European Journal of Cardiovascular Medicine

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, with myocardial infarction (MI) being a major contributor. Surviving an MI represents not only a physical health crisis but also a profound psychological stressor. Among the various psychological sequelae, depression is particularly common, with studies reporting that approximately 15% to 30% of patients develop major depressive disorder (MDD) after an MI [1]. Depression in this context is not merely comorbidity; it has been shown to significantly impair recovery, reduce quality of life, increase the risk of recurrent cardiac events, and even elevate mortality risk [2].

Several demographic factors may predispose post-MI patients to developing depression, including age, gender, educational level, marital status, and employment. For instance, female gender, lower income, and lack of social support have been associated with a higher likelihood of depressive symptoms following MI [3]. These disparities underscore the complex interplay between social determinants of health and mental well-being in cardiac populations. Understanding these associations is crucial for early identification and targeted intervention, particularly in resource-constrained settings where access to mental health care may be limited.

Despite the growing recognition of this issue globally, there remains a paucity of region-specific data, especially from developing countries, where sociocultural and economic factors may uniquely shape health outcomes. A focused investigation into how demographic characteristics influence depression among post-MI patients can inform tailored mental health strategies and policy interventions within tertiary care settings.

Type of study: Institution based, Cross Sectional, Observation study.

Place of study: KPC Medical College and Hospital, Kolkata, West Bengal and Ramakrishna Mission Seva Pratishthan, Vivekananda Institute of Medical Sciences, Kolkata, West Bengal.

Study duration: 18 months.

Sample size: 130 post-myocardial infarction patients.

Inclusion Criteria:

• All patients with Myocardial Infarction [confirmed by qualified cardiologist both clinically and by operational definition*] which happened at least 3 months back
• Age: 18 – 65 years
• Education level: At least 4 years of schooling and able to converse in Bengali or English or Hindi.
• Willing to participate in the study, those who gave informed consent.

Exclusion Criteria:

1. Patients with other co-morbidities which directly affect the
2. Any other major physical illness-

• Uncontrolled Diabetes Mellitus(DM) - HbA1c > 8% (Acc. to American college of Physicians)
• Uncontrolled hypertension (HTN) –systolic BP of > 140mm and/or 90mm
• (Acc. to Standard Treatment Guidelines of Hypertension by The Ministry of Health & Family Welfare Government of India)
• Connective tissue disorder
• Thyroid Disorders (hyperthyroid/hypothyroid)
• Neurological disorder
• Past history of psychiatric illness or already on psychiatric

3. Substance dependence-active (excluding tobacco use)
4. Pregnancy/ Postpartum

Study parameter:

• Prevalence
• Age
• Gender
• Religion
• Marital status
• Education
• Domicile

Statistical Analysis:

Data were entered into Excel and analyzed using SPSS and GraphPad Prism. Numerical variables were summarized using means and standard deviations, while categorical variables were described with counts and percentages. Two-sample t-tests were used to compare independent groups, while paired t-tests accounted for correlations in paired data. Chi-square tests (including Fisher’s exact test for small sample sizes) were used for categorical data comparisons. P-values ≤ 0.05 were considered statistically significant.

Table 1: Prevalence of depression

Table 2: Distribution of Age according to presence or absence of Depression

Table 3: Distribution of Gender, Religion, Marital Status, Educational Status and Domicile according to presence or absence of Depression

Figure 1: Prevalence of depression

Figure 2: Distribution of Education according to presence or absence of Depression

Out of the total study population 32.3 % presented with depressive symptoms (n = 42) and 67.7 % (n= 88) did not exhibit any symptoms of depression. The value of z is 5.7056. The value of p is < .00001. The result is significant at p < .05.

The mean age of patients without depression (n= 88) was 53.01 and the mean age of patients with depression (n=42) was 52.17. There is no statistical significance (P=0.5561) between age of patient and presence or absence of depression.

36.4% (n=16) of the females presented with depression whereas 30.2% (n=26) of the males had depression. There is no statistically significant difference between gender and depression status (p= 0.553 > 0.05).

Out of 130 patients, 90 were Hindu of which 32.2% (n= 29) had depression, 35 were Muslim of which 28.6% (n= 10) presented with depression, 3 patients were Christian with 66.7 %( n= 2) having depression and 2 patients were Sikh with 50 % (n= 1) having depression. The data is not statistically significant (p = 0.546 > 0.05).

There is no significant difference in marital status between patients presenting with depression and patients without any depressive symptoms in the study population (p= 0.909 > 0.05).

