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. Author manuscript; available in PMC: 2021 Apr 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Feb 6;209:107895. doi: 10.1016/j.drugalcdep.2020.107895

Perception of treatment need among adults with substance use disorders: Longitudinal data from a representative sample of adults in the United States

Scott J Moeller a, Jonathan M Platt b, Melody Wu b, Renee D Goodwin b,c
PMCID: PMC7418940  NIHMSID: NIHMS1607119  PMID: 32078975

Abstract

Background:

Most individuals with substance use disorders (SUDs) do not seek treatment. Lack of perceived treatment need (PTN) is one contributing factor, but little is known about PTN over time. We estimated whether PTN changed over three years among those with SUDs in the United States and identified select variables, including sociodemographics and symptom burden, that predict malleability vs. stability of PTN.

Methods:

Data were from Waves 1 (collected 2001-2002) and 2 (collected 2004-2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC); 1,695 adults who met DSM criteria for alcohol or non-alcohol SUD at Wave 1 and maintained ≥1 diagnostic symptom at Wave 2 were included.

Results:

Most individuals with SUDs (77.2%) did not perceive a need for treatment at Wave 1 baseline. Only about 1 in 8 individuals not perceiving a need for treatment in Wave 1 came to perceive a need in Wave 2 (adjusted odds ratio=0.18, 99% confidence interval=0.11-0.29). In contrast, about half the individuals who perceived a need for treatment in Wave 1 no longer did so in Wave 2, despite maintaining ≥1 SUD symptom. Married respondents, and respondents with more SUD symptoms, were more likely to transition from low- to high-PTN status three years later. Respondents with incomes >$35,000 were less likely to transition to high-PTN status three years later.

Conclusions:

PTN was more likely to decline than increase over time. Low PTN appears to be stable among adults with SUDs in the United States, presenting a potentially enduring barrier to treatment-seeking.

Keywords: treatment, substance use disorder, National Epidemiologic Survey on Drugs and Related Conditions (NESARC), self-perception, barrier to treatment

1. INTRODUCTION

Substance use disorders (SUDs) are highly prevalent (Grant et al., 2016) and, in many cases, are accompanied by catastrophic life consequences at the individual and population level (Degenhardt and Hall, 2012; Gilmore et al., 2016). Despite available treatments for SUDs (Benishek et al., 2014; Donoghue et al., 2015; Magill and Ray, 2009; Nosyk et al., 2015), only a minority of individuals with a SUD ultimately seeks treatment (Blanco et al., 2015; Compton et al., 2007; Grant et al., 2016; Hedden and Gfroerer, 2011; Mojtabai and Crum, 2013; Urbanoski et al., 2017). There are many reasons why treatment use among individuals with SUDs remains low (Ali et al., 2016), including but not limited to pessimistic efficacy beliefs (Mojtabai and Crum, 2013), lack of health insurance (Ilgen et al., 2011), cultural norms (Gopalkrishnan and Babacan, 2015), and/or a paucity of resources (McLellan and Meyers, 2004).

In addition to these sociocultural factors, one presumably fundamental obstacle in accessing services is that often individuals suffering with SUDs do not perceive a need for treatment; that is, their perceived treatment need (PTN) is low. A number of studies have documented predictors of PTN, including sociodemographic, mental health, and/or legal factors (Booth et al., 2013; Booth et al., 2014; Borders et al., 2015; Edlund et al., 2006; Grella et al., 2009; Mojtabai and Crum, 2013; Mojtabai et al., 2002; Oleski et al., 2010), as well as various substance use-related predictors (Edlund et al., 2009; Falck et al., 2007; Glass et al., 2015; Wu and Ringwalt, 2004). A crucial gap in the literature, however, is that most prior studies investigating PTN have been cross-sectional [for exceptions, see Charuvastra et al., 2002; Mojtabai and Crum, 2013]. A cross-sectional association with PTN is an important first step, but it cannot specify the temporal direction of the relationship. Furthermore, such studies offering a snapshot of PTN do not account for the fact that PTN may be a dynamic process that evolves (or potentially devolves) over time.

