Ageing is a universal biological process characterized by a progressive decline in physiological functions, often accompanied by social, psychological, and environmental challenges. It is not a disease but a natural stage of life, and with advances in healthcare and improvements in living conditions, the proportion of older adults in the global population is steadily increasing. This demographic shift, commonly referred to as the “greying of the world,” has profound implications for healthcare systems, especially in the domain of mental health.^[1]

Globally, the population aged 60 years and above is projected to nearly double from 12% in 2015 to 22% by 2050. In absolute numbers, this represents a rise from 900 million to 2 billion people. In India, demographic transition has led to a rapidly growing elderly population, with estimates suggesting that individuals aged 60 and above will constitute over 10% of the population in the coming years. Census data indicates that the elderly population in India increased from 4.3 crores in 1981 to 9.2 crores in 2011, and is projected to reach 31.6 crores by 2051. States like Kerala, Tamil Nadu, Punjab, and Himachal Pradesh lead in the proportion of elderly, while Karnataka itself has 7.7% of its population aged 60 and above.^[2]

Older adults often face a range of health problems due to physiological decline, multiple chronic conditions, and loss of functional independence. These are compounded by psychological and social stressors such as bereavement, social isolation, poverty, and changing family structures. The shift from joint families to nuclear households has reduced the traditional support systems for older adults, leaving many vulnerable to loneliness, neglect, and elder abuse. Mental disorders in the elderly, including depression, anxiety, cognitive impairment, psychosis, and substance use disorders, are common yet frequently underdiagnosed. The stigma associated with psychiatric illness further limits help-seeking behaviours._[3]

The World Health Organization (WHO) estimates that over 20% of adults aged 60 and above suffer from a mental or neurological disorder (excluding headache disorders), contributing significantly to global disability-adjusted life years (DALYs). Depression and dementia are among the leading causes of disability in this age group, with anxiety disorders, substance use problems, and suicide risk also posing serious public health concerns. Co-morbid physical illnesses such as hypertension, diabetes, and cardiovascular diseases are prevalent and may exacerbate psychiatric symptoms, while untreated psychiatric conditions can worsen the prognosis of physical disorders.^[4]

In the Indian context, multiple studies have identified depression as the most common psychiatric morbidity among the elderly, followed by dementia, anxiety disorders, psychosis, and substance use. Factors such as female gender, widowhood, low education, low socioeconomic status, unemployment, and chronic physical illness have been consistently associated with higher psychiatric morbidity. However, despite the burden, there is limited systematic research into the prevalence and socio-demographic profile of psychiatric disorders among geriatric populations in tertiary care settings.^[5]

 Aim

To determine the prevalence and socio-demographic profile of psychiatric disorders in the geriatric population attending a tertiary care psychiatry outpatient department.

Objectives

1. To assess the prevalence of psychiatric disorders in elderly patients attending the psychiatry outpatient department of a tertiary care hospital.
2. To describe the socio-demographic profile of elderly patients diagnosed with psychiatric disorders.
3. To identify the distribution of various psychiatric disorders and their association with socio-demographic variables.

Source of Data

The study was conducted among elderly patients attending the Psychiatry Outpatient Department (OPD) of a tertiary care teaching hospital. Data were collected directly from patients and their relatives after obtaining written informed consent.

Study Design

A hospital-based, cross-sectional observational study.

Study Location

Psychiatry Outpatient Department, at tertiary care teaching hospital catering to both urban and rural populations.

Study Duration: Total duration of 18 months.

Sample Size

100 elderly patients, calculated based on a prevalence rate of psychiatric disorders in the elderly from previous literature, with an allowable error of 20% and a confidence level of 95%.

Inclusion Criteria

• Patients aged 60 years and above attending the psychiatry OPD.
• Patients (or their primary caregivers) willing to give informed consent.

 Exclusion Criteria

• Patients with severe medical conditions that made interviewing impossible.
• Those unwilling to participate.

