H, Hockenberry JM, Borders TF, Druss BG. Impact of Medicaid expansion on Medicaid-covered utilization of buprenorphine for opioid use disorder treatment. Med Care. 2017;55(4):336–41. Friedman J, Shover CL. Charting the fourth wave: Geographic, temporal, race/ethnicity and demographic trends in polysubstance fentanyl overdose deaths in the United States, 2010–2021. Addiction. 2023;118(12):2477–85. Magura S, Marshall T. The effectiveness of interventions intended to improve employment outcomes for persons with substance use disorder: an updated systematic review. Subst Use Misuse. 2020;55(13):2230–6. Tables Table 1. Demographic Characteristics of Working-Aged Men (18-64y) by NEET Status, United States, 2022 National Survey on Drug Use and Health Characteristic Full Sample ages 18-64 weighted % (95% CI) Not NEET ages 18-64 weighted % (95% CI) NEET ages 18-64 weighted % (95% CI) p-value Sexual orientation Heterosexual 88.9 (87.9-89.8) 91.3 (90.3-92.2) 86.1 (83.3-88.6) < 0.001 Gay 4.1 (3.5-4.7) 3.7 (3.1-4.4) 7.5 (5.3-10.4) Bisexual 3.8 (3.3-4.3) 3.8 (3.3-4.4) 4.5 (3.2-6.4) Race/ethnicity NH Asian 6.2 (5.4-7.2) 6.9 (5.9-7.9) 1.8 (1.1-2.9) < 0.001 NH Black / African American 12.2 (11.4-13.1) 11.5 (10.6-12.4) 18.5 (15.6-21.8) Hispanic 19.9 (18.2-21.9) 19.7 (17.7-21.8) 18.9 (15.2-23.2) NH Native American / Alaskan Native 0.5 (0.4-0.7) 0.5 (0.4-0.7) 0.6 (0.3-1.2) NH Native Hawaiian / Pacific Islander 0.6 (0.4-1.0) 0.6 (0.4-1.0) 0.4 (0.1-1.4) NH Multiracial 1.9 (1.5-2.2) 1.8 (1.5-2.2) 2.1 (1.4-3.2) NH White 58.6 (56.7-60.5) 59.1 (57.1-61.0) 57.7 (52.9-62.4) Age < 0.001 18-25, years 17.6 (16.7-18.5) 18.4 (17.5-19.5) 11.8 (10.2-13.6) 26-34, years 20.3 (19.1-21.5) 21.1 (19.8-22.5) 14.0 (11.5-16.9) 35-49, years 31.5 (30.5-32.5) 31.8 (30.5-33.0) 27.6 (24.3-31.3) 50-64, years 30.6 (29.1-32.2) 28.7 (27.0-30.4) 46.6 (41.8-51.4) Annual family income < 0.001 < $20-000 14.5 (13.5-15.6) 10.4 (9.4-11.4) 45.9 (41.4-50.5) $20,000-$49,999 24.0 (22.6-25.4) 22.7 (21.4-24.1) 32.4 (27.4-37.7) $50,000-$74,999 14.1 (13.3-14.9) 14.9 (14.0-15.9) 8.3 (6.5-10.5) $75,000 and above 47.5 (45.7-49.2) 52.0 (50.3-53.8) 13.4 (10.4-17.2) Education < 0.001 Less than high school 10.0 (9.2-10.8) 8.4 (7.6-9.2) 20.8 (17.8-24.3) Highschool diploma 29.2 (27.9-30.5) 27.2 (25.7-28.8) 42.7 (38.0-47.5) Some college 29.6 (28.3-30.8) 30.5 (29.2-31.8) 25.5 (21.3-30.2) College degree 31.3 (29.9-32.6) 34.0 (32.5-35.5) 11.0 (8.6-14.0) Relationship status < 0.001 Married 45.1 (43.6-46.6) 47.3 (45.8-48.7) 29.5 (24.8-34.5) Never married 42.3 (40.8-43.9) 41.6 (40.1-43.1) 48.5 (43.1-53.8) Divorced / widowed / separated 12.6 (11.4-14.0) 11.2 (10.0-12.5) 22.1 (17.8-27.1) Urbanicity < 0.001 Large metropolitan 55.6 (53.3-57.9) 56.3 (53.8-58.7) 50.6 (46.3-54.8) Small metropolitan 32.2 (30.5-34.1) 32.4 (30.4-34.4) 31.8 (27.7-36.3) Non-metropolitan 12.1 (10.7-13.7) 11.4 (10.0-12.9) 17.6 (13.8-22.3) Note. NEET = Not in employment, education, or training; NH = Non-Hispanic; CI = Confidence interval. All ps significant at the Bonferroni-correct level of p<.007. Table 2. Substance Use Treatment, Disorders, and Recent Use Among Working‑Aged Men (18‑64 y) by NEET Status, United States, 2022 National Survey on Drug Use and Health Characteristic Not NEET ages 18-64 weighted % (95% CI) NEET ages 18-64 weighted % (95% CI) p-value Substance use treatment (past-year) Any substance use treatment 1.8 (1.4-2.2) 4.6 (2.4-8.7) .008 Substance use disorder (past-year) Alcohol use disorder 16.3 (15.3-17.4) 11.9 (9.9-14.3) .002 Cannabis use disorder 10.2 (9.3-11.2) 12.7 (10.4-15.3) .025 Cocaine use disorder 0.9 (0.6-1.2) 1.3 (0.6-2.6) .366 Methamphetamine use disorder 0.8 (0.6-1.1) 3.5 (2.2-5.7) <.001 Prescription opioid use disorder 1.7 (1.3-2.3) 5.5 (3.8-7.8) <.001 Heroin use disorder 0.4 (0.3-0.7) 1.3 (0.7-2.7) .007 Opioid use disorder (heroin or prescription opioid) 2.0 (1.5-2.8) 6.2 (4.4-8.6) <.001 Prescription stimulant use disorder 0.8 (0.6-1.0) 0.7 (0.3-1.6) .65 Tranquilizer/sedative use disorder 0.7 (0.5-1.0) 2.4 (1.7-3.5) <.001 Any substance use disorder (drug or alcohol use disorder) 24.5 (23.2-26.0) 27.2 (24.1-30.5) .117 Recent substance use (30-day) Alcohol 61.3 (59.9-62.7) 34.1 (29.9-38.4) <.001 Cannabis 20.7 (19.5-22.0) 26.6 (23.0-30.6) .005 Cocaine 1.4 (1.1-1.8) 1.6 (0.9-2.9) .649 Methamphetamine 0.7 (0.4-1.0) 3.2 (1.9-5.5) .005 Heroin 0.3 (0.1-0.5) 1.3 (0.6-2.7) .048 Fentanyl 0.1 (0.1-0.3) 0.3 (0.1-1.1) .311 Prescription stimulant misuse 0.8 (0.6-0.9) 0.6 (0.2-1.7) .672 Prescription pain reliever misuse 1.0 (0.7-1.4) 1.4 (0.9-2.4) .309 Tranquilizers 0.5 (0.4-0.7) 0.8 (0.3-1.7) .486 Sedatives 0.1 (0.0-0.2) 0.3 (0.0-1.7) .478 Nicotine dependence 10.1 (9.1-11.3) 24.4 (20.7-28.5) <.001 Past-month substance use frequency 1 Cannabis (days used in past 30 days) .064 1-2 days 16.6 (14.8-18.4) 9.7 (6.5-12.9) 3-5 days 13.0 (11.1-14.9) 9.2 (6.2-12.3) 6-19 days 19.4 (17.1-21.7) 24.6 (16.1-33.1) 20-30 days 51.1 (48.0-54.2) 56.5 (47.2-65.8) Heroin (days used in past 30 days) .307 1-2 days 8.8 (2.4-15.2) 4.2 (0.9-7.4) 3-5 days 11.2 (4.1-18.4) 8.4 (1.7-15.0) 6-19 days 12.2 (3.5-20.9) 35.7 (9.2-62.2) 20-30 days 67.7 (41.9-93.5) 51.8 (20.1-83.5) Methamphetamine (days used in past 30 days) <.001 1-2 days 16.9 (6.1-27.7) 5.3 (1.1-9.4) 3-5 days 9.4 (4.1-14.6) 6.9 (2.2-11.5) 6-19 days 9.9 (5.1-14.6) 64.6 (41.2-88.0) 20-30 days 63.9 (44.5-83.3) 23.3 (9.6-37.0) Alcohol consumption patterns, mean (SE) Days per week drank alcohol, past year 1.8 (0.04) 0.6 (0.05) <.001 Binge alcohol frequency, past 30-days 1.5 (0.05) 0.9 (0.10) <.001 Days had one or more drinks, past 30 days 5.3 (0.12) 2.5 (0.24) <.001 Usual number of drinks per day, past 30 days 1.9 (0.03) 1.2 (0.11) <.001 NEET = Not in education, employment, or training; CI = confidence interval; SUD = substance use disorder; NDSS = Nicotine Dependence Syndrome Scale. Bold=p<0.0045 Bonferroni corrected. 1 Substance use frequency variables exclude all non-users (0 days). Prescription drug use in this table refers to misuse. All estimates survey‑weighted. Table 3. Multivariable Models Examining NEET Status as a Risk Factor for Past-Year Substance Use Disorders Among Working-Aged Men (18-64y) in the United States, 2022 Separate outcome variables NEET men vs not-NEET men (independent variable). Adjusted OR (95% CI) a Substance use treatment (past-year) Any substance use treatment 2.09 (1.06-4.14) Substance use disorders (past-year) Alcohol use disorder 0.68 (0.54-0.85) Cannabis use disorder 1.40 (1.13-1.74) Cocaine use disorder 1.13 (0.49-2.59) Methamphetamine use disorder 3.10 (1.82-5.28) Prescription pain reliever use disorder 2.88 (1.82-4.53) Heroin use disorder 2.23 (0.81-6.14) Any opioid use disorder (heroin or prescription pain reliever) 2.68 (1.71-4.20) Prescription stimulant use disorder 0.95 (0.39-2.32) Prescription tranquilizer/sedative use disorder 3.54 (1.97-6.37) Any substance use disorder (drug or alcohol use disorder) 1.13 (0.94-1.36) OR = odds ratio; CI = confidence interval; SUD = substance use disorder. a Adjusted for age, race/ethnicity, urbanicity, and marital status. We utilized a Bonferroni statistical correction, bolded values indicate significance at a p < .0045. Additional Declarations Competing interest reported. Keith Humphreys declared a personal financial interest as a non-executive director for Indivior PLC, a company that makes buprenorphine. This role began in November 2023. Wayne Kepner declared that he has no known competing financial interests or personal relationships that could be perceived as influencing the work reported in this paper. 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Keith Humphreys declared a personal financial interest as a non-executive director for Indivior PLC, a company that makes buprenorphine. This role began in November 2023.\nWayne Kepner declared that he has no known competing financial interests or personal relationships that could be perceived as influencing the work reported in this paper.","formattedTitle":"Substance use and treatment utilization patterns of working-age American men who were not in employment, education, or training (NEET) during the COVID-19 pandemic","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe decline in American male workforce participation has created a growing population of men who are \u0026lsquo;not in employment, education or training (i.e., \u0026lsquo;NEET\u0026rsquo;); estimates suggest that 11% of prime-aged men (aged 25\u0026ndash;54) were not participating in the labor force in 2019 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Traditional metrics of unemployment typically capture active jobseekers; it excludes individuals who are long-term unemployed, not looking for work, and at risk of permanent workforce exclusion. Thus, the NEET concept is the central focus of this study because it provides a broader framework that includes such individuals.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic created disruptions to both employment and substance use patterns among American men. During the pandemic, labor force participations rates among prime-aged men (\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) dropped to 86.4% (in April of 2020), meaning that 13.6% were not in the labor force (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). COVID-19 and measures associated with it (e.g., social distancing measures, business closures) exerted substantial effects both on substance use patterns and treatment seeking for substance use disorders (SUD) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These disruptions created complex, potentially reinforcing cycles, as SUD may perpetuate NEET status (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Employment upheavals also varied by industry which led to significant disparities among vulnerable socio-economic populations who were disproportionately impacted by pandemic-related job losses and substance-related harms (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Therefore, there is an urgent public health need to better understand the demographic profile, substance use behaviors, and SUD treatment utilization patterns of NEET men in the wake of the COVID-19 pandemic (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These data can help policymakers and public health officials develop targeted interventions to address the complex relationship between NEET status and SUD while supporting workforce reintegration.