Abstract

Background

Substance use disorders (SUDs), encompassing alcohol (AUDs) and drug use disorders (DUDs), are significant global public health concerns. While SUDs are well-documented worldwide, data on their prevalence and impact in Saudi Arabia remain scarce. This study investigates the epidemiology and burden of SUDs in Saudi Arabia using data from the Saudi National Mental Health Survey (SNMHS).

Methods

The SNMHS is a nationally representative cross-sectional epidemiological household survey, consisting of a sample of 4,004 participants aged 15鈥�65. The survey employed a stratified multistage clustered sampling design and used the WHO CIDI 3.0 to determine diagnoses. Descriptive statistics and multivariate binary logistic regression were used to analyze the data.

Results

The lifetime, 12-month, and 30-day prevalence of any SUD were 4.03%, 1.88%, and 0.78% (p鈥�<鈥�0.05), respectively. DUDs were more prevalent than AUDs overall. SUDs were significantly associated with younger age, lower education, low income, exposure to traumatic events, family burden, and childhood adversities. High psychiatric comorbidity and role impairment were observed. Treatment seeking was moderate, with only 44.9% of those with lifetime SUDs seeking any form of treatment.

Conclusions

SUDs in Saudi Arabia are more prevalent than previously thought, associated with significant psychiatric comorbidities and role impairment. Despite this, treatment seeking remains inadequate. These findings underscore the need for targeted prevention and intervention programs tailored to the demographic and cultural context of Saudi Arabia.

Introduction

Substance use disorders (SUDs) are major psychiatric conditions associated with substantial burden and disability worldwide [1, 2]. SUDs encompass alcohol use disorders (AUDs; including alcohol abuse and dependence), and drug use disorders (DUDs; including drug abuse and dependence) [3]. According to the World Health Organization, 283听million people were diagnosed with AUDs as of 2016, and 36听million people were diagnosed with DUDs in 2019 [1].

Global epidemiological data has demonstrated that alcohol-related disorders are more prevalent than drug-related disorders in the community [4, 5]. Globally, AUDs have an average lifetime prevalence of 8.6%, 12-month prevalence of 2.2%, and 30-day prevalence of 0.8% [5]. Whereas DUDs have a global average lifetime prevalence of 3.5%, 12-month prevalence of 0.7%, and 30-day prevalence of 0.3% [4]. However, these estimates varied widely across countries; AUDs were less prevalent in the Middle East relative to other regions; while DUDs were more prevalent in Western countries and less prevalent in Asia, the Middle East, Latin America, and Africa [4, 5]. Moreover, specific substance types and influences (norms, availability, risk perceptions) vary widely across countries and cultures [6]. In Saudi Arabia, alcohol has been identified as one of the most used substances among Saudi residents seeking addiction treatment [7]. Additionally, amphetamines, specifically the novel drug Captagon, account for a significant portion of substance misuse in the country [8]. The abuse of Gat is also common in the KSA and stands out particularly given its cultural significance, often easily acquired and smoked in community and family gatherings [8].

Further, it has been previously established that SUDs are characterized by high levels of impairment and psychiatric comorbidity and minimal or delayed treatment-seeking [7, 9,10,11]. Substance disorders have also been significantly associated with several sociodemographic risk factors including male gender, younger age, lower income, lower levels of educational attainment, and being unmarried [4, 5, 7, 12, 13]. Other reported risk-factors for the development of SUDs include the presence of adverse childhood experiences [14], familial burden [15], as well as exposure to traumatic events [16, 17].

Saudi Arabia and other Islamic countries employ strong prohibitions against the consumption and proliferation of alcohol and illicit drugs; nevertheless, SUDs have been reported in the country and recognized by the local authorities as a public health issue [7, 18, 19]. Addressing SUDs among the Saudi population requires a better understating of its scale and associated risk factors; however, information on SUDs in Saudi Arabia is scarce due to the lack of community-wide studies on the topic [7, 18, 19]. Indeed, much of the available epidemiological data on SUD in Saudi Arabia is derived either from studies conducted within particular population subgroups, clinical studies, or studies that did not include females in their samples and had considerable methodological limitations [7, 19]. In addition, results from international studies may not be representative of, or applicable to, the Saudi population, given its specific socioeconomic aspects, cultural norms and religious beliefs [12, 13, 20,21,22,23]. Therefore, while global epidemiological estimates of SUD can be relevant to regional policy in some respects, they may fall short in others due to the unique cultural and religious landscape of the Kingdom of Saudi Arabia (KSA) and other countries in the Middle East and North Africa (MENA) region.

