https://orcid.org/0000-0002-0471-7995
Figures
Abstract
The association of subjective mental health-related quality of life (MHRQOL) and treatment use among people experiencing common substance use disorders (SUDs) is not known. Furthermore, the association of a given substance’s legal status with treatment use has not been studied. This work aims determine the association of MHRQOL with SUD treatment use, and how substance legal status modulates this relationship. Our analysis used nationally-representative data from the NESARC-III database of those experiencing past-year SUDs (n = 5,808) to compare rates of treatment use and its correlates among three groups: those with illicit substance use disorders (ISUDs); those with partially legal substance use disorders, i.e., cannabis use disorder (CUD); and those with fully legal substance use disorders, i.e., alcohol use disorder (AUD). Survey-weighted multiple regression analysis was used to assess the association of MHRQOL with likelihood of treatment use among these three groups, both unadjusted and adjusted for sociodemographic, behavioral, and diagnostic factors. Adults with past-year ISUDs were significantly more likely to use treatment than those with CUD and AUD. Among those with ISUDs, MHRQOL had no significant association with likelihood of treatment use. Those with past-year CUD saw significant negative association of MHRQOL with treatment use in unadjusted analysis, but not after controlling for diagnostic and other behavioral health factors. Those with past-year AUD had significant negative association of MHRQOL with treatment use in both unadjusted and adjusted analysis. If legalization and decriminalization continue, there may be a greater need for effective public education and harm reduction services to address this changing SUD landscape.
Citation: Havlik JL, Rhee TG, Rosenheck RA (2024) Association of mental health related quality of life and other factors with treatment seeking for substance use disorders: A comparison of SUDs rooted in legal, partially legal, and illegal substances. PLoS ONE 19(4): e0302544. https://doi.org/10.1371/journal.pone.0302544
Editor: Lakshit Jain, University of Connecticut Health Center: UConn Health, UNITED STATES
Received: June 30, 2023; Accepted: April 9, 2024; Published: April 29, 2024
Copyright: © 2024 Havlik et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data that support findings of this study are available from the National Institute on Alcohol Abuse and Alcoholism (NIAAA), specifically their NESARC-III dataset. Restrictions apply to the availability of these data, which were used by us under a data use agreement for this study; we are not legally authorized to replicate these data in a public repository. Data are available from NIAAA [https://www.niaaa.nih.gov/research/nesarc-iii] with the permission of the NESARC-III Data Access Committee.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
With progressive decriminalization and legalization of cannabis within the United States, and potential near-term FDA approval of treatment with potentially therapeutic psychedelic substances, there appears to be a general trend toward legalization and decriminalization of many previously illicit substances [1,2]. Use of these substances is not without consequences. On the positive side, decriminalization (where use of previously illegal substances remains illegal, but the legal system does not prosecute these crimes) and legalization (where previously illicit substance use is no longer a crime) have been heralded as a way to advance health equity while reducing stigma associated with treatment seeking [3–6]. On the other hand, societal costs of substance use disorders (SUDs), related health care expenditures, law enforcement expenditures, and indirect costs such as unemployment have been estimated at nearly 6% of US gross domestic product annually, more than the costs of common medical conditions such as heart disease, stroke, and obesity, though the economic and social effects of legalization and decriminalization are not known [7–11]. With such high prevalence and costs of illness, determining rates and correlates of treatment seeking is of interest to the medical community, especially examining the association of the comparative legal status of various substances with rates and correlates of treatment use, an issue that has been little studied [12,13].
Several studies have been devoted to describing the impacts of legal status on the extent of substance use, primarily in association with progressive decriminalization and legalization of cannabis in the United States [14–17]. This literature is mixed on the question of whether and how cannabis legalization has impacted rates of cannabis use and cannabis use disorder (CUD), as well as rates of treatment use. Some studies suggest legalization is associated with increased cannabis use but not treatment seeking for CUD [18–23]. Concerningly, multiple studies using national databases have found that cannabis legalization has been associated with increased use of remaining illicit substances, suggesting general illicit substance use may increase as another substance is legalized [24,25].
Several studies have reported high rates of medical and psychiatric comorbidities in various SUDs [26–31]. While the prevalence of some of these comorbidities are well-established in the literature, there is limited data comparing sociodemographic, co-morbid diagnostic and Health Related Quality of Life (HRQOL) among those seeking treatment for substances with different legal statuses, i.e., totally legal (e.g., alcohol), partially legal with variations across states in legality and enforcement (cannabis), and universally illegal (e.g., heroin, amphetamines, illegally sold prescription drugs). Some recent data has provided a limited characterization of treatment use by legal status of the involved substances, finding adults with SUDs related to illegal substances such as heroin were both more likely to need and to receive treatment than those with the entirely legal AUD [32]. Still, it remains unclear whether differences in the legal status of various substances are associated with differences in comorbidities and personal experiences as reflected in Mental HRQOL (MHRQOL), a standardized measure of quality of life attributable to one’s mental health. Importantly, differences in MHRQOL may be associated with differences in the likelihood of use of SUD treatment, as our group has note in previous work [33].