Procedure and Methodology

Patients fulfilling the inclusion criteria were recruited consecutively. A semi-structured proforma was used to collect socio-demographic details (age, gender, religion, education, marital status, occupation, residence, and socioeconomic status) and medical history, including any family history of psychiatric illness. Clinical evaluation was supplemented with standardized diagnostic instruments:

Mini International Neuropsychiatric Interview (M.I.N.I.) Plus: for psychiatric diagnosis according to ICD-10 criteria.

Mini Mental Status Examination (MMSE) and Hindi MMSE (HMSE): for cognitive assessment.

Delirium Rating Scale Revised (DRS-R-98): for evaluation of delirium.

Modified Kuppuswamy Socioeconomic Scale: for socioeconomic status classification.

Sample Processing

Each patient underwent a detailed psychiatric interview and mental status examination, supplemented by physical examination and relevant laboratory investigations when indicated. Diagnoses were confirmed by qualified psychiatrists based on clinical assessment and standardized scales.

Statistical Methods

Data were entered into Microsoft Excel and analyzed using IBM SPSS version 20. Descriptive statistics (mean, standard deviation, frequency, and percentage) were used to summarize continuous and categorical variables. Associations between socio-demographic variables and psychiatric diagnoses were tested using Chi-square or Fisher’s exact test for categorical variables, and Student’s t-test or ANOVA for continuous variables. A p-value < 0.05 was considered statistically significant.

Data Collection

Data were collected directly during OPD visits, with inputs from both patients and caregivers when required. The study ensured confidentiality, voluntary participation, and adherence to ethical guidelines, with approval from the institutional ethics committee.

Table 1: Prevalence (any psychiatric diagnosis) and socio-demographic profile (N = 100)

Note: By design, all included attendees had at least one psychiatric diagnosis; “prevalence” here pertains to the Psychiatry-OPD geriatric caseload, not community prevalence.

In the present study, all 100 participants (100%; 95% CI: 96.4–100.0) attending the geriatric psychiatry OPD had at least one psychiatric diagnosis by design. The mean age of the cohort was 67.70 ± 10.34 years (95% CI: 65.67–69.73), with no statistically significant difference between males and females (Welch t=0.894, p=0.374). Females constituted 52% of the sample, a proportion not significantly different from an equal male-female distribution (p=0.764). Age-band analysis revealed that nearly half of the patients were in the 60–64 year group (46%), followed by 22% aged 65–69 years, 18% aged 70–74 years, 10% aged 75–79 years, and only 4% aged ≥80 years, with a highly significant deviation from a uniform distribution (p<0.001). Most patients were Hindu (82%), with Muslims (12%) and other religions (6%) comprising the remainder (p<0.001). Educational attainment was low, with 57% being illiterate and only 5% being graduates (p<0.001). The majority were married (84%), 14% were widowed, and 2% separated/divorced (p<0.001). Occupationally, housewives (37%) and farmers (29%) predominated, followed by retired/unemployed individuals (26%) and those in business (8%) (p=0.037). Slightly more resided in urban areas (57%) than rural (43%) (p=0.316). Socioeconomically, 30% belonged to the upper-lower class and 28% to the lower class, with fewer in the middle or upper classes (p<0.001). Medical comorbidities were common: 34% had hypertension, 12% diabetes, 7% joint pain, and 10% other chronic conditions; 37% reported no comorbidity (p<0.001). A family history of psychiatric illness was present in 17% (p<0.001).

Table 2: Prevalence of specific psychiatric disorders (N = 100)

Omnibus test: Variation across categories was non-uniform (χ²=47.96, df=8, p=1.01×10⁻⁷).

Regarding the pattern of psychiatric morbidity, depression was the most prevalent diagnosis (26%; 95% CI: 18.4–35.4%), followed by anxiety disorders (21%; 14.3–29.7%), psychotic disorders (13%), dementia (12%), substance use disorders (10%), bipolar affective disorder (5%), somatoform disorder (2%), and delirium (2%). Other disorders, including adjustment disorder, delusional disorder, and pathological grief, comprised 9%. The differences in distribution across diagnostic categories were statistically significant (χ²=47.96, df=8, p≈1.0×10⁻⁷), indicating that certain disorders were substantially more frequent than others.

