Hypertension is a leading modifiable risk factor for cardiovascular diseases, accounting for a significant burden of global morbidity and mortality (1). Despite the availability of effective antihypertensive therapies, poor medication adherence remains a major challenge in achieving optimal blood pressure control (2). Adherence to antihypertensive medication is a complex and multifactorial behavior influenced by factors such as patient education, healthcare access, socioeconomic status, and cultural beliefs (3).

Geographical disparities, particularly between urban and rural populations, have been identified as critical determinants of health outcomes in hypertensive patients (4). Rural populations often face additional barriers, including limited healthcare infrastructure, irregular follow-up, and lower health literacy, which may contribute to suboptimal adherence and poorer hypertension management (5). On the other hand, urban residents may experience different challenges such as polypharmacy, time constraints, and higher stress levels that may also affect adherence patterns (6).

Real-world data derived from clinical registries offer valuable insights into the patterns of antihypertensive medication use across different demographic settings, enabling better understanding of adherence behaviors outside controlled clinical environments (7). While several studies have examined adherence in specific populations, comparative assessments between urban and rural cohorts using registry-based data remain limited, especially in low- and middle-income countries.

This study aims to bridge this knowledge gap by evaluating and comparing antihypertensive medication adherence among urban and rural patients using data from a real-world hypertension registry. The findings can inform targeted strategies to enhance adherence and reduce hypertension-related complications in diverse populations.

A total of 800 adult patients (age ≥18 years), with a confirmed diagnosis of essential hypertension for at least one year and currently on antihypertensive therapy, were recruited. Of these, 400 were from urban areas and 400 from rural regions. Patients with secondary hypertension, cognitive impairments, or those unwilling to provide informed consent were excluded.

Adherence Assessment Tool:

Medication adherence was evaluated using the 8-item Morisky Medication Adherence Scale (MMAS-8), a validated questionnaire widely used in chronic disease management. Scores ranged from 0 to 8 and were categorized as follows: high adherence (score = 8), medium adherence (score 6 to <8), and low adherence (score <6).

Data Collection:

Sociodemographic data (age, gender, education, occupation), clinical parameters (duration of hypertension, comorbidities, number of medications), and healthcare access indicators (distance from healthcare facility, frequency of follow-up visits) were extracted from the registry and verified through direct patient interviews conducted by trained healthcare workers.

Statistical Analysis:

Descriptive statistics were used to summarize the characteristics of the study population. The chi-square test was employed to compare adherence levels between urban and rural groups. Logistic regression was performed to identify independent predictors of low adherence. A p-value <0.05 was considered statistically significant. All analyses were conducted using SPSS version 26.0 (IBM Corp., Armonk, NY, USA).

A total of 800 hypertensive patients were enrolled, equally distributed between urban (n=400) and rural (n=400) populations. The mean age of participants was 54.3 ± 11.2 years, with 52.5% being female. The baseline demographic and clinical characteristics are presented in Table 1.

Table 1: Baseline Characteristics of Study Participants (N = 800)

Adherence levels assessed using the MMAS-8 scale showed significant differences between groups. High adherence was reported in 52.5% of urban participants compared to 37.8% in rural participants. Conversely, low adherence was more common in rural areas (29.5%) than in urban areas (18.3%) (Table 2).

Table 2: Comparison of Antihypertensive Medication Adherence (MMAS-8 scores)

Multivariate logistic regression revealed that rural residence (OR = 1.78; 95% CI: 1.21–2.63), illiteracy (OR = 2.11; 95% CI: 1.46–3.05), and irregular follow-up visits (OR = 1.59; 95% CI: 1.12–2.25) were independently associated with low adherence (Table 3).

Table 3: Multivariate Logistic Regression for Predictors of Low Adherence

These findings demonstrate that geographical location and educational status significantly impact antihypertensive medication adherence (Tables 1–3).

The present study aimed to evaluate and compare antihypertensive medication adherence between urban and rural populations using a registry-based real-world dataset. The findings highlight a significant disparity in adherence levels, with urban participants exhibiting better adherence compared to their rural counterparts. These results are consistent with prior studies that identified geographical and sociodemographic differences as influential factors in medication-taking behavior (1,2).

High adherence among urban participants may be attributed to better healthcare access, greater health literacy, and more frequent physician interactions (3,4). In contrast, rural populations often face systemic barriers such as limited healthcare infrastructure, transportation difficulties, and low awareness of hypertension management, which collectively contribute to poor adherence (5,6). Educational status emerged as a strong determinant of adherence, aligning with previous research that underscores the role of literacy in understanding medication regimens and disease consequences (7,8).

The use of the MMAS-8 scale in this study provided a reliable and validated measure for assessing adherence behaviors in real-world settings (9). The significantly higher prevalence of low adherence in rural participants (29.5%) raises concerns about the long-term risk of cardiovascular complications in this group. Similar findings have been reported in studies from India and other low- and middle-income countries where rural patients are disproportionately affected by poor adherence and uncontrolled hypertension (10,11).

Furthermore, multivariate analysis in our study identified rural residence, illiteracy, and irregular follow-up visits as significant independent predictors of low adherence. This is consistent with existing evidence suggesting that improving continuity of care and providing community-based support can enhance adherence among disadvantaged groups (12). Interestingly, polypharmacy did not emerge as a statistically significant predictor, although it has been implicated in reduced adherence in other contexts (13). This may reflect better medication management or caregiver support in our study population.

To bridge the adherence gap, multifaceted strategies must be implemented. These may include health education campaigns tailored to rural communities, strengthening primary care systems, training community health workers, and adopting mobile health interventions to support medication reminders and follow-ups (14,15). Policymakers must recognize the rural-urban divide in adherence and allocate resources accordingly to ensure equitable hypertension control.

Despite the strengths of using real-world registry data and validated assessment tools, this study has limitations. The cross-sectional design precludes causal inference, and self-reported adherence measures may be subject to recall bias. Future longitudinal studies are needed to evaluate adherence trends and assess the effectiveness of targeted interventions across diverse populations.

This study highlights significant differences in antihypertensive medication adherence between urban and rural populations, with rural patients demonstrating lower adherence levels. Key contributing factors include educational status, access to healthcare, and frequency of follow-up. Addressing these disparities through targeted educational, infrastructural, and community-based interventions is essential to improve hypertension outcomes in underserved regions.

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