European Journal of Cardiovascular Medicine

Chronic medical illnesses are long-term health conditions requiring ongoing medical treatment and regular follow-up. Diseases such as diabetes mellitus, hypertension, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), ischemic heart disease, arthritis, and malignancies contribute significantly to the healthcare burden in India. With rapid urbanization, changing dietary habits, sedentary lifestyle, tobacco consumption, and increasing life expectancy, the prevalence of non-communicable diseases has risen substantially in India. Chronic illnesses not only affect physical functioning but also produce considerable psychological, social, and economic stress. Psychiatric morbidity refers to the presence of psychological disorders or symptoms in individuals suffering from medical illnesses. Depression and anxiety are the most common psychiatric disorders observed in patients with chronic diseases. Persistent symptoms, repeated hospital visits, financial burden, fear of complications, disability, and dependence on family members contribute to emotional distress. Quality of life (QOL) is a multidimensional concept that includes physical health, mental wellbeing, social functioning, occupational performance, and environmental satisfaction. Psychiatric disorders negatively affect quality of life by reducing motivation, impairing interpersonal relationships, decreasing productivity, and worsening treatment adherence. Chronic medical illnesses such as diabetes mellitus, cardiovascular disease, chronic kidney disease (CKD), and chronic respiratory disorders now account for the majority of morbidity and mortality worldwide, reshaping the clinical landscape of hospitals and outpatient care alike. As populations age and survival with noncommunicable diseases improves, patients are living longer with complex, multimorbidity profiles—and, critically, with an often-overlooked burden of psychiatric symptoms and disorders. Against this backdrop, the present study examines psychiatric morbidity among adults with chronic medical conditions in a tertiary-care setting, with the twin aims of describing its pattern and identifying key sociodemographic and clinical correlates.1 The co-occurrence of chronic physical disease and mental disorders is not incidental. Global estimates underscore the high prevalence of common mental disorders, particularly depressive and anxiety disorders, across all regions and age groups, with substantial years lived with disability attributable to these conditions.¹ In parallel, the broader epidemiologic transition has driven a steep rise in noncommunicable diseases, which now dominate the global disease burden in both fatal and non-fatal outcomes, increasingly concentrating in low- and middle-income countries as well as among disadvantaged groups within wealthier nations.² Within this changing burden, the interface between mental and physical health has become a major determinant of clinical outcomes, quality of life, and health system costs. Multiple mechanistic pathways plausibly link chronic illness and psychiatric morbidity. Biological routes include chronic inflammation, hypothalamic–pituitary–adrenal axis dysregulation, autonomic imbalance, and neuroendocrine perturbations; behavioral routes include reduced physical activity, maladaptive coping, and substance use; and social routes include poverty, caregiving strain, stigma, and health-care barriers. These pathways operate bidirectionally, with mental disorders increasing the risk of incident chronic disease, and chronic disease elevating the risk of subsequent psychopathology. The diabetes–depression dyad is emblematic: longitudinal syntheses show that depression confers a substantially increased risk of incident type 2 diabetes, while diabetes modestly elevates the risk of later depression, supporting a bidirectional model with shared and reciprocal drivers.³ Cardiovascular disease illustrates the clinical salience of these links. Depressive symptoms and disorders are more prevalent in patients with coronary heart disease than in the general population and have been associated with adverse prognosis, including higher mortality and recurrent events.⁴ Emotional distress can amplify physiologic stress responses, impair adherence to cardioprotective regimens, and reduce participation in rehabilitation—mechanisms that plausibly explain worse outcomes. In routine cardiology practice, however, depression and anxiety often remain under-recognized and undertreated, despite their potential to influence angina control, functional status, and rehospitalization. A similar pattern holds in chronic respiratory disease. Individuals with chronic obstructive pulmonary disease (COPD) carry a high burden of anxiety and depressive symptoms, which are in turn associated with increased exacerbation risk, poorer exercise tolerance, and diminished health-related quality of life. Meta-analytic evidence suggests associations running in both directions—COPD increases risk of depression and anxiety, and these conditions worsen COPD outcomes—underscoring the clinical payoff of systematic case-finding and integrated psychosocial support.⁵ CKD adds further complexity. Patients across the CKD continuum, including those not yet on dialysis, frequently experience major depressive episodes and other psychiatric syndromes, driven by symptom burden, dietary and fluid restrictions, uncertainty about prognosis, uremia-related neurocognitive effects, and health-service disruptions. Evidence indicates a meaningful point prevalence of major depression in CKD populations and links psychiatric morbidity to hospitalization and mortality risk.⁶ For nephrology teams, early detection of mental disorders is therefore not only patient-centered but potentially prognostically important. From a health-systems vantage, psychiatric morbidity in chronic disease undermines self-management, medication adherence, and glycemic or blood-pressure control; it also increases acute care use and total costs. Conversely, collaborative approaches that integrate mental health into routine chronic-disease management improve both psychological and biomedical outcomes. In randomized trials among patients with comorbid depression and poorly controlled diabetes and/or coronary heart disease, team-based care that combines measurement-based treatment, care management, and specialist supervision produced superior control of hemoglobin A1c, blood pressure, and LDL cholesterol, alongside greater depression remission, compared with usual care.⁷  These findings support a “treat-to-target” paradigm spanning mind and body.