To our knowledge, there is little available information on patterns of PTN over time among adults with a SUD, highlighting the need for prospective, longitudinal data to enhance our understanding of these relationships over time. For example, building off the Transtheoretical Model of Behavior Change that predicts, among other outcomes, treatment receptivity (Callaghan et al., 2005; Prochaska et al., 1992; Willoughby and Edens, 1996), PTN and motivation to change behavior likely progress through a series of stages. The beginning stage is denial or low recognition of a problem or need for change (Precontemplation, potentially corresponding to low PTN), followed by increased recognition and motivation to confront the problem (Contemplation) and finally by behaviors or thoughts meant to initiate or sustain positive change (Action, Maintenance) (with the latter three stages potentially corresponding to high PTN). Thus, examining PTN as a dynamic, longitudinal process appropriately acknowledges that the evolution of PTN in the same individuals may be theoretically nuanced and complex. Longitudinal data can be used to evaluate whether PTN is likely or unlikely to change over time (e.g., from low PTN to high PTN), following theoretical frameworks such as the Transtheoretical Model, and which factors predict such changes. Such information can aid in identifying whether and to what degree certain groups may benefit from outreach to increase insight and/or treatment access, given that high PTN has predicted future use of treatment services among individuals with SUD (Charuvastra et al., 2002; Mojtabai and Crum, 2013).

The overall goals of the current study were to investigate the degree to which PTN changes over time and to identify predictors of becoming PTN-high among individuals meeting criteria for SUD using a longitudinal, nationally representative dataset of the United States (US) population aged 17+. First, we estimated the frequency of PTN changes from baseline to three-year follow-up. Second, we tested whether change in PTN (from low PTN to high PTN) over this three-year period is predicted by sociodemographic factors and (separately) SUD symptom burden. Knowledge gained from examining longitudinal predictors of PTN could help identify subpopulations unlikely to seek services due to persistently low PTN status.

2. METHODS

2.1. Design

Data were analyzed using National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) Waves 1 and 2. The NESARC is a nationally representative sample of the US adult population, and it is arguably the only nationally representative data with this level of detail on substance use and substance use disorders. The study used a two-wave multistage stratified design in which primary sampling units, housing units, and group-quarter units were stratified to oversample certain under-represented sociodemographic groups, specifically non-Hispanic Black, Hispanic, and young (aged 18-24) adults. The final data were weighted according to the demographic distribution of the US population based on the 2000 census. Experienced lay interviewers completed structured Wave 1 interviews of 43,093 respondents in 2001-2002. Wave 2 interviews occurred three years later among 34,653 (80%) of the Wave 1 respondents who were aged 17 and older at baseline (Grant and Kaplan, 2005; Grant, B. et al., 2003).

2.2. Participants

Our goal was to investigate the factors affecting whether individuals with persistent substance use problems (i.e., symptoms maintained over three years, thus capturing individuals who presumably stand to benefit from an intervention of some kind) perceive a need for treatment, and whether such perceptions change over time. Accordingly, we analyzed a subset of NESARC respondents meeting the following criteria. First, included participants met Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria (see Measures, below) for alcohol use disorder (AUD) or non-alcohol SUD at Wave 1. We did not include individuals with emerging SUD between Waves 1 and 2 (i.e., 8.9% of NESARC respondents did not have an SUD at Wave 1, but did so at Wave 2). Second, included participants continued to have ≥1 SUD symptom at Wave 2. We excluded individuals who fully eliminated substance-related problems between Waves 1 and 2, for whom PTN would be irrelevant (i.e., among individuals with AUD or SUD at Wave 1, 32.4% were excluded, reporting no SUD symptoms at Wave 2). [Note that this 32.4% represents a subset of the sample whose SUD diagnosis remitted between Waves 1 and 2 (i.e., among those with AUD or SUD at Wave 1, 49.0% reported <2 SUD symptoms at Wave 2).] Collectively, these inclusion criteria yielded a final analytical sample size of N=1,695. For more information on the entire NESARC sample vs. the PTN analytic sample vis-à-vis symptom counts at Waves 1 and 2, see Supplementary Table 1.

