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Official websites use. Share sensitive information only on official, secure websites. Other tools evaluated chronotype and sleep and mood disturbances. This study included participants. Higher ORT-OUD scores were associated with a family and personal history of alcohol and substance consumption and higher insomnia and anxiety scores. The opioid crisis is considered a global health issue \[ 1 \]. It originated in the s with the surge in opioid analgesics prescribing, particularly oxycodone, resulting in overdoses and fatalities attributed to their use; these have steadily increased ever since \[ 2 \]. A second wave of deaths due to heroin occurred in Finally, a third wave of deaths emerged in , primarily attributed to synthetic opioids, notably fentanyl and its analogs \[ 4 \]. The opioid crisis was declared a national public health emergency on October 27, As of today, over , overdose deaths occurred during the month period ending in April , according to data from the Centers for Disease Control and Prevention CDC \[ 6 \]. In the United States alone, more than million opioid prescriptions are recorded annually, with an exponential increase in prescriptions over the past fifteen years, accompanied by a rise in the number of hospitalizations and deaths caused by opioid overdose \[ 7 \]. Moreover, the increased accessibility of opioids has led to massive medication hoarding and diverted consumption for nonmedical purposes \[ 8 \], contributing to persistent issues such as Opioid Use Disorder OUD. In Lebanon, data on opioid consumption are scarce. However, there has been longstanding evidence of nonmedical use of prescription drugs, and recent years have witnessed a significant surge in opioid and psychoactive substance use \[ 9 , 10 \]. The Lebanese Ministry of Public Health \[ 11 \] reported a six-fold increase in opioid prescriptions from to Prescription opioids seem to be readily available, as almost two-thirds Moreover, Lebanon has experienced numerous conflicts, political turmoil, and monetary instability since , further aggravated by the recent economic collapse and the COVID pandemic \[ 14 \]. Since October , the country has been grappling with overlapping crises, political unrest, sporadic violence, uncertainty about the future, and a lingering sense of insecurity. The massive blast at the port of Beirut on August 4, , further weakened the Lebanese population, making it vulnerable to mental disorders, including anxiety, sleep disorders, and post-traumatic stress disorder \[ 15 \]. This climate has likely increased stress and distress levels among the Lebanese population, precipitating mental health and sleep problems that may contribute to medication abuse and use disorders, both in prescription and illegal drugs \[ 16 \]. More importantly, a study revealed variability in the frequency of follow-up by Lebanese practitioners regarding opioid prescriptions, with a substantial proportion of these practitioners not assessing additional risk factors before prescribing opioids to patients, which may further contribute to increasing OUD cases \[ 17 \]. OUD stands as the most lethal consequence of opioid use and is at the core of the opioid crisis. It can lead to several social and economic harmful repercussions \[ 18 \], affecting various aspects of the quality of life, including physical health, psychological health, social relationships, and environment \[ 19 \]. Consequently, prioritizing primary prevention strategies becomes crucial, focusing on identifying risks through scalable prevention services and techniques. Identifying patients at risk of OUD before initiating chronic opioid therapy is crucial in preventing abuse and implementing sustainable prescription drug monitoring programs. Rating scales play a considerable role in this process by helping identify individuals at higher risk of developing OUD. Hence, several scales have been developed to detect substance use-related health risks and substance use disorders \[ 21 — 25 \]. Some of these scales are designed to screen for multiple substances, including opioids, in adults already taking opioid medications for pain management. However, most of these tools comprise 17 to 24 items, thus requiring a considerable amount of time to complete and calculate the total risk score during evaluation, except for the revised version of ORT ORT-OUD. While ORT-OUD has been validated in various languages, it has not been validated in Arabic \[ 21 , 29 \], and no study in Lebanon has yet evaluated the risk of developing an OUD after opioid treatment or its correlation with other disorders. The secondary objective was to assess the correlates of the risk scores with sociodemographic and clinical factors, including sleep disorders, chronotype, anxiety, and depression. Snowball sampling was applied to recruit the sample. The survey was shared on social media platforms because of pandemic-related restrictive measures and to ensure better