In this study we use survey data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)–III to compare concurrent characteristics and services use among three distinct groups of SUDs defined by the legal status of the substances involved: legal, partially legal, and illegal. Our specific focus is on differences in subjective MHRQOL and its independent association with the likelihood of receiving treatment, as explored in a recent study of AUD based on NESARC-III [34]. We consider the following questions: 1) What are the significant differences in sociodemographic and clinical characteristics between diagnostically-defined groups that differ in the legal status of the substances at the root of their SUDs, 2) Are there differences in rates and correlates of treatment use between SUDs that differ in their legal statuses, including in the association with MHRQOL? And 3) To what extent are differences in subjective MHRQOL, reflecting personal subjective distress, associated with differences in receipt of SUD treatment for substances of differing legal status, both in unadjusted analyses and net of socio-demographic characteristics, co-morbid mental and medical disorders, and behavioral characteristics that are related to both MHRQOL and SUDs and thus are potential confounders? Through this investigation we seek to evaluate the independent role of subjective MHRQOL on use of services for SUDs with different legal statuses, differences that may be important if recent trends towards legalization and decriminalization continue.
Materials and methods
Data source and study sample
We used restricted data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III), sponsored by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) [35]. The NESARC-III is a nationally representative cross-sectional survey, conducted from April 2012 through June 2013, of physical and mental health diagnoses, well-being, and disabilities among non-institutionalized civilian adults aged 18 or older with a focus on alcohol and other SUDs. Participants in the NESARC survey gave electronically documented oral informed consent in a manner approved by the National Institutes of Health institutional review board [36]. In NESARC-III, the Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5) was used to identify psychiatric disorders using Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) criteria [37].
In this study, we included the entire sample of adults aged 18 or older who met criteria for any SUD (n = 5,808 unweighted). We classified the sample into three groups, stratified by substance legality, with no overlap between groups. One “legal” use disorder group was defined by inclusion of adults with past year AUD (n = 4,321), the only entirely legal substance for adults considered in our classification, as nicotine use disorder was not used to define this group. The next “partially legal” group was defined as including adults with past year CUD (n = 828), a substance that is legal in the form of both medical and recreational marijuana use with considerable variability across states. The final, “illicit” group was defined as including adults with past year illicit SUDs (ISUDs (n = 659), including cocaine use disorder, heroin use disorder, amphetamine or stimulant use disorder, hallucinogen use disorder, club drug use disorder, prescription opioid use disorder, sedative use disorder, or other unspecified drug use disorder. Adults who met criteria for more than one group were classified in the more illegal group (e.g., a person with AUD and CUD was placed in the “partially legal” group).
Measures
Mental Health-Related Quality of Life (MHRQOL).
MHRQOL is a patient-reported indicator of health status and assesses subjective evaluation of the impact of disease on bio-psycho-social wellbeing from a mental health perspective [38]. Developed as part of the Medical Outcomes Study (MOS), the 12-item Short Form (SF-12) survey is a standardized questionnaire asking patients about their health states such as physical functioning, social functioning, role limitations, emotions, and general health [39]. Based on these 12 items, we constructed the mental component summary variable (MCS), a standardized measure of MHRQOL, using standard scoring algorithms that score the national average values on these measures as 50 and each standard deviation as a difference of 10 points above or below this average [40,41].
SUD treatment use.
Treatment use for our cohort was defined as receiving potentially definitive treatment for any SUD. This measure addressed receipt of outpatient or residential treatments that would potentially lead to an improved likelihood of abstinence or clinically meaningful reduction in use. These services included treatment at a family services agency, outpatient clinic including outreach and day or partial programs, private physician, psychiatrist, psychologist, social worker, rehabilitation program, or other professional agency in the past year. They did not include crisis intervention, emergency room (ER) services, or inpatient hospitalization as these services offer time-limited assistance and not ongoing, potentially definitive treatment for SUDs.
Sociodemographic variables.
Sociodemographic variables included: age, sex, race/ethnicity, marital status, employment (i.e., a job or business, either full-time or part-time including unpaid work), family income, education, and health insurance [42–44]. Urban vs. rural residence was also documented.
Psychiatric and substance use disorders.
Past-year psychiatric disorder diagnoses available in NESARC-III based on the AUDADIS and DSM 5 criteria [45] include major depressive disorder (hierarchical), dysthymia (hierarchical), bipolar I disorder, generalized anxiety disorder, post-traumatic stress disorder, eating disorders, specific phobias, and panic disorder. Hierarchical disorders included specific disorders also meeting criteria for more general DSM-5 diagnoses, e.g., all in sample with listed major depressive disorder (hierarchical) also met criteria for the more general diagnosis of major depressive episode. We further constructed a summary three-level variable representing number of psychiatric disorders in the past year: none, one, or two or more, in the interest of parsimonious analysis.
Behavioral and other diagnostic factors.
Behavioral and diagnostic factors accounted for in NESARC-III were selected for inclusion in our model based upon hypothesis-driven association with legality of substances used. Behavioral factors assessed in our study sample included MHRQOL, number of lifetime violent experiences, total 2-week contacts, perceived social support (assessed using the Interpersonal Support Evaluation List-12 (ISEL-12) [46], trouble with police in the past year, veteran status, and combat experience. Diagnostic variables of interest included chronic pain and medical comorbidities. Medical comorbidities were assessed as a summary three-level variable representing number of comorbid medical disorders: none, one, or two or more, in the interest of parsimonious analysis.