2.3. Measures

2.3.1. AUD and SUD Assessment

At each Wave, diagnoses were recorded according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) using the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version (AUDADIS-IV), a fully structured diagnostic interview designed for use by professional interviewers who are not clinicians (Grant, B.F. et al., 2003; Ruan et al., 2008). In the AUDADIS-IV, symptom questions were asked separately for alcohol and ten types of drugs (sedatives, tranquilizers, opioids, amphetamines, cannabis, cocaine, hallucinogens, inhalants, heroin, and other drugs), and were used to ascertain DSM-IV abuse and dependence status. To ensure sufficient analytical sample sizes, and considering that AUD and other SUDs both had low overall PTN as shown below, drugs and alcohol were considered as a single SUD category. The reliability and validity of the AUDADIS-IV are well documented in numerous national and international psychometric studies conducted in clinical and general populations, the latter being for whom it was designed (Blanco et al., 2008; Conway et al., 2006; Cranford et al., 2011; Delforterie et al., 2015; Elliott et al., 2016; Fenton et al., 2012; Hoertel et al., 2014; Keyes et al., 2008; Keyes and Hasin, 2008; Keyes et al., 2010; Keyes et al., 2012; Oquendo et al., 2010; Roberts et al., 2018; Verdura Vizcaino et al., 2014). To adapt as closely as possible to DSM-5 criteria, all abuse and dependence symptoms were pooled, and those who endorsed at least two symptoms were considered to meet criteria for a SUD. However, it should be noted that this approach does not exactly reproduce DSM-5 criteria, given the addition of craving and the elimination of the legal problem criterion. Supplementary Table 2 displays frequencies and percentages of SUDs for the analytical sample (and the entire NESARC sample).

2.3.2. Change in Perceived Treatment Need Status

Respondents were asked whether they sought treatment for each substance they reported having used. At Wave 1, respondents were asked if they ever sought treatment; at Wave 2, they were asked if they sought treatment since their Wave 1 interview. Additional questions were asked about specific sources of help (e.g., alcoholics/narcotics anonymous, outpatient clinic) to capture all potential sources of treatment thoroughly. If the respondent reported never having sought treatment, they were then asked whether they ever thought about seeking help but did not actually pursue it. From the responses to these two questions, respondents were classified as PTN-low if they reported no need for treatment. Respondents were classified as PTN-high if they either (1) sought treatment or (2) perceived a need for treatment but did not seek it. These latter two categories were collapsed as a single category to attain sufficient analytical sample sizes. We defined PTN status change as becoming PTN-high at Wave 2, among those who were PTN-low at Wave 1.

2.3.3. Predictors of Change in Perceived Treatment Need Status

We examined PTN change among sociodemographic groups, including sex (women vs. men), marital status (never married, married/cohabiting, divorced), race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Other; note that Other was not included in the analyses due to small sample size), age (17-34, 35-54, 55+), education status (some high school, high school degree/GED, some college, college degree), and annual income ($0-$19,999, $20,000-$34,999, $35,000-$49,999, $50,000+). We also examined two variables indicating whether the respondent had a parent with SUD (yes vs. no) and (separately) whether the respondent had a grandparent with SUD (yes vs. no). Each of these two variables allowed us to test whether a family history of SUD was related to PTN change. Finally, we examined whether symptom burden was related to PTN change. To do this, we summed the total number of SUD symptoms reported by each respondent and created indicator variables of 4-5 and 6+ (vs. 2-3) SUD symptoms at Wave 1. Supplementary Table 3 displays the correlations between number of symptoms at Wave 1 and Wave 2, by substance.

2.4. Analyses

Data were weighted to reflect the complex design of the NESARC samples using Taylor series linearization. Analyses were conducted using SAS v9.4 with survey procedures to account for the multi-stage sampling. First, we examined the prevalence of PTN-low versus PTN-high at Waves 1 and 2. PTN status was estimated for the overall sample and stratified by sociodemographic and symptom burden groups. Second, we estimated the odds of becoming PTN-high at Wave 2 among those who were PTN-low at Wave 1 vs. remaining PTN-low. Third, we estimated the odds of becoming PTN aware for each sociodemographic group and at each level of symptom burden. Odds ratios (ORs) were estimated with logistic regression models, unadjusted and adjusted for sociodemographic confounding variables. ORs were only adjusted for variables that could plausibly be considered confounders (i.e., only unadjusted ORs for sex are presented because none of the analytical variables meet criteria for confounding). We controlled for the following sociodemographic variables: sex, age, marital status, income, education, race, parent and grandparent history of alcohol use disorder. Age was not correlated with income (r=0.03) or education (r=−0.103). Education and income were moderately correlated (r=0.44), but nonetheless reflect distinct variables in the analytical sample. Therefore, it is statistically appropriate to adjust for income and education separately. To ensure reliable results given the number of analyses, we specified a conservative to α=0.01 threshold.

