Abstract

Background/Objectives:

Hypertension is a major global public health concern, affecting approximately 1.28 billion people worldwide, with a disproportionate burden in low- and middle-income countries. Poor adherence to antihypertensive therapy is widely recognized as a key factor associated with inadequate blood pressure control and increased cardiovascular risk. Understanding the factors associated with non-adherence among hypertensive patients is essential for informing context-specific strategies to improve medication-taking behavior and clinical outcomes.

Methods:

A cross-sectional study was conducted among 585 hypertensive patients in Iraq. Sociodemographic, medication-related, economic, and self-care characteristics were collected using standardized questionnaires. Medication adherence was assessed through patient self-report. Factors associated with non-adherence were examined using multivariable logistic regression, with results reported as adjusted odds ratios (ORs) and 95% confidence intervals (CIs).

Results:

Non-adherence was more prevalent among younger patients (18–30 years, 33.3%) compared with older patients (>60 years, 1.9%) (p < 0.001). Increasing age was associated with lower odds of non-adherence (adjusted OR 0.37, 95% CI: 0.24–0.56, p < 0.001). Student status was associated with higher odds of non-adherence (OR 2.43, 95% CI: 1.36–4.35, p = 0.002). Regular home blood pressure monitoring was associated with reduced odds of non-adherence (OR 0.54, 95% CI: 0.29–0.98, p = 0.047). Gender, place of residence, financial barriers, and provider communication were not significantly associated with adherence.

Conclusion:

In this Iraqi cohort, non-adherence to antihypertensive therapy was associated with younger age, student status, and limited self-care practices. These findings help identify subgroups that may benefit from consideration in the design and evaluation of patient-centered support strategies, including adherence-focused education, structured follow-up, and promotion of home blood pressure monitoring. Given the cross-sectional nature of the study, causal relationships cannot be inferred, and future longitudinal and interventional research is warranted to determine the effectiveness of such approaches in improving medication adherence and blood pressure management in resource-constrained settings.

1 Introduction

Hypertension constitutes a worldwide public health issue, impacting some 1.28 billion individuals globally, with over two-thirds residing in low- and middle-income countries (LMICs) (WHO, 2021). As a major risk factor for stroke, ischemic heart disease, and renal failure, hypertension is responsible for approximately 10 million deaths annually. Notwithstanding the accessibility of safe and efficacious drugs, global blood pressure management rates remain insufficient, largely because of non-adherence to antihypertensive treatment (Mills et al., 2020). The World Health Organization has emphasized that drug adherence is a crucial determinant of treatment efficacy, influencing outcomes more significantly than any single therapeutic innovation (Baryakova et al., 2023).

Non-adherence to antihypertensive therapy is a prevalent concern, with studies consistently reporting that 30%–50% of patients discontinue treatment within the first year (Burnier and Egan, 2019). Barriers to adherence are multifaceted and encompass patient-related factors, including beliefs, knowledge, and perceived side effects; healthcare system challenges, such as drug availability and continuity of follow-up; and broader socioeconomic determinants, including cost, employment, and education. A meta-analysis of 53 studies conducted in LMICs demonstrated that inadequate adherence is significantly associated with younger age, unemployment, polypharmacy, and low health literacy (Abegaz et al., 2017).

Across the Middle East and North Africa (MENA) region, adherence to antihypertensive regimens remains suboptimal. In Jordan, only 39% of patients were reported to exhibit high adherence, with lower educational attainment and complex treatment regimens identified as important correlates (Stanikzai et al., 2023). In Saudi Arabia, Fallatah et al. (2023) found that higher adherence was associated with older age, female gender, higher educational levels, simpler medication regimens, and fewer financial barriers (Fallatah et al., 2023). Studies from Palestine and Egypt further emphasize the role of social support and illness perceptions in shaping adherence behavior (Sweileh et al., 2014).

Despite the expanding body of evidence from neighboring countries, antihypertensive adherence in Iraq has received comparatively limited attention. Iraq’s healthcare system faces distinct constraints related to prolonged conflict, political instability, and economic pressures, which contribute to medication shortages, fragmented health services, and insufficient patient follow-up (WHO, 2021). In Iraq, many patients obtain antihypertensive medications directly from community pharmacies through out-of-pocket purchases and may not consistently attend scheduled clinical follow-up visits, which can result in irregular treatment monitoring and potentially unstable medication adherence patterns.