\u003c/p\u003e \u003cp\u003eMultiple studies have identified bi-directional links between substance use disorders and NEET status among men. In a national longitudinal cohort, Manhica et al., demonstrated that NEET status among young adults (ages 17\u0026ndash;24) was associated with an increased risk of later SUD diagnosis among males 25 and older (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In a study of 4,758 young Swiss men in their early 20s, previous cannabis use, and daily tobacco smoking predicted an increased likelihood of being NEET over 15 months (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Rodwell demonstrated that adolescents (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) who used cannabis frequently (defined as at least weekly use) had a 74% higher likelihood of being NEET in young adulthood (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) compared to those who used cannabis infrequently or not at all. Although Rodwell\u0026rsquo;s study included both males and females, males had higher rates of frequent cannabis use during adolescence (19.3% of males versus 11.9% of females). Finally, Compton et al., showed that unemployed men and women aged 18 and older (defined as without a job in the past four weeks) had 40% higher rates of heavy alcohol use and 79% higher rates of illicit drug use than employed individuals (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, existing research on NEET men has typically focused on young adults and frequently lacked diverse samples, limiting generalizability to the context of the COVID-19 pandemic. Although the link between substance use and NEET status in men is robust, specific knowledge about substance use patterns among NEET men during this period remains limited.\u003c/p\u003e \u003cp\u003ePandemic-related social and economic disruptions reshaped both substance-use behaviors and pathways to treatment which may have lasting implications (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The US experienced an average increase in alcohol use (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), drug overdose rates and SUD hospitalizations (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and a decrease in tobacco use (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), with mixed evidence of increases in cannabis use (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The COVID-19 pandemic created barriers to SUD treatment seeking through social distancing measures, but the relaxation of regulations on telehealth and medications for opioid use disorder may have helped counter forces that reduced treatment access (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Evidence on changes in health services access for SUD are varied and fragmented across different study settings (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Moreover, these changes may have had disproportionate impacts among vulnerable populations such as NEET men. Therefore, understanding their specific behavioral patterns during this period offers critical insights into responses to large-scale societal stressors. For example, changes in the type of substances used may signal increased need for workforce training and program development for specific treatment strategies (e.g. medications for opioid use disorder, contingency management). Therefore, characterizing substance use and treatment utilization patterns using national data from this timeframe is essential for developing targeted and effective interventions to enhance public health and enhance workforce integration.\u003c/p\u003e \u003cp\u003eTo help fill these knowledge gaps, we examined sociodemographic factors, substance use patterns, and SUD treatment utilization among working-age male participants in the 2022 National Survey on Drug Use and Health (NSDUH). Our primary objectives are to: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) characterize substance-specific use patterns among working-age NEET vs non-NEET men, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) examine their patterns of SUD treatment utilization, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) identify specific demographic variables for potential targeted interventions. These data can help develop policies and programs designed to help NEET men re-enter the workforce or education, mitigate the negative SUD-related consequences of NEET status, and increase national preparedness for future labor shocks (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eWe analyzed data from the 2022 National Survey on Drug Use and Health (NSDUH), an annual cross-sectional federal government survey of non-institutionalized individuals in the United States. The NSDUH obtains a nationally representative probability sample through a complex survey design for all 50 states and the District of Columbia (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Surveys are administered via computer-assisted interviewing and audio computer-assisted self-interviewing, though self-report data may be subject to recall and social desirability bias, particularly for sensitive substance use behaviors. The analytic sample included 27,047 men of working age (18\u0026ndash;64 years) from the 2022 NSDUH dataset. The NSDUH\u0026rsquo;s weighted interview response rates are between 60.4% and 69.3%. To address potential nonresponse bias, the survey implements demographic-based weight adjustments, comprehensive nonresponse bias analyses, and targeted follow-up with nonrespondents (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The study used publicly available and de-identified data and was exempt from IRB review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cp\u003eThe primary outcome was NEET status, defined as : 1) not working in the past year, 2) not currently enrolled in school or training programs, and 3) not retired. This definition captures longer-term disengagement from work and education rather than short-term unemployment. NEET status was coded as a binary variable. Cases with missing data on employment, education, or retirement status were excluded from analyses. All other variables had complete data due to NSDUH\u0026rsquo;s imputation procedures. We considered the following demographic characteristics: age (18\u0026ndash;25, 26\u0026ndash;34, 35\u0026ndash;49, and 50\u0026ndash;64 years), race/ethnicity (Non-Hispanic (NH) Asian, NH Black/African-American, Hispanic, NH Native American/Alaskan Native, NH Native Hawaiian/Pacific Islander, NH Multiracial, and NH White), education (less than high school, high school graduate, some college, and college graduate), annual family income (\u0026lt;\u003cspan\u003e$\u003c/span\u003e20,000, \u003cspan\u003e$\u003c/span\u003e20,000-\u003cspan\u003e$\u003c/span\u003e49,999, \u003cspan\u003e$\u003c/span\u003e50,000-\u003cspan\u003e$\u003c/span\u003e74,999, and \u0026gt;\u003cspan\u003e$\u003c/span\u003e75,000), relationship status (never married, married, and unmarried with the latter including widowed, divorced and separated individuals), and urbanicity (large metropolitan, small metropolitan, and non-metropolitan).\u003c/p\u003e \u003cp\u003ePast-month substance use was assessed for each substance through self-report.\u003c/p\u003e \u003cp\u003eThe specific substances included alcohol, ecstasy/molly, LSD, cannabis, cocaine, heroin, fentanyl, methamphetamine, prescription opioids, prescription stimulants, prescription pain relievers and tranquilizers/sedatives. Misuse was defined as use of prescription drugs in any way a doctor did not direct (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). We assessed past-month nicotine dependence defined by NSDUH using the Nicotine Dependence Syndrome Scale (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). We further assessed substance use frequencies of cannabis, heroin, methamphetamine and alcohol. SUD outcomes (i.e., past year disorder) for alcohol and drugs were based on criteria from the DSM-5 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Substance use treatment was categorized as a binary variable that included receiving treatment for alcohol or drug use at any location, such as a hospital, rehabilitation facility, mental health center, emergency room, or doctor's office, including phone or video treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe estimated the prevalence of NEET status among working-age men and compared demographic characteristics, substance use behaviors, and SUD treatment utilization patterns between NEET and non-NEET men. Bivariable comparisons were conducted using Rao-Scott chi-square to determine differences for each of these conditions with respect to NEET status. To examine the association between NEET status and our outcomes of interest, we developed logistic regression models and adjusted for selected demographic characteristics (age, race/ethnicity, marital status, and urbanicity).