Data were missing for 4.7% of the PTN responses (n=79). Therefore, the main analysis used multiple imputation for these missing data with 15 combined datasets, with corrected standard errors (Rubin, 2004). Imputation models included all observed model covariates described above. Below, we report all the effects that were statistically significant at p<0.01 in the adjusted analyses.

3. RESULTS

3.1. Change in Perceived Treatment Need Status

Table 1 details the frequency of treatment awareness overall, by sociodemographic groups, and by symptom burden. Among individuals with SUD at Wave 1 (and who continued to report at least one SUD symptom at Wave 2; n=1,695), more had low PTN (n=1,302, 77.2%) than high PTN (n=393; 22.8%). Low PTN was reported for both AUD (76.8%, unadjusted) and non-alcohol SUDs (62.8%, unadjusted). Among the 1,302 individuals with low PTN at Wave 1, only about 1 in 8 (n=171; 14.1%) changed their perceptions to high PTN by Wave 2. In adjusted logistic regressions, those with SUD who were PTN-low at Wave 1 were significantly less likely to change PTN status by Wave 2 than to remain PTN-low [adjusted odds ratio (aOR)=0.18 (99% confidence interval (CI)=0.11, 0.29)] (Table 2). In contrast, among the 393 individuals with high PTN at Wave 1, about half (n=225; 51.8%) changed their perceptions to low PTN by Wave 2. Thus, PTN was more likely to wane than increase over time.

Table 1.

Frequency of perceived treatment need among those with Wave 1 substance use disorder and at least one Wave 2 substance use disorder symptom

Wave 2 PTN
n (%) Total (%) High % Low % p-value
Total 1695 (100.0) 339 (21.1) 1356 (78.9) <0.0001
Wave 1 PTN
 High 393 (22.8) 168 (48.2) 225 (51.8) <0.0001
 Low 1302 (77.2) 171 (14.1) 1131 (84.0)
Sex
 Men 832 (67.5) 110 (14.6) 722 (85.4) 0.1826
 Women 470 (32.5) 61 (13.1) 409 (86.9)
Age
 17-34 796 (63.8) 108 (14.7) 688 (85.3) 0.0561
 35-54 440 (32.2) 56 (13.7) 384 (86.3)
 55+ 66 (4.1) 7 (9.6) 59 (90.4)
Marital status
 Married/cohabiting 455 (40.7) 64 (15.3) 391 (84.7) 0.1109
 Divorced 231 (13.1) 39 (16.1) 192 (83.9)
 Never married 616 (46.2) 68 (12.6) 548 (87.4)
Income
 $0-$19,999 496 (39.1) 92 (16.3) 496 (83.7) 0.001
 $20,000-$34,999 291 (21.5) 43 (13.4) 291 (86.6)
 $35,000-$49,999 166 (12.1) 20 (11.9) 166 (88.1)
 $50,000+ 178 (13.1) 16 (10.4) 178 (89.6)
Race/ethnicity
 Non-Hispanic White 820 (74.8) 114 (13.9) 706 (86.1) 0.1926
 Non-Hispanic Black 202 (9.6) 27 (15.7) 175 (84.3)
 Hispanic 237 (10.5) 25 (12.0) 212 (88.0)
 Other 43 (5.1) 5 (18.7) 38 (81.3)
Education
 Some high school 161 (12.0) 28 (17.7) 133 (82.3) <0.0001
 High school degree/GED 376 (28.1) 57 (17.1) 319 (82.9)
 Some college 478 (36.9) 65 (14.6) 413 (85.4)
 College degree 287 (23.0) 21 (8.0) 266 (92.0)
Parent alcohol problems
 No 863 (67.7) 104 (13.1) 759 (86.9) 0.0214
 Yes 439 (32.3) 67 (16.4) 372 (83.6)
Grandparent alcohol problems
 No 853 (64.5) 104 (13.9) 749 (86.1) 0.3998
 Yes 449 (35.5) 67 (14.6) 382 (85.4)
Symptoms of SUD severity
 2-3 652 (47.7) 56 (9.3) 596 (90.7) <0.0001
 4-5 444 (35.5) 63 (15.2) 381 (84.8)
 6+ 206 (16.8) 52 (25.5) 154 (74.5)
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Abbreviations: PTN=perceived treatment need; PTN-high=aware of a need for treatment; PTN-low=unaware of a need for treatment; SUD=substance use disorder.