Analysis
First, we investigated differences in sociodemographic, behavioral, and diagnostic characteristics for our three categories of SUDs (ISUD, CUD, and AUD), through three sets of pairwise comparisons (ISUD vs CUD, CUD vs AUD, and ISUD vs AUD). Comparisons of categorical variables were based on bivariable logistic regression analyses with odds ratios as indicators of effect sizes while continuous variables were compared with Cohen’s d and associated P-values as indicators of statistical significance and effect size differences. P-values were assessed as significant if <0.05.
Next, after identifying significant differences in these characteristics among our three groups, we examined differences in rates of treatment use within each of the groups, through 1) both unadjusted pairwise bivariable odds ratios comparisons and 2) with adjustment for all significantly different sociodemographic, behavioral, and diagnostic characteristics identified above.
Third, we subsequently quantified differences in sociodemographic, behavioral, and diagnostic characteristics between treatment users and non-users within each SUD group using logistic regression and Cohen’s d as described above.
Finally, we investigated the independent association of MHRQOL and likelihood of SUD treatment within each of our three SUD groups. We constructed three sets of models for each group. The first model was a bivariable logistic regression assessing the unadjusted association MHRQOL with likelihood of SUD treatment. The second model added covariates representing sociodemographic variables such as age, sex, and race that may confound this association. The third model added additional covariates representing behavioral factors such as trouble with police in the past year and diagnostic factors like number of diagnosed non-SUD psychiatric disorders.
As we aimed to control for three sets of factors in nine multivariable analyses, we undertook a Bonferroni correction for multiple comparisons by considering test results significant at p<0.05/9 = 0.005. We used Stata version 17.0 for all analyses and employed the svy commands in Stata to account for the complex survey sampling design of the NESARC-III (e.g., unequal probability of selection, clustering and stratification) [47].
The study procedures were approved by the Institutional Review Board (#2000022543) at Yale School of Medicine. Further details of the survey, including descriptions, questionnaires, sampling methodology and datasets, are available on the NESARC- III website [35].
Results
Sample
Overall, 16.0% (n = 5,808) of the entire study sample, representative of 36,887,003 adults, met criteria for an included SUD within the past 12 months: 659 (11.4%) with ISUDs, representative of 4.1 million adults, 828 (14.3%) with CUD, representative of 5.1 million adults, and 4,321 with AUD (74.4%), representative of 27.7 million adults (Table 1, rows 2–3).
SUD legality-stratified differences in background characteristics and comorbidities
There were significant differences between pairs of substance groups on many sociodemographic, psychiatric, medical, and behavioral measures (Table 1). The group with past year ISUD (whether treated or not) was older, more likely to be male, less likely to be employed, and had lower incomes than those who met criteria for CUD or AUD. The group with ISUDs had a significantly greater risk of psychiatric disorders compared to those with CUD or AUD. The group also had a higher number of lifetime violent experiences, lower amount of self-assessed social support, and higher likelihood of experiencing chronic pain than those with CUD or AUD. This group with ISUD had a higher number of medical comorbidities than those with CUD or AUD. Notably, the group with ISUD had significantly lower MHRQOL as reflected by their MCS scores (40.6 ± 11.9) than those with CUD (46.1 ± 11.1) and AUD (48.4 ± 10.1) (Table 1).
Examining other differences among groups, those with CUD were more likely to be younger than those with ISUD or AUD, were less likely to be white than those with ISUD or AUD, and were more likely to be non-Hispanic black (Table 1). While they did not have as great a risk for past-year psychiatric disorders as those with ISUD, they had significantly greater risk of these disorders than those with AUD (Table 1).
Those with AUD were most likely to be married (34.4%), had higher incomes, were more likely to be employed, have higher levels of education, and have fewer psychiatric disorders than the other two groups.
Differences in rates of treatment by substance legality
Overall, 627 patients (10.7%), representing 3.8 million adults, were treated for SUDs in the past 12 months, while 5,181 patients (89.2%), representing 33.1 million adults, did not receive treatment. Treatment rates varied significantly by legality class of substances of use (Table 2): 26.9% of those with past-year ISUDs, representing 1.1 million US adults, received SUD treatment, while just 10.3% of those with past-year CUD (representing 612,389 US adults) and 8.5% of those with past-year AUD (representing 2.1 million US adults) received treatment. Those with ISUDs had a significantly higher likelihood of receiving treatment than those with CUD (Odds ratio: 2.62, P<0.001) or AUD (Odds ratio: 4.35, P<0.001); these differences persisted after adjustment for sociodemographic, behavioral, and diagnostic characteristics. Those with CUD had significantly higher likelihood of receiving treatment than those with AUD (Odds ratio: 1.66, P<0.01), a difference that was no longer statistically significant after adjusting for sociodemographic, behavioral, and diagnostic characteristics.
Correlates of SUD treatment use
Within legality-based groups of substances, treatment status was associated with significant differences in diverse characteristics, diagnoses, and comorbidities (Table 3). Stratified by age, those with ISUDs, receiving SUD treatment were less likely to be 65+; those receiving treatment for AUD were more likely to be 45–64 and less likely to be 18–44. Those with AUD receiving SUD treatment were significantly more likely to be unmarried. Stratifying by family income, those receiving treatment for AUD were more likely than others with AUD to have an income of <$20,000 per year and less likely to have an income of over $40,000 per year. Those with AUD who received treatment were also less likely to be employed, and more likely to have completed less than a high school education than others with AUD.
