Factors influencing adherence to anti-tuberculosis treatment in Morocco: a cross-sectional study from the Béni Mellal-Khénifra region

Abstract

Objectives

Adherence to anti-tuberculosis treatment is essential for achieving successful outcomes and preventing the emergence of drug-resistant strains. This study aimed to evaluate adherence levels and identify factors associated with non-adherence among tuberculosis patients in the Béni Mellal-Khénifra region of Morocco. We hypothesized that sociodemographic, clinical, and behavioral factors influence adherence.

Methods

A facility-based cross-sectional study was conducted from January 2023 to December 2024 in 2 Tuberculosis and Respiratory Disease Diagnostic Centers in the Béni Mellal-Khénifra region. A total of 481 patients who had been on treatment for at least 2 months were recruited using convenience sampling. Data were collected through structured, pre-tested questionnaires administered in face-to-face interviews and verified against treatment cards and medical records. Adherence was defined as taking ≥90% of prescribed doses. Multivariable logistic regression was used to identify independent predictors of non-adherence.

Results

Among the 481 participants, 8.1% were non-adherent. Significant predictors of non-adherence included forgetfulness (adjusted odds ratio [AOR], 38.84; 95% confidence interval [CI], 11.35–132.88), adverse effects (AOR, 14.26; 95% CI, 3.17–64.13), male sex (AOR, 6.77; 95% CI, 1.45–31.60), rural residence (AOR, 4.42; 95% CI, 1.37–14.25), self-adjusted dosing (AOR, 5.83; 95% CI, 1.20–28.34), stopping treatment after symptom improvement (AOR, 6.56; 95% CI, 1.34–32.14), and missed follow-up visits (AOR, 6.74; 95% CI, 1.56–29.19).

Conclusion

Although overall adherence was high, 8.1% of patients were non-adherent. Strategies to improve adherence should focus on addressing forgetfulness, managing side effects, enhancing access in rural areas, and reinforcing patient education and follow-up systems to improve treatment outcomes in this and similar settings.

Graphical abstract

Introduction

Adherence to anti-tuberculosis (TB) treatment refers to the extent to which patients follow the prescribed therapeutic regimen. This includes taking medications as directed, attending follow-up appointments, and adopting lifestyle modifications recommended by healthcare providers [1]. The World Health Organization (WHO) emphasizes that adherence is a critical determinant of treatment success, as poor adherence may lead to prolonged infectiousness, treatment failure, disease relapse, and the emergence of drug-resistant TB strains [1]. Given TB’s communicable nature and the need for long-term, rigorous treatment regimens, adherence is crucial both for individual recovery and public health [1]. Osterberg and Blaschke highlight that medication adherence is a key driver of treatment outcomes, yet non-adherence remains widespread across chronic conditions, including TB [2]. Extending this framework, Vrijens et al. [3] classify adherence into 3 phases—initiation, implementation, and discontinuation—each of which plays an essential role in ensuring the effectiveness of long-term therapies.

Currently, no universally accepted standard exists for measuring adherence to anti-TB treatment [4]. The WHO notes the difficulty of defining adherence empirically, as there is no clear threshold that guarantees successful therapeutic outcomes. Instead, adherence is often inferred from practical indicators such as treatment success rates within the directly observed therapy, short-course (DOTS) strategy [1]. Research studies commonly measure adherence as the proportion of prescribed doses actually taken. While some studies define adequate adherence as taking at least 80% of prescribed doses [5], others apply more stringent thresholds of 90% or higher [6–10]. These differences reflect varying perspectives on the level of medication intake required to prevent treatment failure, relapse, or drug resistance. Despite the lack of a universal definition, the WHO consistently stresses the importance of maintaining high adherence levels to ensure therapeutic success and to curb the development of multidrug-resistant TB (MDR-TB) [1].

Medication adherence is influenced by a wide range of factors, which can be grouped into 5 key dimensions: the nature of the disease, characteristics of the treatment regimen, patient-related aspects, the demographic and socio-economic context, and the health care system [11,12].

