Prevalence and Determinants of Geriatric Depression in Gopalganj, Bangladesh: A Cross-Sectional Study

Prevalence and Determinants of Geriatric Depression in Gopalganj, Bangladesh: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and Determinants of Geriatric Depression in Gopalganj, Bangladesh: A Cross-Sectional Study Mohammad Kamal Hossain, Md Rafi Hasan, Md Sabbir Hossain, Muhammad Habibulla Alamin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6336673/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Geriatric depression has become a significant concern. This study addresses the growing concern regarding geriatric depression by investigating its prevalence and associated socio-demographic and socioeconomic factors among elderly individuals in the Gopalganj District of Bangladesh. Methods: This cross-sectional study employed a multi-indicator survey to investigate various health issues among elderly individuals. The study utilized a two-stage cluster sampling method to select a sample of 400 elderly participants of both sexes. Depressive symptoms were measured using the 15-item Geriatric Depression Scale. Results: The results indicated a high prevalence of geriatric depression among elderly individuals, with approximately 48.8% experiencing moderate to severe depression. This study highlights high depression rates among elderly individuals, particularly among women, lower-income groups, the illiterate, and the unemployed. Education, occupation, income, smoking habits, age, and family structure play a significant role in geriatric depression. Additionally, the study revealed that social support, marital status, and living arrangements significantly influence the mental well-being of elderly individuals. Conclusion: The results of this study emphasize that policymakers must urgently address the mental health of elderly individuals, with a specific focus on women and those financially reliant on their families, especially sons. Geriatric depression Elderly Mental health Depression Bangladesh Geriatric depression scale (GDS) Figures Figure 1 Introduction Geriatric depression (GD) is becoming one of the most significant global public health concerns [1]. Depression among the elderly is characterized by a core cluster of debilitating symptoms, including persistent low mood, anhedonia, fatigue, disturbances in appetite and sleep patterns, feelings of guilt and worthlessness, difficulties with concentration and decision-making, and suicidal ideation, all of which can significantly impair an older adult's physical, mental, social, and occupational functioning [2]. Mental health is significantly influenced by age. As individuals age, they face various challenges that affect their psychological and interpersonal health, alongside the physical health changes that occur with aging. Natural brain aging, deteriorating physical conditions, and brain diseases all play a role in the increased prevalence of mental and behavioral disorders. Consequently, older adults must navigate these difficulties while adjusting to the complexities of aging. Furthermore, elderly individuals face an elevated risk of developing mental and behavioral problems owing to factors such as disability stemming from diverse illnesses, social isolation, limited support from family members, diminished ability to make personal decisions, and financial reliance [3–5]. Among all mental health conditions, older adults most frequently experience depression, which significantly affects their quality of life and independence. Depression in the elderly can have major clinical and social consequences, but early diagnosis, treatment, and intervention can improve the quality of life, prevent suffering and premature death, and maintain independence while also reducing healthcare costs and mortality [6,7]. Research has suggested that depression is more prevalent among older females. Along with aging, several demographic factors contribute to this condition in the elderly population. These include residing in rural areas, lacking formal education, having a lower socio-economic status, and being unemployed. Marital status is also a key factor as individuals who are single, divorced, widowed, or living alone are more likely to experience depression. In addition, beyond population characteristics, diverse social and psychological elements have been linked to depression in older adults. Feelings of loneliness, insufficient social and family support, and dependence on others are the major contributors. A lack of emotional bonds in the family, reduced interaction with offspring, and encountering challenging life circumstances further elevate risk. Moreover, low perception of personal health, absence of spiritual involvement, and increased use of emotional coping methods have also been associated with depression in the elderly. Depression has been linked to lifestyle and dietary factors such as the absence of hobbies, irregular eating habits, substance use or smoking, and inadequate physical activity [6,8–10]. Approximately 900 million people worldwide, or 12% of the global population, are currently 60 years of age or older. It is anticipated that by 2050, the global elderly population will surpass two billion, with approximately 80% residing in developing nations, such as Bangladesh. The World Health Organization indicates that globally, 7% of older adults experience depression and 15% have some form of mental health disorder [11]. Elderly individuals often suffer from multiple chronic illnesses while facing a lack of social networks and support. Additionally, owing to financial constraints, they frequently consume meals deficient in essential vitamins and minerals. These limitations often increase vulnerability to depression [12–14]. Depression contributes significantly to long-term disability in older adults. While numerous studies have explored depression in the elderly, including its symptoms, causes, and suicidal tendencies, they have yielded varying and unreliable results regarding its global prevalence [15–18]. The rate of depression in the elderly population of Bangladesh is growing alarmingly, with roughly half of those affected remaining undiagnosed. Along with unhealthy aging, financial hardship creates a double burden for the elderly in the country. Many older adults experience poor health, abuse, neglect, and exploitation. To address these issues, policymakers urgently need a sustainable public health strategy supported by data on elderly mental health and its socio-demographic determinants. This study aims to explore the prevalence of