access to all the Lebanese regions to enhance the representativeness of the sample. All Lebanese adults over 18 with access to the Internet were eligible to participate no incentive was offered in return for their participation. A total of respondents from the general population filled out the questionnaire, which required around 20 min to complete. To assess test-retest reliability, the ORT-OUD scale was administered twice to a subsample of the general population who agreed to be contacted by phone. At least a one-month interval with a maximum of three months separated each call. The recruitment took place at the Skoun addiction center, which offers a free-of-charge program in Beirut, Lebanon. All patients 46 patients in total who were present at Skoun during the inclusion period between May and July were invited to join the study and fill out the questionnaire. Patients had to meet inclusion criteria, i. Patients were asked to fill out a paper version of the questionnaire to enhance the completion of the survey and were supported by a research assistant who ensured that all questions were addressed; of note, the research assistant did not interfere during the process, except for providing guidance to participants in completing the questionnaire. Comrey and Lee suggested a minimum of ten observations per variable to perform an exploratory factor analysis \[ 30 \] when assessing construct validity. Since the revised ORT is a 9-item questionnaire, a minimum of 90 patients was required for this study. For the epidemiological study, the minimum sample size was calculated using the Epi-info software. The online questionnaire was available in English and Arabic, the native language in Lebanon Appendix 1 , and consisted of four parts. The first assessed the sociodemographic characteristics of the participants, including age, gender, weight, height, marital status, nationality, highest educational level, employment status and occupation, religion, current household monthly income, socioeconomic status, and medical history of chronic and mental illness. The socioeconomic status was assessed using the crowding index calculated by dividing the number of individuals living in the house by the number of rooms , which was then categorized into quartiles. Other questions were related to medical coverage, smoking and alcohol consumption, and self-perception of the financial situation. The second part of the questionnaire consisted of two validated scales for the evaluation of substance use disorders, i. The chronotype of the participants was also evaluated using the Composite Scale CS \[ 34 \]. Permission from copyright holders was obtained to use the validated scales. Only three subscales were used in this study: opioids, sedatives or sleeping pills, and alcoholic beverages. This selection was based on existing literature that has shown a correlation between OUD mainly and sedatives and alcohol use disorders \[ 37 , 38 \]. Each item was weighted differently, and higher total subscores predicted higher risks of developing related substance use disorder. According to the total score, participants are classified into potential high, moderate, or low-risk level. It was created by unweighting all items and reducing their number to nine by removing one item related to preadolescent sexual abuse. A cut-off point of 2. The selected cut-off score of 2. The PSQI is a self-report questionnaire designed to assess sleep quality and sleep disorders over a one-month period. It consists of 19 items that generate seven component scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping pills, and daytime dysfunction. The total score is calculated by summing the scores of these seven components. The higher the score, the worse the sleep quality \[ 31 \]. The ISI is a self-report instrument used to measure patient perceived insomnia. It targets the subjective symptoms and consequences of insomnia and the level of worry or distress caused by these difficulties. The total score enables to determine the presence and severity of insomnia. Values between 0 and 7 indicate the absence of insomnia, 8—14 sub-threshold insomnia, 15—21 moderate insomnia, and 22—28 severe insomnia \[ 32 \]. The ESS is a subjective measure of sleepiness widely used in sleep medicines. It consists of a list of eight situations where individuals rate their tendency to doze on a scale from 0 no chance of dozing to 3 high chance of dozing. According to scores, sleepiness is categorized as normal 0—10 , mild 11—14 , moderate 15—17 , and severe 18—24 \[ 33 \]. The CS is a question tool used to evaluate the chronotype, which refers to the general preferences regarding the timing of waking up, falling asleep, and peak performance \[ 34 \]. The scores range from 1 to 4 or 5, depending on the question. Scores can indicate an evening circadian typology 22 or less , morning circadian typology higher than 44 , or intermediate circadian typology between 22 and 44 \[ 39 \]. The total score for each subscale