In Morocco, the introduction of the DOTS strategy in 1991 [13], contributed to a significant decline in TB incidence and mortality. Nevertheless, TB remains a major public health concern. According to WHO estimates in 2023, approximately 35,000 individuals were affected by TB, corresponding to an incidence rate of 92 cases per 100,000 population. The disease accounted for 1,954 deaths, of which 1,900 occurred among human immunodeficiency virus (HIV)-negative individuals and 54 among those living with HIV. Furthermore, 590 patients developed MDR-TB or rifampicin-resistant TB [14]. In 2021, Morocco’s National TB Program (NTP) reported 29,327 new TB cases (incidence rate, 80 per 100,000 population), including 295 MDR-TB cases [15].

In the Béni Mellal-Khénifra (BMK) region specifically, 1,353 TB cases were reported, corresponding to an incidence rate of 51 per 100,000 population [15], highlighting the continuing burden of TB in this region. Despite this, data on adherence to anti-TB treatment in Morocco remain limited. Previous studies have reported widely varying non-adherence prevalence, ranging from 20.5% [16] to 53.1% [17]. To address this knowledge gap, the present study aimed to assess adherence levels and identify determinants of non-adherence among TB patients in the BMK region of Morocco.

Materials and Methods

Study Design and Setting

We conducted a facility-based cross-sectional study in the BMK region, located in central Morocco, from January 1, 2023, to December 31, 2024. The region consists of 5 prefectures with a total population of 2,525,801 [18].

All TB cases in the region are registered and treated under the DOTS strategy at 5 designated Tuberculosis and Respiratory Disease Diagnostic Centers (TRDDCs). These facilities provide comprehensive TB services, including diagnosis, treatment, and prevention, free of charge.

In Morocco, newly diagnosed TB patients undergo a 6-month standardized chemotherapy regimen consisting of 2 phases: a 2-month intensive phase with a fixed-dose combination of RHZE (Rifampicin, Isoniazid, Pyrazinamide, and Ethambutol), followed by a 4-month continuation phase with RH (Rifampicin and Isoniazid). Patients collect their medications from primary health centers every 2 weeks and are scheduled for follow-up visits at the TRDDCs every 2 months [16]. Routine provider-initiated HIV testing is also offered to all TB patients.

Of the 5 TRDDCs in the BMK region, 2 centers—Béni Mellal and Fqih Ben Salah—were purposively selected for this study due to their high TB burden and geographic representativeness. According to the Moroccan Ministry of Health and Social Protection, these 2 centers accounted for approximately 50% of the region’s TB cases in the years prior to the study [15]. Their inclusion ensured a representative urban–rural mix, capturing patients from diverse socio-geographic contexts.

Participants

The study population comprised TB patients aged 15 years or older receiving treatment at the selected centers, regardless of disease site (pulmonary or extrapulmonary). Eligibility required patients to have completed at least 2 months of anti-TB therapy at the time of data collection, ensuring they had sufficient treatment experience for adherence assessment.

Sample Size and Sampling Method

Participants were recruited through convenience sampling from TB patients attending the selected TRDDCs during the study period (January 2023–December 2024). Eligible patients present during the researchers’ scheduled visits and who provided consent were enrolled.

A total of 555 patients were initially screened. Of these, 39 were excluded because they were younger than 15 years, and 35 were excluded for having received less than 2 months of anti-TB treatment. The final sample size (n=481) reflected the available eligible patients during the study period, which was adequate for performing multivariable analysis on adherence outcomes.

Variables and Data Sources

Data were collected using a structured, interviewer-administered questionnaire designed to capture factors influencing adherence. These included sociodemographic variables (e.g., age, household income), clinical characteristics (e.g., type of TB, comorbidities), behavioral risk factors (e.g., smoking, alcohol use), treatment-related aspects (e.g., side effects, follow-up), and TB-related knowledge.