depression among the elderly population in Gopalganj District, Bangladesh, and to identify the social and demographic factors that increase the risk. The results of this study could offer important information to help policymakers and healthcare professionals create effective and focused programs. These programs could play a vital role in improving mental health support for older adults in Bangladesh. Materials and Methods Data Source and Study Design In this study, the population included adults aged 55 years or older. While the typical cutoff age for seniors is 60 years, we adopted 55 years as the threshold, in line with the Bangladesh Association of Geriatrics. A field study was conducted between July 2023 and June 2024 to collect the data. Data and related information were obtained using a predefined questionnaire. The mental health of the participants was assessed using the 15-question Geriatric Depression Scale (GDS-15). Additionally, socio-demographic information and elderly status variables were gathered. Well-trained field investigators conducted interviews with all the selected participants. 400 individuals were included in the study and participated in interviews. Trained personnel were recruited, and they worked under direct supervision to ensure accurate data collection. This cross-sectional descriptive study was conducted at the household level. It employs a multi-indicator survey approach to explore a range of diverse health issues in the elderly population. The study took place in Gopalganj Pourashava, comprising 15 administrative Wards. A two-stage random sampling method was employed. In the first stage, 7 out of the 15 wards were selected using simple random sampling. In the second stage, a minimum sample size of 400 elderly individuals was randomly chosen from these selected wards, with at least 55 elderly individuals of both sexes randomly selected within each ward. The Ward Councilor's office provided essential information to facilitate the data collection. Trained personnel collected socio-demographic information using a structured questionnaire. Sample Size Determination A two-stage cluster sampling technique was employed to select a representative sample of elderly individuals (aged 55 years and above) from the Gopalganj Pourashava areas. The Pourashava comprises 15 administrative wards. Initially, seven out of the fifteen wards, representing approximately half, were chosen using a basic random selection process. To ensure a statistically robust study, a minimum sample size was calculated. Where, n= sample size and = utilized a two-sided 95% confidence interval (1.96), p = assumed population proportion (0.5 for unknown prevalence), and d = precision level of 0.05, with a maximum of 0.10. However, to account for potential complexities and to simplify data collection, approximately 400 elderly individuals were ultimately selected from the urban areas of the Gopalganj District. In the second stage, within each selected ward, at least 55 elderly individuals, encompassing both males and females, were randomly selected. This was performed to ensure a minimum sample size of 400 elderly individuals per ward. Dependent Variable The Geriatric Depression Scale (GDS) is a depression assessment tool specifically designed for use with older individuals. It has been successfully translated into many languages (Chinese, Dutch, French, Greece, Hindi, etc.) and has been widely validated in a wide variety of elder populations and settings [19–22]. The Geriatric Depression Scale (GDS) is a well-known and valid self-administered questionnaire used to evaluate depression among the elderly. The GDS initially consists of 30 questions (Long Form). It requires yes/no responses about feelings over the past week [19,23]. This tool is frequently used and is simple to administer when checking for depression in the elderly. A shorter 15-item version (GDS-15) was later created, with questions most strongly linked to depression [23]. The Geriatric Depression Scale (GDS) assesses a range of symptoms, including emotional, cognitive, and behavioral indicators. In this scale, scores between 0 and 15 are used to measure depression, with higher scores representing increased severity. Ten questions were scored positively for depression, while a negative response to questions 1, 5, 7, 11, and 13 was indicative of depressive symptoms. The abbreviated 15-question version of the Geriatric Depression Scale (GDS-15) is particularly useful for older adults who are physically unwell, have mild to moderate dementia, or experience difficulty in focusing or fatigue. In this study, the GDS-15 was used to assess geriatric depression within the study area with data gathered through a 15-question survey. A simple "yes/no" response format was utilized by most administrations. The variables related to geriatric depression are presented in the following table [Table 1] . Table 1. Indicators of geriatric depression were assessed by the scale (short form). No. Questions Answer 1. Are you basically satisfied with your life? [ No =1, Yes=0] 2. Have you dropped many of your activities and interests? [No=0, Yes =1] 3. Do you feel that your life is empty? [No=0, Yes =1] 4. Do you often get bored? [No=0, Yes =1] 5. Are you in good spirits most of the time? [ No =1, Yes=0] 6. Are you afraid that something bad is going to happen to you? [No=0, Yes =1] 7. Do you feel happy most of the time? [ No =1, Yes=0] 8. Do you often feel helpless? [No=0, Yes =1] 9. Do you prefer to stay at home, rather than going out and doing new things? [No=0, Yes =1] 10. Do you feel you have more problems with memory than most? [No=0, Yes =1] 11. Do you think it is wonderful to be alive now? [ No =1, Yes=0] 12. Do you feel pretty worthless the way you are now? [No=0, Yes =1] 13. Do you feel full of energy? [ No =1, Yes=0] 14. Do you feel that your situation is hopeless? [No=0, Yes =1] 15 Do you think that most people are better off than you are? [No=0, Yes =1] Total Score 15 Source: https://web.stanford.edu/~yesavage/GDS.html In the GDS-15, the responses marked in bold indicate depressive symptoms. One point was assigned to each of the bold answers. A score of > 5 points suggests depression. A score of ≥ 10 points is almost always indicative of depression. A score of > 5 points should warrant a follow-up comprehensive assessment. To determine an individual's GDS score, the total number of points accumulated from the bold responses was calculated as follows: Once the geriatric depression assessment was completed, we categorized the scores using the following scale: Table 2. Geriatric depression cut points. 