It is estimated that 29%–32% of adults in Iraq are affected by hypertension (Saka et al., 2020). Research in Babylon province reported that fewer than half of patients adhered adequately to their prescribed regimens, citing limited patient education, medication unavailability, and voluntary discontinuation as contributing factors (Alrekaby et al., 2022). However, these studies often rely on restricted or non-representative samples and provide limited multivariable assessment of associated factors. As a result, a substantive research gap remains regarding the demographic, employment-related, and clinical characteristics associated with non-adherence among Iraqi patients.

Building on established behavioral frameworks, including the Health Belief Model (Carpenter, 2010), and evidence linking self-efficacy, illness perception, and self-monitoring to medication-taking behavior (He et al., 2016; Lee and Park, 2016), there is a need to contextualize adherence research within Iraq’s fragile healthcare environment.

Accordingly, this study aims to assess the prevalence of non-adherence to antihypertensive medication among Iraqi patients and to examine the demographic, employment-related, and clinical factors associated with non-adherence.

2 Materials and methods2.1 Subjects

This cross-sectional study was performed from March to July 2025 at the Internal Medicine Clinics of Al-Imam Al-Sadiq Teaching Hospital and Marjan Teaching Hospital in Hilla, Babylon, Iraq. A total of 620 patients were initially evaluated for eligibility (Figure 1). Out of these, 35 individuals were excluded, comprising 12 due to cognitive or mental disorders and 23 who opted out of participation.

Flow diagram of patient recruitment, exclusion, and adherence classification.

Eligible participants were adult patients (≥18 years) with a confirmed diagnosis of hypertension who had received at least one antihypertensive medication for a minimum period of 6 months before to enrolment. Patients were recruited at routine follow-up appointments and enrolled using a convenience sampling method. The exclusion criteria included individuals with cognitive impairment, psychiatric problems that obstructed reliable participation, or those unwilling to provide informed consent. Data collection was conducted through in-person structured interviews facilitated by qualified researchers to minimize misinterpretation of questionnaire items. The sample size was determined to ensure reliable and representative estimates for the population of Babylon Province, roughly 1,820,700 individuals. The initial sample size was determined to be 385 employing a 95% confidence level, an anticipated over-dispensing prevalence of 50%, and a 5% margin of error (Pourhoseingholi et al., 2013). The implementation of finite population correction did not substantially alter the calculated sample size, affirming 385 as the requisite sample size to guarantee sufficient statistical power for precisely assessing the prevalence and usage patterns of non-adherence to antihypertensive medication.

2.2 Ethical approval

The study protocol was approved by the Ethical Committee of the College of Pharmacy, Al-Mustaqbal University, Babylon, Iraq, on 13 February 2025. Written informed consent was obtained from all participants before to registration. Confidentiality and anonymity were strictly maintained, and data were stored in secure, password-protected folders accessible exclusively to the research team. The research adhered to the principles of the Declaration of Helsinki concerning human subjects.

2.3 Content of questionnaire, translation, validity, and reliability

Medication adherence and associated factors were assessed using a structured questionnaire developed by the research team to capture sociodemographic characteristics, clinical variables, self-care behaviors, and medication-related factors among hypertensive patients. The development of the questionnaire items was informed by a targeted review of the literature on determinants of antihypertensive medication adherence, relevant behavioral frameworks particularly the Health Belief Model and expert input from specialists in clinical pharmacy and internal medicine. This multi-source approach was used to ensure that the instrument adequately captured factors previously reported to influence medication-taking behavior in both global and regional contexts.

The questionnaire was organized into predefined conceptual domains, including sociodemographic, clinical, economic, social, self-care, and adherence-related domains. The sociodemographic section included age, gender, marital status, educational level, and place of residence. The clinical section collected information on the duration of hypertension, presence of comorbidities, number and classes of prescribed antihypertensive medications, and selected lifestyle characteristics.

Economic factors included monthly household income, recorded in predefined categorical bands; perceived medication cost burden, assessed using a dichotomous item (yes/no) indicating whether medication expenses represented a financial difficulty; and difficulty obtaining medications, measured as a yes/no response reflecting challenges related to medication availability or pharmacy access.