Knowledge of TB was assessed using 5 questions addressing etiology, transmission, symptoms, treatment duration, and side effects. Each correct answer was awarded 1 point. Participants scoring ≥60% (at least 3 of 5 correct answers) were classified as knowledgeable, while those scoring <60% were considered non-knowledgeable.

The questionnaire underwent rigorous translation (French to Arabic and back-translation) and pretesting to ensure linguistic validity and cultural appropriateness.

Two trained investigators (M.D. and S.B.) conducted face-to-face exit interviews with eligible participants. Informed consent was obtained after explaining study objectives, confidentiality procedures, and the voluntary nature of participation. All questionnaires were completed anonymously to preserve confidentiality.

Adherence was assessed using a triangulated approach combining patient self-reports, treatment card verification, and medical record review. In cases of discrepancy, adherence classification was determined through clinician-led review of patients’ full medical records. Participants were considered adherent if they had taken ≥90% of prescribed doses during the treatment period. This threshold was selected based on WHO operational guidance [19] and is consistent with guidelines from Morocco’s NTP [20], which emphasize near-complete adherence to prevent relapse, treatment failure, and drug resistance. The ≥90% cut-off has also been adopted in previous studies in high-burden settings [6–9,21], supporting its validity as a rigorous standard for assessing TB treatment adherence.

Bias Control

Measures were taken to minimize recall and social desirability bias by triangulating adherence data and conducting interviews privately using behaviorally neutral language. Interviewers received standardized training to avoid suggestive phrasing, and participants were assured of strict confidentiality. Selection bias is acknowledged as a limitation due to the use of non-probability sampling, which was required by logistical constraints.

Statistical Methods

Data were analyzed using IBM SPSS Statistics ver. 25.0 (IBM Corp.). Descriptive statistics, including frequencies and percentages, were used to summarize participant characteristics and adherence patterns. To identify factors associated with adherence, bivariate binary logistic regression analyses were performed. Variables with a p-value ≤0.05 were included in the multivariable logistic regression model to adjust for confounding.

Prior to multivariable analysis, collinearity among independent variables was assessed using variance inflation factors (VIF) from a linear regression model. All VIF values were below 3.5, indicating no evidence of problematic multicollinearity.

Multivariable logistic regression was then conducted to identify independent predictors of treatment adherence. Statistical significance was defined as p≤0.05 in the final model. Model fit was evaluated using the Hosmer-Lemeshow goodness-of-fit test, while model performance was assessed using Nagelkerke’s R², overall classification accuracy, sensitivity, and specificity. The model demonstrated acceptable fit.

Results of the bivariate analysis were reported as crude odds ratios (CORs), whereas the multivariable analysis provided adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs). No missing data were identified for the variables included in the final analysis.

Ethical Considerations

The study received ethical approval from the Ethics Committee for Biomedical Research at Mohammed Premier University’s Faculty of Medicine and Pharmacy, Oujda (approval number: 2022-08), and administrative authorization from the BMK Regional Directorate of Health and Social Protection.

All participants provided informed consent after receiving detailed explanations of study objectives, potential risks, and benefits. For adults (≥18 years), verbal consent was obtained, while minors (<18 years) gave verbal assent accompanied by parental permission. Participation was entirely voluntary, and participants retained the right to withdraw at any stage without consequences.

Results

Participants

A total of 555 patients were screened for eligibility. Of these, 39 were excluded for being under 15 years of age, and 35 were excluded for having received less than 2 months of treatment. The final sample included 481 participants.

Descriptive Characteristics of Study Participants

The study population comprised 481 TB patients, with a nearly equal sex distribution (51.1% male, 48.9% female). Most participants were adults aged 35–59 years (41.3%) or 15–34 years (38.9%), with the majority residing in urban areas (57.4% vs. 42.6% rural). Nearly half were married (49.2%), while 36.4% reported being single (Table 1). Clinically, extrapulmonary TB (54.3%) was more common than pulmonary TB (45.7%), with new diagnoses predominating (91.1%). Reported comorbidities included diabetes mellitus (13.7%) and HIV co-infection (1.2%). Behavioral risk factors included smoking (31.2%), alcohol use (15.8%), and illicit drug use (20.2%) (Table 2).