Table 2.

Odds of perceived treatment need at Wave 2 among those with Wave 1 substance use disorder and at least one Wave 2 substance use disorder symptom

Unadjusted Adjusted
OR 99% CI OR 99% CI
Wave 1 SUD PTN-low (vs. PTN-high)† 0.15 (0.09, 0.23)* 0.18 (0.11, 0.29)*
Symptoms of SUD severity (vs. 2-3)†
 4-5 1.70 (1.14, 2.54)* 1.68 (1.13, 2.51)*
 6+ 3.10 (2.43, 3.95)* 3.08 (2.42, 3.93)*
Sex (vs. men) 0.82 (0.46, 1.45)
Age (vs. 55+)
 17-34 1.78 (0.88, 3.60)
 35-54 1.58 (0.81, 3.08)
Race/ethnicity (vs. NH White)
 NH Black 1.23 (0.70, 2.16)
 Hispanic 0.59 (0.31, 1.13)
 Other 0.64 (0.16, 2.60)
Education (vs. college degree) †
 Some high school 1.67 (1.12, 2.49)* 1.51 (0.86, 2.63)
 High school degree/GED 1.44 (1.05, 1.97)* 1.47 (0.93, 2.34)
 Some college 1.26 (0.94, 1.70) 1.52 (0.99, 2.33)
Marital status (vs. never married†
 Married 2.99 (2.34, 3.81)* 2.10 (1.50, 2.94)*
 Divorced 2.31 (1.65, 3.22)* 1.60 (0.98, 2.61)
Income (vs. $0-19,999) †
 $20-34,999 0.68 (0.52, 0.88)* 0.82 (0.58, 1.17)
 $35-49,999 0.75 (0.55, 1.04) 0.53 (0.32, 0.88)*
 $50,000+ 0.48 (0.34, 0.69)* 0.48 (0.29, 0.81)*
Parent alcohol problems‡ 1.17 (0.63, 2.15) 1.19 (0.65, 2.19)
Grandparent alcohol problems 0.94 (0.52, 1.70)
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Abbreviations: NH=non-Hispanic; PTN=perceived treatment need; PTN-high=aware of a need for treatment; PTN-low=unaware of a need for treatment; SUD=substance use disorder.

†

Adjusted for sex, age, marital status, income, education, race, parent and grandparent history of alcohol use disorder. ORs were only adjusted for variables that could plausibly be considered confounders (i.e., only unadjusted ORs for sex are presented because none of the analytical variables meet the criteria for confounding).

‡

Adjusted for grandparent alcohol problems.

*

Significant at the alpha=.01 level.

3.2. Predictors of Change in Perceived Treatment Need Status

Table 2 presents the odds of changing’s one PTN status from Wave 1 to Wave 2 as a function of sociodemographics and number of diagnostic symptoms, among individuals with SUD at Wave 1 and at least one SUD symptom at Wave 2. Married respondents (vs. never-married respondents) had higher odds of transitioning from low PTN at Wave 1 to high PTN at Wave 2. Respondents with incomes above $35,000 (specifically, respondents who earned $35-49,999 or who earned $50,000+ vs. respondents with incomes below $19,999) had lower odds of transitioning from low PTN at Wave 1 to high PTN at Wave 2. That is, a higher relative income and no spouse were associated with stable perceptions of low treatment need.

Beyond sociodemographics, respondents with higher disease burden had higher odds of transitioning from low PTN at Wave 1 to high PTN at Wave 2. Specifically, compared with having 2-3 SUD symptoms, respondents with 4-5 symptoms had 1.68 times the adjusted odds of increasing their PTN three years later (99% CI=1.13-2.51). Similarly, respondents with 6+ symptoms had 3.08 times the adjusted odds of increasing their PTN three years later (99% CI=2.42-3.93).