\u003c/p\u003e \u003cp\u003eTo account for potential family-wise error resulting from multiple comparisons, we used familywise Bonferroni statistical correction for the 7 demographic variables (α\u0026thinsp;=\u0026thinsp;0.007; 0.05/7) and the 11 outcomes variables (α\u0026thinsp;=\u0026thinsp;0.0045; 0.05/11) to determine significance. We also ensured that multicollinearity was not present. Weights were included in all analyses and R studio version 4.3.2 was used to analyze data (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The analysis incorporated NSDUH\u0026rsquo;s complex survey design using cluster weights, strata weights, and calibrated person-level weights (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.1 Sample characteristics\u003c/h2\u003e\n \u003cp\u003eThe largest proportions of respondents were heterosexual (88.9%, 95% CI: 87.9\u0026ndash;89.8), non-Hispanic White (58.6%, 95% CI: 56.7\u0026ndash;61.0), aged 35\u0026ndash;49 (31.5%, 95% CI: 30.5\u0026ndash;32.5) or 50\u0026ndash;64 (30.6%, 95% CI: 29.1\u0026ndash;32.2), married, (45.1% CI: 43.6\u0026ndash;46.6), and resided in large metropolitan areas (55.6%, 95% CI: 53.3\u0026ndash;57.9). An estimated 11.1% (95% CI: 10.1\u0026ndash;12.1) of working aged (18\u0026ndash;64) males in the US were not in employment, education or training (i.e., NEET) representing an estimated 10.6\u0026nbsp;million individuals (95% CI: 9.7\u0026ndash;11.6\u0026nbsp;million). Table\u0026nbsp;1 presents demographic characteristics according to NEET status. Significant racial/ethnic disparities were observed with Non-Hispanic Black men showing higher NEET rates (18.5% vs 11.5%) and Non-Hispanic Asian men showing lower rates (1.8% vs 6.9%). Individuals reporting gay sexual orientation had significantly higher NEET rates compared to individuals reporting heterosexual orientation (7.5% vs 3.7%). Compared to non-NEET men, NEET men were significantly more likely to be older (46.6% aged 50\u0026ndash;64 vs 28.7%), have lower income (45.9% earning \u0026lt;$20,000 vs 10.4%), and less likely to be married (29.5% vs 47.3%). They were also less likely to be college graduates (11.0% vs 34.0%) and more likely to live in non-metropolitan areas (17.6% vs 11.4%). All demographic differences were statistically significant (ps\u0026thinsp;\u0026lt;\u0026thinsp;0.007).\u003c/p\u003e\n \u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.2 Primary analyses\u003c/h2\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;2, NEET men had a distinctly different substance‑use and treatment profile than their non‑NEET counterparts. NEET men were more than twice as likely to have received any past-year substance use treatment (4.6% vs 1.8%; p\u0026thinsp;=\u0026thinsp;0.008), and had higher rates of several substance use disorders including methamphetamine (3.5% vs 0.8%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), prescription opioid (5.5% vs 1.7%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and tranquilizer/sedative use disorders (2.4% vs 0.7%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). NEET men were three times more likely to report opioid use disorder (heroin or prescription opioid) than non-NEET men (6.2% vs 2.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Surprisingly, NEET men had significantly lower rates of alcohol use disorder compared to non-NEET men (11.9% vs 16.3%; p\u0026thinsp;=\u0026thinsp;0.002). The overall prevalence of any past‑year SUD did not differ significantly between groups (27.2% vs 24.5%; p\u0026thinsp;=\u0026thinsp;0.117).\u003c/p\u003e\n \u003cdiv\u003e\u003c/div\u003e\n \u003cp\u003eWhen looking at recent use, NEET men showed significant differences in nicotine dependence and measures of alcohol consumption but fewer differences regarding other substances. Specifically, NEET demonstrated more than twice the rate of nicotine dependence in the past 30 days compared to non-NEET men (10.1% vs. 24.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but reported fewer binge drinking episodes (0.89 vs. 1.48 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fewer days drinking per week (0.61 vs. 1.81 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and fewer drinks per day (1.20 vs. 1.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Past 30-day alcohol consumption was significantly lower in NEET men (61.3% vs 34.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Frequency distributions suggest that when NEET men used methamphetamine, they are less likely to be either casual users (1\u0026ndash;2 days: 5.3% vs 16.9%) or daily users (20\u0026ndash;30 days: 23.3% vs 63.9%) and show higher use rates at intermediate frequencies (6\u0026ndash;19 days: 64.6% vs 9.9%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u0026quot;\u003c/p\u003e\n \u003cp\u003eIn our adjusted models (Table\u0026nbsp;3), with all else being equal, working-aged NEET men were at lower odds of past-year alcohol‑use disorder but substantially increased odds of several SUDs. After applying the Bonferroni‑corrected \u0026alpha;\u0026thinsp;=\u0026thinsp;0.0045, NEET status was associated with lower odds of alcohol‑use disorder (aOR\u0026thinsp;=\u0026thinsp;0.68, 95% CI\u0026thinsp;=\u0026thinsp;0.54\u0026ndash;0.85), and higher odds of cannabis use disorder (aOR\u0026thinsp;=\u0026thinsp;1.40, 95% CI\u0026thinsp;=\u0026thinsp;1.13\u0026ndash;1.74), methamphetamine use disorder (aOR\u0026thinsp;=\u0026thinsp;3.10, 95% CI\u0026thinsp;=\u0026thinsp;1.82\u0026ndash;5.28), prescription pain reliever use disorder (aOR\u0026thinsp;=\u0026thinsp;2.88, 95% CI\u0026thinsp;=\u0026thinsp;1.82\u0026ndash;4.53), any opioid use disorder (heroin or prescription; aOR\u0026thinsp;=\u0026thinsp;2.68, 95% CI\u0026thinsp;=\u0026thinsp;1.71\u0026ndash;4.20, and prescription tranquilizer/sedative use disorder (aOR\u0026thinsp;=\u0026thinsp;3.54, 95% CI\u0026thinsp;=\u0026thinsp;1.97\u0026ndash;6.37). No significant differences emerged for cocaine, heroin, prescription‑stimulant, or any drug and alcohol use disorder, and the association with past‑year substance use treatment engagement fell below the Bonferroni-adjusted threshold for significance. Overall, the adjusted findings highlight a distinct substance use pattern in which NEET men carry disproportionate risks for high‑harm prescription and illicit drug disorders while exhibiting comparatively lower alcohol involvement.\u003c/p\u003e\n \u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Principal findings\u003c/h2\u003e \u003cp\u003eIn this nationally representative study, NEET men had substantially higher odds of prescription tranquilizer/sedative use disorder, methamphetamine use disorder, and opioid use disorder compared to their counterparts. We estimated that 11.1% of working-aged men (18\u0026ndash;64) were not engaged in employment, education or training in 2022 during the COVID-19 pandemic. This is remarkable given that the U.S. enjoyed historically low rates of unemployment (less than 4%) at the time of data collection (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our study adds to the literature by providing a more comprehensive picture of the NEET population that shows an overrepresentation of middle-aged and older adults (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Consistent with past research, we found a high prevalence of past-year SUDs among NEET men, but past 30-day use of illicit substances was similar among both groups. Importantly, NEET men showed strikingly lower rates of alcohol use and higher rates of nicotine dependence. Despite NEET men being disproportionately in rural areas where SUD treatment services tend to in short supply, they had slightly higher prevalence of SUD treatment engagement. These findings indicate that public health interventions should be tailored by substance type (e.g., prescription drugs, methamphetamine) and designed with age- and culturally-appropriate components for this vulnerable population.\u003c/p\u003e \u003cp\u003eOur estimate that 11.1% of men (18\u0026ndash;64) were NEET in 2022 closely aligns with previous research indicating that 11% of prime-aged men (aged 25\u0026ndash;54) were not participating in the labor force in 2019 (Bureau of Labor Statistics, 2023; Eberstadt, 2022). NEET men also had significantly higher past-year prevalence of several SUDs which is consistent with previous work that has established independent associations between NEET status and substance use (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast with previous literature, NEET men had significantly lower alcohol use among several key measures including fewer binge drinking episodes, fewer drinking days per week, fewer days with any alcohol consumption in the past month, and fewer drinks per day. This finding contrasts with an earlier study using the same NSDUH dataset which found significantly higher rates of heavy alcohol use and alcohol use disorders among unemployed individuals between 2002 and 2010 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, Compton et al. defined unemployed as those without a job in the last four weeks whereas our study looked at longer-term unemployment which was defined as at 52 weeks. Although alcohol consumption frequency increased in the general population during the COVID-19 pandemic (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), this trend varied among different demographic groups (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur finding of lower alcohol use among NEET men aligns with a \"polarization effect\" in alcohol consumption patterns during the COVID-19 pandemic. A study of Swiss men demonstrated that those reporting higher COVID-19-related health concerns and/or financial anxieties reported a decrease in their alcohol consumption, while those with fewer such concerns reported an increase (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). A large study of over 30,000 adults in the UK found a similar polarization based on socioeconomic position; individuals with low income were more likely to reduce their drinking whereas those with high income were more likely to drink more during the pandemic (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Given that nearly half of the NEET men in our study earned less than \u003cspan\u003e$\u003c/span\u003e20,000 annually and were over the age of 50, it is likely they experienced the heightened health and financial vulnerabilities that led to a reduction in alcohol consumption as a protective or cost-saving response to pandemic-related stressors.