Table 1.

Sociodemographic characteristics of tuberculosis patients (n=481)

Variable	Category	Frequency (%)
Sex	Male	246 (51.1)
	Female	235 (48.9)
Age (y)	15–34	187 (38.9)
	35–59	199 (41.3)
	>60	95 (19.8)
Residence	Urban	276 (57.4)
	Rural	205 (42.6)
Marital status	Single	175 (36.4)
	Married	237 (49.2)
	Divorced	33 (6.9)
	Widowed	36 (7.5)
Body mass index (kg/m2)	≥18.5	344 (71.5)
	<18.5	137 (28.5)
Education status	No formal education	192 (39.9)
	At least primary school	289 (60.1)
Employment	Employed	210 (43.7)
	Unemployed	271 (56.3)
Family income (MAD)	≤3,000	399 (83.0)
	>3,000	82 (17.0)
Health insurance	Yes	344 (71.5)
	No	137 (28.5)
Family size	≥4	335 (69.6)
	<4	146 (30.4)

MAD, Moroccan dirham.

Table 2.

Clinical and behavioral characteristics of TB patients (n=481)

Variable	Category	Frequency (%)
Form of TB	PTB	220 (45.7)
	EPTB	261 (54.3)
Treatment history	New cases	438 (91.1)
	Re-treatment cases	43 (8.9)
HIV status	Sero-negative	475 (98.8)
	Sero-positive	6 (1.2)
Diabetes mellitus	Yes	66 (13.7)
	No	415 (86.3)
Alcohol use	Yes	76 (15.8)
	No	405 (84.2)
Smoking	Yes	150 (31.2)
	No	331 (68.8)
Illicit drug use (primarily cannabis or hashish)	Yes	97 (20.2)
	No	384 (79.8)

TB, tuberculosis; PTB, pulmonary tuberculosis; EPTB, extrapulmonary tuberculosis; HIV, human immunodeficiency virus.

Several patient-related factors were identified as potential barriers to treatment adherence (Table 3). Adverse effects during treatment were reported by 58.2% of participants. Forgetfulness in taking medication was reported by 12.7%; 5.2% admitted to reducing their doses without medical advice, and 4.2% discontinued treatment once symptoms improved. Although the majority of participants (84.2%) could access healthcare facilities within 30 minutes, 15.8% reported longer travel times. In total, 90 participants (18.7%) were classified as knowledgeable about TB, correctly answering at least 3 of the 5 knowledge questions (≥60%). The remaining 81.3% were categorized as non-knowledgeable, highlighting substantial educational gaps within the study population. Despite these challenges, almost all patients (99.4%) expressed satisfaction with their healthcare providers, suggesting that non-adherence was primarily driven by patient-level and systemic factors rather than provider-related issues.

Table 3.

Treatment-related and behavioral characteristics of TB patients (n=481)

Variable	Category	Frequency (%)
Adverse effects during treatment	Yes	280 (58.2)
	No	201 (41.8)
Time to reach to the health facility (min)	≤30	405 (84.2)
	>30	76 (15.8)
Satisfied with health service provider’s behavior	Yes	478 (99.4)
	No	3 (0.6)
Forgetfulness	Yes	61 (12.7)
	No	420 (87.3)
Taking a lower dose without informing the physician	Yes	25 (5.2)
	No	456 (94.8)
Stopping medication when symptoms improve	Yes	20 (4.2)
	No	461 (95.8)
Missing 1 or more follow-up visits during treatment	Yes	30 (6.2)
	No	451 (93.8)
Knowledge of TB	Knowledgeable	90 (18.7)
	Non-knowledgeable	391 (81.3)

TB, tuberculosis.