Table 3: Socio-demographic correlates: Common Mental Disorders (CMD) vs Severe Mental Disorders (SMD)* (N = 100)

When grouped into common mental disorders (CMD: depression, anxiety, somatoform, adjustment/pathological grief) versus severe mental disorders (SMD: psychotic, BPAD, substance use, dementia, delirium), CMD accounted for 58% and SMD for 42% of cases. Older age (≥70 years) was significantly associated with higher odds of SMD; only 22.4% of CMD cases were in this age group compared with 45.2% of SMD cases (OR=0.35 for CMD vs SMD, p=0.016). Female sex, urban residence, low socioeconomic status, and medical comorbidity showed non-significant trends toward different distributions between CMD and SMD groups.

Table 4: Selected disorder–socio-demographic associations (2×2 tests; N = 100)

Bivariate association analyses between specific socio-demographic factors and particular diagnoses revealed that females had significantly higher odds of depression compared with males (OR=2.65, p=0.041). Older age (≥70 years) was strongly associated with dementia (OR=8.48, p<0.001), and male sex was significantly linked to substance use disorders (OR=11.77, p=0.005). Rural residence showed a non-significant trend toward association with psychotic disorders (OR=2.38, p=0.148), and low socioeconomic status was not significantly associated with depression (p=0.177). Medical comorbidity presence was not significantly linked to anxiety disorders (p=0.368).

Sample structure and socio-demographics (Table 1). OPD cohort was older (mean 67.7 ± 10.3 y) with a left-skewed age banding—46% in 60–64 y and only 4% ≥80 y—mirroring clinic-based Indian datasets where help-seeking peaks soon after the retirement threshold and drops in the “old-old” due to mobility and caregiver barriers. Randhawa K. (2023)^[6] reported a similarly front-loaded urban geriatric curve (largest band 65–69 y) in Pune community screening, with progressive rise in neurocognitive morbidity in the older tail. The sex mix in OPD (52% women) is close to Goa’s tertiary clinic series (female preponderance 62%) and Delhi’s community survey (55% women), consistent with women’s greater survivorship and care-seeking for affective symptoms. Illiteracy (57%) and concentration in lower SES strata (upper-lower 30%, lower 28%) in sample align with Chandigarh’s hospital registry (≈60% with primary or no schooling; hypertension/diabetes common)³ and Mysuru’s urban survey showing lower-middle/low SES dominance. Marital status in clinic (84% married; 14% widowed) differs from community frames—Delhi had a higher widowed fraction-likely reflecting spousal accompaniment boosting OPD access. Urban residence (57%) in series tracks the urban bias seen in hospital work-ups from Vadodara and Chandigarh. Comorbidity patterns were also typical: hypertension led (34%), then diabetes (12%), echoing multisite Indian geriatric psychiatry cohorts where vascular/metabolic multimorbidity co-travels with late-life mood and cognitive disorders.

Disorder profile (Table 2). Depression headed  caseload (26%, 95% CI 18.4–35.4) with anxiety second (21%). That ordering is the rule rather than the exception in India: Delhi’s community study found depression 23.6% and anxiety 10.8%; Vadodara medical OPD flagged depression in 20% of geriatric attendees⁷; Goa’s psychiatric hospital cohort showed mood disorders as the largest block, with depressive episodes the most frequent subtype. Nepalese tertiary data are strikingly close to s: Taquet M et al.(2022)^[7] observed depression 26.7% and anxiety 23.3% among elderly psychiatry OPD attendees. Dementia (12%) in  series sits in the mid-teens band commonly seen across Indian urban samples - 14.9% in Pune and is lower than community dementia screen-positivity because clinics under-capture very old, frail adults. Psychotic disorders (13%) are similar to tertiary registers (≈12–13%), and substance-use disorders (10%) show the expected male loading (Table 4), aligning with hospital-based estimates of 8–12% in elderly men. The highly non-uniform omnibus distribution (χ² p≈10⁻⁷) is therefore consistent with regional evidence: depressive and anxiety syndromes dominate clinic throughput, while neurocognitive, psychotic, and substance-use conditions constitute substantial but smaller slices. McGrath JJ et al.(2023)^[8]