Although prior studies contributed significantly to the epidemiological profile of SUDs on a global scale, differences in sample sizes, methodologies, assessment instruments, and importantly, the particular sensitivity of SUDs to cultural norms and attitudes, preclude clear inferences on the current state of substance use and disorders in the country [24]. We therefore aim to offer a comprehensive report on the epidemiology and burden of SUD in Saudi Arabia, using data from the Saudi National Mental Health Survey (SNMHS), by determining the prevalence, persistence, and comorbidity profile of SUDs in the country as well as their associations with certain sociodemographics, functional impairment, and treatment seeking behaviors. Given the unique scope and sample characteristics of our study relative to those conducted in similar contexts, we believe the implications of our findings can prove valuable in assisting policymakers and healthcare professionals in their efforts to combat the detrimental effects of substance consumption in the country and region.

Methodology

Study design, sampling, participants

As detailed elsewhere [25,26,27,28,29,30], the SNMHS is a nationally representative cross-sectional household survey focusing on Saudi citizens aged 15鈥�65 living in KSA. A stratified multistage clustered area probability sample of 4,302 households was selected as the first-stage sampling units in the remaining 11 administrative areas of the country. The household screening rate was 84% and the conditional interview response rate in screened households was 73%, with an estimated individual-level response rate of 61%. A total of 4,004 interviews were completed during the period from 2014 to 2016. We attempted to interview one randomly selected male and one randomly selected female in households that contained both males and females in the selected age range and only one randomly selected respondent in households in which eligible residents were either all male or all female.

We used a two-part case鈥揷ontrol sampling design to reduce the interview burden on respondents who did not meet criteria for any of the core mental disorders assessed in the SNMHS. All respondents completed Part I of the interview, which assessed core disorders. All Part I respondents who met lifetime criteria for any of these disorders plus a probability subsample of other Part I respondents were then administered Part II, which assessed disorders of secondary interest and a wide range of correlates. A total of 1,977 respondents were administered Part II of the interview. The Part I sample was weighted to adjust for differential probabilities of selection within and between households and to match sample distributions to population distributions on the cross鈥恈lassification of key socio鈥恉emographic and geographic data. The Part II sample was then additionally weighted for the under-sampling of Part I respondents without core disorders, resulting in the prevalence estimates of core disorders in the weighted Part II sample being identical to those in the Part I sample. In accordance with our aim, only the Part II sample was included in this paper.

Field procedures

All interviews were carried out face-to-face by trained lay interviewers. The interview and training materials were translated and adapted using a standardized evidence-based protocol (TRAPD: translation, revision, adjudication, pretesting, documentation) [31, 32]. Details of training, field, quality procedures are described elsewhere [33, 34]. Study procedures conformed to the international standards set by the Declaration of Helsinki. Written informed consent was obtained from respondents prior to beginning each interview. These consent procedures were approved by the Institutional Review Board at the King Faisal Hospital and Research Center.

Measures

Substance use disorders

Diagnoses were based on the WHO Composite International Diagnostic Interview (CIDI 3.0) [32, 33], a fully structured, lay-administered interview based on both ICD-10 [35] and DSM-IV diagnoses [3]. The SUDs assessed were alcohol use disorders (AUDs; including alcohol abuse disorders (AADs) and alcohol dependence disorders (ADDs)) and drug use disorders (DUDs; including drug abuse disorders (DADs) and drug dependence disorders (DDDs)).

The substance-use sections of the survey collected information regarding lifetime and recent (in the prior 12 months and 30 days) use, followed by clinical features associated with substance use syndromes. The substances assessed were alcohol (beer, ale, wine, wine coolers, and hard liquor like vodka, gin or whiskey) and drugs (sedatives or tranquilizers, stimulants, painkillers, Marijuana/Hashish/Gat, Cocaine, club drugs, hallucinogens, opioids, and inhalants or solvents).