This was an observational, cross-sectional study conducted at a tertiary care teaching hospital. The study was designed to assess the psychiatric morbidity among patients suffering from chronic medical illnesses. A total of 76 patients with established diagnoses of chronic medical illnesses were included in the study. Patients were recruited from the outpatient and inpatient departments of medicine and allied specialties. Inclusion Criteria • Patients aged 18 years and above. • Patients with a confirmed diagnosis of chronic medical illness (e.g., diabetes mellitus, hypertension, chronic kidney disease, chronic obstructive pulmonary disease, ischemic heart disease, or other long-standing medical disorders). • Patients who provided informed consent. Exclusion Criteria • Patients with previously diagnosed primary psychiatric disorders prior to the onset of medical illness. • Patients with acute confusional states, delirium, or other severe cognitive impairments that could interfere with assessment. • Patients unwilling to participate. Data collection was carried out using a semi-structured proforma designed to record comprehensive information on each participant. Sociodemographic details such as age, sex, education level, marital status, employment status, socioeconomic status, and place of residence were obtained. Clinical parameters included the type and duration of chronic medical illness, presence of comorbidities, treatment details, and history of substance use. To assess psychiatric morbidity, standardized and validated instruments were employed. The General Health Questionnaire-28 (GHQ-28)was administered to evaluate overall psychological distress, while the Hospital Anxiety and Depression Scale (HADS)was used to screen for the presence of anxiety and depressive symptoms. For diagnostic confirmation of psychiatric disorders, the Mini-International Neuropsychiatric Interview (MINI)was conducted. Statistical Analysis All data were entered and analyzed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were applied: means and standard deviations for continuous variables, and frequencies and percentages for categorical variables. Chi-square test and Fisher’s exact test were used for comparison of categorical data. Independent sample t-test or Mann-Whitney U test was applied for continuous variables, depending on data distribution. Logistic regression analysis was performed to determine predictors of psychiatric morbidity among patients with chronic medical illnesses. A p-value of <0.05 was considered statistically significant.

Table 1. Sociodemographic Profile of Patients (n=76)

Sociodemographic Characteristics

The study included 76 patients with chronic medical illnesses. As shown in Table 1, the majority of participants belonged to the middle-aged group of 46–60 years (34.21%), followed by those aged above 60 years (26.32%). Patients aged 31–45 years accounted for 23.68%, while the youngest age group of 18–30 years comprised 15.79% of the study population. Gender distribution revealed a slight male predominance with 55.26% males and 44.74% females. In terms of marital status, most patients were married (76.32%), whereas 13.16% were unmarried and 10.53% were widowed or divorced. These findings reflect those middle-aged and married individuals formed the bulk of the study cohort.

