Investigating Sex Differences in Opioid Use Disorder Risk Factors: Insights from Cross-Section Lebanese Study Population
Bibliographic record
Abstract
Introduction Opioid use disorder (OUD) is a significant public health concern, and understanding the risk factors associated with OUD is crucial for effective prevention and management strategies. However, limited information is available regarding the role of sex differences in OUD risk factors. Women have often been excluded from clinical studies to create more homogeneous samples and simplify the analysis of treatment effects. The underrepresentation of women in clinical trials and the lack of sex stratification, typically limited to binary comparisons without considering gender dynamics, raise concerns about potential sex disparities. Given the emerging evidence suggesting the possibility of sex differences in the likelihood of developing OUD, further research is needed to investigate and understand these potential disparities to optimize the individualized management of OUD. Objectives The primary objective of this study was to examine and identify any sex-related variations in OUD risk variables within the Lebanese community. By pinpointing sociodemographic, psychiatric, and other factors related to sleep and chronotype, we aim to elucidate their impact on the onset and progression of OUD in both males and females. Methods A cross-sectional study was conducted among 581 Lebanese adults using an online questionnaire that included sociodemographic questions, validated scales for substance use disorders and sleep disorders, and assessments for depression and anxiety. Multivariate analyses were performed to identify associations between risk factors and OUD scores in both male and female populations. Results Common risk factors for OUD were identified, including family and personal history of substance use disorder, co-occurrence of sedative and alcohol misuse, and psychiatric illnesses. Sex-specific risk factors were also observed. Among women, the ASSIST-opioids subscore was significantly associated with the Pittsburgh Sleep Quality Index (B=0.143) and Insomnia Severity Index (B=0.286) scores. Men demonstrated a correlation between ORT-OUD and younger age (B=0.882). Waterpipe consumption was negatively correlated with the ORT-OUD score in men (B=-0.018). Conclusions Our study emphasizes the importance of examining sex differences in risk factors for OUD, particularly within the Lebanese population. By acknowledging these gender-specific risk factors, interventions can be customized to address the distinct vulnerabilities of each sex. This approach could potentially improve prevention efforts, facilitate early identification, and implement treatment strategies tailored to the specific needs of individuals with OUD. Further research is needed to delve into the underlying mechanisms and develop targeted interventions for enhanced management of OUD. Disclosure of Interest None Declared
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".