Study of Prevalence of Depression in Geriatric Patients Visiting Geriatric OPD of a Public Tertiary Care Centre

Aging is a natural process which everyone goes through from birth to death. Physical, emotional and social problems abound in old age. Age-related cognitive decline, comorbid conditions, lack of financial, personal autonomy and loneliness are significant factors that increase incidence of mental health disorders among the elders. “Aging India” is now a significant public health issue. A nation is classified as a “Graying Nation” by the UN when the percentage of its population over 60 years old reaches 7%. Nearly 7.7% (77 million) of India's overall population was elderly at the start of the millennium, making up 8.6% (104 million) in 2011 and 9.4% (125 million) in 2017. By 2050, the proportion of older adults in India is predicted to reach 19% (324 million), thanks to advances in healthcare technologies and epidemiological change [1]. 

Like diabetes or hypertension, depression is a true and treatable medical condition and should not be considered as a normal part of aging and needs attention, [2] and it is not just having “the blues” or the emotional turmoil which one feels after the loss of a loved one. As per WHO, it is a prolonged period of sadness, lack of interest, loss of pleasure in activities and low self-worth being the hallmark of depression. Approximately about 3.8% of the population have depression, including 5% of adults (4% in men and 6% in women) and 5.7% of adults > 60 years. WHO reports the prevalence of depression in the elderly varies between 10% to 20% in various societies. It was 13% to 43% in some of the 121 community-based studies in India [3]. Limited funding, lack of skilled healthcare professionals and the social stigma attached to mental diseases act as barriers to providing effective therapy for mental health diseases [4]. To screen for and quantify the severity of this iceberg phenomenon it was decided to study depression in all the geriatric patients visiting the health care providers for non-psychiatric health issues to detect hidden depression for early diagnosis and treatment.

It is a cross-sectional study conducted in Sion hospital. Figure 1 shows urban areas which are in the close vicinity of Sion hospital. The patients of this areas visit Sion hospital for their medical needs. It was conducted as a part of project course work for Executive Post Graduate Diploma in Hospital Administration (EPGDHA). The prime goal of this project work was to comprehend the entire depression issue among the elderly population that visits the Sion Hospital’s Medicine, Geriatric outpatient for routine medical illness. The objectives were to find the prevalence rate and determinants of hidden depression in geriatric patients in community-based settings, where there is a need to assess and improve primary health care services.

 Figure 1: Map of Urban areas in close vicinity of Sion Hospital.

(Sion Hospital Map - Mumbai, Maharashtra, India (mapcarta.com))

The sampling method used was purposive sampling. The study was conducted from 23 April 2024 to 28 May 2024. For calculation of sample size, we estimated a sample size of 174 subjects for an estimated prevalence of 41%, 95% confidence level, 5% and 10% contingency. All patients who visited the Medicine, Geriatric OPD were included in the study as per the inclusion and exclusion criteria till the sample size reached 174. The study was started after the Institutional Ethics Committee (IEC) approval of Sion hospital with IEC No. IEC/17/24 and individual consent of the patients were taken. Inclusion Criteria were patients in the age group 60-80 years visiting OPD and those who gave voluntary consent for study. Patients having a previous history of psychiatric illness, cognitive impairment and Alzheimer's Disease were excluded from the study. 

Data was collected with help of Demographic Performa and questionnaire of Geriatric Depression Scale (GDS) was taken by face-to-face interview. GDS was utilized to assess depression, it has 30 questions which enquire participants about their moods during the previous week. A rating of 1 or 0 is given for each question. Minimum score is 0 and maximum score is 30. Grading: Severe Depressives- 20-30, Mild Depressives- 10-19 and Normal- 0-9. Marathi and Hindi languages translation of the questionnaire was done. Each question was clearly stated to the participants, if any doubts they were clarified. Each interview took approximately 20-25 minutes, those who had high depression score were referred to Psychiatry OPD of Sion Hospital for further management. The collected data was compiled and analyzed by Statistical Package for Social Sciences (SPSS) software (Version 26). Descriptive statistics were used to summarize characteristics of the study subjects. Pearson's Chi-square test is used to study the association between several categorical variables. Additionally, correlation and cross tabulation between various dependent variables was performed. To determine the relationship between the covariates and dependent variables, multinomial logistic regression was used. Since all tests were tested at a 5% significance level, a relationship was considered significant if its ‘P’ value was < 0.05.