Prevalence of Non-Adherence

The overall prevalence of non-adherence was 8.1% (39/481), defined as taking less than 90% of prescribed doses, while 91.9% (442/481) adhered to their treatment regimen.

Factors Associated with Non-Adherence

Bivariate analysis identified several statistically significant predictors of treatment non-adherence (p≤0.05) across sociodemographic, clinical, and behavioral domains (Table 4). Among sociodemographic factors, male sex (p=0.004) and rural residence (p=0.034) were both significantly associated with non-adherence. Clinically, adverse treatment effects (p=0.001) and pulmonary TB (p=0.009) were significant predictors. Behavioral factors strongly associated with non-adherence included medication forgetfulness (p<0.001), self-adjusted dosing (p<0.001), premature treatment cessation (p<0.001), and missed follow-up visits (p<0.001). These 8 variables were retained in the final multivariable logistic regression model to ensure stability and interpretability.

Table 4.

Bivariate logistic regression analysis of factors associated with treatment non-adherence among TB patients (n=481)

Variable	Category	Adherents (n, %)	Non-adherents (n, %)	COR (95% CI)	p
Sex	Male	217 (88.2)	29 (11.8)	3.01(1.43–6.31)	0.004
	Female	225 (95.7)	10 (4.3)	1
Residence	Rural	182 (88.8)	23 (11.2)	2.05 (1.05–3.99)	0.034
	Urban	260 (94.2)	16 (5.8)	1
Marital status	Single	158 (90.3)	17 (9.7)	1.18 (0.27–8.15)	0.646
	Married	219 (92.4)	18 (7.6)	0.90 (0.07–71.34)	0.635
	Divorced	32 (97.0)	1 (3.0)	0.34 (0.29–24.24)	0.388
	Widowed	33 (91.7)	3 (8.3)	1
Family size	<4 Members	129 (88.4)	17 (11.6)	1.87 (0.39–5.62)	0.559
	≥4 Members	313 (93.4)	22 (6.6)	1
Body mass index (kg/m²)	<18.5	123 (89.8)	14 (10.2)	1.45 (0.33–5.29)	0.696
	≥18.5	319 (92.7)	25 (7.3)	1
Age (y)	15–34	79 (90.9)	17 (9.1)	1.48 (0.28–16.34)	0.461
	35–59	183 (92.0)	16 (8.0)	1.29 (0.21–5.95)	0.901
	>60	89 (93.7)	6 (6.3)	1
Health insurance	No	121 (88.3)	16 (11.7)	1.85 (0.44–5.89)	0.475
	Yes	321 (93.3)	23 (6.7)	1
Employment	Employed	188 (89.5)	22 (10.5)	1.75 (0.29–10.37)	0.548
	Unemployed	254 (93.7)	17 (6.3)	1
Education status	At least primary school	265 (91.7)	24 (8.3)	1.07 (0.31–8.34)	0.567
	No formal education	177 (92.2)	15 (7.8)	1
Family income (MAD)	≤3,000	366 (91.7)	33 (8.3)	1.14 (0.89–27.16)	0.068
	>3,000	76 (92.7)	6 (7.3)	1
Adverse effects during treatment	Yes	246 (87.9)	34 (12.1)	5.42 (2.08–14.11)	0.001
	No	196 (97.5)	5 (2.5)	1
Treatment history	Re-treatment cases	29 (67.4)	14 (32.6)	7.97 (0.75–28.69)	0.100
	New cases	413 (94.3)	25 (5.7)	1
Time to health facility (min)	>30	63 (82.9)	13 (17.1)	3.00 (0.24–5.96)	0.838
	≤30	379 (93.6)	26 (6.4)	1
HIV status	Sero-positive	5 (83.3)	1 (16.7)	2.30 (0.001–1371.39)	0.919
	Sero-negative	437 (92.0)	38 (8.0)	1
Smoking	Yes	129 (86.0)	21 (14.0)	2.83 (0.11–21.58)	0.765
	No	313 (94.6)	18 (5.4)	1
Illicit drug use	Yes	80 (82.5)	17 (17.5)	3.50 (0.59–92.80)	0.120
	No	362 (94.3)	22 (5.7)	1
Alcohol use	Yes	64 (84.2)	12 (15.8)	2.62 (0.46–32.82)	0.214
	No	378 (93.3)	27 (6.7)	1
Diabetes mellitus	No	380 (91.6)	35 (8.4)	1.43 (0.32–17.68)	0.403
	Yes	62 (93.9)	4 (6.1)	1
Form of TB	PTB	198 (90.0)	22 (10.0)	1.59 (1.20–39.82)	0.009
	EPTB	244 (93.5)	17 (6.5)	1
Forgetfulness	Yes	32 (52.5)	29 (47.5)	37.16(16.63–83.00)	<0.001
	No	410 (97.6)	10 (2.4)	1
Taking a lower dose without informing the physician	Yes	10 (40.0)	15 (60.0)	27.00(10.98–66.38)	<0.001
	No	432 (94.7)	24 (5.3)	1
Stopping medication when symptoms improve	Yes	5 (25.0)	15 (75.0)	54.62(18.32–162.85)	<0.001
	No	437 (94.8)	24 (5.2)	1
Missing 1 or more follow-up visits during treatment	Yes	22 (73.3)	8 (26.7)	4.93(2.03–11.97)	<0.001
	No	420 (93.1)	31 (6.9)	1
Knowledge of TB	Knowledgeable	86 (95.6)	4 (4.4)	2.11 (0.45–52.15)	0.193
	Non-knowledgeable	356 (91.0)	35 (9.0)	1

