European Journal of Cardiovascular Medicine

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, posing a significant public health burden across all regions and age groups. CVD encompasses a broad range of conditions, including coronary artery disease, heart failure, stroke, and peripheral vascular disease. The World Health Organization (WHO) estimates that approximately 17.9 million people die annually from CVDs, representing 32% of all global deaths, with over three-quarters occurring in low- and middle-income countries (LMICs) [1]. While biological risk factors such as hypertension, diabetes mellitus, dyslipidemia, and obesity are well-established contributors, growing evidence suggests that socioeconomic determinants play a pivotal role in the incidence and progression of cardiovascular conditions [2].

The incidence of CVD is influenced not only by individual lifestyle choices but also by broader social determinants of health such as income, education, occupation, and access to healthcare. Individuals from lower socioeconomic strata often experience higher rates of behavioral risk factors, including tobacco use, unhealthy diets, physical inactivity, and inadequate health literacy, which collectively increase their cardiovascular risk [3]. Additionally, economic disadvantages often limit access to timely and effective healthcare services, preventive screenings, and medications, compounding the burden of disease and leading to poorer outcomes [4].

Prevalence patterns of CVD are not uniform across populations. Epidemiological studies have consistently demonstrated a higher prevalence of CVD and associated risk factors in economically disadvantaged populations, both in urban and rural settings [5]. Urbanization, although associated with better access to healthcare infrastructure, has also contributed to sedentary lifestyles and dietary transitions that favor processed, high-fat, and high-sugar foods. Conversely, rural populations often face barriers such as low health awareness, poor access to diagnostic facilities, and underdeveloped emergency medical systems, further widening the gap in cardiovascular outcomes [6].

The interplay between socioeconomic factors and CVD is further evident in disparities in awareness, treatment, and control of risk factors. For example, individuals with higher education levels and incomes are more likely to recognize the symptoms of myocardial infarction, seek prompt medical care, and adhere to secondary prevention strategies [7]. On the contrary, marginalized populations are less likely to receive guideline-recommended interventions, including statins, antihypertensives, and cardiac rehabilitation, due to cost-related or systemic barriers [8]. Moreover, chronic stress induced by financial instability, job insecurity, and social marginalization has been linked to sustained elevations in cortisol levels and sympathetic nervous system activity, contributing to endothelial dysfunction, arterial stiffness, and atherogenesis [9].

From a public health perspective, addressing the socioeconomic determinants of cardiovascular health is essential for achieving equitable and effective disease prevention. Integrated strategies targeting poverty alleviation, health education, community engagement, and universal health coverage are essential components of a long-term solution. In particular, understanding the localized burden of CVD through population-based studies enables healthcare systems to allocate resources more effectively and tailor interventions to the needs of high-risk groups [10].

In this context, the present clinical study aims to assess the incidence and prevalence of cardiovascular disease within a defined population and analyze the influence of key socioeconomic factors such as income level, educational status, employment, and living environment on disease outcomes. This investigation seeks to contribute to the growing body of evidence supporting the integration of social determinants into cardiovascular risk assessments and policy-making for more inclusive and impactful health interventions.

Study Design and Setting:

This was a hospital-based cross-sectional clinical study conducted over a period of 12 months at a tertiary care teaching hospital. The study was approved by the Institutional Ethics Committee and informed consent was obtained from all participants before enrollment.

Study Population:

A total of 300 patients aged ≥30 years who were diagnosed with cardiovascular disease (CVD), including coronary artery disease, cerebrovascular accident (stroke), heart failure, or peripheral arterial disease, were included in the study. Diagnosis was confirmed by a cardiologist based on clinical evaluation, ECG findings, echocardiography, or angiographic evidence.

Inclusion Criteria:

• Adults aged 30 years and above
• Diagnosed case of any form of cardiovascular disease
• Willing to give informed consent and participate

Exclusion Criteria:

• Patients with congenital heart disease or rheumatic heart disease
• Critically ill patients unable to respond to the questionnaire
• Unwilling or non-consenting individuals

Data Collection Tools:

A structured proforma was used to collect demographic details, clinical data, and socioeconomic indicators. This included:

• Age, gender, BMI
• Type and duration of CVD
• Lifestyle factors: smoking, alcohol use, physical activity, diet
• Comorbidities: diabetes, hypertension, dyslipidemia
• Socioeconomic variables: monthly income (categorized per Modified BG Prasad scale), educational level, occupation, residential status (urban/rural), and access to healthcare services

Assessment of Socioeconomic Status:

Socioeconomic status was categorized using the Modified BG Prasad Scale (2023) based on monthly per capita income, adjusted for the Consumer Price Index (CPI). Education was classified as illiterate, primary, secondary, or graduate/postgraduate. Employment status was grouped as employed (formal/informal), unemployed, and retired.

Clinical Assessment:

Anthropometric measurements including height, weight, and BMI were recorded using standard methods. Blood pressure was measured using a calibrated sphygmomanometer. History of prior cardiovascular events, medication use, and adherence were recorded. Diagnostic confirmation of cardiovascular events was supported by medical records.

Outcome Measures:

The primary outcomes were:

1. Incidence of CVD among newly diagnosed patients during the study period
2. Prevalence of existing CVD cases in the outpatient and inpatient populations
3. Association of socioeconomic factors with type and severity of CVD

Statistical Analysis:

All collected data were entered into Microsoft Excel and analyzed using SPSS software version 25.0.