0-5 Normal 6-10 Mild or Moderate Depression 10+ Severe Depression Source: https://web.stanford.edu/~yesavage/GDS.html The GDS results were then incorporated into the model using the following categorization: Independent Variables This study examined the influence of ten independent variables on the outcome of interest. The study considered Age was grouped into seven intervals: 55–59, 60–64, 65–69, 70–74, 75–79, 80–84, and above. Education level was classified as Illiterate, Primary, Secondary, Higher Secondary, and Graduate. Occupation is categorized into four categories: government, private sector, business, and other occupations. Family income is grouped into three levels: lower, middle, and upper. This study considered family structure, which was defined as nuclear, joint, or extended. Additionally, marital status was recorded as married, widowed, or divorced. Smoking behavior was categorized as regular, frequent, or no habit. The accommodation type was classified as Kacha House, Semi-Paka House, or Paka House. Living arrangements were considered in terms of whether individuals lived alone (yes/no). Lastly, breadwinner status identifies whether the primary financial provider is the individual, their spouse, son, or daughter. Despite the country's religious diversity, with four major faiths, this study's sample only included individuals identifying as Muslim, Hindu, or Other. Family income served as a measure of socioeconomic status, classifying individuals into lower (less than 10,000 BDT), middle (10,000-20,000 BDT), or upper classes (over 20,000 BDT) [24]. Statistical Analysis This study focused on determining the prevalence of depression among the elderly population in the Gopalganj district and identified associated demographic or socio-economic risk factors. Frequency distribution was the primary method used to analyze the characteristics of the sample data. Bivariate analysis played a vital role in investigating the associations among the current health status of the elderly, their place of residence, and other socio-demographic characteristics. To determine the relationship between geriatric depression and different demographic and socioeconomic variables, a Chi-Square test was used. A binary logistic regression model was used to identify the most significant socio-demographic predictors. SPSS (IBM SPSS Statistics 25) was used for data management and statistical analysis, whereas Python was employed to generate visual representations such as graphs and charts. The first phase of the analysis involved univariate analysis, employing frequency distributions to describe the socio-demographic profile of the elderly population, encompassing variables such as age, sex, religion, and occupation. During the second stage, the Chi-square test was applied to examine the association between the dependent and independent variables. The null hypothesis was then considered as follows: H 0 : There is no association between geriatric depression and a specific factor. The alternative hypothesis is, H 1 : There exists an association between geriatric depression and a specific factor. In the third stage, binary logistic regression was used to analyze the relationship between response and explanatory variables. This statistical technique is commonly employed when the outcome variable has two categories, such as: 'yes' or 'no.' The analysis aimed to identify the most significant socio-demographic and socio-economic factors affecting the elderly population. Results Background Characteristics of the Elderly This study surveyed 400 elderly individuals, both male and female, from Gopalganj Pourashava, Bangladesh. Among them, 34.7% exhibited moderate depression, 13.8% experienced severe depression, and 51.5% were at normal levels [Fig. 1A]. The study sample consisted of a majority of male (62.7%) and Muslim (81.3%) elderly individuals, with 37.3% female participants, 18.3% Hindu, and a small fraction (0.3%) identified as other religions. Table 3 presents the demographic profiles of the elderly participants with a focus on gender and religious affiliation. The study included individuals aged 55 and above, with the largest segment (20.5%) falling within the 55–59 age bracket. The subsequent age groups showed a gradual decline in the number of participants. Notably, within the 55–59 age range, females outnumbered males (64.6% vs. 35.4%) and the majority were identified as Muslim (80.5%). The table also reveals that the 55–59 age group contained the highest number of Muslim and Hindu participants. Conversely, the 55–59 age group had the fewest male participants, while the 80–84 age group had the fewest female participants. The 85 + age group had the lowest representation of both Muslim and Hindu elderly individuals. These results were consistent with those reported by Rahman et al. (2017) [ 25 ]. Table 3 presents the socio-economic profiles of the elderly participants. A small majority (51.6%) were literate, ranging from primary to graduate education, while 48.5% were illiterate. Among literate individuals, primary education was the most common level attained (27.8%), followed by higher secondary education (4.5%). Literate males exhibited a higher literacy rate than females, and Muslims formed the majority of the literate group. Regarding occupation, 4.8% were in government services, 16% were in business, and 78.3% were homemakers or engaged in other activities. Notably, females were more than twice as likely as males to work in government services (68.4% vs. 31.6%). Finally, 51% of the elderly were classified as middle-income, with Muslims comprising 77% and Hindus comprising 23% of this group. The housing situation of the elderly in the Gopalganj district varied, with 49% living in Paka houses, 39.75% living in Semi Paka houses, and 11.25% in Kacha houses. A significant majority of Muslim elderly individuals resided in Paka housing, whereas Hindu elderly were less represented in Kacha housing. Elderly individuals living alone were uncommon, with 99.3% living with their families. Financial dependence on sons was prevalent, affecting 66.75% of the elderly, with males showing a higher reliance. Self-reliance was more common among males and Muslims, while females typically relied on their spouses. Muslim elderly individuals were more likely to depend on their sons (78.7%), and Muslim female elderly participants were significantly more reliant on their spouses (81.6%) compared to Hindu female elderly participants. These findings highlight notable gender and religious differences in