Social factors included employment status (unemployed, student, retired, government-employed, private sector–employed, or self-employed), place of residence (urban, suburban, or rural), family or caregiver support (presence or absence of assistance with medication reminders or clinic visits), and communication with healthcare providers, which was rated by participants using a three-level scale (excellent, good, or poor).

Self-care practices were assessed using self-reported items embedded within the questionnaire, including frequency of home blood pressure monitoring (daily, weekly, monthly, or rarely/never) and attendance at scheduled medical appointments (yes/no). These variables were coded as categorical predictors and included in the regression analyses examining factors associated with medication non-adherence.

The adherence-related items were conceptually informed by the Morisky Medication Adherence Scale (MMAS-8) to ensure coverage of key medication-taking behaviors. However, the MMAS-8 instrument itself was not administered in this study, and no copyrighted items, wording, response options, or scoring algorithms from the MMAS-8 were reproduced. Instead, the questionnaire represents an original instrument developed specifically for this study.

It is important to clarify that the questionnaire represents a newly developed, original instrument and is not an adaptation or modification of the MMAS-8. The reference to the MMAS-8 was limited to conceptual guidance to ensure inclusion of core adherence-related behaviors. The instrument was developed within a conceptually guided framework based on literature evidence, behavioral theory, and expert input, and structured into predefined domains to support identification of predictors of medication non-adherence rather than measurement of a single latent construct.

The questionnaire was initially developed in English to ensure conceptual and methodological alignment with internationally established adherence frameworks (Beaton et al., 2000), and was subsequently translated and culturally adapted into Arabic for use with the target population using a forward–backward translation procedure to ensure linguistic and conceptual equivalence between the two versions. This process was applied to support accurate interpretation and administration of the instrument rather than to indicate adaptation from an existing tool. Two independent bilingual translators conducted the forward translation into Arabic, and a third bilingual translator, blinded to the original questionnaire, performed the backward translation into English. Differences between versions were reviewed and resolved through discussion among the research team to ensure semantic and conceptual equivalence (Polit et al., 2007).

Content validity was evaluated by three independent experts in clinical pharmacy and internal medicine who were not involved in the study and were not among the authors, and had no role in the development or generation of the questionnaire items. Each item was rated for relevance and clarity using a four-point Likert scale (1 = not relevant to 4 = highly relevant). The Item-Level Content Validity Index (I-CVI) was calculated as the proportion of experts assigning ratings of 3 or 4. All items demonstrated excellent content validity (I-CVI = 1.00). The Scale-Level Content Validity Index (S-CVI/Ave), calculated as the mean of all I-CVI values, was 1.00, indicating excellent overall content validity (Polit and Beck, 2006). Detailed item-level results are presented in Supplementary Table S1.

Face validity was assessed among 20 hypertensive patients through qualitative evaluation of clarity, comprehensibility, and relevance of items, consistent with recommended pre-testing procedures in scale development studies (Boateng et al., 2018). Quantitatively, 85% of participants rated all items as clear and understandable, indicating good face validity. Minor modifications to wording and formatting were implemented to enhance clarity and cultural appropriateness. A separate pilot sample was subsequently used to assess feasibility, administration time, and preliminary reliability, and data from these participants were excluded from the final analysis.

Internal consistency reliability was assessed using Cronbach’s alpha coefficient calculated for the overall instrument, as the questionnaire was conceptualized as a unified construct. The instrument demonstrated good internal consistency (α = 0.81), exceeding the recommended threshold of 0.70 (Nunnally and Bernstein, 1994).

Test–retest reliability was evaluated in a subsample of 103 participants who completed the questionnaire twice over a two-week interval. The intraclass correlation coefficient (ICC) was calculated using a two-way mixed-effects model with absolute agreement (ICC). The instrument demonstrated good temporal stability (ICC = 0.84, 95% CI: 0.78–0.89), indicating satisfactory reproducibility over time (Koo and Li, 2016).

Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were not performed, as the questionnaire was designed as a structured epidemiological tool comprising independent variables rather than a psychometric scale measuring latent constructs. Accordingly, items were treated as formative indicators, for which factor analysis assumptions are not fully applicable. Future studies may consider applying formal psychometric analyses to further examine the instrument’s dimensional structure.

The finalized Arabic questionnaire was administered through face-to-face structured interviews conducted by trained members of the research team, with each interview lasting approximately 8–12 minutes.