CMD vs SMD contrasts (Table 3). Grouping shows older age (≥70 y) doubling the share of SMD (45.2% vs 22.4% for CMD; OR 0.35 for CMD vs SMD, p=0.016). This tracks the epidemiologic gradient whereby neurocognitive disorders (and delirium risk) rise with age, shifting case-mix toward SMD in the “ng-old” to “old-old” transition. Non-significant trends-more CMD among women and urban residents; more SMD with lower SES and comorbidity-are directionally concordant with multi-setting studies: women carry greater CMD (depression/anxiety) burden, while lower SES and multimorbidity correlate with cognitive and severe psychotic syndromes in clinic populations. Power constraints (N=100) and 2×2 collapsing likely widened CIs around those effects. Shi L et al.(2020)^[9]

Specific associations (Table 4). Three signals are both statistically and clinically coherent and match prior literature: (i) female sex depression (OR 2.65, p=0.041), paralleling Delhi community data and clinic cohorts; (ii) age ≥70:  dementia (OR 8.48, p<0.001), in line with the steep late-life prevalence climb documented across Indian urban studies and WHO synthesis; and (iii) male sex: substance-use disorders (OR 11.77, p=0.005), consistent with male-skewed late-onset/relapsing alcohol-use presentations in tertiary registries. The rural-psychosis trend (OR 2.38, p=0.148) is plausible—later help-seeking and fewer early-intervention pathways outside cities—but under-powered here. Likewise, the low-SES→depression trend (OR 1.91, p=0.177) echoes community signals but likely needs a larger frame to stabilize. Anxiety’s weak link with medical comorbidity in  dataset (OR 1.61, p=0.368) contrasts with some primary-care findings where cardiometabolic disease co-occurs with GAD/depressive symptoms; the psychiatric OPD filter (where depression is the dominant affective phenotype) may attenuate a standalone anxiety–comorbidity effect. Auerbach RP et al. (2018)^[10]

The present hospital-based study highlights that psychiatric morbidity is highly prevalent among geriatric patients attending a tertiary care psychiatry outpatient department, with depressive and anxiety disorders forming the largest diagnostic categories. Dementia, psychotic disorders, and substance use disorders constituted a significant proportion, particularly among the older age groups and male patients. Socio-demographic factors such as female sex, low educational attainment, lower socioeconomic status, and presence of medical comorbidities were common in the study cohort, underscoring the complex interplay between biological, psychological, and social determinants in late-life mental health. These findings reinforce the need for targeted screening, early intervention, and integrated care models within geriatric health services to address both psychiatric and medical needs, especially for high-risk subgroups.

1. Hospital-based sampling: As the study was conducted in a tertiary care OPD, the findings reflect caseload prevalence rather than community prevalence, limiting generalizability to the broader elderly population.
2. Cross-sectional design: The study captured data at a single time point, precluding assessment of temporal changes, causal relationships, or illness trajectories.
3. Selection bias: Elderly individuals with severe physical disability, advanced dementia, or lack of caregiver support may have been underrepresented due to reduced likelihood of attending the OPD.
4. Recall bias: Reliance on patient and caregiver reporting for history could have led to inaccuracies, especially in cognitively impaired participants.
5. Limited sample size: Although adequate for descriptive profiling, subgroup analyses (e.g., individual disorder–socio-demographic associations) were underpowered, resulting in wide confidence intervals for some estimates.
6. Diagnostic constraints: While standardized tools were used, cultural and language nuances might have influenced symptom reporting and interpretation.

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