socio-demographic and socio-economic factors among elderly individuals in Gopalganj [ Table 3 ] . Table 3 Background characteristics of the elderly with respect to gender and religion. Characteristics Response Total (%) Male (251) Female (149) Muslim (326) Hindu (73) Others (1) n (%) n (%) n (%) n (%) n (%) Total (%) 400 62.7 37.3 81.5 18.3 0.3 Age 55–59 82 (20.5) 29 (35.4) 53 (64.6) 66 (80.5) 16 (19.5) 0 60–64 69 (17.3) 33 (47.8) 36 (52.2) 54 (78.3) 14 (20.3) 1 (1.4) 65–69 58 (14.5) 30 (51.7) 28 (48.3) 43 (74.1) 15 (25.9) 0 70–74 64 (16.0) 42 (65.6) 22 (34.4) 53 (82.8) 11 (17.2) 0 75–79 48 (12.0) 41(85.4) 7 (14.6) 39 (81.3) 9 (18.8) 0 80–84 44 (11.0) 43 (97.7) 1 (2.3) 39 (88.6) 5 (11.4) 0 85 and over 35 (8.8) 33 (94.3) 2 (5.7) 32 (91.4) 3 (8.6) 0 Education Illiterate 194 (48.5) 128 (66.0) 66 (34.0) 152 (78.4) 41 (21.1) 1 (0.5) Primary 111 (27.8) 63 (56.8) 48 (43.2) 96 (86.5) 15 (13.5) 0 Secondary 51 (12.8) 33 (64.7) 18 (35.3) 40 (78.4) 11 (21.6) 0 Higher Secondary 18 (4.5) 12 (66.7) 6 (33.3) 13 (72.2) 5 (27.8) 0 Graduate 26 (6.5) 15 (57.7) 11 (42.3) 25 (96.2) 1 (3.8) 0 Occupation Govt. Service 19 (4.8) 6 (31.6) 13 (68.4) 16 (84.2) 3 (15.8) 0 Private Service 4 (1.0) 1 (25.0) 3 (75.0) 3 (75.0) 1 (25.0) 0 Business 64 (16.0) 61 (95.3) 3 (4.7) 60 (93.8) 4 (6.3) 0 Others 313 (78.3) 183 (58.5) 130 (41.5) 247 (78.9) 65 (20.8) 1 (0.3) Family Income Lower 24 (6.0) 17 (70.8) 7 (29.2) 14 (58.3) 9 (37.5) 1 (4.2) Middle 204 (51.0) 122 (59.8) 82 (40.2) 157 (77.0) 47 (23.0) 0 Upper 172 (43.0) 112 (65.1) 60 (34.9) 155 (90.1) 17 (9.9) 0 Type of Family Nuclear 66 (16.5) 35 (53.0) 31 (47.0) 59 (89.4) 6 (9.1) 1 (1.5) Joint 322 (80.5) 207 (64.3) 115 (35.7) 260 (80.7) 62 (19.3) 0 Extended 12 (3.0) 9 (75.0) 3 (25.0) 7 (58.3) 5 (41.7) 0 Marital Status Married 354 (88.5) 224 (63.3) 130 (36.7) 287 (81.1) 66 (18.6) 1 (0.3) Widowed 44 (11.0) 26 (59.1) 18 (40.9) 37 (84.1) 7 (15.9) 0 Divorced 2 (0.5) 1 (50.0) 1 (50.0) 2 (100.0) 0 0 Smoking Behavior Regular 23 (5.8) 23 (100.0) 0 19 (82.6) 4 (17.4) 0 Frequently 48 (12.0) 46 (95.8) 2 (4.2) 43 (89.6) 5 (10.4) 0 No Habit 329 (82.3) 182 (55.3) 147 (44.7) 264 (80.2) 64 (19.5) 1 (0.3) Accommodation Type Kacha House 45 (11.25) 27 (60) 18 (40) 29 (64.4) 15 (33.3) 1 (2.2) Semi Paka House 159 (39.75) 107 (67.3) 52 (32.7) 125 (78.6) 34 (21.4) 0 Paka House 196 (49) 117 (59.7) 79 (40.3) 172 (87.8) 24 (12.2) 0 Living Alone No 397 (99.3) 250 (63.0) 147 (37.0) 325 (81.9) 71 (17.9) 1 (0.3) Yes 3 (0.8) 1 (33.3) 2 (66.7) 1 (33.3) 2 (66.7) 0 Breadwinner Self 78 (19.5) 67 (85.9) 11 (14.1) 70 (89.7) 8 (10.3) 0 Spouse 49 (12.25) 0 49 (100) 40 (81.6) 8 (16.3) 1 (2) Son 267 (66.75) 182 (68.2) 85 (31.8) 210 (78.7) 57 (21.3) 0 Daughter 6 (1.5) 2 (33.3) 4 (66.7) 6 (100) 0 0 Prevalence of Depression The prevalence of depression among the elderly was as follows: 34.7% experienced moderate depression, 13.8% suffered from severe depression, and 51.5% were in the normal depression category [Fig. 1A] . This study found that depression is prevalent in the elderly population, with 48.6% exhibiting depressive symptoms and an average depression score of 9.056. Gender disparity was evident, with women showing a higher rate of depression (52.3%) than men (46.3%) [ Table 4 ]. Table 4 Classification of elderly based on GDS-15 scores (N = 400). Gender Mean (SD) Mild or moderately depressed N (%) Severely depressed N (%) Total N (%) Male 9.0172 85 (33.9) 31 (12.4) 116 (46.3) Female 9.1154 54 (36.2) 24 (16.1) 78 (52.3) Total 9.0566 139 (34.8) 55 (13.8) 194 (48.6) According to Fig. 1B, 53 .8% of individuals with no smoking habit were observed to have a normal depression level, 30.1% experienced moderate depression, and 16.1% fell into the severe category. Among frequent smokers, 50.0% exhibit moderate depression, 45.8% are in the normal category, and 4.2% fall into the severe category. For regular smokers, 69.6% experience moderate depression, and 30.4% are in the normal category [Fig. 1B]. Figure 1C suggests that 79.2% of lower-income individuals experience moderate depression, 16.7% are in the normal category, and 4.2% fall into the severe category. In the middle-income class, 41.2% have normal depression, 39.2% experience moderate depression, and 19.6% fall into the severe category. For the upper-income class, 68.6% are in the normal category, 23.3% experience moderate depression, and 8.1% fall into the severe category [Fig. 1C]. According to Fig. 1D, 88 .5% of graduates fall into the normal depression category, while 7.7% experience moderate depression and 3.8% have severe depression. Among individuals with higher secondary education, 66.7% are in the normal category, 33.3% have moderate depression. For those with secondary education, 68.6% are in the normal category, 23.5% experience moderate depression, and 7.8% fall into the severe category. In the primary education group, 45.9% are in the normal category, 33.3% have moderate depression, and 20.7% experience severe depression. Among illiterate individuals, 43.8% fall into the normal category, 42.3% have moderate depression, and 13.9% experience severe depression [Fig. 1D]. Relationship Between Geriatric Depression with Socio-demographic and Socio-economic Variables Table 5 examines the relationship between geriatric depression and several socio-demographic and socioeconomic variables. Notably, age demonstrated a significant relationship with depression levels. The study population was distributed across various age brackets: 36.6% were aged 55–59, 42.0% were 60–64, 39.7% were 65–69, 35.9% were 70–74, 41.7% were 75–79, 15.9% were 80–84, and 20.0% were 85 and older. Within these age groups, a considerable proportion experienced mild to moderate depression. However, severe depression prevalence varied, with 4.9% in the 55–59 age group, 10.1% in the 60–64 age group, 12.1% in the 65–69 age group, 12.5% in the 70–74 age group, 8.3% in the 75–79 age group, 31.8% in the 80–84 age group, and 31.4% in the 85 + age group exhibiting severe depressive symptoms. Statistical analysis revealed a strong association between age and geriatric depression, rejecting the null hypothesis (H 0 : there is no association between geriatric depression and age of the elderly). The relationship was highly significant, with a p-value below 0.001 (p < 0.001). Many other studies strongly support the statement that the association between chronological age and geriatric depression is statistically significant [ 26 – 30 ]. The study demonstrated a significant association between education level and depression (p < 0.001), revealing that illiteracy is a strong predictor of depressive symptoms. Specifically, over