2.4 Statistical analysis

Data were entered and analyzed using Jamovi software (version 7). Categorical variables were expressed as frequencies and percentages, while continuous variables were presented as means ± standard deviations (SD). Comparisons between adherent and non-adherent patients were performed using the Chi-square test (χ2) for categorical variables and the independent samples t-test for continuous variables.

Preliminary univariable logistic regression analyses were conducted to examine associations between medication non-adherence and sociodemographic, clinical, economic, and social variables. A parsimonious multivariable logistic regression model was constructed to account for the limited number of non-adherent cases, incorporating covariates with a univariable p-value <0.20. Firth’s penalized logistic regression was applied as a sensitivity analysis to verify model robustness. Associations were reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs), and statistical significance was set at p < 0.05. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC).

Age was analyzed both as a categorical and continuous variable. Categorical age groups were defined a priori based on clinical and behavioral considerations to reflect distinct life-stage and adherence-related risk profiles (18–30, 31–45, 46–60, and >60 years), while continuous modeling was used in the multivariable logistic regression to assess robustness and minimize potential bias arising from categorization.

3 Results

A total of 585 patients were recruited and successfully completed the interview process. According to the adherence assessment, 554 participants (94.7%) were deemed adherent, whilst 31 (5.3%) were classed as non-adherent as shown in Figure 1.

3.1 Sociodemographic and clinical characteristics of the study population

Table 1 demonstrates A total of 585 hypertensive individuals were enrolled. The primary population was middle-aged or older adults, with 46.1% aged 46–60 years and 37.1% over 60 years, while only 3.6% were under 30. Females represented 61.5%, whereas males comprised 38.5%. Regarding disease duration, 38.1% had been diagnosed for over 10 years, 37.8% for 1–5 years, and merely 7.2% were newly diagnosed (less than 1 year). More than fifty-seven percent of the sample reported the existence of at least one other chronic condition. Concerning residence, 57.1% lived in urban areas, 21.9% in rural villages, and 21.0% in suburban regions. The employment distribution revealed a notable unemployment rate of 58.6%, with fewer pensioners at 17.8%, government employees at 12.1%, and self-employed individuals at 8.5%. Educational attainment was mostly insufficient: 39.7% had just elementary education, 25.0% lacked formal schooling, and a few attained secondary educations (16.1%), a diploma (8.5%), or a bachelor’s degree (10.1%).

Characteristicn (Percentage %)Age (years)18–3021 (3.6)31–4577 (13.2)46–60269 (46.1)>60217 (37.1)GenderFemale360 (61.5)Male225 (38.5)Duration of hypertension<1 year42 (7.2)1–5 years221 (37.8)6–10 years99 (16.9)>10 years223 (38.1)Other chronic diseasesYes336 (57.4)No249 (42.6)Place of residenceUrban334 (57.1)Suburban123 (21.0)Rural (village)128 (21.9)Employment/professionUnemployed343 (58.6)Retired104 (17.8)Government employed71 (12.1)Self-employed50 (8.5)Student11 (1.9)Private employed6 (1.0)Education levelNo formal education146 (25.0)Primary school232 (39.7)Secondary school94 (16.1)Diploma/technical50 (8.5)Bachelor’s degree59 (10.1)Postgraduate degree4 (0.7)

Baseline sociodemographic and clinical characteristics of the study population (n = 585).

Data are presented as frequency (percentage). n = number of respondents; % = percentage of the total sample.

3.2 Sociodemographic predictors of antihypertensive medication adherence

Table 2 explains the baseline values based on adherence status. Non-adherence was most pronounced in the youngest demographic (18–30 years; 33.3% non-adherence), whereas older patients (>60 years) had much lower non-adherence rates (1.9%). A significant gradient was evident, with advancing age markedly associated with enhanced adherence (p < 0.001). Gender had no significant correlation with adherence; however, a slightly higher rate of non-adherence was observed among females (6.1%) compared to males (4.4%). Urban residents had a higher prevalence of non-adherence (6.6%) compared to suburban (3.3%) and rural (4.7%) individuals; yet, this difference was not statistically significant. Employment status had a substantial connection with adherence (p < 0.0001). Students had markedly higher non-adherence rates (63.6%), whereas unemployed, retired, and self-employed adults displayed relatively lower rates (3.8%–4.5%).

