Gender Differences in Sociodemographic and Clinical Characteristics of Methamphetamine Use in Saudi Arabia

Abstract

Objectives:

To explore gender-related variations in sociodemographic factors, clinical presentations, and psychosocial characteristics among methamphetamine users in Saudi Arabia.

Methods:

A retrospective cross-sectional study was conducted using the electronic health records of 1,181 patients (91% male, 9% female) treated at multiple Erada Mental and Addiction Centers across Saudi Arabia (2020–2024). Data on sociodemographic characteristics, methamphetamine use patterns, psychiatric comorbidities, and psychosocial functioning were collected and analyzed using validated tools, including the Addiction Severity Index, Beck Depression and Anxiety Inventory, and Quality of Life Scale.

Results:

Female participants were younger than males (mean age 25.3 vs 28.5 years; p < 0.001) and initiated methamphetamine at a later age (21.4 vs 19.2 years; p < 0.001). Females had greater proportion of daily use (65.4% vs. 60%, p = 0.035) but consumed smaller amounts per day (1.5 vs. 1.8 g, p = 0.041). They also exhibited higher rates of psychiatric comorbidities, including depression (50% vs. 30.0%, p < 0.001) and anxiety (40% vs. 20%, p < 0.001), and reported more severe psychosocial impairments, including lower quality of life (54.8 vs. 62.4, p < 0.001), higher unemployment rates (59.8% vs. 30%, p < 0.001), and more family conflicts (65.4% vs. 50%, p < 0.001). In contrast, Legal issues were more common among males (40% vs. 29.9%, p = 0.002).

Conclusions:

Female methamphetamine users demonstrate greater vulnerability with earlier onset, more psychiatric comorbidities, and worse psychosocial outcomes. Targeted interventions are needed for female methamphetamine users in Saudi Arabia.

Introduction

Methamphetamine use has become a major public health concern with grave consequences for communities and healthcare systems. In Saudi Arabia, the use of methamphetamine has increased in recent years, mirroring global trends [1]. Methamphetamine is a highly potent stimulant of the central nervous system linked to substantial adverse health effects, including addiction, cardiovascular complications, and mental health disorders, such as anxiety and depressive disorders [2]. In addition to its health effects, methamphetamine use contributes to social problems, including increased criminal activity and disrupted family relationships [3].

Demographic characteristics, particularly gender, play a significant role in drug use patterns and related health outcomes. Research has consistently shown gender differences in drug addiction behaviors, including the severity of health consequences, frequency of use, and age of onset [4]. For example, compared with men, women are more likely to start using drugs earlier in life and may experience more severe health problems. Gender disparities in the social environment of drug use, such as stigma and access to treatment, further complicate this situation [5]. Gender differences in drug addiction and dependency have been well-documented for various substances. However, women often exhibit higher levels of cocaine and drug dependence [6]. Additionally, notable gender differences exist in other aspects of drug use, such as age at initiation, escalation rates, and the amount consumed [7]. Women may escalate their use of cocaine or amphetamines more rapidly and typically begin using these substances at a younger age [8]. They also tend to experience stronger cravings and more substance-related health issues. However, some studies have suggested that there are no significant gender differences in certain aspects of drug addiction [9].

Difference in susceptibility to biological, behavioral, and environmental risk factors between genders has been observed in the onset and progression of psychiatric disorders [10]. Evidence suggests that females who engaged in substance abuse are more likely than men to suffer from severe physical and psychological outcomes, including higher rates of depression, emotional distress, anxiety, and suicidality [11].

Although previous studies have examined methamphetamine use in various populations, there is limited research on gender differences in Saudi Arabia. This study was undertaken to address this research deficiency by investigating the sociodemographic, clinical, and psychosocial characteristics of male and female methamphetamine users seeking treatment in Saudi Arabia. We hypothesized that: i) Female methamphetamine users have an earlier age of initiation and higher rates of psychiatric comorbidities than male users. ii) Females report more severe psychosocial impairment, including a lower quality of life and higher rates of unemployment and family conflict. iii) Males have higher rates of legal problems and more frequent use of methamphetamine.

Methods

This cross-sectional study used retrospective data from patients’ medical records (EHR) who treated at multiple Erada Mental and Addiction Treatment Centers across Saudi Arabia. Data were collected from records spanning from 2020 to 2024. This study focused on patients with a primary diagnosis of methamphetamine use disorder, as documented in their medical records.