Examining diagnostic and behavioral variables, those receiving treatment for SUDs across all groups tended to have more than one psychiatric disorder compared to those not receiving treatment. They also tended to have higher rates of comorbid substance use.
Notably, MHRQOL was not significantly different among those with ISUDs receiving and not receiving SUD treatment. Among those with CUD, those receiving SUD treatment had significantly lower MHRQOL (Cohen’s d -0.51, P<0.001), were more likely to report trouble with police in the past year, and were more likely to be veterans than those not receiving treatment.
As in those with CUD, those with AUD receiving SUD treatment had significantly lower MHRQOL (Cohen’s d -0.62, P<0.001) than those not receiving SUD treatment. Those receiving treatment for CUD and AUD, but not ISUD, as compared to those not receiving treatment, had a significantly higher likelihood of reporting a history of violent experiences, and to report trouble with the police in the past year. Those with CUD who received treatment were more likely to be veterans, and reported significantly less social support than those who did not receive treatment. Those who received treatment for AUD were more likely to report chronic pain and to have 2 or more medical comorbidities than those not receiving treatment for AUD.
Multivariate group comparisons on measures of MHRQOL
Multivariable-adjusted comparisons within legality-based groups of likelihood of seeking treatment focused on the independent contribution (or lack thereof) of MHRQOL to the likelihood of seeking treatment net of other factors. Unadjusted regression coefficients are presented in columns 2, 5 and 8 of Table 4 and reflect differences in MHRQOL between in treated and non-treated subgroups within each legality group. In unadjusted bivariable logistic regression, higher MHRQOL is significantly and negatively associated with likelihood of treatment in those with CUD (-4.1% for each additional point of MCS) and AUD (-5.0% for each additional point of MCS), but there was no significant association among those with ISUD (Table 4, Columns 2, 5 and 8). These associations between MHRQOL and likelihood of treatment persist, though slightly attenuated, after adjusting for sociodemographic differences, (Table 4, Columns 6 and 9). After further adjusting for mental health diagnoses and behavioral indicators, higher MHRQOL remains a significant negative correlate of treatment seeking only for the AUD group (Table 4, Column 10). In this final set of models for the AUD group, each additional point in the MCS score was associated with 3.6% lower odds of receiving SUD treatment.
Discussion
This study used data from a nationally representative sample of US adults to investigate the relationship between MHRQOL and SUD treatment use among individuals with current SUDs involving substances with different legal statuses. The results revealed, first, a significantly greater likelihood of treatment use among those with ISUDs as compared to partially legal CUD and fully legal AUD and second, significant differences in sociodemographic and clinical characteristics among groups classified by legal status of the substances underlying their SUDs. Third, the results revealed further differences between these groups in individual characteristics associated with treatment use, especially a significant association between lower MHRQOL and treatment use among individuals with the partially legal CUD and entirely legal AUD. In contrast, no significant association was observed between lower MHRQOL and treatment use among those with ISUDs. After adjusting for demographic factors, other mental health diagnoses, and behavioral indicators, the correlation of lower MHRQOL and greater treatment use remained significant for AUD but not for CUD, suggesting a more robust relationship between subjective distress and treatment use for SUDs based on fully legal substances than among partially legal or illegal SUDs.
This robust constellation of findings offers no clear interpretation. The divergent impact of substance legality on treatment use suggests that treatment use may be at least partially influenced by the legal status of the substances being used. One might have expected that this difference was driven by interactions with the police and legal system which might be expected to create pressure to obtain treatment among those with ISUDs, far more than for CUD or AUD. However, while reports of trouble with the police were more common among those with ISUDs, they had no significant relationship to treatment within this group. In contrast, contact with police, although less frequent, was significantly associated with treatment among those with CUD and AUD. It is possible that fear of negative interaction with the police or legal system, rather than actual interactions with police or the legal system, drive greater proportions of those with ISUDs to seek treatment. These fears would be expected to be lower among those with CUD and AUD and would likely be reduced among those with current ISUDs if patterns of legalization and decriminalization continue.
Surprisingly, while age greater than 65 and having more than one co-morbid psychiatric disorder were significantly associated with SUD treatment in bivariate analysis of the ISUD group, no specific risk factors were independently associated with treatment use among those with ISUDs on multivariate analysis, suggesting that amidst their many problems, what leads those with ISUDs to treatment is not a distinct set of individual characteristics measured in NESARC-III. It may be that treatment use reflects unmeasured factors other than individual characteristics, for example greater local availability of treatment facilities or some other unmeasured neighborhood characteristics. Proximity to substance use treatment, i.e., small area variations in service availability, that are not captured in NESARC-III beyond delineating residents as “urban” or “rural,” have been well characterized as factors associated with variations in treatment use in prior literature and may account for variation in use of treatment [48–50]. It is also possible that interaction effects of sex and MHRQOL-affecting comorbidities could have played a role, as women with opioid use disorder have been reported to exhibit more psychiatric comorbidities than men [51]. Another factor not captured in this analysis is the local availability of religious institutions that have programs to facilitate entry into SUD treatment that are also undocumented in NESARC-III [52,53].