Persistence

In line with other WMH surveys [36, 37], disorder persistence was indirectly estimated from the proportion of lifetime cases that had a disorder in the 12-months preceding the interview (12-months/lifetime), while episode persistence was indirectly estimated from the proportion of 12-month cases that had an episode in the 30-days preceding the interview (30-day/12-months).

Risk-factors/correlates

The correlates covered gender (male/female), age, education, income, marital status, employment (employed/unemployed/student), exposure to traumatic events (yes/no), and the presence of family burden (yes/no) and childhood adversities (yes/no). Due to low frequencies, age was grouped into different cohorts for substance disorders (15鈥�24, 25鈥�34, and 35鈥�+鈥墆ears old) and substance consumption (15鈥�22, 23鈥�30, 31鈥�49, and 50鈥�+鈥墆ears old). Education was classified as: low (primary: 0鈥�6 years), low-average (secondary school: 7鈥�9 years), high-average (high school & 3 years of college: 10鈥�15 years), and high (post-graduate: 16鈥�+鈥墆ears). Family income status was calculated based on total family income from all sources divided by the number of people in the household to average the income per capita for each household. Income was classified based on the median of the entire sample as follows: low (values less than 50% of the median), low-average (values up to the median), high-average (values between one and three times the median), and high (values more than three times the median). Marital status was coded as either married, never married, previously married (separated/widowed/divorced), or not currently married (previously married or never married).

Role impairment

The Sheehan Disability Scale (SDS) [38, 39] was used to assess impairment due to alcohol or drug consumption across four domains: home, work, relationships, and social life. Level of impairment was classified as follows: severe (SDS score of 7鈥�10), severe or moderate (SDS score of 4鈥�10), or any (SDS score of 1鈥�10).

Comorbidity

The CIDI 3.0 was used to assess other DSM-IV including anxiety, mood, impulse-control, eating, and substance use disorders. Participants who were diagnosed with one or more DSM-IV disorder, including SUD, were considered comorbid.

Treatment

All part II respondents were asked if they ever received treatment from 14 different types of professionals for issues with emotions, nerves, mental health or use of alcohol or drugs. If they had reported receiving treatment, questions were asked about age at first obtaining treatment, treatment in the past 12 months, and, when 12-month treatment was reported, number of visits to each of these types of professionals. Summary measures of 12-month treatment were created separately for the healthcare sector and the non-healthcare sector. Healthcare sector treatment was further divided into treatment in the general medical sector and the mental health specialty sector. Non-healthcare sector was classified into human services and complementary-alternative medicine (CAM). We did not distinguish between inpatient and outpatient treatment, but all inpatient treatment was categorized as mental health specialty treatment.

Lifetime treatment was measured by administering a smaller number of treatment questions at the end of each diagnostic section when respondents are asked whether they ever in their life saw a medical doctor or any other professional about the disorder assessed in that section of the interview and, if so, their age when they first sought treatment for the disorder. Responses to these disorder-specific questions and the more general treatment questions were combined in our descriptive analyses of treatment prevalence but only disorder-specific responses were used to make projections of eventual lifetime treatment [27].

Analysis

Descriptive statistics such as frequencies, percentages and standard errors were calculated for categorical variables using cross-tabulations. All the cross-tabulations were done using the PROC SURVEYFREQ procedure in SAS 9.2 (SAS Institute Inc., Cary, NC, USA). Multivariate binary logistic regression was used to compute odds ratios with 95% CIs and to assess the presence and degree of association between the substance disorders and sociodemographic variables and other correlates. The significance of the logistic regression model was calculated using the Wald chi-square test with p鈥�<鈥�0.05. The multivariate binary logistic regression models were generated using the PROC LOGISTIC procedure.