\u003c/p\u003e \u003cp\u003eNEET men had substantially higher rates of past-year SUDs for multiple substances, but measures of past 30-day substance use were not significantly different between the two groups. This might suggest that some individuals reduced or ceased their substance use after becoming NEET due to financial constraints. This discrepancy might also indicate that comparable use of illicit substances might lead to more severe levels of functional impairment in the context of long-term unemployment, socioeconomic differences and the COVID-19 pandemic (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Social distancing measures, telehealth prescribing of prescription medications, lower prices for synthetic drugs and mental health co-morbidity may partially explain this specific substance use profile (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). For example, NEET status was shown to be associated with severe symptoms of anxiety and moderate/severe symptoms of depression and concurrent mental health and substance problems (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Our population's older age and over-representation of Non-Hispanic Black males may also partially explain our findings due to increased intersectional vulnerability (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our sample, NEET status was disproportionately concentrated among older working age adults, which contrasts with much of the existing literature that predominantly focuses on adolescent and young adult NEET populations (Mawn et al., 2017; O'Dea et al., 2014; Rodwell et al., 2018; Stea et al., 2024). This suggests that COVID-19 may have uniquely affected older adult workers in terms of employment challenges compared to previous economic downturns (Li and Mutchler, 2021). It could also be that older men have been using substances for more years, which may have increased the severity of their disorder and simultaneously entrenched their NEET status (Kuerbis, 2020; Silver et al., 2022). The significant overrepresentation of men who identify as Non-Hispanic Black in the NEET population underscores the need for culturally tailored interventions. NEET men were significantly more likely to lack a high school diploma and far less likely to hold college degrees. These demographic patterns suggest disparities around the impact of COVID-19 on employment sectors especially among those requiring in-person work and those employing men without college credentials (Moen et al., 2020). The concentration of NEET status among older working age adult men represents a significant shift from the traditional view of NEET as a youth-centered problem.\u003c/p\u003e \u003cp\u003eAlthough NEET men showed higher rates of treatment seeking, this difference attenuated after adjustment which suggests that interventions should prioritize enhancing treatment effectiveness and retention as well as substance-specific programs. Previous studies show mixed results, with some indicating a treatment gap among long-term unemployed individuals, while others suggest unemployment rates significantly correlate with substance use treatment admissions (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Potential mechanisms for the attenuation include differential treatment access by rurality, demographic profile (older vs younger), and the availability of substance specific treatment options (e.g. methamphetamines).\u003c/p\u003e \u003cp\u003eThe high prevalence of illicit substance use among the NEET population is cause for concern due to the potential for serious harms related to hospitalizations and fatal overdose especially among vulnerable populations (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). For example, hospitalizations for stimulant use increased by 42.6% for adults aged 55\u0026ndash;64 in California from 2017\u0026ndash;2021 (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Beginning in 2020, over 50% of all drug overdose deaths among adults aged 25\u0026ndash;64 are estimated to have occurred among males with at most a high school-level education (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). During the COVID-19 pandemic, Black individuals experienced the largest percentage increase in drug overdose mortality (48.8%), surpassing overdose rates among White individuals for the first time since 1999 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). These data suggest that a high proportion of NEET men in our sample are at increased risk of mortality due to their demographic and substance use profiles and likely account for a disproportionate number of overdose fatalities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Implications\u003c/h2\u003e \u003cp\u003eNEET men during the COVID-19 pandemic display unique substance use behaviors that have higher risk profiles which warrant immediate attention. Our findings highlight the urgent need for an integrated model of treatment for NEET men that addresses substance use, employment, and socio-economic barriers to workforce reentry and risk mitigation. For example, increasing educational attainment and improving economic conditions could have the potential to decrease unemployment rates and reduce substance use disorders simultaneously. Promising approaches include substance-specific programs targeting prevalent drugs among NEET men (e.g. methamphetamine, opioids), contingency management interventions that simultaneously address employment and substance use, re-education programs that target low-income communities or justice-involved individuals, and policy changes that extend health coverage for individuals transitioning out of employment to maintain treatment continuity (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Focusing on enhancing work-related recovery capital for NEET men engaged in treatment may have indirect benefits for SUD outcomes (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e); professional treatment programs (e.g. physician\u0026rsquo;s health programs) have high rates of success (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvidence-based interventions such as overdose rescue medications and medications for opioid use disorder are critical tools for treatment and should be and made easily available for low-income and uninsured populations and accessible in a wide variety of settings (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The widespread use of synthetic drugs in the post-COVID era, and the new epidemic of stimulant and opioid co-use make these interventions critically important (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Early prevention strategies aimed at reducing harmful substance use among unemployed men is warranted, especially when considering that stimulant use disorders have no FDA-approved medication-based treatment options. Future studies should consider adapting substance use prevention programs tailored to unemployed youth to middle-aged and older adults of different cultural backgrounds (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Because the COVID-19 pandemic likely influenced our findings related to the NEET population, follow-up studies are warranted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations\u003c/h2\u003e \u003cp\u003eOur study is limited due to the self-report nature of the survey which is therefore susceptible to recall and social desirability bias. Additionally, self-report of drug class used may lead to misidentification of specific substances. Multiple testing could be a limitation, but test results (ps\u0026thinsp;\u0026lt;\u0026thinsp;0.0045) remained significant in light of a Bonferroni correction (alpha\u0026thinsp;=\u0026thinsp;0.0045). In addition, our study is limited to community-living NEET men aged 18\u0026ndash;64 and may not be representative of certain vulnerable subpopulations such as individuals experiencing homelessness who do not use shelters or those in institutional settings. Also, the NSDUH does not include a diagnostic interview; responses to survey questions are not a direct clinical diagnosis. However, proxy diagnoses in the NSDUH are used extensively in the literature and have been validated in previous studies. Additionally, while our study examines potential COVID-19 impacts on NEET men's substance use patterns, the cross-sectional nature of our data limits causal inference regarding pandemic-related effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eMore than one in ten U.S. working-aged men were NEET in 2022, and they showed elevated risk of tranquilizer/sedative, methamphetamine, and prescription-opioid and use disorders while showing unexpectedly lower alcohol use. These findings call for multi-level evidence-based interventions that integrate substance-specific treatments (e.g., MOUD, contingency management for stimulants) with vocational assistance, and healthcare policies that maintain insurance coverage during periods of long-term unemployment; targeted interventions should be age-appropriate and culturally tailored.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eKeith Humphreys declared a personal financial interest as a non-executive director for Indivior PLC, a company that makes buprenorphine. This role began in November 2023.Wayne Kepner declared that he has no known competing financial interests or personal relationships that could be perceived as influencing the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e \u003ch2\u003eKeith Humphreys\u003c/h2\u003e \u003cp\u003eConceptualization, Investigation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Supervision, and Funding acquisition.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eWayne Kepner\u003c/strong\u003e \u003cp\u003eConceptualization, Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Project administration, and Funding acquisition.