The study included 1,181 patients (91% male, 9% female) who met the inclusion criteria. The sample size was determined using G*Power software with the following parameters: α = 0.05 (2-tailed), Power (1-β) = 0.80, expected effect size (medium) = 0.25 (based on prior studies on gender differences in substance use), and anticipated dropout rate = 10%

Participants eligible for inclusion were adults aged 18 years or older who had a primary diagnosis of methamphetamine use disorder based on the criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; criteria as documented in the EHR; Saudi nationals treated at participating centers; and positive urine screening for methamphetamine at admission (documented in the EHR).

The exclusion criteria were incomplete or missing records for key variables (such as age, gender, and diagnosis) and patients with severe medical or psychiatric conditions that could interfere with diagnosis (such as active psychosis or severe cognitive impairment).

Data were extracted from the EHR system using a standardized data extraction form. The following information was collected: age, gender, marital status, educational level, occupational status, housing situation, socioeconomic status, age at first methamphetamine use, duration of use, frequency of use (daily, several times per week, weekly or less), route of administration (smoking, intranasal, so on), amount typically used (g/day), psychiatric comorbidities (such as depressive disorders, anxiety disorders, trauma-related disorders, medical complications (such as cardiovascular issues, liver damage), previous treatment attempts, Quality of Life Scale (Arabic version) scores, social support networks (strong, moderate, or poor), employment problems (yes/no), family conflict (yes/no), and legal problems (yes/no).

The following validated tools were used to assess patients during treatment, and the results were recorded in the EHR. The Structured Clinical Interview for DSM-5 (SCID-5) was used to diagnose substance use disorders and psychiatric comorbidities. The Arabic version of the Addiction Severity Index (ASI) was used to assess the severity of addiction across multiple domains (medical, employment, legal, and so on). The severity of anxiety and depression was evaluated using the Arabic editions of the Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II). The Arabic version of the Quality-of-Life Scale was used to evaluate overall quality of life.

Missing data were addressed using multiple imputation methods, with less than 5% missing data for any variable. Sensitivity analyses confirmed that the results were robust to the methods used to handle the missing data.

Research staff were trained in standardized data extraction procedures to ensure consistency. A subset of records (10%) was independently reviewed by two researchers to ensure accuracy (κ = 0.92). Regular audits were conducted to verify the completeness and accuracy of extracted data.

Statistical analysis

All statistical analyses were performed using Statistical Package for Social Sciences, version 23.0 (IBM, Armonk, NY), with statistical significance set at p < 0.05 (2-tailed). Continuous variables were expressed as means ± standard deviations, while categorical variables were presented as frequencies and percentages. Between-group comparisons were conducted using appropriate statistical tests based on data characteristics: Chi-square tests for categorical variables, independent t-tests for normally distributed continuous variables, and Mann-Whitney U tests for non-normally distributed continuous variables. To examine associations between outcomes and independent variables while controlling for potential confounders, we employed multiple logistic regression for categorical outcomes and multiple linear regression for continuous outcomes, adjusting for age, educational level, socioeconomic status, and duration of methamphetamine use. Effect sizes were calculated to quantify the magnitude of observed differences: Cohen’s d for continuous variables (interpreted as small [0.2], medium [0.5], and large [0.8]) and odds ratios (ORs) with 95% confidence intervals for categorical variables. The robustness of our findings was further validated through post-hoc power analyses and sensitivity testing, ensuring adequate statistical power (>80%) for all primary outcome measures. Multiple imputation was employed to address minimal missing data (<5% for any variable), with sensitivity analyses confirming the stability of results across imputed and complete-case datasets.

Approval of the study was obtained from the ethics committee of Hail University. (protocol Number: RG-24 022). Confidentiality was maintained using coded identifiers. All data were stored on secure, password-protected servers with restricted access to authorized personnel only. Only de-identified data were used to protect patient privacy. The study adhered to the Declaration of Helsinki guidelines for ethical research.