A total of 174 individuals were studied. Of these 66.7% participants were males and 33.3% were females. About 48.9% participants were in the range of 60-65 years. The median age of study participants was 65 years (IQR: 61-70). The majority of the study participants 55.7%, belongs to Hindu religion. 67.8% were married, 76.4% lived in joint families, 70.1% were literate and 74.1% were unemployed. Their median GDS was 14 (IQR: 5-24). Of the total, 25.3% suffered from mild depression, 34.5% suffered from severe depression and rest 40.2% were normal. Table 1 gives their demographic profile along with the GDS.

Table 1: Socio demographic profile of the study participants (n = 174).

SD = Standard Deviation

IQR = Interquartile Range

As depicted in figure 2, it was observed that participants aged 60-65 years exhibits the highest median GDS of 16 (IQR: 4-24), while those in 65-70 years’ group have lowest median score at 11 (IQR: 5-21), indicating lower prevalence of depressive symptoms in this age range. The group aged more than 70 years has a median score of 15 (IQR: 6-26), indicating slightly elevated depressive symptoms compared to 65-70 years’ group but lower than 60-65 years’ group.

 Figure 2: Geriatric depression scores across different age groups.

As demonstrated in table 2, geriatric depression was found to be significantly associated with gender, with a high prevalence of severe depression observed in females as compared to males (44.8% versus 29.3% , p = 0.021). Additionally, a higher prevalence of severe depression was significantly associated with factors like skipping meals (58.3%, p < 0.001), having an irregular exercise routine (37.9%, p < 0.001), not practicing yoga (35.8%, p = 0.009), shorter sleep duration (60.7%, p < 0.001) and spending more leisure time alone (65.5%, p < 0.001). The prevalence of severe depression was found higher among participants with no income, who had history of illness and currently living alone with the rates of 87.5%, 34.9% and 38.9% respectively. However, no significant association was found with age groups, religion, marital status, family type, current living arrangement, families’ annual income, education, occupation, Comorbid conditions and dietary habits both Mild and Severe depression. In the multivariate analysis, the duration of sleep less than six hours and skipping meals were identified as significant predictors which independently increases the risk of depression.

Table 2: Association of socio demographic variables with geriatric depression scale.

*. Significance attained using Chi Square test of association at 5% Level.

#. Data pooling done in order to apply Continuity Correction to Chi Square test.

Table 3 shows various predictive factors influencing depression by using multivariate analysis. Insufficient sleep ( < 6 hours) and meal skipping emerged as independent predictors of both mild and severe depression.

Table 3: Predictive factors influencing depression severity using Multivariate analysis.

Pearson’s Goodness of Fit : χ² = 44.069 df = 268 p value = 0.303 (good fit)

Nagelkerke R-Square = 0.606

*. Significance attained using Multinomial Regression Analysis at 5% Level

^. Figures inside parenthesis represents 95% CI OR: Odds Ratio

CI : Confidence Interval df: Degrees of Freedom

With increased longevity mental health issues in geriatric population are rising steadily. In this study 25.3% suffered from Mild depression and 34.5% suffered from Severe depression. The results of this investigation are consistent with an estimated 34.4% prevalence in India, as reported in the global aging and adult health Wave-1 study and a systematic review by Pilania et al., [5,6] was out in six nations between 2007 and 2010 revealed that the prevalence of depression was lowest in China (2.6%), and highest in India (27.1%), Mexico (23.7%), Ghana (11%), South Africa (6.4%), and Russia (15.6%). 

Geriatric depression showed significant association with not doing exercise and yoga, sleeping less than 6 hours, skipping meals, spending lesser leisure time with others. It was more in females as compared to males. As seen in study, prevalence of depression is seen more in females than males may be due to widowhood, staying alone, poor health, poverty and cognitive decline [7]. The study conducted by [8] suggested on similar line that physical exercise supported the protective effects of physical activity on depression for older adults. The mechanism behind the antidepressant effects of exercise is still not well understood. 