4. DISCUSSION

SUDs are highly prevalent in the US and around the world, contributing to job loss, financial problems, social and occupational impairment, and premature mortality (Degenhardt and Hall, 2012; Grant et al., 2016). According to the National Survey on Drug Use and Health (NSDUH) (2017 data), approximately 19.7 million people aged 12 or older (7.2% of this population) had a SUD related to their use of alcohol or illicit drugs in the past year, including 14.5 million people who had an AUD and 7.5 million people who had an illicit drug use disorder. Lack of PTN has been identified as a primary barrier to receiving treatment (Wallace et al., 2009). Results from the current study suggest that (1) the majority of individuals with SUDs in the US do not perceive a need for treatment; (2) this perception appears to remain mostly fixed over three years; and (3) change in PTN over three years among individuals with SUD is predicted to some degree by SUD severity, as well as marital status and income.

The primary contribution of this study is that, to our knowledge, we provide the first evidence of longitudinal persistence of low PTN among a representative sample of US adults. In particular, the majority of individuals with SUDs do not perceive a need for treatment, and such perceptions of low need for treatment are relatively stable over time, at least over a three-year period. Simultaneously, perception of high need for treatment was comparably more malleable over this same time period, moving toward low PTN. These findings, considered together, may have the effect of perpetuating low PTN among individuals with SUD. Given the seeming intransigence of low PTN over time (and its emergence in respondents with initially high PTN), our results speak to the need for developing outreach campaigns to highlight the benefits of treatment, educate the public and primary health providers on the symptoms and signs of SUDs, and increase awareness that there are treatments available that can help. Such efforts are crucial because prior results have shown that those who perceive a need for treatment are more likely to enter treatment in the future (Charuvastra et al., 2002; Mojtabai and Crum, 2013). A better understanding of barriers to treatment and predictors of these barriers can help to develop strategies to increase access to services.

Although most individuals maintained low PTN over three years as discussed above, select sociodemographic characteristics and symptom severity predicted a change in perceptions over three years. The most robust sociodemographic predictors, significant even at our conservative α=0.01 threshold and robust to potential confounders, were being currently married and having incomes above $35,000. These two variables predicted greater odds and lower odds, respectively, of transitioning toward higher PTN (awareness of treatment need or the actual seeking of treatment) after three years. These findings are consistent with prior cross-sectional research. For marriage, the experience of living with a spouse has previously been associated with higher PTN (Ding et al., 2014), possibly due to household conflict related to substance use. Perhaps having a spouse, with the potential for such conflict, may serve to change PTN over time. With respect to income, prior studies have shown that higher relative income is associated with lower likelihood of treatment use (Oleski et al., 2010; Urbanoski et al., 2017). Similar results have emerged with education, such that higher education is associated with a lower likelihood of treatment contact (Blanco et al., 2015). It is possible that those with greater socioeconomic status afforded by higher relative incomes (and higher educational attainment, which in our study was initially associated with lower odds of increasing PTN but did not survive adjustment for covariates; Table 2) have resources that enable maintenance of the perception that substance use is not adversely affecting their lives. Importantly, neither income nor education is confounded with age: these variables were uncorrelated in the sample, and our analyses with income controlled for age as a covariate. Overall, current results advance prior research by showing that some of the same factors that cross-sectionally correlate with low PTN also prospectively predict the stability and evolution of PTN over time. Future research is needed to ascertain the mechanisms underlying the relationships.