Table 2. Distribution of Chronic Medical Illnesses

Distribution of Chronic Medical Illnesses

The spectrum of chronic medical conditions is depicted in Table 2. Diabetes mellitus was the most common chronic illness, affecting 28.95% of the patients, followed by hypertension (23.68%). Ischemic heart disease was present in 15.79%, while 13.16% of participants were diagnosed with chronic kidney disease (CKD). Chronic obstructive pulmonary disease (COPD) and other chronic respiratory illnesses were observed in 10.53% of cases. A smaller proportion of patients (7.89%) had other long-standing medical conditions, including autoimmune and chronic liver diseases. These data highlight that metabolic and cardiovascular illnesses were the most prevalent underlying chronic conditions among the study population.

Table 3. Psychiatric Morbidity among Patients

Psychiatric Morbidity

Psychiatric morbidity was observed in a significant proportion of patients, as presented in Table 3. Depression was the most frequent diagnosis, identified in 26.32% of patients, followed by anxiety disorders in 18.42%. Adjustment disorder was diagnosed in 13.16%, while somatoform disorders were seen in 7.89%. Importantly, 34.21% of patients did not meet criteria for any psychiatric disorder. Overall, psychiatric morbidity was present in nearly two-thirds of the sample (65.79%), underscoring the substantial burden of psychological disorders among patients with chronic medical illnesses.

Table 4. Association between Gender and Psychiatric Morbidity

*Chi-square test applied; *p < 0.05 significant

Gender and Psychiatric Morbidity

The association between gender and psychiatric morbidity is shown in Table 4. Among females, 76.47% had psychiatric morbidity compared to 57.14% of males. Conversely, psychiatric morbidity was absent in 42.86% of males compared to only 23.53% of females. This association was statistically significant (p = 0.041), suggesting that female patients were more likely to experience psychiatric morbidity than males.

Table 5. Type of Chronic Medical Illness and Psychiatric Morbidity

*Chi-square test applied; *p < 0.05 significant

Type of Chronic Medical Illness and Psychiatric Morbidity

As presented in Table 5, psychiatric morbidity was most prevalent among patients with CKD (80.00%) and diabetes mellitus (72.73%). COPD/respiratory illness also showed a high rate (75.00%), though the association narrowly missed statistical significance (p = 0.062). By contrast, patients with ischemic heart disease (50.00%) and hypertension (55.56%) had lower rates of psychiatric morbidity. Statistical analysis demonstrated significant associations for diabetes mellitus (p = 0.032) and CKD (p = 0.041), indicating that these illnesses independently contributed to higher psychiatric morbidity.

Table 6. Multiple Logistic Regression Analysis of Factors Associated with Psychiatric Morbidity (n=76)

*Logistic regression model applied; *p < 0.05 considered statistically significant

Predictors of Psychiatric Morbidity (Regression Analysis)

The results of the multiple logistic regression analysis are summarized in Table 6. After adjusting for confounding factors, several independent predictors of psychiatric morbidity were identified. Female gender was associated with a two-fold increased risk (Adjusted OR = 2.10, 95% CI = 1.05–4.75, p = 0.038). Low socioeconomic status also significantly predicted morbidity (Adjusted OR = 2.72, 95% CI = 1.14–6.48, p = 0.024). Among clinical variables, diabetes mellitus (Adjusted OR = 2.48, 95% CI = 1.09–5.65, p = 0.031) and chronic kidney disease (Adjusted OR = 3.12, 95% CI = 1.18–8.25, p = 0.021) were significant independent predictors. Although older age (>60 years) and unemployment showed increased odds, they did not reach statistical significance. Duration of illness beyond five years also did not independently predict morbidity. These findings emphasize that female gender, low socioeconomic status, diabetes, and CKD are the strongest contributors to psychiatric morbidity in this patient group.