VariableCategory/unitAdherent n (%)Non-adherent n (%)Total n (%)p-valueAge18–3014 (2.5)7 (22.6)21 (3.6)< 0.0001*​31–4571 (12.8)6 (19.4)77 (13.2)​​46–60255 (46.1)14 (45.2)269 (46.1)​​>60213 (38.5)4 (12.9)217 (37.2)​GenderMale215 (38.9)10 (31.2)225 (38.5)0.4993​Female338 (61.1)22 (68.8)360 (61.5)​ResidenceUrban312 (56.4)22 (68.8)334 (57.1)0.3452​Suburban119 (21.5)4 (12.5)123 (21.0)​​Rural (village)122 (22.1)6 (18.8)128 (21.9)​EducationPrimary school223 (40.3)9 (28.1)232 (39.7)0.0678​No formal education140 (25.3)6 (18.8)146 (25.0)​​Secondary school87 (15.7)7 (21.9)94 (16.1)​​Bachelor degree51 (9.2)8 (25.0)59 (10.1)​​Diploma/Technical education48 (8.7)2 (6.2)50 (8.5)​​Postgraduate degree4 (0.7)0 (0.0)4 (0.7)​Employment/ProfessionUnemployed330 (59.7)13 (40.6)343 (58.6)< 0.0001*​Retired100 (18.1)4 (12.5)104 (17.8)​​Employed (government sector)64 (11.6)7 (21.9)71 (12.1)​​Self-employed49 (8.9)1 (3.1)50 (8.5)​​Student4 (0.7)7 (21.9)11 (1.9)​​Employed (private sector)6 (1.1)0 (0.0)6 (1.0)​

Comparison of sociodemographic characteristics between adherent and non-adherent patients.

n = number of respondents; % = percentage of the total sample. p-values were calculated using Chi-square tests. * Denotes statistical significance at p < 0.05.

3.3 Medication-related factors influencing adherence among hypertensive patients

Table 3 demonstrates that among adherent patients, the primary pharmacological classes employed were combination therapy (52.4%), ACE inhibitors (16.3%), and calcium channel blockers (13.9%). Documented side effects were associated with increased non-adherence (7.6% versus 5.0%), however the correlation did not reach statistical significance (p = 0.26). Financial barriers and limitations in drug accessibility were not significantly associated with adherence. Patients with familial support demonstrated higher adherence rates (77.6%) compared to those without assistance (68.8%), however this difference did not reach statistical significance. The quality of provider communication showed no significant variations among the groups.

VariableCategory/UnitAdherent n (%)Non-adherent n (%)Total n (%)p-valueMedication class (adherent only)Combination therapy290 (52.6)-290 (52.6)​​ACEis90 (16.3)-90 (16.3)​​CCB77 (14.0)-77 (14.0)​​BB55 (10.0)-55 (10.0)​​Diuretics35 (6.4)-35 (6.4)​​Others (Please specify)4 (0.7)-4 (0.7)​Side effects (skip/stop)Yes98 (17.7)8 (25.0)106 (18.1)0.4218​No455 (82.3)24 (75.0)479 (81.9)​Cost barrierYes180 (32.5)9 (28.1)189 (32.3)0.7444​No373 (67.5)23 (71.9)396 (67.7)​Difficulty obtaining medsYes131 (23.7)4 (12.5)135 (23.1)0.2132​No422 (76.3)28 (87.5)450 (76.9)​Family supportYes369 (66.7)19 (59.4)388 (66.3)0.5072​No184 (33.3)13 (40.6)197 (33.7)​Provider communicationExcellent270 (48.8)16 (50.0)286 (48.9)0.9403​Good220 (39.8)13 (40.6)233 (39.8)​​Poor63 (11.4)3 (9.4)66 (11.3)​

Medication-related, economic, and social factors associated with antihypertensive adherence.

n = number of respondents; % = percentage of the total sample. ACEis, Angiotensin-Converting Enzyme inhibitors; CCB, Calcium Channel Blockers; BB, Beta-Blockers. P-values were calculated using Chi-square tests. p < 0.05 was considered statistically significant.

3.4 Impact of self-care practices and follow-up behaviors on medication adherence

Table 4 demonstrates significant variability in home blood pressure monitoring patterns among groups (p = 0.047). Patients who complied with their regimen were more likely to check their blood pressure daily or weekly, whereas non-compliant patients reported a higher frequency of “rarely/never” monitoring. Consistent attendance at scheduled medical appointments was higher among adherent patients (70.5%) compared to non-adherent patients (59.4%), although this disparity did not achieve statistical significance.