half (56.28%) of illiterate participants experienced depression, a substantially higher rate compared to the 43% of literate individuals who reported depressive symptoms. Regarding the educational attainment profile of the literate elderly, 33.3% had completed primary-level education, 23.5% had attained secondary-level education, 33.3% had achieved higher secondary-level education, and 7.7% had graduated. Within these distinct educational cohorts, the prevalence of mild to moderate depressive symptomatology was observed. Additionally, the severe depression rates were 20.7% in the primary education group, 7.8% in the secondary education group, and 3.8% in the graduate education group. Occupation significantly influences geriatric depression (p < 0.001): 55% of depressed individuals were housewives or in "others" occupations, while rates were substantially lower for business (29.7%), private service (25%), and government employees (10.6%). This highlights a strong link between occupational status and depression risk. The data revealed that the majority of elderly respondents (83.4%) were experiencing depression, and these individuals predominantly belonged to lower Family Income families. In contrast, a significant level of depression was found among elderly persons from middle-class (58.8%) and upper-class (31.4%) income backgrounds also. The analysis concluded that an elderly individual's family income level is highly associated (p < 0.001) with their likelihood of developing geriatric depression. The study's results establish a clear connection between marital status and the prevalence of geriatric depression. The majority of elderly respondents who were widowed (81.8%) were found to be depressive, whereas only around 44.4% of married elderly individuals were depressive. These results suggest that married elder people are less likely to experience depression compared to widowed elderly individuals. The strong statistical significance (p < 0.001) supports the conclusion that marital status is a key factor linked to geriatric depression. Most of the studies on elderly health agreed that married elder people are less depressed than unmarried ones [ 25 – 28 ]. The findings revealed a significant association (p < 0.001) between elderly individuals' smoking behaviors and geriatric depression. Notably, 46.2% of non-smoking respondents were identified as depressive, in contrast to substantially higher rates observed among those with frequent (54.2%) and regular (69.6%) smoking habits. The study indicates that smoking is a notable risk factor for depression in older adults. Additionally, accommodation type significantly impacts geriatric depression (p < 0.001): 84.5% of those in "kacha" houses reported depression, compared to 62.2% in "semi-paka" and 29% in "paka" houses. Finally, a significant association (p < 0.001) exists between depression and the elderly respondent's status as the family breadwinner. The majority of elderly individuals (83.3%) were identified as depressive when the primary breadwinner in the household was the daughter. Furthermore, substantial proportions of elderly respondents were also found to be depressive in families where the breadwinner was the daughter (83.3%), the son (55.0%), the spouse (32.6%), or the elderly individuals themselves (33.3%). However, the analysis revealed no statistically significant relationships between geriatric depression and variables such as the elderly respondents' gender, type of family, religious affiliation, and living alone status [ Table 5 ]. Table 5 Association of depression with socio-demographic and socio-economic variables. Characteristics Normal Mild or Moderate Depression Severe Depression P-value n (%) n (%) n (%) Age 55–59 48 (58.5) 30 (36.6) 4 (4.9) 0.000 60–64 33 (47.8) 29 (42.0) 7 (10.1) 65–69 28 (48.3) 23 (39.7) 7 (12.1) 70–74 33 (51.6) 23 (35.9) 8 (12.5) 75–79 24 (50.0) 20 (41.7) 4 (8.3) 80–84 23 (52.3) 7 (15.9) 14 (31.8) 85 and over 17 (48.6) 7 (20.0) 11 (31.4) Sex/ Gender Male 135 (53.8) 85 (33.9) 31 (12.4) 0.408 Female 71 (47.7) 54 (36.2) 24 (16.1) Education Illiterate 85 (43.8) 82 (42.3) 27 (13.9) 0.000 Primary 51 (45.9) 37 (33.3) 23 (20.7) Secondary 35 (68.6) 12 (23.5) 4 (7.8) Higher Secondary 12 (66.7) 6 (33.3) 0 (0.0) Graduate 23 (88.5) 2 (7.7) 1 (3.8) Occupation Govt. Service 17 (89.5) 1 (5.3) 1 (5.3) 0.000 Private Service 3 (75.0) 1 (25.0) 0 (0.0) Business 45 (70.3) 18 (28.1) 1 (1.6) Housewives or Others 141 (45.0) 119 (38.0) 53 (16.9) Family Income Lower 4 (16.7) 19 (79.2) 1 (4.2) 0.000 Middle 84 (41.2) 80 (39.2) 40 (19.6) Upper 118 (68.6) 40 (23.3) 14 (8.1) Type of Family Nuclear 40 (60.6) 21 (31.8) 5 (7.6) 0.373 Joint 159 (49.4) 114 (35.4) 49 (15.2) Extended 7 (58.3) 4 (33.3) 1 (8.3) Marital Status Married 197 (55.6) 127 (35.9) 30 (8.5) 0.000 Widowed 8 (18.2) 11 (25.0) 25 (56.8) Divorced 1 (50.0) 1 (50.0) 0 (0) Smoking Behavior Regular 7 (30.4) 16 (69.6) 0 (0.0) 0.000 Frequently 22 (45.8) 24 (50.0) 2 (4.2) No Habit 177 (53.8) 99 (30.1) 53 (16.1) Religion Muslim 175 (53.7) 105 (32.2) 46 (14.1) 0.165 Hindu 31 (42.5) 33 (45.2) 9 (12.3) Others 0 (0.0) 1 (100) 0 (0.0) Accommodation Type Kacha House 7 (15.6) 31 (68.9) 7 (15.6) 0.000 Semi Paka House 60 (37.7) 74 (46.5) 25 (15.7) Paka House 139 (70.9) 34 (17.3) 23 (11.7) Living Alone No 205 (51.6) 137 (34.5) 55 (13.9) 0.473 Yes 1 (33.3) 2 (66.7) 0 (0.0) Breadwinner Self 52 (66.7) 26 (33.3) 0 (0.0) 0.000 Spouse 33 (67.3) 13 (26.5) 3 (6.1) Son 120 (44.9) 97 (36.3) 50 (18.7) Daughter 1 (16.7) 3 (50.0) 2 (33.3) Results on Binary Logistic Regression Analysis of Depression and Socio-demographic and Socio-economic Variables A binary logistic regression analysis was conducted to identify key socio-demographic factors for geriatric depression. The Geriatric Depression Scale (GDS) was used as the binary dependent variable, while various sociodemographic factors served as independent variables. The findings, summarized in Table 6 , indicate that age, marital status, occupation, family income, breadwinner status, smoking habits, and accommodation type have a significant association with depression within the entire group studied. The results reveal that, older adults in age groups 60–64 (OR = 1.769, 95% CI: 1.383–2.196, p = 0.037), 65–69 (OR = 1.579, 95% CI: 1.295–2.056, p = 0.024), and 75–79 (OR = 1.403, 95% CI: 1.128–1.275, p = 0.012) had a higher likelihood of experiencing depression compared to those aged 55–59. Marital status emerged as a critical factor, with widowed individuals showing a significant risk of depression (OR = 9.113, 95% CI: 3.44–24.09, p = 0.000) than their married