Results

Prevalence & persistence

As shown in Table听1, the lifetime, 12-month, and 30-day prevalence of any SUD was 4.03%, 1.88%, 0.78%, respectively. The overall prevalence was higher for all DUDs relative to AUDs. DADs were the most common type of DUD, with a lifetime, 12-month, and 30-day prevalence of 3.16%, 1.43%, and 0.52%, respectively.

Furthermore, we found high estimates of SUDs persistence in our sample. Approximately half of participants with any lifetime SUD (46.61%) reported disorder persistence and over one-third (41.52%) reported episodic persistence.

The highest persistence was recorded for participants within the DUD category, with a prevalence of 55.90% for DDDs and 45.16% for DADs; while the lowest persistence was recorded for participants within the AUD category, in that only 10% of those with lifetime AUDs reported past-year use. However, roughly half of those with 12-month ADDs (59.83%) and DDDs (48.58%) reported persistence of symptoms into the 30-days preceding the interview.

Table 1 Prevalence of substance disorders

Risk-factors

As shown in Table听2, there was a strong association between age cohort and lifetime AUDs. Relative to those with high education, those with low education (OR鈥�=鈥�2.01) had higher odds for any SUD.

Participants who were unemployed or were students were at significantly less risk for any substance use disorder relative to those who were employed (ORs鈥�=鈥�0.39 & 0.33). Further, there was a very strong association between the presence of childhood adversities and developing any SUD (OR鈥�=鈥�3.90), AUDs (OR鈥�=鈥�43.6), and DUDs (OR鈥�=鈥�12.6). Higher odds for any SUD were also significantly associated with exposure to traumatic events (OR鈥�=鈥�3.85) and the presence of family burden (OR鈥�=鈥�1.80).

Table 2 Associations of risk factors with lifetime substance disorders

Comorbidity

Lifetime psychiatric comorbidity was observed in the majority of respondents with any lifetime SUD (76.1%) as well as in participants with lifetime DUDs (88.5%), and AUDs (70%). Overall, the highest lifetime comorbidity was found with anxiety disorders, followed by eating disorders among participants with lifetime AUDs (70%) and impulse disorders among participants with lifetime DUDs (39.2%).

Further, 12-month psychiatric comorbidity was observed in 71.4% of respondents with any 12-month SUD and in 82.7% of respondents with 12-month DUDs. Overall, 12-month comorbidity was highest with anxiety disorders, followed by mood, impulse, and eating disorders (See Table听3).

Table 3 Comorbidity of substance use disorders with other DSM-IV disorders

Role impairment

As shown in Fig.听1, AUDs had a higher impact on impairment while DUDs had a wider impact. Severe role impairment in any domain was observed in 24.5% of participants with any 12-month SUD, in 48.1% of participants with 12-month DUD, and in 67.2% of participants with 12-month AUD. The greatest role impairment was found for the domain of relationships among those with any 12-month SUD (13.5%), equally in the domains of relationships and social life for those with 12-month AUDs (67.2%), and equally in the domains of work and social life for those with 12-month DUDs (45.6%).

Fig. 1

Severe role impairment (SDS) associated with 12-month substance disorders

Treatment prevalence

Less than half of respondents with any lifetime SUD (44.9%), reported ever seeking any treatment; however, most of this treatment was sought from the healthcare sector (38.4%), particularly the mental health specialty sector (29.5%). Lifetime treatment rates were higher for respondents with lifetime DUDs (56.6%) than those with lifetime AUDs (27%).

Further, over one-third of respondents with any 12-month SUD (43.3%) reported seeking treatment in the 12-months preceding the interview, with most seeking treatment from the healthcare sector (34.9%), yet no difference was observed for treatment from the general medical (17.9%) vs. mental health specialty (18%) sectors. Further, roughly half of those with 12-month DUDs (52.2%) sought any past-year treatment, with a similar proportion receiving care from the mental health specialty and general medicine sectors (See Table听4).

Table 4 Prevalence of treatment among substance disorders

Substance consumption prevalence

For lifetime substance consumption, painkillers were most prevalent (7.11%), followed by alcohol (6%), other drugs (stimulants, cocaine, club drugs) (3.58%), Marijuana/Hashish/GAT (3.2%), and sedatives/tranquilizers (1.63%). Past-year reports of substance consumption were similarly highest for painkillers (67.32%), followed by sedatives/tranquilizers (41.07%), alcohol (29.55%), then by Marijuana/Hashish/GAT (21.52%) and other drugs (stimulants, cocaine, club drugs) (0.35%) (Supplementary Table 1).