TB, tuberculosis; COR, crude odds ratio; CI, confidence interval; MAD, Moroccan dirham; HIV, human immunodeficiency virus; PTB, pulmonary tuberculosis; EPTB, extrapulmonary tuberculosis.

Multivariable logistic regression revealed several independent predictors of non-adherence (Table 5). Forgetfulness remained the strongest behavioral factor (AOR, 38.84; 95% CI, 11.35–132.88; p<0.001), followed by adverse effects (AOR, 14.26; 95% CI, 3.17–64.13; p<0.001). Male sex (AOR, 6.77; 95% CI, 1.45–31.60; p=0.015) and rural residence (AOR, 4.42; 95% CI, 1.37–14.25; p=0.013) were also significant predictors. Pulmonary TB showed elevated odds (AOR, 2.60; 95% CI, 0.84–8.02) but did not reach statistical significance (p=0.096). Other significant behavioral predictors included self-adjusted dosing (AOR, 5.83; 95% CI, 1.20–28.34; p=0.029), premature cessation of treatment (AOR, 6.56; 95% CI, 1.34–32.14; p=0.020), and missed follow-up visits (AOR, 6.74; 95% CI, 1.56–29.19; p=0.011). These findings underscore the multifactorial nature of TB treatment non-adherence and highlight the need for multidimensional, patient-centered strategies to improve adherence outcomes.

Table 5.

Multivariable logistic regression model to identify independent predictors of treatment non-adherence of TB patients (n=481)