is the sum of the seven items ranging from 0 to A score between 0 and 7 indicates no anxiety or depression, while scores of 8 to 10 suggest mild anxiety or depression, 11 to 14 moderate anxiety or depression, and 15 to 21 severe anxiety or depression \[ 35 \]. The translation procedure started after getting the approval from Professor Martin Cheatle, the author of the scale. Independent professional translators conducted both the translation and back-translation. The research team and translators compared the original English version with the back-translated version to ensure that the questions had the same meaning, making the necessary corrections as needed. Cultural adaptation of the items was not performed during this process. The scale was then piloted following the finalization of the translation. It was administered to 56 bilingual participants in both languages. Its reliability was found to be excellent, with a single measures intraclass correlation coefficient of 0. The final version of the questionnaire was deemed easy to understand and complete. A pilot test was conducted among ten individuals who were not part of the study to ensure the clarity of the questions. Based on their feedback, one question in the sociodemographic section was reformulated for better comprehension. The responses from the pilot study were not included in the final database. Descriptive statistics were calculated for all variables in the study. The Kolmogorov-Smirnov test was used to verify the normality of the continuous variables. Means and standard deviations were shown for normally distributed variables, and medians and interquartile ranges for non-normally distributed variables. Frequencies and percentages were displayed for dichotomous and multinomial variables. This analysis was performed on the nine items of the ORT-OUD scale, and a Varimax rotation was applied since the extracted factors were not found to be significantly correlated. The number of factors corresponding to Eigenvalues greater than one were retained. The test-retest reliability was evaluated by the intraclass correlation coefficient ICC, mean measurement for the scores of participants with repeated measures. ICC values less than 0. Bivariate and multivariable analyses were performed for sample 1 general population , taking the opioid use risk scores as dependent variables. The Mann-Whitney test was used for comparing two groups, and the Kruskal-Wallis test was used for comparing three or more groups when the continuous variable did not follow a normal distribution. Finally, multivariable analyses were conducted to account for potential confounding factors. Sample 1 consisted of participants from different regions of Lebanon, with The mean age was Of the total participants, Sample 2 comprised 46 patients with a previous diagnosis of OUD. Table 1 summarizes the sociodemographic characteristics of the included population. None of the scale items was removed; the items converged to a solution of four factors with an Eigenvalue over 1, explaining a total of The four factors were: History of substance abuse 3 items , history of alcohol abuse 2 items , history of illegal drug abuse 2 items , and psychological factors 2 items. A Kaiser-Meyer-Olkin measure of sampling adequacy of 0. Results are summarized in supplementary data. Details are presented in Tables 4 , 5 and 6. In this study, the ORT-OUD was translated into Arabic and validated in the general population sample 1 , and its criterion validity was confirmed in a clinical sample of participants with OUD sample 2. Construct validity analysis resulted in the distribution of items on four factors, i. These factors demonstrated rational explanations, appropriate sampling adequacy, anti-image correlations, and communalities. It is worth noting that the initial validation of the ORT-OUD scale in the original paper determined the discriminant predictive validity and the receiver operating characteristic ROC curve in two samples of chronic nonmalignant pain patients taking long-term opioid therapy; the first sample of patients developed OUD after starting opioid therapy, while the other one displayed no evidence of OUD. Furthermore, to the best of our knowledge, no previous research has conducted the translation and validation of the ORT-OUD scale. Nevertheless, the average ICC between the test and retest indicated good reliability \[ 42 \]. OUD patients who use nonmedical sedatives and tranquilizers often exhibit higher rates of polysubstance use and other substance use disorders \[ 44 , 45 \]. This correlation highlights the potential risk of drug-drug interactions and fatal opioid overdoses associated with the simultaneous consumption of opioids and sedatives such as benzodiazepines \[ 46 \]. Moreover, several studies among OUD patients have found correlations between the use of nonmedical sedatives and tranquilizers and sociodemographic characteristics, such as female gender, younger age, and indicators of opioid use severity \[ 47 — 49 \]. These characteristics have been associated with a higher likelihood of using nonmedical sedatives and tranquilizers, making them essential to understanding the patterns of substance use and polysubstance use among individuals with OUD. Another study among OUD patients revealed that This finding indicates good convergent validity of the Arabic version of the ORT-OUD, affirming its usefulness in assessing opioid use risk and its relationship with other substance use disorders. The difference in prevalence rates between the two scales can be attributed to their different approaches to assessing OUD risk. Indeed, ORT-OUD evaluates the risk of developing OUD by considering various well-known risk factors such as age, family, personal history of substance abuse, and mental health conditions, while the ASSIST-opioid subscale focuses more on current substance use habits, thus leading to lower estimated prevalence \[ 21 , 24 \]. Interestingly, one of these studies evaluated the association between hurricane exposure and the risk of opioid-abusive behavior. Our study was conducted in the context of multiple crises, including the COVID pandemic, an economic collapse described as one of the worst crises of the past century \[ 15 , 54 \], and the devastating Beirut port explosion in August considered one of the largest non-nuclear blasts ever recorded in history, with more than deaths, injured, and , displaced Lebanese citizens \[ 14 , 15 , 55 \]. Nevertheless, as data on OUD before these crises are lacking, no conclusions can be drawn as to whether these crises might have potentially affected the high prevalence values reported in this paper; more robust, larger-size epidemiological studies would provide a better understanding of prevalence trends over time. Another valuable factor is the lack of comprehensive evaluation and follow-up by healthcare practitioners when prescribing opioids. It has been reported that a significant number of healthcare practitioners do not thoroughly assess patients for potential risk factors before prescribing opioids in Lebanon. Additionally, inadequate follow-up and insufficient communication about possible adverse effects are common among healthcare practitioners in Lebanon. This lack of proper evaluation and follow-up can contribute to the emergence of an opioid epidemic by increasing the likelihood of people developing OUD \[ 17 \]. This study also aimed to explore the association between sociodemographic and clinical data and the risk of OUD in the Lebanese population. It is well-established that a family history of substance use disorder is a risk factor for OUD in patients with chronic nonmalignant pain \[ 56 — 60 \]. Furthermore, research has demonstrated that teenagers with a family history of alcohol or drug abuse and a lack of pro-social skills are more prone to transition quickly from occasional use to severe patterns of abuse or dependence \[ 61 \]. Thus, understanding these factors helps elucidate the etiopathology and trajectory of addictive behaviors. Finally, social risk factors, such as connection with deviant peers, popularity, bullying, and gang affiliation, can help in shaping positive beliefs and attitudes toward drug use. Therefore, friends and family provide immediate access to substances and also serve as role models for behavior and drug use \[ 62 , 63 \]. When exploring sleep patterns, a positive and significant correlation was observed between the risk of developing OUD and sleep disorders, as evaluated by the ISI. The association between illegal psychoactive substance use and sleep problems appears to be bidirectional \[ 64 \]. Sleep problems have been found to increase the risk of developing substance use disorders \[ 65 — 67 \], which, in turn, might lead to sleep problems \[ 67 — 70 \]. Evidence suggests that chronic use of some illicit substances results in chronic sleep alterations, distinct from the acute effects of these substances \[ 71 \]. Another study has even shown that suvorexant, an orexin-blocking sleep medication approved for the treatment of insomnia, can also decrease opioid-induced cravings \[ 73 \]. Our study is the first to assess the relationship between chronotype and the risk of OUD in the Lebanese population. While it yielded inconclusive results with the Composite Scale used to evaluate chronotype, other studies found a connection between circadian preferences, such as eveningness, and substance use disorder in young adults and adolescents \[ 74 , 75 \]. Regarding mood and other psychiatric disorders, our results revealed that individuals with high anxiety scores as evaluated by the HADS-A and those with psychiatric illnesses were more likely to develop OUD. A strong association exists between opioid- and anxiety-related symptoms and disorders \[ 76 \], which are more common and more strongly associated with the use of prescribed opioids than other substances \[ 76 — 78 \]. Furthermore, individuals with a genetic predisposition for OUD are