Sociodemographic correlates of substance consumption

Relative to males, females were at significantly higher odds to consume painkillers (OR鈥�=鈥�1.67), but at lower odds to consume alcohol (OR鈥�=鈥�0.23) and Marijuana/Hashish/Gat (OR鈥�=鈥�0.12). We found a strong association between age and consumption of alcohol, sedatives/tranquilizers, and Marijuana/Hashish/Gat. Relative to respondents in the 15鈥�24 age group, those in the 25鈥�50鈥�+鈥塧ge group were more likely to consume Marijuana/Hashish/Gat (OR鈥�=鈥�7.23鈥�9.51), those in the 25鈥�34 and 50鈥�+鈥塧ge groups were more likely to consume alcohol (OR鈥�=鈥�1.93 & 2.33), while those in the 35鈥�50鈥�+鈥塧ge group were less likely to consume sedatives and tranquilizers (ORs鈥�=鈥�0.21 & 0.02).

Further, higher levels of educational attainment were associated with lower odds of consuming sedatives/tranquilizers (ORs鈥�=鈥�0.10鈥�0.24); but with higher odds of consuming painkillers (ORs鈥�=鈥�2.73鈥�4.06,). Higher household income was significantly associated with lower odds of drug consumption. For instance, respondents with high income were significantly less likely to consume alcohol (OR鈥�=鈥�0.5) and Marijuana/Hashish/Gat (OR鈥�=鈥�0.43), while those with high-average income were less likely to consume Marijuana/Hashish/Gat (OR鈥�=鈥�0.18) and other drugs (OR鈥�=鈥�0.23). Relative to being married or never married, being previously married was a significant predictor for consumption of sedatives/tranquilizers and painkillers (See Table听5).

Table 5 Associations of socio-demographic characteristics with lifetime alcohol and drug consumption

Discussion

To the best of our knowledge, this study is the first to explore the epidemiology of substance use disorders using a nationally representative community sample in Saudi Arabia. Overall, the lifetime, 12-month, and 30-day prevalence of any SUD among our sample was 4.03%, 1.88%, and 0.78%, respectively. Generally, our results yielded higher prevalence estimates for DUDs than AUDs, contrary to prior global findings. This could be attributed to the largely easier access to drugs relative to alcohol in the country, which typically requires illegal means to acquire and consume [7].

The lifetime prevalence rates of DUDs in our sample are similar to other MENA countries such as Lebanon and Iraq but relatively lower than a number of Western countries [4, 40]. Whereas the 12-month and 30-day prevalence rates as well as persistence patterns fall in line with results of the majority of WMH countries [4]. Furthermore, lifetime, 12-month, and persistence prevalence rates of AUDs in our sample were relatively lower than those reported in most WMH countries, yet similar to rates from Iraq [41] and other Arab countries [6]. This could be due to the certain shared legal and cultural attitudes towards alcohol across MENA states [6]; whereas inconsistencies could reflect variations in study methodological features [42].

Sociodemographic correlates

Consistent with a large body of literature [6, 41, 43], we found SUDs to be associated with younger age, lower educational attainment, low family income, as well as exposure to traumatic events, family burden, and childhood adversities.

As previously found, younger adults were at higher risk for AUDs than older adults in our sample [41]. Prior studies identified peer pressure, social and academic stressors, curiosity, as well as familial alcohol consumption as some of the underlying causes of alcohol consumption among youth [6, 18, 19, 44]. This is particularly concerning given the long-term effects of early-onset AUD on employability, overall functionality, early-onset mortality [40].