Results

The study included 1,181 participants (91% male, 9% female). Male participants were significantly older than females (mean age 28.5 ± 7.2 versus [vs.] 25.3 ± 6.8 years; p < 0.001, Cohen’s d = 0.45, 95% confidence interval [CI]: 0.30–0.60). Females had higher odds of being divorced/separated (odds ratio [OR] = 1.59, 95% CI: 1.18–2.14, p = 0.003) and lower odds of attaining university education (OR = 0.79, 95% CI: 0.65–0.96, p = 0.015). Employment disparities were pronounced, with females having threefold lower odds of employment (OR = 0.28, 95% CI: 0.19–0.44, p < 0.001) (Table 1).

Table 1.

- Sociodemographic characteristics by gender.

Characteristic	Males (n = 1,074)	Females (n = 107)		P-value	Effect size (95% CI)	Adjusted OR (95% CI)*
Age (years), mean ± SD	28.5 ± 7.2	25.3 ± 6.8	<0.001		d = 0.45 (0.30–0.60)	—
Marital status, n(%)			0.003
Single	645 (60.1%)	59 (55.3%)	—		Ref.	Ref.
Married	322 (30.0%)	32 (29.9%)	—		OR = 1.12 (0.72–1.75)	1.10 (0.70–1.73)
Divorced/Separated	107 (10.0%)	16 (14.8%)	—		OR = 1.59 (1.18–2.14)	1.55 (1.15–2.09)
Education level, n(%)			0.015
Primary or less	215 (20.0%)	26 (24.0%)	—		Ref.	Ref.
Secondary	537 (50.0%)	54 (50.0%)	—		OR = 0.85 (0.52–1.39)	0.88 (0.54–1.43)
University or higher	322 (30.0%)	27 (26.0%)	—		OR = 0.79 (0.65–0.96)	0.81 (0.66–0.99)
Employment status, n(%)			<0.001
Employed	644 (60.0%)	32 (29.9%)	—		Ref.	Ref.
Unemployed	322 (30.0%)	64 (59.8%)	—		OR = 3.48 (2.20–5.58)	3.10 (2.05–4.68)
Student	108 (10.0%)	11 (10.3%)	—		OR = 1.10 (0.53–2.28)	1.05 (0.50–2.20)

*Adjusted for age, education, and marital status. Bold represents statistically significant (p < 0.05). Effect sizes: Cohen’s d (small ≥0.2, medium ≥0.5, large ≥0.8). CI: confidence interval, OR: odd ratio, SD: standard deviation.

Females initiated methamphetamine use later than males (21.4 ± 4.8 vs. 19.2 ± 4.2 years; p < 0.001, Cohen’s d = 0.52, 95% CI: 0.37–0.67) but reported higher rates of daily use (65.4% vs. 60%; OR = 1.26, 95% CI: 1.02–1.56, p = 0.035). Despite this, their daily consumption was lower (1.5 ± 0.8 vs. 1.8 ± 0.9 g/day; p = 0.041, Cohen’s d = -0.34, 95% CI: -0.49 to -0.19) (Table 2).

Table 2.

- Clinical characteristics and methamphetamine use patterns.

Characteristic	Males (n = 1,074)	Females (n = 107)	P-value	Effect size (95% CI)	Adjusted OR (95% CI)*
Age of First Use (years), mean ± SD	19.2 ± 4.2	21.4 ± 4.8	<0.001	d = 0.52 (0.37–0.67)	—
Duration of Use (years), mean ± SD	7.1 ± 4.3	6.2 ± 3.9	0.023	d = -0.21 (-0.36 to -0.06)	—
Route of administration, n (%)			0.012
Smoking	859 (80.0%)	86 (80.4%)	—	OR = 1.03 (0.65–1.63)	1.01 (0.64–1.60)
Intranasal	215 (20.0%)	21 (19.6%)	—	Ref.	Ref.
Frequency of use, n(%)			0.035
Daily	644 (60.0%)	70 (65.4%)	—	OR = 1.26 (1.02–1.56)	1.24 (1.00–1.53)
Several times/week	322 (30.0%)	29 (27.1%)	—	OR = 0.85 (0.54–1.34)	0.87 (0.55–1.37)
Weekly or less	108 (10.0%)	8 (7.5%)	—	OR = 0.70 (0.33–1.49)	0.72 (0.34–1.52)
Amount Used (g/day), mean ± SD	1.8 ± 0.9	1.5 ± 0.8	0.041	Cohen’s d = -0.34 (-0.49 to -0.19)	—

*Adjusted for age and duration of use. Bold represents statistically significant (p < 0.05). OR: odd ratio, CI: confidence interval, SD: standard deviation.