Variable	Category	Adherents (n, %)	Non-adherents (n, %)	COR (95% CI)	AOR (95% CI)	p
Sex	Male	217 (88.2)	29 (11.8)	3.01 (1.43–6.31)	6.77 (1.45–31.60)	0.015
	Female	225 (95.7)	10 (4.3)	1	1
Residence	Rural	182 (88.8)	23 (11.2)	2.05 (1.05–3.99)	4.42 (1.37–14.25)	0.013
	Urban	260 (94.2)	16 (5.8)	1	1
Adverse effects during treatment	Yes	246 (87.9)	34 (12.1)	5.42 (2.08–14.11)	14.26 (3.17–64.13)	<0.001
	No	196 (97.5)	5 (2.5)	1	1
Form of TB	PTB	198 (90.0)	22 (10.0)	1.59 (1.20–39.82)	2.60 (0.84–8.02)	0.096
	EPTB	244 (93.5)	17 (6.5)	1	1
Forgetfulness	Yes	32 (52.5)	29 (47.5)	37.16 (16.63–83.00)	38.84 (11.35–132.88)	<0.001
	No	410 (97.6)	10 (2.4)	1	1
Taking a lower dose without informing the physician	Yes	10 (40.0)	15 (60.0)	27.00 (10.98–66.38)	5.83 (1.20–28.34)	0.029
	No	432 (94.7)	24 (5.3)	1	1
Stopping medication when symptoms improve	Yes	5 (25.0)	15 (75.0)	54.62 (18.32–162.85)	6.56 (1.34–32.14)	0.020
	No	437 (94.8)	24 (5.2)	1	1
Missing 1 or more follow-up visits during treatment	Yes	22 (73.3)	8 (26.7)	4.93 (2.03–11.97)	6.74 (1.56–29.19)	0.011
	No	420 (93.1)	31 (6.9)	1	1

TB, tuberculosis; COR, crude odds ratio; AOR, adjusted odds ratio; CI, confidence interval; PTB, pulmonary tuberculosis; EPTB, extrapulmonary tuberculosis.

Discussion

Key Results

This study assessed adherence to anti-TB treatment and its determinants among patients in the BMK region of Morocco. We found a non-adherence rate of 8.1%, defined as taking less than 90% of prescribed doses. This rate is comparable to findings from Ethiopia (8.4%) [22] and China (9.4%) [9], which used similar definitions, and aligns with reports of approximately 10% non-adherence in studies employing monthly dose-count methods [23]. However, our rate is notably lower than those reported in India (15.5%) [24], China (12.2%) [8], and Ethiopia (24.5%[7]; 21.2% [10]). These differences may reflect variations in study methodology, the evolution of NTPs, or differences in patient characteristics.

Interpretation

Sociodemographic factors

Multivariable analysis identified several independent predictors of non-adherence. Male patients had significantly higher odds of non-adherence, consistent with results from Argentina [25] and India [24,26].

Although prior studies have suggested that employment responsibilities and time constraints may hinder men’s ability to adhere to treatment schedules, our data did not identify employment status as a significant predictor, nor did we observe interaction effects between sex and employment. Thus, this explanation should be interpreted cautiously. Nonetheless, the observed sex disparity highlights the importance of developing sex-sensitive adherence strategies. Interventions should consider men’s social roles and potential structural barriers by incorporating workplace-based programs or mobile adherence support.

Rural residence was also strongly associated with non-adherence, consistent with findings from Ethiopia [21], where geographic distance and limited transportation were major barriers. Decentralizing services through mobile clinics or community health worker programs could improve access and continuity of care for rural populations.

Behavioral and Treatment-Related Factors

Treatment-related side effects were another major determinant of non-adherence. In line with findings from Ethiopia [22,27], Somalia [28], and China [8,9,29], our results suggest that adverse events such as gastrointestinal or dermatologic reactions contribute to treatment discontinuation. Providing comprehensive pre-treatment counseling and timely management of side effects may help mitigate this barrier and improve adherence.

Missed follow-up visits were also strongly associated with non-adherence, likely reflecting broader socioeconomic constraints, including transportation costs and logistical challenges, especially in rural areas. Similar barriers have been reported in Brazil [30]. Providing transportation support or financial incentives may help mitigate these obstacles and reduce missed appointments. In parallel, expanding access to telemedicine follow-ups—via phone or video—offers a practical and increasingly viable solution. Morocco has made substantial progress in this regard, with a national telemedicine program launched in 2018 and integrated into the Ministry of Health and Social Protection’s “Plan Santé 2025” [31]. This initiative aims to equip 160 rural sites by 2025, serving more than 2 million people in medically underserved areas [32]. These efforts, supported by national legislation and high mobile phone penetration, reflect the country’s readiness to implement digital health solutions [31]. A recent scoping review by Olowoyo et al. [33] confirmed that telemedicine interventions can effectively improve treatment adherence and outcomes among TB patients, making it a promising area for continued investment and locally tailored implementation in Morocco.