Furthermore, it has been largely suggested that the association between developing substance disorders and certain sociodemographic factors is bidirectional [6]. For instance, associations between SUDs and low educational attainment may be due to individuals adopting negative substance-use behaviors to cope with poor academic performance. Additionally, individuals suffering academically may have a larger proclivity for deviant behaviors and social engagements that enable substance use [45]. On the other hand, SUDs may lead to unsatisfactory academic performance and diminished educational motivation [45]. We were also able to confirm previous findings of a significant link between exposure to childhood adversities [46, 47], family burden [15], and traumatic [17] events with the development of SUDs in the general population. Nevertheless, directionality cannot be assumed given the nature of the study.

Interestingly, in large contrast to previous findings [6, 24], we found employment to be a risk factor for developing AUDs and SUDs. This could possibly be attributed to the high amount of stress associated with maintaining jobs or even the fear of job-loss driving people to consume substances and developing substance-related disorders as a consequence [24]. Additionally, given that alcohol and certain drugs can be both expensive and not widely available locally, having a stable income is generally needed to acquire them [24]. Nevertheless, further research is required to understand the association between employment and SUDs in the context of the Arab world.

In further contrast to prior global findings [41], we did not find gender differences to be a significant risk factor for any form of SUD. However, while not significant, we were able to replicate prior reports of female gender as a protective factor against any SUD [21]. It is important to note that within Arab contexts, substance-related behaviors can be considered as particularly dishonoring for women and their families by extension; which could have in turn contributed to their reluctance to report on such matters [6, 44].

Role impairment

In line with previous studies [4, 5], a large proportion of participants reported having at least one area of life in which they experienced severe impairment associated with substance-related disorders. Previous reports found that impaired functioning is more pronounced for comorbid mental disorders than for any disorder alone [48]. This is of particular cause for concern given that SUDs are highly comorbid, which could lead to even higher functional impairment and poorer treatment outcomes.

Mental Disorder comorbidity

In line with global findings [5, 11, 49], SUDs among our sample were highly comorbid with other psychiatric disorders, particularly anxiety, mood, and impulse disorders. It has been previously posited that the significant association between substance use disorders and mental comorbidity can be attributed to the 鈥榮elf-medication鈥� hypothesis which assumes that the unhealthy consumption of substances is used as a maladaptive coping mechanism to reduce the symptoms and severity of other mental disorders [50, 51]. Conversely, it has also been proposed that SUDs may result in physiological strains that increase vulnerability to internalizing disorders, or that SUDs and internalizing disorders share no etiology but simply negatively affect each other [50]. Nevertheless, it is important to note that while it is possible to speculate on the driving mechanisms behind such comorbidities, we cannot conclude on any such hypotheses nor assume causality and directionality given the cross-sectional design of our study as well as the complex nature of substance/mental comorbidities. Indeed, previous reports suggest that SUDs, mood, anxiety, and impulse-control disorders may share underlying pathways, such that as symptoms from one disorder progress over time they are likely to exacerbate symptoms from another disorder [5]. Given that there is likely no one pattern of comorbidity between these disorders, further research is needed to ascertain the common mechanisms behind psychiatric comorbidity by understanding their specific etiologies and their influence on each other [5].

Substance use

With regard to substance consumption, intriguingly, painkillers were the most prevalent form of substance used in our sample. This may be due to the limited availability of certain hardcore drugs (e.g. cocaine, opioids, hallucinogens) relative to the much more widespread and affordable access to painkillers, inhalants, and solvents [19]; as well as the misconception that prescription or over-the-counter drugs may be safer to use although chronic extra-medical use of such drugs could lead to addiction and dependence in the long-term [6].

We were able to replicate prior findings from KSA of males being more likely to consume alcohol and marijuana/hashish/GAT than women, which is consistent with the local culture in that males have more means to acquire alcohol while women are more liable to using primitive, homemade substances and painkillers [18].

Additionally, while we were not able to replicate previous associations between marital status and risk for SUDs [42]; we did consistently find that those who were previously married were at higher risk for substance consumption relative to those who were married or unmarried [6], particularly for painkillers and sedatives/tranquilizers. This has several important implications for policy design since continuous consumption of such substances could transition to abuse/dependence in the long-term [44]. Further research is needed to understand what local factors underlie the transition from substance use and consumption to dependence and abuse at both the group and individual levels.