counterparts. Additionally, occupation was identified as an important determinant of depression among the elderly. The logistic regression analysis identified several occupational categories associated with increased depression risk compared to government service employees. Individuals working in private service showed 1.901 times increase in the odds of depression (p = 0.048), while those in business had 2.181 times increase (p = 0.014). Notably, individuals classified within "other" occupations exhibited the highest risk, with 3.612 times increase in the odds of depression (p = 0.003). Specifically, housewives or those categorized as "others" were 3.61 times (95% CI: 1.597–5.656, p = 0.003) more likely to experience depression than elderly government job holders. Similarly, elderly business owners were 2.18 times (95% CI: 1.382–6.459, p = 0.014) more likely to experience depression compared to government job holders. Furthermore, family income demonstrated a significant association with geriatric depression. Elderly individuals from upper-income families were significantly less likely to be depressed (OR = 0.330, 95% CI 0.081–0.983, p = 0.000) than those from lower-income families. Likewise, elderly individuals from middle-income families also exhibited lower odds of depression (OR = 0.352, 95% CI 0.095–0.837, p = 0.017) compared to those from lower-income families. Religious affiliation was found to be an important determinant, with Hindu elderly individuals experiencing lower depression compared to the Muslim elderly (OR = 0.968, 95% CI: 0.496–0.973, p = 0.023). The role of the family’s primary breadwinner also had a significant influence on depression risk. Elderly individuals living in households where the son was the main breadwinner were 3.77 times more likely to experience depression (95% CI: 1.335–10.665, p = 0.012) compared to those who were financially independent. Smoking habits further influenced depression prevalence among the elderly. Individuals who did not smoke had a significantly lower chance of being depressed (OR = 0.125, 95% CI: 0.029–0.538, p = 0.005) compared to regular smokers. Additionally, living conditions played a crucial role in mental health outcomes. Elderly individuals residing in semi-pucca houses had a lower likelihood of depression (OR = 0.284, 95% CI: 0.106–0.760, p = 0.012) compared to those in kachcha houses. The risk was even lower for those living in pucca houses (OR = 0.086, 95% CI: 0.029–0.252, p = 0.000). The study identifies key risk factors for geriatric depression, including older age, lower family income, unmarried or widowed status, unemployment, Muslim religious affiliation, dependence on a son as the main breadwinner, and living in kachcha houses. In contrast, education, gender, living alone, and the type of family structure did not show a significant relationship with depression. [Table 6 ] . Table 6 Binary logistic regression analysis of depression with socio-demographic and socio-economic variables. Characteristics Adjusted OR OR 95% CI for OR P value Lower Upper Age 55–59 (Ref.) 60–64 1.769 1.383 2.196 .037 65–69 1.579 1.295 2.056 .024 70–74 0.505 0.184 1.383 .184 75–79 1.403 1.128 1.275 0.012 80–84 0.466 0.133 1.636 0.233 85 and above 0.445 0.114 1.735 0.243 Gender Male (Ref.) Female 1.583 0.756 3.317 0.223 Education Illiterate (Ref.) Primary 1.249 0.708 2.202 0.443 Secondary 0.651 0.282 1.501 0.314 Higher Secondary 0.674 0.177 2.574 0.564 Graduate 0.697 0.138 3.522 0.662 Occupation Service (govt.) (Ref.) Service (private) 1.901 1.342 9.365 0.048 Business 2.181 1.382 6.459 0.014 Housewives or Others 3.612 1.597 5.656 0.003 Family Income Lower (Ref.) Middle 0.352 0.095 0.837 0.017 Upper 0.330 0.081 0.983 0.000 Type of Family Nuclear (Ref.) Joint 1.008 0.454 2.238 0.985 Extended 0.824 0.164 4.133 0.814 Marital Status Married (Ref.) Widowed 9.113 3.446 24.094 0.000 Divorced 12.026 0.535 27.222 0.117 Smoking Habit Regular (Ref.) Frequently 0.422 0.102 1.749 0.234 No Habit 0.125 0.029 0.538 0.005 Religion Muslim (Ref.) Hindu 0.968 0.496 0.973 0.023 Accommodation Type Kacha House (Ref.) Semi Paka House 0.284 0.106 0.760 0.012 Paka House 0.086 0.029 0.252 0.000 Living Alone No (Ref.) Yes 2.231 0.086 5.584 0.628 Breadwinner Self (Ref.) Spouse 1.485 0.425 5.182 0.535 Son 3.773 1.335 10.665 0.012 Daughter 3.263 0.275 8.755 0.349 Discussion This study is the first to examine the factors contributing to depressive symptoms within the geriatric population in the Gopalganj district of Bangladesh. The present study found a GD prevalence rate of 48.6%, including 34.8% and 13.8% moderately and severely depressed, respectively. Studies conducted in rural Bangladeshi communities revealed a high depression rate of 84.3% in two southern regions of the country [ 26 ], across multiple villages in the Dhamrai Upazila, Dhaka, the rate was 60% [ 25 ] and 59% prevalence in rural Mirzapur, Tangail, including 42% with mild and 17% with severe depressive symptoms [ 16 ]. Studies conducted in Indian communities show depression rates between 8.9% and 62.16% [ 31 – 34 ]. Research across ten European nations, focusing on those 50 and older, showed depression prevalence between 18% and 37% [ 35 ]. The observed variations in depression prevalence across studies are likely attributable to diverse geographical locations, cultural contexts, variations in study instruments, varied study environments, different sample sizes, and diverse selection methods. Despite these differences, this study's findings align with research conducted in other countries and settings. It was confirmed that depression is universally related to older age, since this research also demonstrated a significant association between older age and a higher probability of depression. Some studies support our findings [ 36 , 37 ]. In addition to age, studies from Mexico, South Africa, India, and other low- and middle-income nations consistently show that women are more prone to depression than men [ 38 ]. In this study, no significant association is found between depression and gender. However, the authors did not find a contact link between gender and depression [ 39 ]. This study's results suggest that higher education levels correlate with a reduced prevalence of depressive symptoms in elderly individuals. While univariate analysis demonstrated a significant association between increased education and lower depression rates, this relationship was not confirmed in the binary logistic regression analysis. This finding aligns with previous research, which has consistently demonstrated a link between low educational