Forgetfulness emerged as the strongest predictor of non-adherence, corroborating prior evidence from Zegeye et al. [34] and Adane et al. [23]. This highlights the need for simple, cost-effective strategies such as SMS reminders, pillboxes, and family involvement in medication routines. Routine clinical assessments that include screening for forgetfulness could help identify at-risk patients early and enable timely interventions.

Behavioral misconceptions, such as self-adjusting doses or discontinuing treatment when symptoms improve, were also important contributors to non-adherence. These behaviors reflect an inadequate understanding of TB treatment requirements, consistent with findings from India [24] and Eritrea [35]. Targeted health education, particularly through community health workers, can help correct these misconceptions and reinforce the importance of completing the full course of therapy.

Strengths and Limitations

This study provides robust evidence on TB treatment adherence in the BMK region by using methodological triangulation and a comprehensive analysis of clinical, behavioral, and structural factors. Adherence was evaluated using a validated threshold aligned with WHO recommendations, strengthening both reliability and comparability across studies. While the cross-sectional design limits causal inference and the use of convenience sampling may have introduced selection bias, the study nonetheless identifies critical and actionable barriers to adherence that are highly relevant for public health planning and TB control programming.

Generalizability

In Morocco, TB treatment is provided exclusively through public health facilities known as TRDDCs, where medications are dispensed free of charge. The 2 TRDDCs included in this study were purposively selected for their high TB burden and their representation of both urban and rural populations, accounting for nearly half of the region’s TB cases. Therefore, the findings are likely generalizable to similar semi-urban and rural populations within Morocco and to countries with comparable public-sector TB programs. However, caution should be exercised when applying these results to settings with different healthcare delivery structures or greater private-sector involvement.

Based on these findings—and consistent with the review by Volmink and Garner [36], which suggests that addressing social and systemic barriers is more effective than restrictive approaches that compromise patient autonomy—we recommend 3 key policy actions: (1) the NTP should integrate digital reminder tools and structured side-effect management into routine care; (2) the Ministry of Health and Social Protection should prioritize sex-sensitive strategies and decentralize TB services in rural areas by strengthening community health worker networks; and (3) policymakers should implement transportation subsidies to improve access for vulnerable populations. Additionally, motivating healthcare personnel involved in TB control through targeted incentives could enhance service delivery. To reinforce these efforts, we also advocate for patient-centered education initiatives and future longitudinal mixed-methods research to further examine and address persistent adherence challenges in Morocco and other high-burden contexts.

Conclusion

This study demonstrates a high overall adherence rate to anti-TB treatment among patients in the BMK region. Nonetheless, a subset of patients continues to experience significant barriers to adherence, including forgetfulness, adverse drug effects, male sex, rural residence, and missed follow-up visits. These results highlight the multifactorial and context-specific nature of non-adherence, shaped by clinical, behavioral, and structural determinants.

To overcome these challenges, targeted interventions are required. These include enhanced patient education, proactive side-effect management, digital reminder tools, and expanded healthcare access in rural areas. Incorporating community-based support and integrating psychosocial care into TB programs may further strengthen adherence. Sustained improvements in adherence are critical not only for optimizing individual outcomes but also for advancing Morocco’s national TB control strategy and preventing the emergence and spread of drug-resistant TB.

HIGHLIGHTS

• A cross-sectional study identified an 8.1% non-adherence rate among 481 tuberculosis (TB) patients in Morocco.

• Major risk factors included forgetfulness, adverse effects, male sex, and rural residence.

• Stopping treatment upon symptom improvement and missed follow-up visits also increased the risk of non-adherence.

• Tailored education, reminder systems, and rural service access are essential.

• The findings support patient-centered strategies to sustain TB control and reduce drug resistance.