In operationalizing SUD severity, we used number of SUD symptoms. We found a dose-response relationship between the number of SUD symptoms and increasing odds of changing from low PTN at Wave 1 to high PTN at Wave 2. This finding is consistent with prior work suggesting that low PTN was a more common reason for not seeking treatment among individuals with mild compared with more severe SUDs (Mojtabai et al., 2011); here, to our knowledge, we report the first longitudinal evidence for this relationship. Furthermore, our overall findings demonstrate that most respondents did not perceive a need for treatment (i.e., they were part of the PTN-low group), regardless of the number of SUD symptoms they endorsed. Thus, many individuals with moderate SUD as well as a sizable proportion of individuals with severe SUD symptoms remained untreated and potentially unaware either of a problem or that treatment might be helpful in addressing that problem. Even for individuals with milder SUD, it still might be societally impactful to provide more extensive public education about the common symptoms and signs of substance use problems. Another avenue could be more widespread implementation of proactive screening and brief behavioral interventions in primary care settings within a Screening, Brief Intervention, and Referral to Treatment (SBIRT) framework (Jonas et al., 2012; Rehm et al., 2016; Watkins et al., 2018). These approaches have the potential to enhance PTN, clinical insight, and behavioral self-awareness (Garland et al., 2013; Goldstein et al., 2009; Moeller and Goldstein, 2014; Verdejo-Garcia et al., 2013); prevent the onset of more severe impairment (Ettner et al., 2006); and reduce societal stigma surrounding SUDs (Kulesza et al., 2014; Yang et al., 2017). Future studies may also incorporate additional severity information, such as the presence of co-occurring disorders, which tend to increase PTN among individuals with SUDs (Urbanoski et al., 2017) while simultaneously decreasing perceptions that treatment needs have been met (Urbanoski et al., 2007, 2008).

A limitation of our study is that, for reasons of ensuring adequate sample size, we included participants who endorsed as few as one SUD symptom at Wave 2; such individuals therefore would not meet diagnostic criteria at follow-up. Accordingly, the prevalence of individuals with actual SUD who do not perceive a need for treatment might be lower than the numbers reported here (for the Wave 2 NESARC assessment). Nevertheless, percentages of those not perceiving a need for treatment exceeding 75% are in line with responses from other national surveys, such as the NSDUH [e.g., (Moeller et al., 2019)]. A second limitation, again due to sample size constraints, is that we collapsed individuals who perceived a need for treatment but did not seek it and individuals who sought treatment into a single group. This approach was necessary given our study goal of examining respondents with persistent substance use symptoms, defined as (1) having a non-tobacco SUD at baseline and (2) maintaining symptomatology three years later. It was these focused inclusion criteria – not an overall low prevalence of SUD in the population – that resulted in a somewhat reduced analytic sample size. While it is likely that most individuals who were seeking treatment perceived at least some need for it, it is also possible that external factors, such as a mandate from court, could be the sole reason for seeking treatment in some cases. Third, there is a possibility of censoring by death or other adverse health outcome. For example, individuals with more severe SUD (who are more likely to have high PTN) may have experienced more adverse consequences, rendering them less likely to respond during Wave 2. However, we emphasize that even individuals with severe SUD (6+ symptoms) were, as a group, more likely than not to have low PTN. Fourth, to ensure adequate sample sizes, we collapsed across AUD and non-alcohol SUDs in the analyses. Because alcohol is legal at the US federal level whereas the other substances are not, the type of SUD could be an effect modifier on PTN. Although we note that PTN was also low in non-alcohol SUDs, partially reducing this concern, future studies may consider examining PTN and treatment-seeking behavior for each substance separately, toward disentangling the effects of comorbidity. Fifth, our approach for assigning diagnoses does not account for the addition of craving and removal of legal problems as diagnostic criteria in DSM-5. Finally, as stated earlier, individuals who reduced or eliminated problematic substance use from Wave 1 to Wave 2 were outside the scope of this study and thus not included in the analyses. It would be informative for future studies to evaluate, among this unique population, which factors promote such recovery (and/or, in the opposite population, which factors might precipitate the emergence of new problems).

In conclusion, the majority of individuals meeting criteria for SUD, encompassing AUDs and non-alcohol SUDs, did not report needing treatment, and they maintained these perceptions over the next three years despite maintaining at least some degree of problematic use. This was especially true in unmarried adults and those with higher relative incomes (i.e., above $35,000). Outreach campaigns could be developed to inform the public about the signs and symptoms of SUDs and facilitate the implementation of SBIRT-type approaches in primary health care settings; in doing so, it could be potentially useful to focus resources on subgroups identified to have the greatest risk of having low PTN. Such campaigns may help increase awareness of substance-related problems and therefore potentially a need for treatment, which could in turn have vast benefits for individuals and society.

Supplementary Material

Supplement

Acknowledgments

Funding/Support

This work was supported by the National Institutes of Health/National Institute on Drug Abuse [grant numbers # K01DA037452, T32DA031099, R01DA20892]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Declarations of Interest: none

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