attainment and a higher likelihood of depressive symptoms [ 40 – 44 ]. In Bangladesh, individuals with limited education often face financial hardship due to restricted access to well-paying jobs or positions offering retirement benefits. Thus, they endure poverty in their elderly years, and this is known to correlate with a higher incidence of depression [ 40 ]. To mitigate the high prevalence of depression related symptoms among elderly individuals in Bangladesh, sustained focus on increasing educational attainment is essential. Furthermore, unemployment was found to be a key factor in predicting geriatric depression in the binary logistic regression analysis. Specifically, elderly individuals those without formal employment, such as housewives or those in "other" occupations, experienced higher rates of depression compared to those who were employed or actively serving. This result is similar with previous research. Being unemployed causes economic uncertainty and financial dependence, making individuals more vulnerable to psychological problems such as depression [ 45 ]. Within the Bangladeshi context, elderly individuals are often relegated to unemployment and dependence on family members, leading to financial strain and limited access to nutritious food, healthy living conditions, and necessary healthcare. The current study found that family income was significantly associated with GD. Notably, the elderly from higher-income families exhibited lower rates of depressive symptoms compared to those from lower-income families. Previous studies also supports our results [ 24 ]. The traditional Bangladeshi joint family structure is widely recognized as a beneficial support system for individuals who are emotionally vulnerable, financially insecure, unemployed, or elderly and infirm. Contrary to this, individuals residing in nuclear family settings have been observed to experience higher rates of depression. Our study's findings corroborate this, demonstrating a statistically significant association between living in a nuclear family and increased depression risk compared to living in a joint family. This can be attributed to the comprehensive support network provided by relatives in joint families, where responsibilities are shared across various aspects of life. Distributing responsibilities in this way improves the financial and social well-being of older family members. Prior research has consistently shown a link between family structure and depression [ 46 ]. This study revealed that married individuals exhibited a lower likelihood of experiencing depression compared to those who were unmarried, separated, divorced, or widowed. This finding is aligned with the results of previous studies [ 26 , 47 ] and inconsistent with the other [ 33 ]. A study comparing depression across Europe showed that unmarried, widowed, or divorced individuals reported higher levels of depressive symptoms than married individuals [ 48 ]. This result is consistent with the results of a study in China, where there was a significant increase in depression among widows (9.2% vs 4.9% married and 4.5% unmarried), never to be married or divorced again [ 49 ]. The death or divorce of a spouse in later life can lead to mental health issues like depression, due to the profound sense of loss and emptiness. Elderly individuals who are widowed or divorced require targeted support and monitoring to mitigate adverse health outcomes, including depression. Additionally, our study demonstrates that non-smoking elderly individuals experience lower rates of depressive symptoms compared to those who smoke. Similar to our findings, a community-based study done in India reported that depression was more common among those who were not using tobacco [ 50 ]. Elderly individuals who are not Muslim exhibited lower rates of depression compared to Muslim elderly, which aligns with previous research [ 26 ]. Depression rates among the elderly appear to be influenced by their living situation. In this study, it was observed that elderly individuals residing in basic, unrefined "kacha" houses had higher rates of depression compared to those living in more substantial "semi-paka" or "paka" houses. The quality of an elderly person's living environment seems to be a significant factor affecting their risk of developing depressive symptoms [ 26 , 33 ]. The findings indicate an association between the main breadwinner role and depression in elderly individuals, suggesting that this role is associated with poorer mental health [ 51 ]. The study reveals a significant prevalence of depression, particularly mild depression, among the elderly population in the Gopalganj district of Bangladesh. These findings underscore the need for targeted policies and programs designed to enhance the mental well-being of older adults in Bangladesh. Policy Recommendations This study emphasizes the critical need for policy interventions aimed at enhancing the mental health and general well-being of Bangladesh's elderly population, especially in urban regions like Gopalganj. A significant association was observed between depression and socio-demographic factors, such as age, education level, socioeconomic status, and occupation. Policymakers should develop targeted interventions for the well-being of the elderly population. The government can integrate mental health services into primary healthcare systems. Which ensures easy access to counseling and psychological support for the elderly population. This will benefit individuals from disadvantaged economic backgrounds and those with minimal formal education. Community-based programs can be introduced to promote social engagement. Participating in social programs, such as elderly clubs, support networks, and awareness initiatives, can help reduce isolation. Additionally, financial support systems, such as pension schemes for elderly individuals, particularly widowed and dependent women should be expanded. This will help address the economic factors contributing to depression. Public health policies must address lifestyle factors, such as smoking habits and chronic illnesses. Limitations This study's findings are subject to several limitations. Firstly, the results are specific to the Gopalganj district and may not be generalizable to the broader population of Bangladesh. Secondly, as this study used a cross-sectional approach, we cannot determine cause-and-effect relationships. Therefore, future research should prioritize longitudinal studies with sufficiently large, nationally representative samples to provide a more comprehensive