• Descriptive statistics such as mean, standard deviation, and percentages were used for baseline characteristics.
• The Chi-square test was used to analyze associations between categorical variables (e.g., income group vs. CVD type).
• Student’s t-test or ANOVA was applied for comparing continuous variables across groups.
• Binary logistic regression analysis was conducted to identify independent socioeconomic predictors of severe CVD presentation (e.g., myocardial infarction, stroke).
• A p-value < 0.05 was considered statistically significant.

Ethical Considerations:

The study complied with the ethical standards of the Declaration of Helsinki. Confidentiality of participants was maintained, and all data were anonymized for analysis purposes. Participants were free to withdraw from the study at any point.

Out of the 300 study participants, 180 (60%) were male and 120 (40%) were female, with a mean age of 57.8 ± 11.3 years. The most common cardiovascular diagnosis was coronary artery disease (47.3%), followed by stroke (26.3%), heart failure (17%), and peripheral vascular disease (9.3%). A significantly higher burden of CVD was observed among patients from lower socioeconomic backgrounds (p < 0.001).

Table 1: Demographic and Clinical Profile of Study Participants

Finding: Middle-aged adults (46–60 years) formed the largest affected group. Coronary artery disease was the most prevalent diagnosis.

Table 2: Socioeconomic Status and CVD Distribution

Finding: A statistically significant association was observed between lower socioeconomic status and higher incidence of stroke and advanced CVD. The middle and lower classes together accounted for nearly 75% of the cases.

Table 3: Association Between Educational Status and Treatment Adherence

Finding: Higher education level was significantly associated with better adherence to prescribed cardiovascular treatment. Among graduates/postgraduates, adherence was 67.9%, compared to only 28.6% in illiterate patients.

Table 4: Multivariate Logistic Regression: Socioeconomic Predictors of Severe CVD (MI or Stroke)

Finding: Independent predictors of severe CVD (defined as myocardial infarction or stroke) included lower socioeconomic status, smoking, illiteracy, diabetes, and age >60. Male gender showed increased risk but was not statistically significant

The current study assessed the incidence, prevalence, and influence of socioeconomic determinants on cardiovascular disease (CVD) outcomes in a clinical population. The findings highlight a significant association between lower socioeconomic status (SES), poor education, and increased risk and severity of CVD, corroborating existing literature that emphasizes the critical role of social determinants in cardiovascular health outcomes [11].

Coronary artery disease emerged as the most common diagnosis in our study (47.3%), followed by stroke and heart failure, aligning with national epidemiological trends reported in the Global Burden of Disease Study for India [12]. The clustering of CVD in the 46–60 year age group reflects the earlier onset of cardiovascular complications in developing countries, likely due to a convergence of metabolic risk factors and underdiagnosed comorbidities [13].

A key finding of the study was the disproportionate burden of CVD among individuals belonging to the lower and lower-middle socioeconomic classes. The Modified BG Prasad classification revealed that over 49% of patients with severe cardiovascular presentations (e.g., stroke, myocardial infarction) belonged to these strata. This aligns with studies indicating that poverty exacerbates health inequities by limiting access to primary prevention, diagnostics, and continued care [14]. Income insecurity often results in deferred medical care, reduced compliance with medications, and lower participation in follow-up, thus worsening outcomes [15].

Educational status, a robust proxy for health literacy, demonstrated a statistically significant relationship with treatment adherence. Patients with secondary or higher education had better medication compliance and follow-up behaviors compared to those with no formal schooling. Similar associations have been documented in cohort studies conducted in urban India and sub-Saharan Africa, where low literacy was linked to poor awareness of CVD symptoms, inappropriate self-medication, and failure to adopt heart-healthy behaviors [16].

Tobacco use and diabetes emerged as significant predictors of severe CVD in our regression model, consistent with the INTERHEART study and multiple region-specific reports [17]. However, the socioeconomic gradient in smoking behavior may partly explain this linkage; lower SES groups often exhibit higher tobacco consumption, both due to occupational stress and aggressive marketing in resource-poor communities [18].

Urban-rural differentials were also noted in our subgroup analyses (data not shown here). Urban participants had greater access to diagnostics but were more likely to be physically inactive and consume high-sodium processed foods, whereas rural patients faced barriers in healthcare access, delayed diagnosis, and often presented late with complications. These findings are supported by a systematic review by Jeemon et al., which found that despite infrastructure growth, rural cardiovascular mortality remains disproportionately high in South Asia due to weak primary care linkages [19].

Psychosocial stress is another overlooked but vital contributor to CVD progression in low-income populations. Chronic exposure to financial strain, job insecurity, and overcrowded housing leads to persistent sympathetic activation and hormonal changes that increase vascular resistance and accelerate atherosclerosis [20]. Our study, although not focused on stress biomarkers, indirectly supports this model through the observed association between poverty and stroke prevalence.

From a policy perspective, our findings emphasize the need for integrating social determinants into national cardiovascular prevention strategies. Merely addressing hypertension, diabetes, and dyslipidemia without targeting poverty, education, and employment gaps will not yield sustainable gains. Public health interventions should therefore include community-based risk screening, mobile health units in underserved areas, and subsidies for essential cardiovascular medications. Furthermore, targeted educational programs aimed at improving health literacy in marginalized communities could significantly enhance adherence and early symptom recognition.

This clinical study demonstrates a strong association between socioeconomic factors and cardiovascular disease burden. Lower income, poor education, and limited access to care significantly contributed to higher incidence and severity of CVD, particularly stroke and myocardial infarction. Smoking, diabetes, and age over 60 were also identified as independent risk factors. The findings highlight the need for a multifaceted approach integrating socioeconomic risk assessment into routine cardiovascular care. Policies focused on improving health equity, awareness, and accessibility to treatment in lower-income populations are imperative to reduce disparities and improve long-term cardiovascular outcomes in India and similar settings.

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