Psychiatric comorbidities and clinical outcomes

Female participants demonstrated substantially greater psychiatric vulnerability compared to males, with markedly higher prevalence rates across all measured comorbidities. Depression was present in 50% of females versus 30% of males (OR = 2.38, 95% CI: 1.60–3.54; p < 0.001), while anxiety disorders affected 40% of females compared to 20% of males (OR = 2.68, 95% CI: 1.75–4.10; p < 0.001). PTSD symptoms were also more prevalent among females (20.1% vs. 10%; OR = 2.18, 95% CI: 1.32–3.60; p < 0.001). Clinical severity measures reinforced these disparities: females scored significantly higher on the BDI-II (24.6 ± 9.1 vs. 18.4 ± 8.2; p < 0.001; Cohen’s d = 0.75, 95% CI: 0.59–0.91) and exhibited greater addiction severity on the ASI composite score (0.64 ± 0.20 vs. 0.52 ± 0.18; p < 0.001; Cohen’s d = 0.66). Multivariate analysis confirmed that female gender remained an independent predictor of depression after adjusting for age and employment status (adjusted OR = 2.40, 95% CI: 1.65–3.50 (Table 3).

Table 3.

- Psychiatric comorbidities and clinical outcomes.

Characteristic	Males (n = 1,074)	Females (n = 107)	P-value	Effect size (95% CI)	Adjusted OR (95% CI)*
Psychiatric comorbidity, n (%)
Depression	322 (30.0%)	54 (50.0%)	<0.001	OR = 2.38 (1.60–3.54)	2.40 (1.65–3.50)
Anxiety	215 (20.0%)	43 (40.0%)	<0.001	OR = 2.68 (1.75–4.10)	2.70 (1.80–4.05)
PTSD	108 (10.0%)	21 (20.1%)	<0.001	OR = 2.18 (1.32–3.60)	2.20 (1.35–3.58)
Personality disorders	161 (15.0%)	26 (24.0%)	<0.001	OR = 1.82 (1.15–2.88)	1.85 (1.18–2.90)
Clinical severity scores, mean ± SD
BDI-II score	18.4 ± 8.2	24.6 ± 9.1	<0.001	d = 0.75 (0.59–0.91)	—
BAI score	15.2 ± 7.4	20.8 ± 8.3	<0.001	d = 0.75 (0.59–0.91)	—
ASI composite score	0.52 ± 0.18	0.64 ± 0.20	<0.001	d = 0.66 (0.50–0.82)	—

*Adjusted for age, employment, and education. Bold represents statistically significant (p < 0.05). BDI-II: Beck depression inventory-II, BAI: Beck anxiety inventory, ASI: addiction severity index, PTSD: post-traumatic stress syndrome.

Psychosocial functioning and quality of life

The study revealed significant gender disparities in psychosocial outcomes, with female methamphetamine users experiencing substantially poorer quality of life compared to their male counterparts (54.8 ± 16.4 vs. 62.4 ± 15.2; p < 0.001), demonstrating a moderate effect size (Cohen’s d = -0.50). This diminished quality of life was accompanied by markedly higher rates of family conflicts among females (65.4% vs. 50%; OR = 1.89, 95% CI: 1.30-2.75, p < 0.001). females were significantly less than males to encounter legal problems (29.9% vs. 40%; OR = 0.64, 95% CI: 0.43-0.96, p = 0.002). The robustness of these quality-of-life findings was confirmed by post-hoc power analysis, which indicated excellent statistical power (99%) to detect these effects at α =0.05 with the observed effect size of 0.50 (Table 4).

Table 4.

Psychosocial functioning and quality of life.