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Source:\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Institute on Drug Abuse of the National Institutes of Health under Award Number T32DA035165, and the William and Katharine Duhamel Addiction Medicine Fund. Dr. Humphreys was supported by a Senior Research Career Scientist award from the Veterans Health Administration. The content is solely the responsibility of the authors and does not represent the official position of the funders.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKeith Humphreys: Conceptualization, Investigation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Supervision, and Funding acquisition.Wayne Kepner: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Project administration, and Funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study came from publicly available datasets from the National Survey on Drug Use and Health (NSDUH). These datasets can be accessed through the Substance Abuse and Mental Health Data Archive (SAMHDA) at https://datafiles.samhsa.gov/ after a brief application process. Additional data from these analyses are available from the authors upon reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBureau of Labor Statistics USDoL. Labor force participation rate for people ages 25 to 54 in May 2023 highest since January 2007 [The Economics Daily]. 2023 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bls.gov/opub/ted/2023/labor-force-participation-rate-for-people-ages-25-to-54-in-may-2023-highest-since-january-2007.htm\u003c/span\u003e\u003cspan address=\"https://www.bls.gov/opub/ted/2023/labor-force-participation-rate-for-people-ages-25-to-54-in-may-2023-highest-since-january-2007.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEberstadt N. Men Without Work: Post-Pandemic Edition (2022): Templeton Foundation Press; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsden J, Brown J, Clark L, Cousijn J, Hall W, Hickman M et al. The impact of the COVID-19 pandemic on addictive disorders-an update. Addiction. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Mutchler JE. Older adults and the economic impact of the COVID-19 pandemic. Older adults and COVID-19. Routledge; 2021. pp. 201\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman JR, Nguemeni Tiako MJ, Hansen H. Understanding and addressing widening racial inequalities in drug overdose. Am J Psychiatry. 2024;181(5):381\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow ND. Collision of the COVID-19 and addiction epidemics. American College of Physicians; 2020. pp. 61\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManhica H, Lundin A, Danielsson A-K. Not in education, employment, or training (NEET) and risk of alcohol use disorder: a nationwide register-linkage study with 485 839 Swedish youths. BMJ open. 2019;9(10):e032888.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManhica H, Yacaman-Mendez D, Sj\u0026ouml;qvist H, Lundin A, Agardh E, Danielsson A-K. Trajectories of NEET (Not in Education, Employment, and Training) in emerging adulthood, and later drug use disorder-a national cohort study. Drug Alcohol Depend. 2022;233:109350.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaggio S, Iglesias K, Deline S, Studer J, Henchoz Y, Mohler-Kuo M, et al. Not in education, employment, or training status among young Swiss men. Longitudinal associations with mental health and substance use. J Adolesc Health. 2015;56(2):238\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCompton WM, Gfroerer J, Conway KP, Finger MS. Unemployment and substance outcomes in the United States 2002\u0026ndash;2010. Drug Alcohol Depend. 2014;142:350\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSohi I, Chrystoja BR, Rehm J, Wells S, Monteiro M, Ali S, et al. Changes in alcohol use during the COVID-19 pandemic and previous pandemics: A systematic review. Alcoholism: Clin experimental Res. 2022;46(4):498\u0026ndash;513.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImtiaz S, Nafeh F, Russell C, Ali F, Elton-Marshall T, Rehm J. The impact of the novel coronavirus disease (COVID-19) pandemic on drug overdose-related deaths in the United States and Canada: a systematic review of observational studies and analysis of public health surveillance data. Substance Abuse Treatment, Prevention, and Policy. 2021;16:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh AK, Janke AT, Kinsman J, Rothenberg C, Goyal P, Malicki C, et al. Emergency department utilization for substance use disorders and mental health conditions during COVID-19. PLoS ONE. 2022;17(1):e0262136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmeda N, G\u0026oacute;mez-G\u0026oacute;mez I. The impact of the COVID-19 pandemic on smoking consumption: a systematic review of longitudinal studies. Front Psychiatry. 2022;13:941575.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehra K, Rup J, Wiese JL, Watson TM, Bonato S, Rueda S. Changes in self-reported cannabis use during the COVID-19 pandemic: a scoping review. BMC Public Health. 2023;23(1):2139.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones CM, Diallo MM, Vythilingam M, Schier JG, Eisenstat M, Compton WM. Characteristics and correlates of US clinicians prescribing buprenorphine for opioid use disorder treatment using expanded authorities during the COVID-19 pandemic. Drug Alcohol Depend. 2021;225:108783.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrawczyk N, Rivera BD, King C, Dooling BC. Pandemic telehealth flexibilities for buprenorphine treatment: a synthesis of evidence and policy implications for expanding opioid use disorder care in the United States. Health Affairs Scholar. 2023;1(1):qxad013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVo AT, Patton T, Peacock A, Larney S, Borquez A. Illicit substance use and the COVID-19 pandemic in the United States: a scoping review and characterization of research evidence in unprecedented times. Int J Environ Res Public Health. 2022;19(14):8883.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandler RF, Lozada ARS. Health status among NEET adolescents and young adults in the United States, 2016\u0026ndash;2018. SSM-population health. 2021;14:100814.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJemberie WB, Stewart Williams J, Eriksson M, Gr\u0026ouml;nlund A-S, Ng N, Blom Nilsson M, et al. Substance use disorders and COVID-19: multi-faceted problems which require multi-pronged solutions. Front Psychiatry. 2020;11:714.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMawn L, Oliver EJ, Akhter N, Bambra CL, Torgerson C, Bridle C, et al. Are we failing young people not in employment, education or training (NEETs)? A systematic review and meta-analysis of re-engagement interventions. Syst reviews. 2017;6:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubstance Abuse and Mental Health Services Administration. 2022 National Survey on Drug Use and Health (NSDUH): Methodological Resource Book (MRB). Rockville, MD; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenter for Behavioral Health Statistics and Quality. 2022 National Survey on Drug Use and Health (NSDUH): Methodological summary and definitions. Rockville, MD; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiffman S, Waters AJ, Hickcox M. The nicotine dependence syndrome scale: a multidimensional measure of nicotine dependence. Nicotine Tob Res. 2004;6(2):327\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association. Diagnostic and statistical manual of mental disorders. Fifth ed. Washington, DC: American Psychiatric Publishing; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllaire J, Boston. MA. 2012;770(394):165\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeeringa SG, West BT, Berglund PA. Applied survey data analysis. chapman and hall/CRC; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEssien LSL, Owens MD. Greg. Unemployment rate returned to its prepandemic level in 2022. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStea TH, Bertelsen TB, Fegran L, Sejersted E, Kleppang AL, Fyhn T. Interventions targeting young people not in employment, education or training (NEET) for increased likelihood of return to school or employment\u0026mdash;A systematic review. PLoS ONE. 2024;19(6):e0306285.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasta M, Karakonstantis S, Koutra K, Dafermos V, Papargiris A, Drakaki M, et al. NEET status among young Greeks: association with mental health and substance use. J Affect Disord. 2019;253:210\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGon\u0026ccedil;alves PD, Moura HF, Do Amaral RA, Castaldelli-Maia JM, Malbergier A. Alcohol use and COVID-19: can we predict the impact of the pandemic on alcohol use based on the previous crises in the 21st century? A brief review. Front Psychiatry. 2020;11:581113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNolte-Troha C, Roser P, Henkel D, Scherbaum N, Koller G, Franke A. Unemployment and Substance Use: An Updated Review of Studies from North America and Europe. Healthcare. 2023;11(8):1182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong S, Fox AM. Enhanced unemployment benefits, mental health, and substance use among low-income households during the COVID-19 pandemic. Soc Sci Med. 2023;328:115973.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabhart F, Gmel G. A polarisation rather than just an increase or a decrease-Exploring different approaches to measure the impact of the COVID-19 pandemic on alcohol consumption after one year. Int J Alcohol Drug Res. 2023;11(2):70\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarnett C, Jackson S, Oldham M, Brown J, Steptoe A, Fancourt D. Factors associated with drinking behaviour during COVID-19 social distancing and lockdown among adults in the UK. Drug Alcohol Depend. 