30.4% (n= 21) of the patients who received primary education presented with depression, whereas 35.0% (n= 14) of those who received secondary education presented with depression. Of the patients who completed university level education, 33.3% (n=7), had depression. There is no significant difference in depression status among varied educational status of the study population (p= 0.881 > 0.05).

Among rural participants, 65.1% showed a positive outcome compared to 69.0% of urban participants. The difference between rural and urban groups was not statistically significant (P = 0.693).

Our study found that 32.3% of the participants exhibited depressive symptoms, while 67.7% did not. This is notably higher than national estimates, which report a prevalence of around 10% (Sagar et al. [4], 2020; Gururaj et al., [5]2016). The elevated percentage in our study may be attributed to the sample’s demographic characteristics or specific local factors such as socioeconomic conditions and access to healthcare.

The mean age of participants with depression was 52.17 years, compared to 53.01 years among those without depression. This small difference was not statistically significant. In contrast, studies such as Math et al. [6] (2019) suggest that depression is often more prevalent among older adults in India. Our findings, however, do not support a strong relationship between age and depression status.

Gender-wise, 36.4% of females and 30.2% of males in our study had depression, though this difference was not statistically significant (p = 0.553). This trend aligns with prior research showing higher depression rates in females globally and in India (Grover et al., [7] 2010; WHO, [8] 2017), possibly due to factors such as gender-based violence, socioeconomic dependence, and hormonal influences.

The analysis showed a slightly higher proportion of positive outcomes in urban participants (69.0%) compared to rural participants (65.1%). However, this difference was minimal and statistically non-significant (P = 0.693), indicating no meaningful association between domicile and outcome. These findings suggest that place of residence may not influence the observed results in this population.

Religious affiliation in our study showed varying depression prevalence: Christians (66.7%), Sikhs (50%), Hindus (32.2%), and Muslims (28.6%). Despite these differences, the association was not statistically significant. Other studies have similarly found that while religion may influence coping mechanisms, it is not a consistent predictor of depression (Kohn et al, [9] 2004).

Regarding marital status, our findings showed no significant association with depression (p = 0.909), although widows (40%) and widowers (37.5%) had higher rates compared to married (31.9%) and unmarried individuals (27.8%). This aligns with national data indicating increased vulnerability to depression among widowed and divorced individuals (Sagar et al., 2020).

In terms of educational status, depression was slightly more common in those with secondary education (35.0%), followed by university (33.3%) and primary education (30.4%). These findings differ from those of the National Mental Health Survey (Gururaj et al,[5] 2016), which reported an inverse relationship between educational attainment and depression, suggesting that higher education is generally protective.

Family structure in our study revealed no significant difference: 30.9% of individuals in nuclear families and 34.7% in joint families had depression (p = 0.701). This finding is supported by Grover et al. [7] (2010), who noted that family type alone does not predict mental health outcomes unless contextualized by family dynamics and support quality.

Similarly, family size did not have a significant association with depression (p = 0.578), although the percentages fluctuated across groups. Prior studies (Kohn et al., [9] 2004) suggest that both small and large families may offer distinct protective or risk factors depending on interpersonal dynamics and resource availability.

Occupation-wise, the highest prevalence of depression was found among homemakers (46.2%), followed by semi-skilled (38.5%) and highly skilled workers (38.9%). The retired group had no cases of depression. Despite these differences, the association was not statistically significant (p = 0.377). National data shows mixed outcomes based on occupational categories, but generally indicates higher mental health risks among less skilled and economically vulnerable populations (Gururaj et al.,[5] 2016).

Lastly, income levels did not significantly differ between the depressed and non-depressed groups. The data shows the distribution of responses across varying family sizes, with percentages indicating differing trends—for instance, families with 6 members had the highest proportion in one category (86.7%), while the single 10-member family was entirely in the opposite category (100%). There is no significant difference between presence or absence of depression across different family sizes having head count from 1 to 11 (p = 0.578 > 0.05).While other studies (Charlson et al,[6] 2016) found that lower income is a strong predictor of depression, our study did not replicate this trend, possibly due to limitations in income data accuracy or other confounding socioeconomic factors.

The study reveals that 32.3% of post-myocardial infarction patients experience depression. Despite examining demographic factors like age, gender, religion, marital status, education, family type, and occupation, none showed a significant association with depression. The findings suggest that depression is influenced by multiple complex factors, not just demographic characteristics. Healthcare providers should adopt a holistic approach to mental health, regardless of these factors. Further research with larger sample sizes and more detailed psychological assessments is needed to understand the underlying causes and interventions for depression in this population.

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