In this tertiary-care cohort, psychiatric morbidity affected 65.79% of patients with chronic medical illnesses, with 34.21% free of diagnosable disorder. This burden aligns with health-systems evidence showing that people living with long-term conditions are 2–3 times more likely to experience common mental disorders than the general population; the magnitude in our study is at the upper end of that spectrum, plausibly reflecting a hospital-based sample and use of structured case ascertainment.8 Most participants were 46–60 years (34.21%) or >60 years (26.32%), and 76.32% were married. Indian consultation-liaison psychiatry reports indicate that general-hospital psychiatric morbidity commonly clusters in middle-aged adults who attend medical services for chronic conditions, consistent with our age distribution and setting; reported screening-based morbidity rates around 31–34.5% in unselected medical samples are lower than ours, again reflecting our focus on chronic illness and use of diagnostic interviews.9  Psychiatric morbidity was more frequent in women (76.47%) than men (57.14%, p = 0.041). This mirrors robust meta-analytic evidence of higher depression burden among females in population-representative samples (female–male ratio typically ~1.7:1 across measurement approaches), providing an explanatory backdrop to the gender effect that also persisted in our adjusted model (Adjusted OR 2.10, 95% CI 1.05–4.75).10  In our sample, diabetes (28.95%) and hypertension (23.68%) were the most common illnesses, followed by IHD (15.79%), CKD (13.16%), and COPD (10.53%). Cross-national analyses show that depression with chronic diseases—particularly angina/IHD and diabetes—is associated with the largest decrements in overall health status; our finding of substantial psychiatric morbidity across cardiometabolic conditions (e.g., morbidity in diabetes 72.73% and in IHD 50.00%) is therefore directionally concordant with the literature, while reflecting broader diagnostic coverage beyond depression alone.11  We found depression 26.32% and anxiety disorders 18.42%, with additional adjustment (13.16%) and somatoform disorders (7.89%). A systematic review of medical outpatients estimated point prevalence of depressive symptoms/disorders at 27.0% (range 17–53%), closely matching our depression figure, and highlighting comparable burdens across clinical departments when standardized tools are used.12  Psychiatric morbidity among patients with diabetes in our cohort was 72.73%, and diabetes independently predicted morbidity (Adjusted OR 2.48, 95% CI 1.09–5.65). Classic meta-analytic data show depression prevalence 17.6% in type 2 diabetes versus 9.8% in controls (OR 1.6), underscoring an elevated baseline risk; our higher estimate likely reflects inclusion of anxiety/adjustment alongside depression and the greater psychosocial load in tertiary-care attendees.13  CKD showed the highest illness-specific burden in our study (80.00%) and independently predicted morbidity (Adjusted OR 3.12, 95% CI 1.18–8.25). This direction accords with a CKD meta-analysis reporting depressive-symptom prevalence around 27% overall (with dialysis studies often higher, range 5–58%), again suggesting that our broader diagnostic net and hospital-based sampling capture additional non-depressive morbidity alongside depression.14 Low SES independently increased odds of psychiatric morbidity in our model (Adjusted OR 2.72, 95% CI 1.14–6.48). A foundational meta-analysis demonstrated a graded association between disadvantaged SES and depression across 51 prevalence, 5 incidence, and 4 persistence studies, supporting SES as a consistent determinant of mental-health inequality and aligning with the strength of the effect we observed.15

Psychiatric morbidity is highly prevalent among patients with chronic medical illnesses and significantly impairs quality of life. Depression and anxiety adversely affect physical health, psychological wellbeing, social functioning, and environmental satisfaction. Early screening, psychiatric consultation, counseling, and psychosocial support should be integrated into routine management of chronic medical illnesses. This study highlights a high prevalence of psychiatric morbidity (65.79%) among patients with chronic medical illnesses, with depression and anxiety being the most common disorders. Female gender, low socioeconomic status, diabetes, and chronic kidney disease emerged as significant independent predictors. These findings underscore the need for routine psychiatric screening and integration of mental health services into chronic disease management at tertiary-care hospitals. Early identification and collaborative care can improve both psychological well-being and medical outcomes in this vulnerable population.

1. World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates. Geneva: WHO; 2017. Available from: https://apps.who.int/iris/bitstream/handle /10665/254610/WHO-MSD-MER-2017.2-eng.pdfWHO Apps

2. Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2018: a systematic analysis for the Global Burden of Disease Study 2018. Lancet. 2019;396(10258):1204–1222. Available from: https://www.thelancet.com/journals/ lancet/article/PIIS0140-6736(20)30925-9/fulltextThe Lancet

3. Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: a meta-analysis. Arch Intern Med. 2008;168(21):2219–2227. Available from: https:// pubmed.ncbi.nlm.nih.gov/19033418/PubMed