In contrast to ISUDs, multiple individual factors were associated with treatment on multivariate analysis among those with CUD and AUD, especially low MHRQOL, and among those with AUD, multiple psychiatric co-morbidities. The significant association of low MHRQOL and treatment use among those with CUD and AUD suggests that for these disorders, subjective dissatisfaction with life plays an especially important role in treatment seeking for partially or fully legal SUDs, in contrast to ISUDs. Treatment appears to be more “elective” in those for whom legal substances are the basis for their SUD and thus more driven by subjective HRQOL.
Findings from this study have noteworthy policy implications. If legalization becomes more common, treatment rates for currently illicit substances may decrease and come to resemble those of currently legal or partially legal substances. Treatment use may also become more prominently influenced by public attitudes, which may be shaped by public service announcements and anti-drug information campaigns. Unfortunately, previous research has shown models of education such as D.A.R.E and most other public service messaging around curbing illicit substance use do not appear to be effective and more research is thus needed to develop effective public education interventions that encourage those suffering from legal SUDs to enter treatment and address their SUDs [54,55]. While overall quality of life for those with SUDs may improve as previously illicit substances become decriminalized (and fears of legal punishment are reduced), the risk of dying or other adverse health effects from excessive use of newly legalized substances, as is currently the case with alcohol, remains substantial and may be more effectively addressed by newly developed public information campaigns highlighting serious health risks [56].
Since improvements in substance-related MHRQOL due to legalization of previously illicit substances, could decrease rates of treatment use as people with ISUDs feel “good enough” to think they do not “need” treatment, new and more effective methods of reducing harm among those who choose not to seek treatment may also become increasingly important tools (50,51). Until public education campaigns improve their record of limited efficacy in encouraging treatment use, methods such as harm reduction may take on a greater role in caring for those with newly decriminalized SUDs, e.g., providing safe spaces for substance use where life-saving help in the event of overdose or other medical complications can be readily available [57,58].
Several limitations of this study deserve comment. First, the most recent available NESARC survey was conducted between 2012 and 2013, and substance use habits, attitudes and socio-economic conditions of the United States population have shifted since this time. However, data from this early period of decriminalization/legalization still may be informative for future developments and policy discussion. Among the major changes since 2013 are the legalization or decriminalization of recreational cannabis in many states, the vast increase in opioid overdose deaths due to the illegal prescribing and marketing of long-acting opiates such as oxycodone [59], and the growing availability of fentanyl and analogs in unstandardized and tragically often lethal doses [60,61]. Furthermore, we classified opioid use disorder in our “illegal” category, when many opioids are obtained legally and prescribed through physicians [62]. In addition, use of both legal and illegal substances increased during the COVID-19 pandemic [63–66]. Whether these changes would change the findings reported here is not known. Second, some underserved populations (e.g., homeless individuals and prisoners) were not surveyed in NESARC-III, and thus this study may underestimate the numbers of adults with all SUDs, but especially ISUDs which can result in incarceration. Here too, it is unclear whether inclusion of these respondents in NESARC-III would have changed our results. Third, the cross-sectional nature of this study meant the effects of receiving definitive and longitudinal treatment for SUDs on MHRQOL could not be assessed. Finally, consistent with other epidemiologic surveys, NESARC- III is not a clinician-administered interview yielding diagnoses but relies on the AUDADIS-5, a fully structured interview [45]. However, the reliability and validity of NESARC-III have been well documented in a number of studies [67–69].
Conclusions
Despite these limitations, to our knowledge this is the first study to characterize the differences in proportions and correlates of adults receiving treatment for SUD by the legal status of the substances involved. More specifically, we have identified distinct differences in the strength of association of poor MHRQOL along with other factors, with treatment use, especially for partially legal and legal substances, which has not been previously noted. As legalization progresses, if indeed it does, there may be less latent legal pressure for treatment seeking and a greater need for effective methods of public education on the long-term adverse effects of SUDs to motivate use of treatment, as well as for harm reduction interventions for those who make hazardous, if legal, use of harmful substances. While legalization and decriminalization of previously illegal substances is unlikely to proceed rapidly in the near future, the analyses presented here may add useful information for discussions that will inevitably be controversial.
Supporting information
S1 Checklist. STROBE statement—checklist of items that should be included in reports of observational studies.
https://doi.org/10.1371/journal.pone.0302544.s001
(DOCX)
References
- 1. Hammond D, Goodman S, Wadsworth E, Rynard V, Boudreau C, Hall W. Evaluating the impacts of cannabis legalization: The International Cannabis Policy Study. International Journal of Drug Policy. 2020;77: 102698. pmid:32113149
- 2. Carhart-Harris RL, Goodwin GM. The Therapeutic Potential of Psychedelic Drugs: Past, Present, and Future. Neuropsychopharmacology 2017 42:11. 2017;42: 2105–2113. pmid:28443617
- 3. Virani HN, Haines-Saah RJ. Drug Decriminalization: A Matter of Justice and Equity, Not Just Health. Am J Prev Med. 2020;58: 161–164. pmid:31735479
- 4. Rouhani S, McGinty EE, Weicker NP, White RH, LaSalle L, Barry CL, et al. Racial resentment and support for decriminalization of drug possession in the United States. Prev Med (Baltim). 2022;163: 107189. pmid:35964775
- 5. Paquette C, Syvertsen J, Pollini R. Stigma at every turn: health services experiences among people who inject drugs. Int J Drug Policy. 2018;57: 104–110. pmid:29715589
- 6.