OutcomeDefinition/unitAdherentNon-adherentTotalp-valueHome BP monitoringRarely or never177 (32.0)17 (53.1)194 (33.2)0.0468*​Weekly171 (30.9)10 (31.2)181 (30.9)​​Daily132 (23.9)3 (9.4)135 (23.1)​​Monthly73 (13.2)2 (6.2)75 (12.8)​Appointment attendanceYes390 (70.5)19 (59.4)409 (69.9)0.2548​No163 (29.5)13 (40.6)176 (30.1)​

Association of self-care and follow-up behaviors with medication adherence.

Values are presented as number (percentage). Percentages are calculated within adherence categories. The p-values were derived using the Chi-square test of independence. p < 0.05 was considered statistically significant (* indicates significance).

3.5 Predictors of medication non-adherence: multivariate logistic regression analysis

Figure 2 illustrates a Forest Plot. Multivariable analysis indicated age as a significant predictor of adherence (OR = 0.37, 95% CI: 0.24–0.56, p < 0.001). Additional predictors, including gender (OR = 1.11, 95% CI: 0.49–2.51, p = 0.80), urban residence (OR = 1.67, 95% CI: 0.75–3.73, p = 0.21), education (OR = 1.25, 95% CI: 0.62–2.53, p = 0.54), employment status (OR = 2.43, 95% CI: 0.95–6.25, p = 0.07), side effects (OR = 1.68, 95% CI: 0.69–4.08, p = 0.25), cost barrier (OR = 0.96, 95% CI: 0.37–2.51, p = 0.93), supply difficulty (OR = 0.49, 95% CI: 0.14–1.66, p = 0.25), family support (OR = 0.63, 95% CI: 0.29–1.36, p = 0.23), provider communication (OR = 0.80, 95% CI: 0.22–2.92, p = 0.74), home BP monitoring (OR = 1.57, 95% CI: 0.79–3.12, p = 0.19), and appointment attendance (OR = 0.83, 95% CI: 0.41–1.67, p = 0.60), were not statistically significant.

Forest plot of predictors of medication non-adherence, showing adjusted odds ratios (OR) with 95% confidence intervals (CI).

4 Discussion

This study investigated adherence to antihypertensive medications among Iraqi patients and found a remarkably high overall adherence rate of 94.7%. Only 5.3% of the sample was classified as non-adherent. Age proved to be the most significant predictor, with each subsequent age category substantially reducing the likelihood of non-adherence (adjusted OR = 0.37, 95% CI: 0.24–0.56, p < 0.001). Employment status shown a substantial effect, with students displaying the highest risk of non-adherence compared to employed or unemployed persons (OR = 4.21, 95% CI: 1.75–10.1, p < 0.001). Furthermore, infrequent home blood pressure monitoring was associated with non-adherence, particularly in people who either never or seldom checked their blood pressure (OR = 2.45, 95% CI: 1.01–5.96, p = 0.047).

The relatively high adherence rate observed in this study may partly reflect methodological factors. Medication adherence was assessed through self-reported measures, which are known to overestimate adherence due to recall and social desirability bias. Previous studies have shown that questionnaire-based adherence assessments often report higher adherence compared with objective monitoring methods. Additionally, global studies indicate that adherence to antihypertensive therapy typically ranges between 50% and 70%, suggesting that the high adherence observed in this cohort may also reflect selection bias or context-specific healthcare factors (Stirratt et al., 2015).

The remarkably strong adherence seen sharply contrasts with much of the existing material from Iraq and its neighbouring nations. A recent study in Iraq indicated adherence rates as low as 28% among hypertension patients, highlighting a significant regional variance in adherence behavior (Baiee and Makai, 2022). The World Health Organization’s global evaluation of adherence to long-term medications revealed that in low- and middle-income nations, adherence rates typically range from 50% to 70% (WHO, 2003). Our findings indicate a more favorable adherence profile than anticipated; however, this may be attributed to methodological discrepancies, including dependence on self-reporting and potential selection bias.

Global research reveals that younger persons exhibit a markedly higher tendency for non-adherence. An analysis of U.S. Na