Domain	Males (n = 1,074)	Females (n = 107)	P-value	Effect Size (95% CI)	Adjusted OR (95% CI)*
Quality of life score, mean ± SD	62.4 ± 15.2	54.8 ± 16.4	<0.001	Cohen’s d = -0.50 (-0.65 to -0.35)	—
Social support, n (%)			0.003
Strong	429 (40.0%)	32 (29.9%)	—	Ref.	Ref.
Moderate	429 (40.0%)	48 (44.9%)	—	OR = 1.45 (0.91–2.30)	1.42 (0.89–2.26)
Poor	216 (20.0%)	27 (25.2%)	—	OR = 1.34 (1.12–1.60)	1.30 (1.09–1.55)
Employment problems, n(%)	322 (30.0%)	64 (59.8%)	<0.001	OR = 3.48 (2.20–5.58)	3.10 (2.05–4.68)
Family conflicts, n(%)	537 (50.0%)	70 (65.4%)	<0.001	OR = 1.89 (1.30–2.75)	1.85 (1.28–2.68)
Legal problems, n(%)	429 (40.0%)	32 (29.9%)	0.002	OR = 0.64 (0.43–0.96)	0.66 (0.44–0.99)

*Adjusted for age, education, and employment status. Bold represents statistically significant (p < 0.05). OR: odd ratio, CI: confidence interval, SD: standard deviation.

Discussion

This study highlights significant gender differences in methamphetamine use patterns, psychiatric comorbidities, and psychosocial outcomes among treatment-seeking individuals in Saudi Arabia. Females demonstrated greater vulnerability, with higher rates of depression, anxiety, and family conflict as well as lower quality of life.

Sociodemographic and use pattern differences

Female participants’ younger ages (25.3 vs. 28.5 years) and later methamphetamine initiation ages (21.4 vs. 19.2 years) are consistent with a large-scale study of 241 methamphetamine users conducted by Daiwile et al. [13], which found that women generally started using the drug 2.8 years later than men.

The higher unemployment rates among female participants (59.8% vs. 30%) paralleled the findings of Weber et al. [14], who reported 57.3% unemployment among female methamphetamine users compared with 34.2% among males in their multi-site study of 182 participants. These employment disparities may reflect both preexisting socioeconomic challenges and the consequences of substance use, as documented in a longitudinal study on employment trajectories among stimulant users [14, 15].

Psychiatric comorbidity and clinical implications

Findings from multiple significant studies are consistent with the increased prevalence of psychiatric comorbidities in female participants. For instance, similar changes in prevalence of depression, anxiety, and PTSD were observed by Salo et al. (48.5% vs. 31.2%) [16], Yen et al. (42.3% vs. 22.1%) [17], and Chen et al. (29.8% vs. 15.2%) [18], respectively.

Psychosocial functioning and treatment considerations

The quality-of-life disparities we found (54.8 vs. 62.4) were consistent with McKetin et al.’s longterm study of methamphetamine users, which found that gender differences in quality of life persisted over a 24-month period. Beyond overall quality of life, methamphetamine use is also linked to important psychosocial and health consequences; evidence from an Asian cohort suggests greater sexual health harms among females, including a higher burden of sexually transmitted infections [19, 20].

Differences in social support were consistent with the findings of Leach et al. [21], who found that male users had superior community integration scores and stronger social networks (OR = 1.42, 95% CI 1.28–1.57). However, our strong social support percentages (40% vs. 29.9%) were marginally lower than the stated rates (48.3% vs. 35.7%), which may be due to geographical differences [21].

The observed gender differences may be influenced by cultural and societal factors unique to Saudi Arabia. For example, the stigma associated with substance use among women may delay treatment-seeking and exacerbate psychosocial challenges. In addition, traditional gender norms may limit women’s opportunities for workforce participation and social support, thereby contributing to poor outcomes.

Clinical implications

These findings highlight the need for gender-specific treatment. For females, interventions should address psychiatric comorbidities and provide targeted support for employment and family relationships. For males, interventions should focus on reducing legal problems and preventing relapses.

Study limitations

First, the cross-sectional design limits causal inferences. Second, the sample was predominantly male, which may have constrained the generalizability of the findings to females. Third, self-reported data may be subject to bias, particularly in culturally conservative settings, such as Saudi Arabia.

In conclusion, the results of this study offer strong evidence for gender differences in methamphetamine use patterns, mental comorbidities, and psychosocial consequences in Saudi Arabia and are corroborated by a large body of recent research. Our findings’ agreement with several extensive studies supports the idea that gender-specific treatment strategies are effective and identifies certain areas that require clinical and policy action. These findings highlight the need for gender-specific treatment strategies and targeted public health interventions. Future research should explore the cultural and societal factors that influence these differences and evaluate the effectiveness of gender-specific interventions.