at increased risk of developing anxiety, stress-related disorders, and major depressive disorder \[ 79 \]. Common mental health disorders and problematic drug use have been found to be associated with the initiation and use of prescribed opioids in the general population \[ 80 \]. Therefore, it is essential to accurately evaluate and identify psychiatric disorders before starting an opioid treatment for pain management \[ 81 \]. Finally, our study revealed that higher waterpipe use was linked to a lower risk of developing OUD, probably because the high nicotine content of waterpipe smoke helps reduce anxiety \[ 82 \], thereby decreasing the need to seek drugs, including opioids. Additionally, anxious individuals may have difficulty self-regulating during stressful situations and may turn to external methods, such as tobacco use, to cope with stress \[ 83 \]. This study has several limitations. However, it was selected because it is the only scale with complete validity and reliability data in Arabic \[ 85 \]. Thus, our results may not be generalized to the entire population. Finally, due to the presence of multiple crises, including the COVID pandemic and the massive Beirut port explosion, the prevalence values reported should be interpreted with caution, as these external factors may have influenced the results. Additionally, the lack of data related to OUD before these crises limits our ability to draw definitive conclusions about the specific influence of these factors. Despite all these limitations, this study is the first to validate an OUD questionnaire in the Lebanese population. This validated tool can now be used in any Arabic-speaking country to assess the risk of OUD before initiating opioid therapy. Moreover, our study is the first nationwide and regional investigation of OUD and potential risk factors, such as sleep disorders, chronotype, and mood disorders. By taking into account modifiable risk factors such as insomnia and anxiety, this scale can help identify people at risk of developing OUD, allowing for targeted interventions to reduce the risk of OUD and improve patient outcomes. Additional file 1 - Supplementary Table 1. Supplementary Table 2. Dolla Karam Sarkis, whose profound influence, enduring wisdom, and unwavering support have left an indelible mark on this work. She is remembered with the utmost reverence. We dedicate this article to her memory, acknowledging her lasting impact on our scholarly endeavors. The authors would like to thank all the participants who agreed to participate in this study. LRK and BM contributed to the design. PS undertook the statistical analysis. HS edited the whole article for English language and intellectual content. BM and LRK supervised the whole process and critically reviewed the article. All authors contributed to and have approved the final manuscript. Our study was conducted in accordance to the Declaration of Helsinki. The topic was explained to all participants in the introductory section of the survey, which also included the consent to participate mandatory to have access to the questionnaire. Participants from the general population group could either maintain their anonymity guaranteed throughout the process of data collection and analysis or allow the principal investigator to contact them again for the re-test part of the scale validation. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. BMC Psychiatry. Find articles by Karam Chamoun. Find articles by Joseph Mouawad. Find articles by Pascale Salameh. Find articles by Hala Sacre. Find articles by Ramzi Haddad. Find articles by Lydia Rabbaa Khabbaz. Find articles by Bruno Megarbane. Find articles by Aline Hajj. Received Mar 5; Accepted Oct 24; Collection date Open in a new tab. Components Factor 1: History of substance abuse Factor 2: History of alcohol abuse Factor 3: History of illegal drug abuse Factor 4: Psychological factors Personal history of substance abuse prescription drugs 0. Multivariate analysis considering the opioid use disorder score as the dependent variable. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Personal history of substance abuse age between 16—45 years. Variables entered in the model: gender, occupation, family income, governate, education, marital status, religion, socioeconomical quartiles, family history of substance abuse, family history of substance abuse illegal drugs , family history of substance abuse prescription drugs , personal history of substance abuse alcohol , personal history of substance abuse illegal drugs , personal history of substance abuse prescription drugs , age between 16—45 years, Psychological disease Attention deficit disorder ADD , obsessive-compulsive disorder OCD , bipolar disorder, schizophrenia , age, weight, height, number of cigarettes per day, number of waterpipes per week, number of alcohol glasses per occasion, HADS-A score, HADS-D score, PSQI score, ISI score, CS score, ESS score.

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