2021;219:108461.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorvell Gustavsson I, Jonsson F. Exploring the experiences of NEET-situated young people within the context of the COVID-19 pandemic using resonance theory. J Appl Youth Stud. 2024;7(3):253\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeller MS, Kiefer E, Campbell S, Bradley K, Mashburn R, Bawa M, et al. Sustained increase of sedative-hypnotic prescribing during the COVID-19 pandemic in a large urban health system: an observational study. J Gen Intern Med. 2021;36(11):3618\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor S, Paluszek MM, Rachor GS, McKay D, Asmundson GJ. Substance use and abuse, COVID-19-related distress, and disregard for social distancing: A network analysis. Addict Behav. 2021;114:106754.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Dios C, Fernandes BS, Whalen K, Bandewar S, Suchting R, Weaver MF, et al. Prescription fill patterns for benzodiazepine and opioid drugs during the COVID-19 pandemic in the United States. Drug Alcohol Depend. 2021;229:109176.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldman-Mellor S, Caspi A, Arseneault L, Ajala N, Ambler A, Danese A, et al. Committed to work but vulnerable: Self‐perceptions and mental health in NEET 18‐year olds from a contemporary British cohort. J Child Psychol Psychiatry. 2016;57(2):196\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZapolski TC, Pedersen SL, McCarthy DM, Smith GT. Less drinking, yet more problems: understanding African American drinking and related problems. Psychol Bull. 2014;140(1):188.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilver SR, Li J, Quay B. Employment status, unemployment duration, and health-related metrics among US adults of prime working age: behavioral risk factor surveillance system, 2018\u0026ndash;2019. Am J Ind Med. 2022;65(1):59\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzagba S, Shan L, Qeadan F, Wolfson M. Unemployment rate, opioids misuse and other substance abuse: quasi-experimental evidence from treatment admissions data. BMC Psychiatry. 2021;21:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScherbaum N, Mikoteit T, Witkowski L, Bonnet U, Specka M, Schifano F, et al. New Access Routes to Undertreated Populations; How Do Problem Substance Users Recruited from an Unemployment Office Differ from Detoxification Treatment Inpatients? Int J Environ Res Public Health. 2021;18(24):13014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKepner W, Courchesne-Krak NS, Satybaldiyeva N, Narasimhan R, Marienfeld CB. Emergency care utilization in persons with substance related diagnoses. Addict Behav Rep. 2024:100573.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan BH, Brennan JJ, Kepner WE, Chen S, Lin S, Carley JA, et al. Trends in stimulant-related emergency department visits among adults in California, 2017\u0026ndash;2021. Am J Emerg Med. 2025;93:94\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu JJ, Seamans MJ, Friedman JR. Drug overdose mortality rates by educational attainment and sex for adults aged 25\u0026ndash;64 in the United States before and during the COVID-19 pandemic, 2015\u0026ndash;2021. Drug Alcohol Depend. 2024;255:111014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman JR, Hansen H. Evaluation of increases in drug overdose mortality rates in the US by race and ethnicity before and during the COVID-19 pandemic. JAMA psychiatry. 2022;79(4):379\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Rumrill S, Chun J, Osak R. Demographic and Vocational Rehabilitation Service Correlates of Employment Outcomes in People With Substance Use Disorders During COVID-19. Rehabilitation Couns Bull. 2024;0(0):00343552241236870.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrone MR, Chosewood LC, Osborne JC, Howard JJ. Workplace supported recovery from substance use disorders: defining the construct, developing a model, and proposing an agenda for future research. Occup health Sci. 2022;6(4):475\u0026ndash;511.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuPont RL, McLellan AT, Carr G, Gendel M, Skipper GE. How are addicted physicians treated? A national survey of physician health programs. J Subst Abuse Treat. 2009;37(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrank RG, Fry CE. The impact of expanded Medicaid eligibility on access to naloxone. Addiction. 2019;114(9):1567\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen H, Hockenberry JM, Borders TF, Druss BG. Impact of Medicaid expansion on Medicaid-covered utilization of buprenorphine for opioid use disorder treatment. Med Care. 2017;55(4):336\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman J, Shover CL. Charting the fourth wave: Geographic, temporal, race/ethnicity and demographic trends in polysubstance fentanyl overdose deaths in the United States, 2010\u0026ndash;2021. Addiction. 2023;118(12):2477\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagura S, Marshall T. The effectiveness of interventions intended to improve employment outcomes for persons with substance use disorder: an updated systematic review. Subst Use Misuse. 2020;55(13):2230\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 714px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Demographic Characteristics of Working-Aged Men (18-64y) by NEET Status, United States, 2022 National Survey on Drug Use and Health\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Sample ages 18-64 weighted % (95% CI)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot NEET ages 18-64 weighted \u0026nbsp; \u0026nbsp; % (95% CI)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEET ages 18-64 weighted \u0026nbsp; \u0026nbsp; % (95% CI)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSexual orientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eHeterosexual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e88.9 (87.9-89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e91.3 (90.3-92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e86.1 (83.3-88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eGay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e4.1 (3.5-4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e3.7 (3.1-4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e7.5 (5.3-10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eBisexual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e3.8 (3.3-4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e3.8 (3.3-4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.5 (3.2-6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNH Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e6.2 (5.4-7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e6.9 (5.9-7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.8 (1.1-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNH Black / African \u0026nbsp; \u0026nbsp;American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e12.2 (11.4-13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e11.5 (10.6-12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e18.5 (15.6-21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e19.9 (18.2-21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e19.7 (17.7-21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e18.9 (15.2-23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNH Native American / Alaskan Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.5 (0.4-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.5 (0.4-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.6 (0.3-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNH Native Hawaiian / Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.6 (0.4-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e0.6 (0.4-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.4 (0.1-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNH Multiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1.9 (1.5-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1.8 (1.5-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2.1 (1.4-3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNH White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e58.6 (56.7-60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e59.1 (57.1-61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e57.7 (52.9-62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e18-25, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e17.6 (16.7-18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e18.4 (17.5-19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e11.8 (10.2-13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e26-34, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e20.3 (19.1-21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e21.1 (19.8-22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e14.0 (11.5-16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e35-49, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e31.5 (30.5-32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e31.8 (30.5-33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e27.6 (24.3-31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e50-64, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e30.6 (29.1-32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e28.7 (27.0-30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e46.6 (41.8-51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual family income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026lt; $20-000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e14.5 (13.5-15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e10.4 (9.4-11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e45.9 (41.4-50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e$20,000-$49,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e24.0 (22.6-25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e22.7 (21.4-24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e32.4 (27.4-37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e$50,000-$74,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e14.1 (13.3-14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e14.9 (14.0-15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e8.3 (6.5-10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;$75,000 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e47.5 (45.7-49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e52.0 (50.3-53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e13.4 (10.4-17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e10.0 (9.2-10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e8.4 (7.6-9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e20.8 (17.8-24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eHighschool diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e29.2 (27.9-30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e27.2 (25.7-28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e42.7 (38.0-47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSome college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e29.6 (28.3-30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e30.5 (29.2-31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e25.5 (21.3-30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eCollege degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e31.3 (29.9-32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e34.0 (32.5-35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e11.0 (8.6-14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelationship status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e45.1 (43.6-46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e47.3 (45.8-48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e29.5 (24.8-34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e42.3 (40.8-43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e41.6 (40.1-43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e48.5 (43.1-53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eDivorced / widowed / separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e12.6 (11.4-14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e11.2 (10.0-12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e22.1 (17.8-27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eLarge metropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e55.6 (53.3-57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e56.3 (53.8-58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e50.6 (46.3-54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSmall metropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e32.2 (30.5-34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e32.4 (30.4-34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e31.8 (27.7-36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNon-metropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e12.1 (10.7-13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e11.4 (10.0-12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e17.6 (13.8-22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. NEET = Not in employment, education, or training; NH = Non-Hispanic; CI = Confidence interval. All ps significant at the Bonferroni-correct level of p\u0026lt;.007.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"696\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 696px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Substance Use Treatment, Disorders, and Recent Use Among Working‑Aged Men (18‑64 y) by NEET Status, United States, 2022 National Survey on Drug Use and Health\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot NEET ages 18-64 weighted % (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEET ages 18-64 weighted % (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance use treatment (past-year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eAny substance use treatment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.8 (1.4-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e4.6 (2.4-8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance use disorder (past-year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eAlcohol use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e16.3 (15.3-17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e11.9 (9.9-14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eCannabis use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e10.2 (9.3-11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e12.7 (10.4-15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eCocaine use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.9 (0.6-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.3 (0.6-2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eMethamphetamine use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.8 (0.6-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e3.5 (2.2-5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003ePrescription opioid use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.7 (1.3-2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e5.5 (3.8-7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eHeroin use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.4 (0.3-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.3 (0.7-2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eOpioid use disorder (heroin or prescription opioid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e2.0 (1.5-2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e6.2 (4.4-8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003ePrescription stimulant use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.8 (0.6-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.7 (0.3-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eTranquilizer/sedative use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.7 (0.5-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e2.4 (1.7-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eAny substance use disorder (drug or alcohol use disorder)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e24.5 (23.2-26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e27.2 (24.1-30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecent substance use (30-day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e61.3 (59.9-62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e34.1 (29.9-38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eCannabis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e20.7 (19.5-22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e26.6 (23.0-30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eCocaine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.4 (1.1-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.6 (0.9-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eMethamphetamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.7 (0.4-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e3.2 (1.9-5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eHeroin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.3 (0.1-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.3 (0.6-2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eFentanyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.1 (0.1-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.3 (0.1-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003ePrescription stimulant misuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.8 (0.6-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.6 (0.2-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003ePrescription pain reliever misuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.0 (0.7-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.4 (0.9-2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eTranquilizers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.5 (0.4-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.8 (0.3-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eSedatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.1 (0.0-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.3 (0.0-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eNicotine dependence\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e10.1 (9.1-11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e24.4 (20.7-28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePast-month substance use frequency\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCannabis (days used in past 30 days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e1-2 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e16.6 (14.8-18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e9.7 (6.5-12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e3-5 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e13.0 (11.1-14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e9.2 (6.2-12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e6-19 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e19.4 (17.1-21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e24.6 (16.1-33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e20-30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e51.1 (48.0-54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e56.5 (47.2-65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeroin (days used in past 30 days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e1-2 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e8.8 (2.4-15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e4.2 (0.9-7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e3-5 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e11.2 (4.1-18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e8.4 (1.7-15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e6-19 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e12.2 (3.5-20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e35.7 (9.2-62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e20-30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e67.7 (41.9-93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e51.8 (20.1-83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethamphetamine (days used in past 30 days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e1-2 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e16.9 (6.1-27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e5.3 (1.1-9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e3-5 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e9.4 (4.1-14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e6.9 (2.2-11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e6-19 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e9.9 (5.1-14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e64.6 (41.2-88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e20-30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e63.9 (44.5-83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e23.3 (9.6-37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption patterns, mean (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDays per week drank alcohol, past year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.8 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.6 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eBinge alcohol frequency, past 30-days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.5 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.9 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDays had one or more drinks, past 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e5.3 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e2.5 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eUsual number of drinks per day, past 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.9 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.2 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 696px;\"\u003e\n \u003cp\u003eNEET = Not in education, employment, or training; CI = confidence interval; SUD = substance use disorder; NDSS = Nicotine Dependence Syndrome Scale. Bold=p\u0026lt;0.0045 Bonferroni corrected. \u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003eSubstance use frequency variables exclude all non-users (0 days). Prescription drug use in this table refers to misuse. All estimates survey‑weighted.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 684px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Multivariable Models Examining NEET Status as a Risk Factor for Past-Year Substance Use Disorders Among Working-Aged Men (18-64y) in the United States, 2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeparate outcome variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEET men vs not-NEET men (independent variable).\u0026nbsp;\u003cbr\u003e Adjusted OR (95% CI)\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance use treatment (past-year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003eAny substance use treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e2.09 (1.06-4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance use disorders (past-year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003eAlcohol use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.68 (0.54-0.85)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003eCannabis use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.40 (1.13-1.74)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003eCocaine use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e1.13 (0.49-2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003eMethamphetamine use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.10 (1.82-5.28)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003ePrescription pain reliever use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.88 (1.82-4.53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003eHeroin use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e2.23 (0.81-6.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003eAny opioid use disorder (heroin or prescription pain reliever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.68 (1.71-4.20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003ePrescription stimulant use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e0.95 (0.39-2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003ePrescription tranquilizer/sedative use disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.54 (1.97-6.37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003eAny substance use disorder (drug or alcohol use disorder)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e1.13 (0.94-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 684px;\"\u003e\n \u003cp\u003eOR = odds ratio; CI = confidence interval; SUD = substance use disorder.\u003cbr\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted for age, race/ethnicity, urbanicity, and marital status.\u003cbr\u003e\u0026nbsp;We utilized a Bonferroni statistical correction, bolded values indicate significance at a p \u0026lt; .0045.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"substance-abuse-treatment-prevention-and-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"satp","sideBox":"Learn more about [Substance Abuse Treatment, Prevention, and Policy](http://substanceabusepolicy.biomedcentral.com)","snPcode":"13011","submissionUrl":"https://submission.nature.com/new-submission/13011/3","title":"Substance Abuse Treatment, Prevention, and Policy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, epidemiology, NEET, opioid use, substance use disorder, substance use treatment, unemployment","lastPublishedDoi":"10.21203/rs.3.rs-7645273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7645273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA growing population of working-aged men are not in employment, education, or training (NEET). The COVID-19 pandemic increased rates of substance use disorders (SUDs) and affected treatment seeking in the general population, but the COVID era substance use patterns among NEET men are unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe estimated the prevalence and correlates of NEET status among working-aged (18\u0026ndash;64) men using data from the 2022 National Survey on Drug Use and Health, a nationally representative survey of non-institutionalized individuals in the United States. We developed logistic regression models to examine associations between NEET status and substance use behaviors and treatment engagement, adjusted for sociodemographic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAn estimated 11.1% of working-aged men were NEET in 2022 representing 10.6\u0026nbsp;million individuals. NEET men were significantly more likely to be older, have lower income, be unmarried, and have lower educational attainment and be Non-Hispanic Black compared to non-NEET men. After adjusting for sociodemographic factors, NEET status was significantly associated with higher odds of prescription tranquilizer/sedative use disorder (aOR\u0026thinsp;=\u0026thinsp;3.54, 1.97\u0026ndash;6.37), methamphetamine use disorder (aOR\u0026thinsp;=\u0026thinsp;3.10, 95% CI: 1.82\u0026ndash;5.28), and prescription pain‑reliever use disorder (aOR\u0026thinsp;=\u0026thinsp;2.88, 1.82\u0026ndash;4.53), while being inversely associated with alcohol use disorder (aOR\u0026thinsp;=\u0026thinsp;0.68, 0.54\u0026ndash;0.85).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMore than 1 in 10 working-aged men were NEET in 2022. Adjusted models showed higher odds of past-year SUDs but lower rates of alcohol use disorder. Targeted interventions should include age-appropriate, culturally tailored, and substance-specific treatment programs to improve public health.\u003c/p\u003e","manuscriptTitle":"Substance use and treatment utilization patterns of working-age American men who were not in employment, education, or training (NEET) during the COVID-19 pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 19:40:03","doi":"10.21203/rs.3.rs-7645273/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-05T18:25:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T17:25:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T23:42:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T21:27:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154507741310224050584218184639823149147","date":"2026-01-26T18:36:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63610001353657417765017214168458394305","date":"2026-01-26T14:56:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254210984260338467902839401334630683969","date":"2026-01-20T01:39:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-19T01:28:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T03:09:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-22T03:08:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Substance Abuse Treatment, Prevention, and Policy","date":"2025-09-18T04:54:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"substance-abuse-treatment-prevention-and-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"satp","sideBox":"Learn more about [Substance Abuse Treatment, Prevention, and Policy](http://substanceabusepolicy.biomedcentral.com)","snPcode":"13011","submissionUrl":"https://submission.nature.com/new-submission/13011/3","title":"Substance Abuse Treatment, Prevention, and Policy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"429afccb-d501-472f-ad38-1347efdd09d5","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T14:09:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 19:40:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7645273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7645273","identity":"rs-7645273","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}