On Multinomial regression analysis, the Nagelkerke R square test value was 0.606 i.e., 61% indicates that the variation of dependent variable can be explained by independent variable where Sleep duration less than 6 hours and Skipping Meals are found significant, and they tend to independently raise the depression risk. Another important risk factor was a lack of adequate sleep. We found that depression was more among those who were having less than 6 hours sleep at night (aOR = 2.587, 95% CI = 1.44-4.64). These results are supported by study of [9] and [10] demonstrated that in community-dwelling older men, level of depressive symptoms had a strong, graded association with sleep disturbances [11] have also revealed that sleep disturbance was associated with current and future depression. 

When comparing individuals with severe depression to those classified as normal, those who slept less than six hours exhibited significantly higher odds of severe depression, with an adjusted odds ratio (aOR) of 32.518 (95% CI: 9.238 - 114.462 , p < 0.001) compared to those who slept six hours or more. Conversely, individuals who do not skip meals had significantly lower odds of severe depression (aOR = 0.116, 95% CI: 0.020 - 0.673, p = 0.016) compared to those who skipped meals. 

Similarly, when comparing individuals with mild depression to those classified as normal, those who slept less than six hours illustrated significantly higher odds of mild depression (aOR = 3.898, 95% CI: 1.307 - 11.623, p < 0.015) compared to those who slept six hours or more. Furthermore, when comparing individuals with severe depression to those with mild depression, individuals who slept less than 6 hours had significantly higher odds of severe depression (aOR = 8.341, 95% CI: 2.559 - 27.189, p < 0.001) compared to those who slept six hours or more. Overall, it can be narrated as insufficient sleep and meal skipping habits can be considered as strong predictors of increased depression risk among geriatric participants (Table 3). 

This emphasizes how complex depression—like any mental illness—is, and that’s why addressing the issue of depression in the community will require a holistic approach of healthcare services that prioritizes social, mental and physical well-being. The high prevalence rates of depression in patients presenting with medical issues suggest that primary care physicians need to be more vigilant for its early screening, diagnosis and treatment. The primary healthcare environment needs to be strengthened by providing proper training to health care workers. It is imperative to establish geriatric wards and geriatric OPDs with doctors, psychiatrists and social workers to provide healthcare services. Government must take initiative for the establishment of geriatric clubs, where senior citizens can socialize and exchange ideas. Non-governmental organizations and volunteer organizations should be involved. Health policy must address the problem of depression, special attention needs to be paid to pertinent social and economic risk factors for depression, like the absence of social security and the dearth of senior citizen health subsidiary care. Special senior citizens medical insurance schemes with economical packages must be introduced by competent authorities as against present health insurance policies which have high premiums for elders.

The prevalence of depression in elderly patients was found to be high visiting the geriatric medical OPD. Inadequate sleep and skipping meals significantly increased depression. So, these determinants should be worked upon, health care plans need to be modified to improve the emotional health of the elderly. This study suggests identifying geriatric depression in its early phases, healthcare professionals and other auxiliary health personnel should be made aware of it at the primary care level. High index of suspicion to be kept for screening of hidden depression symptoms in elders irrespective of their presenting symptoms. 

Limitations of study were that this study's methodology was only intended for screening; it was not intended to take the place of a clinician's diagnosis. Recall bias may have existed, as the prevalence of depression was dependent on self-reporting statistics. The study sample did not receive a confirmed diagnosis of depression as no follow up was done for those sent to the psychiatry OPD. Causation cannot be proved as this was a cross-sectional study and the results could be impacted by various confounders.

NIL.

The authors declare no conflicts of interest.

I want to extend my sincere thanks to Dr Mohan Joshi, Dean Sion Hospital who gave approval for data collection from Medicine OPD of Sion Hospital and Prof Harshad Thakur for his continuous support for my project work on geriatric depression.

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