Decriminalization | LII / Legal Information Institute. [cited 16 Dec 2023]. Available: https://www.law.cornell.edu/wex/decriminalization.
- 7.
Deaths from Excessive Alcohol Use in the United States | CDC. [cited 22 Aug 2022]. Available: https://www.cdc.gov/alcohol/features/excessive-alcohol-deaths.html.
- 8.
Excessive Drinking is Draining the U.S. Economy. [cited 23 Aug 2022]. Available: https://www.cdc.gov/alcohol/features/excessive-drinking.html.
- 9.
Health and Economic Costs of Chronic Diseases | CDC. [cited 23 Aug 2022]. Available: https://www.cdc.gov/chronicdisease/about/costs/index.htm.
- 10. Ward ZJ, Bleich SN, Long MW, Gortmaker SL. Association of body mass index with health care expenditures in the United States by age and sex. PLoS One. 2021;16. pmid:33760880
- 11.
Substance Abuse: Facing the Costs | Health Policy Institute | Georgetown University. [cited 15 Feb 2023]. Available: https://hpi.georgetown.edu/abuse/.
- 12. Owens DK. Interpretation of Cost-Effectiveness Analyses. J Gen Intern Med. 1998;13: 716. pmid:9798822
- 13. Scheim AI, Maghsoudi N, Marshall Z, Churchill S, Ziegler C, Werb D. Original research: Impact evaluations of drug decriminalisation and legal regulation on drug use, health and social harms: a systematic review. BMJ Open. 2020;10: e035148. pmid:32958480
- 14. Allshouse AA, Metz TD. 854: Trends in self-reported and urine toxicology (UTOX)-detected maternal marijuana use before and after legalization. Am J Obstet Gynecol. 2016;214: S444–S445.
- 15. Melchior M, Nakamura A, Bolze C, Hausfater F, el Khoury F, Mary-Krause M, et al. Does liberalisation of cannabis policy influence levels of use in adolescents and young adults? A systematic review and meta-analysis. BMJ Open. 2019;9. pmid:31296507
- 16. Sarvet AL, Wall MM, Fink DS, Greene E, Le A, Boustead AE, et al. Medical marijuana laws and adolescent marijuana use in the United States: a systematic review and meta-analysis. Addiction. 2018;113: 1003–1016. pmid:29468763
- 17. Scheim AI, Maghsoudi N, Marshall Z, Churchill S, Ziegler C, Werb D. Original research: Impact evaluations of drug decriminalisation and legal regulation on drug use, health and social harms: a systematic review. BMJ Open. 2020;10: e035148. pmid:32958480
- 18. Kotlaja MM, Carson J v Cannabis Prevalence and National Drug Policy in 27 Countries: An Analysis of Adolescent Substance Use. Int J Offender Ther Comp Criminol. 2019;63: 1082–1099. pmid:30477367
- 19. Coley RL, Hawkins SS, Ghiani M, Kruzik C, Baum CF. A quasi-experimental evaluation of marijuana policies and youth marijuana use. American Journal of Drug and Alcohol Abuse. 2019;45: 292–303. pmid:30764656
- 20. Cerdá M, Mauro C, Hamilton A, Levy NS, Santaella-Tenorio J, Hasin D, et al. Association Between Recreational Marijuana Legalization in the United States and Changes in Marijuana Use and Cannabis Use Disorder From 2008 to 2016. JAMA Psychiatry. 2020;77: 165–171. pmid:31722000
- 21. Anderson DM, Hansen B, Rees DI, Sabia JJ. Association of Marijuana Laws With Teen Marijuana Use: New Estimates From the Youth Risk Behavior Surveys. JAMA Pediatr. 2019;173: 879–881. pmid:31282944
- 22. Rhee TG, Rosenheck RA. Increasing Use of Cannabis for Medical Purposes Among U.S. Residents, 2013–2020. Am J Prev Med. 2023 [cited 13 Apr 2023]. pmid:36918131
- 23. Rhee TG, Rosenheck RA. Admissions to substance use treatment facilities for cannabis use disorder, 2000–2017: Does legalization matter? Am J Addict. 2022;31: 423–432. pmid:35368113
- 24. Lee Y-H, Chang Y-C, Liu C-T, Arndt S. Is Medical Marijuana Legalization Associated With Prescription Drug Misuse, Illicit Drug Use, or Combination of Both Among Adults in the United States? J Drug Issues. 2020: 566–578.
