Heart attacks, also referred to as myocardial infarction (MI), continue to rank among the world's top causes of morbidity and mortality, including in India.  The psychological effects of MI are still not well understood, despite tremendous advancements in diagnosis, treatment, and secondary prevention techniques.  One of the most common psychiatric comorbidities in patients who have had a MI is depression, which significantly raises the risk of death, poor recovery, and decreased treatment adherence [1]. Co-occurring depression after MI is a significant clinical and public health concern since it has been demonstrated to negatively impact quality of life, social functioning, and cardiovascular outcomes [2]. Myocardial infarction and depression have a complex pathophysiological link.  The bidirectional relationship between mood disorders and heart disease is mostly mediated by biological processes, including autonomic instability, systemic inflammation, platelet activation, and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis [3]. Psychosocial issues, including fear of recurrence, loss of independence, financial load, and changing family dynamics, further enhance the emotional suffering in post-MI patients.  According to studies, between 30 and 45 percent of people have some form of depression after an acute cardiac incident, and many of them go undetected and untreated [4]. In individuals who have had a MI, depression has a substantial impact on their quality of life (QoL) in addition to compromising their mental health.  It has been linked to higher hospital readmission rates, poor lifestyle adherence, and a lack of motivation for cardiac rehabilitation.  Social disengagement and decreased functional ability are common in patients with depressive symptoms, which impedes their overall rehabilitation process.  Therefore, treating depression in patients who have had a MI is crucial to providing comprehensive cardiovascular treatment. Our initiative aims to study the prevalence of depression in post-myocardial infarction patients and to evaluate the influence of socioeconomic factors—such as income, education, employment, and social support—on the severity and occurrence of post-MI depression in a tertiary care hospital setting.

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 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:

• Patients with other co-morbidities which directly affect the study.

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 diastolic.
• (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 treatment.
• Substance dependence-active (excluding tobacco use)
• Pregnancy/ Postpartum

Study Variables:

• Educational level (Illiterate/Primary/Secondary/Graduate and above)
• Occupation (Employed/Unemployed/Homemaker/Retired)
• Monthly income
• Family type
• No of family members
• Domicile

Statistical Analysis:

Data were entered into excel and analysed using SPSS and graph pad 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: Educational Status, and Occupation Distribution according to presence or absence of Depression

Table 2: Table: Distribution of Depression Status by Family Members, Family Type, and Domicile

Table 4: Distribution of Income according to presence or absence of Depression

Educationally, 48 participants with primary education (69.6%) were without depression and 21 (30.4%) had depression; 26 with secondary education (65%) were depression-free and 14 (35%) had depression; 14 university-educated participants (66.7%) were without depression and 7 (33.3%) had depression (p = 0.881). In terms of occupation, 22 unskilled (66.7%) were without depression and 11 (33.3%) had depression; 16 semi-skilled (61.5%) were depression-free and 10 (38.5%) had depression; 29 skilled (78.4%) were without depression and 8 (21.6%) had depression; 11 highly skilled (61.1%) were depression-free and 7 (38.9%) had depression; 7 homemakers (53.8%) were without depression and 6 (46.2%) had depression; 3 retired participants (100%) were without depression and none had depression (p = 0.377).

The distribution of depression among participants was analyzed according to the number of family members, family type, and domicile. Among participants, depression prevalence varied across family sizes, with the highest proportion observed in families of 3 members (39.1%) and the lowest in families of 10–11 members (0%). However, the difference in depression prevalence across family sizes was not statistically significant (p = 0.578). Regarding family type, 30.9% of participants from nuclear families and 34.7% from joint families reported depression, with no significant association observed (p = 0.651). Similarly, depression prevalence was slightly higher in rural participants (34.9%) compared to urban participants (31.0%), but this difference was not statistically significant (p = 0.659).

In our study, the mean monthly income of participants without depression was ₹17,610.23 ± 14,856.41, while those with depression had a mean income of ₹17,950 ± 19,751.21. The difference between the two groups was not statistically significant (p = 0.098).

This was an institution-based, cross-sectional observational study conducted at KPC Medical College and Hospital, Kolkata, West Bengal, and at Ramakrishna Mission Seva Pratishthan, Vivekananda Institute of Medical Sciences, Kolkata, West Bengal. The study was carried out over 18 months and included a total of 130 post-myocardial infarction patients.