4. Nicholson A, Kuper H, Hemingway H. Depression as an aetiologic and prognostic factor in coronary heart disease: a meta-analysis. Eur Heart J. 2006;27(23):2763–2774. Available from: https://academic.oup.com/eurheartj/article/27/23/2763/2887435Oxford Academic

5. Atlantis E, Fahey P, Cochrane B, Smith S. Bidirectional associations between clinically relevant depression or anxiety and COPD: a systematic review and meta-analysis. Thorax. 2013;68(7):639–649. Available from: https://pubmed.ncbi.nlm.nih.gov/23429910/PubMed

6. GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Psychiatry. 2017;9(2):137–150. Available from: https://pubmed. ncbi.nlm.nih.gov/35026139/PubMed

7. Wu Y, Zhu C, Tian M, et al. Changing trends in the global burden of mental disorders from 1990 to 2018 and predicted levels in 25 years: an age–period–cohort analysis. Front Public Health. 2019;11:1271814. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10689059/PMC

8. Naylor C, Parsonage M, McDaid D, Knapp M, Fossey M, Galea A. Long-term conditions and mental health: The cost of co-morbidities. London: The King’s Fund; 2012. Available from: https://assets.kingsfund.org.uk/f/256914/x/a7a77f9f6b/long_term_conditions_and_mental_health_february_2012.pdfassets.kingsfund.org.uk

9. Grover S, State of Consultation-Liaison Psychiatry in India. Indian J Psychiatry. 2011;53(3):201–212. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3221175/PMC

10. Salk RH, Hyde JS, Abramson LY. Gender differences in depression in representative national samples: meta-analyses of diagnoses and symptoms. Psychol Bull. 2017;143(8):783–822. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5532074/PMC

11. Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health. Lancet. 2007;370(9590):851–858. Available from: https://www. thelancet.com/journals/lancet/article/PIIS0140-6736(07)61415-9/abstractThe Lancet

12. Wang J, Wu X, Lai W, et al. Prevalence of depression and depressive symptoms among outpatients: a systematic review and meta-analysis. BMJ Open. 2017;7:e017173. Available from: https://bmjopen.bmj.com/content/7/8/e017173BMJ Open

13. Ali S, Stone M, Peters J, Davies M, Khunti K. The prevalence of co-morbid depression in adults with Type 2 diabetes: a systematic review and meta-analysis. Diabet Med. 2006;23(11):1165–1173. Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1464-5491.2006.01943 .xWiley Online Library

14. Palmer S, Vecchio M, Craig JC, et al. Prevalence of depression in chronic kidney disease: systematic review and meta-analysis. CMAJ. 2013;185(3):E117–E126. Available from: https://pubmed.ncbi.nlm.nih.gov/23486521/PubMed

15. Lorant V, Deliège D, Eaton W, Robert A, Philippot P, Ansseau M. Socioeconomic inequalities in depression: a meta-analysis. Am J Epidemiol. 2003;157(2):98–112. Available from: https://academic.oup.com/aje/article/157/2/98/90059Oxford Academic

16. Singh HP, Kumar P, Goel R, Kumar A. Sex hormones in head and neck cancer: Current knowledge and perspectives. Clin Cancer Investig J 2012;1:2-5.

17. Sodhi, Surinder Pal Singh; Brar, Ramandeep Singh; Singh, Harkanwal Preet; Kaur, Tajinder; Dhawan, Rohan.A rare occurrence of basal cell adenoma of palate. A case report with comprehensive immunohistochemical analysis. Journal of Cancer Research and Therapeutics 11(4):p 1023, Oct–Dec 2015. | DOI: 10.4103/0973-1482.147391

18. Nayar, Amit; Singh, Harkanwal Preet; Leekha, Swati. Pulp tissue in sex determination: A fluorescent microscopic study. Journal of Forensic Dental Sciences 6(2):p 77-80, May–Aug 2014. | DOI: 10.4103/0975-1475.132527

19. Singh H, Bansal P, SH T (September 13, 2018) Denture Stomatitis and Candida albicans in the Indian Population: A Systematic Review and MetaAnalysis. Cureus 15(9): e45182. DOI 10.7759/cureus.45182