- 25. Wong SW, Lin HC. Medical marijuana legalization and associated illicit drug use and prescription medication misuse among adolescents in the U.S. Addictive Behaviors. 2019;90: 48–54. pmid:30359847
- 26. Castillo-Carniglia A, Keyes KM, Hasin DS, Cerdá M. Psychiatric comorbidities in alcohol use disorder. Lancet Psychiatry. 2019;6: 1068–1080. pmid:31630984
- 27. Grant B, Goldstein R, Saha T, al. et. Epidemiology of DSM-5 alcohol use disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA Psychiatry. 2015;72: 757–766. pmid:26039070
- 28. Grant B, Stinson F, Dawson D, al. et. Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004;61: 807–816. pmid:15289279
- 29. Melchior M, Prokofyeva E, Younes N, Surkan P, Martins S. Treatment for illegal drug use disorders: the role of comorbid mood and anxiety disorders. BMC Psychiatry. 2014;14: 89. pmid:24670230
- 30. Preuss U, Gouzoulis-Mayfrank E, Havemann-Reinecke U, al. et. Psychiatric comorbidity in alcohol use disorders: results from the German S3 guidelines. Eur Arch Psychiatry Clin Neurosci. 2018;268: 219–229. pmid:28439723
- 31. Lai H, Cleary M, Sitharthan T, Hunt G. Prevalence of comorbid substance use, anxiety and mood disorders in epidemiological surveys, 1990–2014: a systematic review and meta-analysis. Drug Alcohol Depend. 2015;154: 1–13. pmid:26072219
- 32. Johnson AD, Miller BL. An Examination of Substance Use Disorder Type, Legal Status of Drug, and Treatment Outcomes. https://doi.org/101080/1082608420211949608. 2021;56: 1670–1676. pmid:34279170
- 33. Havlik JL, Rhee TG, Rosenheck RA. Characterization of quality of life among individuals with current treated, untreated, and past alcohol use disorder. Am J Drug Alcohol Abuse. 2023 [cited 10 Nov 2023]. pmid:37788415
- 34. Falker CG, Stefanovics EA, Rhee TG, Rosenheck RA. Women’s Use of Substance Use Disorder Treatment Services: Rates, Correlates, and Comparisons to Men. Psychiatric Quarterly. 2022;93: 737–752. pmid:35661318
- 35.
National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III) | National Institute on Alcohol Abuse and Alcoholism (NIAAA). [cited 10 Aug 2022]. Available: https://www.niaaa.nih.gov/research/nesarc-iii.
- 36. Grant BF, Saha TD, June Ruan W, Goldstein RB, Patricia Chou S, Jung J, et al. Epidemiology of DSM-5 Drug Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions-III. JAMA Psychiatry. 2016;73: 39–47. pmid:26580136
- 37. Hasin DS, Saha TD, Kerridge BT, Goldstein RB, Chou SP, Zhang H, et al. Prevalence of Marijuana Use Disorders in the United States Between 2001–2002 and 2012–2013. JAMA Psychiatry. 2015;72: 1235–1242. pmid:26502112
- 38. Laudet AB. The Case for Considering Quality of Life in Addiction Research and Clinical Practice. Addiction Science & Clinical Practice. 2011;6: 44. pmid:22003421
- 39. Brazier JE, Roberts J. The estimation of a preference-based measure of health from the SF-12. Med Care. 2004;42: 851–859. pmid:15319610
- 40. Gandek B, Ware JE, Aaronson NK, Apolone G, Bjorner JB, Brazier JE, et al. Cross-validation of item selection and scoring for the SF-12 Health Survey in nine countries: Results from the IQOLA Project. J Clin Epidemiol. 1998;51: 1171–1178. pmid:9817135
- 41. Ware JE, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34: 220–233. pmid:8628042
- 42. Na PJ, Stefanovics EA, Rhee TG, Rosenheck RA. “Lives of despair” at risk for “deaths of despair”: tracking an under-recognized, vulnerable population. Soc Psychiatry Psychiatr Epidemiol. 2022;57: 1123–1134. pmid:35028698
- 43. Bommersbach TJ, Rosenheck RA, Petrakis IL, Rhee TG. Why are women more likely to attempt suicide than men? Analysis of lifetime suicide attempts among US adults in a nationally representative sample. J Affect Disord. 2022;311: 157–164. pmid:35598742
- 44. Rhee TG, Gillissie ES, Nierenberg AA, McIntyre RS. Association of current and remitted bipolar disorders with health-related quality of life: Findings from a nationally representative sample in the US. J Affect Disord. 2023;321: 33–40. pmid:36273681
- 45. Hasin DS, Greenstein E, Aivadyan C, Stohl M, Aharonovich E, Saha T, et al. The Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5): procedural validity of substance use disorders modules through clinical re-appraisal in a general population sample. Drug Alcohol Depend. 2015;148: 40. pmid:25604321
- 46.
Cohen S, Mermelstein R, Kamarck T, Hoberman HM. Measuring the Functional Components of Social Support. In: Sarason IG, Sarason BR, editors. Social Support: Theory, Research and Applications. Dordrecht: Springer Netherlands; 1985. pp. 73–94. https://doi.org/10.1007/978-94-009-5115-0_5
- 47.
U.S. Alcohol Epidemiologic Data Reference Manual, Volume 10. [cited 10 Oct 2022]. Available: https://pubs.niaaa.nih.gov/publications/NESARC_DRM3/NESARC3DRM.htm.