In this study, analysis of family characteristics revealed no significant differences in depression prevalence across family sizes (p = 0.578), family type (nuclear vs. joint; p = 0.651), or domicile (rural vs. urban; p = 0.659). Although the highest prevalence was observed in three-member families (39.1%), these variations were not statistically significant. This aligns with prior research suggesting that family composition alone may not be a primary determinant of depression, and psychosocial factors such as family cohesion, communication patterns, and perceived support quality may play a more critical role than numerical family size or structure. Strong interpersonal relationships within the family, irrespective of size or type, are known to buffer psychological distress, while dysfunctional family dynamics can exacerbate vulnerability to depression.

However, a study conducted in Nigeria using the WHOQOL-BREF reported poorer mean scores in the social domain compared to the physical, psychological, and environmental domains, indicating that the quality of social interactions may significantly shape mental health outcomes (Olusina AK et al.[5]). Similarly, a study in China demonstrated better health-related QOL scores, which may be attributable to a robust healthcare infrastructure, better access to services, and shorter duration of untreated illness (Zeng Q et al.[6]). These findings support the notion that the social and environmental milieu—rather than demographic family descriptors—remains a more relevant determinant of mental well-being.

Socioeconomic status, assessed via mean monthly income, was slightly higher among participants with depression (₹17,950 ± 19,751.21) compared to those without depression (₹17,610.23 ± 14,856.41), though this difference was not statistically significant (p = 0.098). While income is often used as a proxy for socioeconomic status, it is increasingly recognized that economic well-being is multidimensional, encompassing financial stability, employment security, living conditions, healthcare accessibility, and perceived financial stress. The absence of a significant association in our study indicates that income alone may not be a reliable predictor of depression in this population.

Kandasamy et al. [7] (2025) similarly emphasized the complex and nonlinear relationship between socioeconomic factors and mental health, particularly among individuals with chronic illnesses such as hypertension. Their findings highlight that psychological distress may persist despite moderate or even adequate income levels if individuals experience financial strain, job insecurity, or limited social support. Conversely, lower income does not uniformly translate to poor mental health if protective factors—such as strong family support networks, community engagement, and effective coping strategies—are present. This multifactorial interaction underscores the importance of evaluating socioeconomic vulnerability in a more holistic manner, considering material deprivation, subjective financial pressure, and the broader social environment rather than income figures alone.

Post-myocardial infarction (MI) depression is consistently linked to poorer long-term outcomes, independent of disease severity and conventional risk factors. Meijer et al. [8] (2013), in an individual-patient-data meta-analysis of ~10,175 post-MI cases, found that each standard deviation increase in depression score was associated with higher all-cause mortality (HR ~1.32) and cardiovascular events (HR ~1.19). Social determinants, particularly low social support, further exacerbate risk: Mookadam F, Arthur HM et al [9] highlighted that inadequate social support, often reflecting poorer socioeconomic resources, contributes to worse post-MI outcomes among depressed patients. Similarly, Bush et al. [10] (2005) emphasized that socioeconomic adversity, psychosocial stress, and lack of support amplify post-MI depression and its prognostic impact. Evidence from non-Western settings, such as the Assam, India study by Dutta et al. [11], shows that socio-demographic factors influence depression in the early post-MI period, even in small cohorts. Large population-based data also link socioeconomic status (SES) to post-MI care: Ohm et al. [12] (2021) found that lower SES in Swedish first-MI survivors was associated with reduced secondary prevention adherence, including less participation in cardiac rehabilitation, poorer risk-factor control, and lower smoking cessation rates, suggesting that socioeconomic disadvantage may indirectly worsen mental health outcomes. Collectively, these studies underscore the interplay between depression, social determinants, and SES in shaping post-MI prognosis.

We concluded that depression was present in 32.3% of the 130 post-myocardial infarction patients in our study; no significant correlations were established with age, gender, marital status, religion, education, occupation, or income. There was no statistically significant difference in the mean income between those with depression and those without. These results suggest that depression following myocardial infarction is probably complex and not limited to any particular socioeconomic or demographic group. The findings highlight the possibility that physiologic alterations and psychological stress after heart attacks may be more responsible for post-MI depression than underlying traits. Therefore, it is crucial to incorporate mental health assessment and counseling into cardiac rehabilitation programs in order to enhance post-MI patients' overall recovery and quality of life.

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