However, it is important to note that sociodemographic profiles and prevalence estimates of substance and alcohol users vary by type of substance across countries and cultures [2]. It may also be the case that such variability in substance use patterns across countries may be attributed to country-specific differences in exposure to particular drugs as well as to risk factors that increase the probability of developing disorders from use [43]. These variations extend even to the Arab world as a result of the different national policies and religious and cultural outlooks towards alcohol and drug consumption [6, 18].

It is important to note that although substance consumption does not constitute a sufficient cause for SUDs on its own, this does not detract from the preventive potential associated with controlled substance availability [52]. Indeed, having an understanding of which substances are most often consumed has important public health implications by allowing for targeted health interventions and public policies [52]. Furthermore, group level factors can play a role in the ability to acquire certain substances [6]. Therefore, understanding how individual and group level factors form a general liability for substance use is important for designing substance-specific interventions [53].

Treatment

Despite the various issues associated with SUDs, only a small number sought treatment for mental health problems both ever (29.5%) and in the past 12-months (18%). Furthermore, only a small proportion of those with AUDs (27%) reported ever seeking any form of treatment, with only 13.4% having sought any healthcare treatment. It has been proposed that this may be due to a low perceived need for treatment among those with AUDs [54]. Stigma towards substance users, legal concerns, as well as possible low perceived need for treatment among those with SUDs may have also contributed to this large treatment gap [27, 54].

It remains unclear whether people initially sought mental health treatment for SUD or for a comorbid mental disorder. It has been previously found that people with a past year SUD were highly likely to seek mental health-specific treatment, even without the presence of a comorbid disorder [54,55,56]. This may occur for a number of reasons, namely that individuals with SUDs may perceive the nature of their problem as a mental health one, and among those with a comorbid mental disorder, they may perceive the latter to be more troubling and seek treatment for it first [54]. It has also been reported that people with SUD may perceive mental health-specific treatment to be more accepting of substance-related problems than other medical sectors [54]. Nevertheless, prior reports show that despite the low number of visitations to health professionals, those who do seek treatment for SUDs are likely to obtain positive outcomes and persist with treatment [54].

While our findings generally follow the global pattern for SUDs treatment [54], they nevertheless pose significant cause for concern given that untreated SUDs may result in increased morbidity, decreased quality of life, and loss of productivity [54]. Our findings therefore point to the importance of developing effective prevention efforts that are tailored to the demographic profile of the substance user in the country.

Implications

The current study has important implications for the understanding of SUDs that can extend to neighboring countries. Indeed, the identification of clinical characteristics of SUDs and prevalence patterns can help guide policymakers and healthcare professionals in identifying who should be the focus of rehabilitation efforts to moderate the treatment gap for substance-related disorders.

Limitations

Several limitations exist with respect to this study. First, as in all cross-sectional studies, our results cannot be used to infer causation or directionality between variables; second, the exclusion of certain groups (e.g., homeless, institutionalized) from our study may have resulted in underestimated prevalence rates; third, recall bias, inevitably associated with self-report measures, may have also contributed to underreporting; fourth, the legal and cultural stance against substance consumption in the country may have resulted in underreporting on substance-related matters [6]. Although, we have undertaken measures to counteract possible underreporting bias in the country due to fear and stigma surrounding culturally-sensitive subjects such as drugs and alcohol. Indeed, the sections on substance use disorders in the survey were employed using ACASI mode to account for such biases. Nevertheless, these limitations are likely to be offset by our large sample size, thorough survey methodology, and carefully validated measures and instruments.

Conclusions

Our findings underscore the concerning prevalence, persistence, associated impairment, and significant co-occurring comorbidity associated with SUDs among our sample. We were also able to identify younger age, lower income, employment, and lower educational levels as significant risk factors for the development of substance-related disorders. Taken together, our results highlight the importance of mitigating the burdens associated with substance use disorders by understanding the substance-use profile in the country and identifying the appropriate targets of prevention and treatment programs.

Regardless of the type of substance consumed, SUDs among our sample were found to be more prevalent than previously assumed and characterized by a persistent and significantly impairing course, psychiatric comorbidities, and inadequate treatment seeking behaviors.