- 48. Cantor JH, Deyoreo M, Hanson R, Kofner A, Kravitz D, Salas A, et al. Patterns in Geographic Distribution of Substance Use Disorder Treatment Facilities in the US and Accepted Forms of Payment From 2010 to 2021. JAMA Netw Open. 2022;5: e2241128–e2241128. pmid:36367729
- 49. Chipp C, Dewane S, Brems C, Johnson ME, Warner TD, Roberts LW. “If only someone had told me…”: lessons from rural providers. J Rural Health. 2011;27: 122–130. pmid:21204979
- 50. Stein BD, Dick AW, Sorbero M, Gordon AJ, Burns RM, Leslie DL, et al. A population-based examination of trends and disparities in medication treatment for opioid use disorders among Medicaid enrollees. Subst Abus. 2018;39: 419–425. pmid:29932847
- 51. Rhee TG, Peltier MR, Sofuoglu M, Rosenheck RA. Do Sex Differences Among Adults With Opioid Use Disorder Reflect Sex-specific Vulnerabilities? A Study of Behavioral Health Comorbidities, Pain, and Quality of Life. J Addict Med. 2020;14: 502–509. pmid:32371659
- 52. Jordan A, Costa M, Nich C, Swarbrick M, Babuscio T, Wyatt J, et al. Breaking through social determinants of health: Results from a feasibility study of Imani Breakthrough, a community developed substance use intervention for Black and Latinx people. Journal of substance use and addiction treatment. 2023; 209057. pmid:37207836
- 53. Grim BJ, Grim ME. Belief, Behavior, and Belonging: How Faith is Indispensable in Preventing and Recovering from Substance Abuse. J Relig Health. 2019;58: 1713. pmid:31359242
- 54. West SL, O’Neal KK. Project D.A.R.E. Outcome Effectiveness Revisited. Am J Public Health. 2004;94: 1027. pmid:15249310
- 55. Werb D, Mills EJ, DeBeck K, Kerr T, Montaner JSG, Wood E. The effectiveness of anti-illicit-drug public-service announcements: a systematic review and meta-analysis. J Epidemiol Community Health. 2011;65: 834–840. pmid:21558482
- 56. Esser MB, Leung G, Sherk A, Bohm MK, Liu Y, Lu H, et al. Estimated Deaths Attributable to Excessive Alcohol Use Among US Adults Aged 20 to 64 Years, 2015 to 2019. JAMA Netw Open. 2022;5: e2239485–e2239485. pmid:36318209
- 57. Ng J, Sutherland C, Kolber MR. Does evidence support supervised injection sites? Canadian Family Physician. 2017;63: 866. Available: /pmc/articles/PMC5685449/. pmid:29138158
- 58. Levengood TW, Yoon GH, Davoust MJ, Ogden SN, Marshall BDL, Cahill SR, et al. Supervised Injection Facilities as Harm Reduction: A Systematic Review. Am J Prev Med. 2021;61: 738–749. pmid:34218964
- 59. Meier B. Pain killer: an empire of deceit and the origin of America’s opioid epidemic. Random House; 2018.
- 60. Palamar JJ, Cottler LB, Goldberger BA, Severtson SG, Grundy DJ, Iwanicki JL, et al. Trends in characteristics of fentanyl-related poisonings in the United States, 2015–2021. https://doi.org/101080/0095299020222081923. 2022;48: 471–480. pmid:35704785
- 61. Jannetto PJ, Helander A, Garg U, Janis GC, Goldberger B, Ketha H. The Fentanyl Epidemic and Evolution of Fentanyl Analogs in the United States and the European Union. Clin Chem. 2019;65: 242–253. pmid:30305277
- 62. Macy B. Dopesick: Dealers, doctors and the drug company that addicted America. Bloomsbury Publishing; 2018.
- 63. Grossman ER, Benjamin-Neelon SE, Sonnenschein S. Alcohol Consumption during the COVID-19 Pandemic: A Cross-Sectional Survey of US Adults. Int J Environ Res Public Health. 2020;17: 1–10. pmid:33316978
- 64. Yeo YH, He X, Ting PS, Zu J, Almario C V., Spiegel BMR, et al. Evaluation of Trends in Alcohol Use Disorder–Related Mortality in the US Before and During the COVID-19 Pandemic. JAMA Netw Open. 2022;5: e2210259–e2210259. pmid:35507347
- 65. Pollard MS, Tucker JS, Green HD. Changes in Adult Alcohol Use and Consequences During the COVID-19 Pandemic in the US. JAMA Netw Open. 2020;3: e2022942–e2022942. pmid:32990735
- 66. Chen PH, Ting PS, Almazan E, Chander G, Cameron AM, Gurakar A. Inter-Hospital Escalation-of-Care Referrals for Severe Alcohol-Related Liver Disease with Recent Drinking During the COVID-19 Pandemic. Alcohol Alcohol. 2022;57: 185–189. pmid:34219147
- 67. Smith SM, Goldstein RB, Grant BF. The association between post-traumatic stress disorder and lifetime DSM-5 psychiatric disorders among veterans: Data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III). J Psychiatr Res. 2016;82: 16–22. pmid:27455424
- 68. Geoffroy PA, Tebeka S, Blanco C, Dubertret C, Le Strat Y. Shorter and longer durations of sleep are associated with an increased twelve-month prevalence of psychiatric and substance use disorders: Findings from a nationally representative survey of US adults (NESARC-III). J Psychiatr Res. 2020;124: 34–41. pmid:32114030
- 69. Browne KC, Stohl M, Bohnert KM, Saxon AJ, Fink DS, Olfson M, et al. Prevalence and Correlates of Cannabis Use and Cannabis Use Disorder Among U.S. Veterans: Results From the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III). Am J Psychiatry. 2022;179: 26–35. pmid:34407625