understanding of geriatric depression and its associated risk factors in the elderly population in Bangladesh. Conclusion This study aimed to determine the prevalence of geriatric depression and identify the socio-demographic factors associated with it among elderly individuals. The study revealed a substantial prevalence of depressive symptoms within this population in Bangladesh. Significant determinants for the development of these symptoms included older age, education level, smoking habits, family income, residence type, occupation, marital status, and breadwinner status. The results suggest that approximately half of the geriatric population in the study area reported experiencing depression, with the oldest age group (60–64 years) at the highest risk. The study also indicates that mild or moderate depression is common in this population. The substantial prevalence of depression observed among elderly individuals in this study indicates a concerning rise in depression within the Bangladeshi community. Understanding the identified key factors associated with geriatric depression can inform the development of comprehensive, multi-faceted strategies aimed at reducing depression and enhancing the quality of life for this population. However, to establish a causal relationship between these risk factors and depression, further longitudinal research is necessary. Declarations Supplementary Information None Acknowledgements We sincerely acknowledge the elderly individuals and their families for their cooperation in data collection. Author Contributions M. K. H.: Conceptualization, Methodology, Formal analysis, Supervision, Writing – original draft, Funding acquisition; M. R. H.: Data curation, Formal analysis, Software, Visualization, Writing – original draft; M. S. H.: Data curation, Formal analysis, Software, Visualization, Writing-original draft; M. H. A.: Data curation, Supervision, Visualization, Validation, Writing – review & editing. All authors read and approved the final manuscript. Funding This work was partially supported by the GSTU Research Cell, administered by Gopalganj Science and Technology University (GSTU), Bangladesh, under the Research and Innovation Grant from the People's Republic of Bangladesh. The funding covered data collection and staff remuneration but did not include publication costs. No funding organization was involved in the research activities of this project. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Written informed consent was obtained from all individual participants included in the study. Ethics approval and consent to participate This study was reviewed and approved by the Ethical Review Board of Gopalganj Science and Technology University, Bangladesh. Under the Declaration of Helsinki for research involving human subjects, we obtained informed consent (written or verbal) from every participant by explaining the study design, purpose, procedure, risk, and benefits before the interview. Consent for publication This manuscript does not contain any individual person’s data in any form (including individual details, images, or videos). Therefore, consent for publication is not applicable. Competing interests The authors declare that they have no competing interests. Clinical trial number Not applicable Author details 1 Department of Statistics, Gopalganj Science and Technology University, Gopalganj-8100, Bangladesh. 2 Department of Statistics, slal University of Science and Technology, Sylhet-3114, Bangladesh. References Ross RE, VanDerwerker CJ, Saladin ME, Gregory CM. 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Int J Adv Res (Indore). 2018;6:238–45. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6336673","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438372367,"identity":"32b365de-be69-45b1-88f9-e481e1079f33","order_by":0,"name":"Mohammad Kamal Hossain","email":"","orcid":"","institution":"Gopalganj Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Kamal","lastName":"Hossain","suffix":""},{"id":438372368,"identity":"d9980f20-9867-41f9-86e3-fe27b123aa3f","order_by":1,"name":"Md Rafi Hasan","email":"","orcid":"","institution":"Shahjalal University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Rafi","lastName":"Hasan","suffix":""},{"id":438372370,"identity":"b78017fc-c8a1-40f2-95ab-3afb25384856","order_by":2,"name":"Md Sabbir Hossain","email":"","orcid":"","institution":"Shahjalal University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Sabbir","lastName":"Hossain","suffix":""},{"id":438372372,"identity":"f2882add-cc15-4a86-b4c5-41a210a05187","order_by":3,"name":"Muhammad Habibulla Alamin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie3RMUvEMBQH8DwCyRLo+g65+hUChUNQ8Ks0S10chAPBLaWQjrfqt9BFXI+ALvcBDm7p7Q49cOiQwVc4B4f2OgrmP4XwfoT/C2MxMX8wCYe6zTWmgkPZHC81A8sHyayuHGvurrJEcq/76ZNEbz4cNG1hnlaiwEmEbY1rcu0z7dXi4SsEY2X1ivB2OSjg0dSaSNqT3dxpY9X7PcLmZpBwNBZ/XtnNLBG8XSA4P0jEkZhnIksMRM4/x4lSa0eE6leigFb0r6hxgrKsqAstmXN/Bi7LnCqWF2aky7WX+30X6CuTdXnoQjpfSf+yPYxs7PcuVN+uP+XTAG28mzoZExMT86/yDQxZV+3XQxp/AAAAAElFTkSuQmCC","orcid":"","institution":"Gopalganj Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Habibulla","lastName":"Alamin","suffix":""}],"badges":[],"createdAt":"2025-03-30 04:08:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6336673/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6336673/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80047814,"identity":"f6caacd2-9d52-487d-b98d-40935dc15146","added_by":"auto","created_at":"2025-04-07 09:57:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":450137,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Distribution of Depression Severity Levels, (B) Depression Levels Across Different Smoking Habits, (C) Depression Levels Across Income Classes, (D) Depression Levels by Educational Attainment.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6336673/v1/d337454a2d649285f651c0ab.png"},{"id":80056544,"identity":"03b69a60-64ac-43f0-b899-07ef9f256bee","added_by":"auto","created_at":"2025-04-07 11:32:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2368099,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6336673/v1/69e1f55b-38bb-4990-9a87-d770a33683d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Determinants of Geriatric Depression in Gopalganj, Bangladesh: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGeriatric depression (GD) is becoming one of the most significant global public health concerns [1]. Depression among the elderly is characterized by a core cluster of debilitating symptoms, including persistent low mood, anhedonia, fatigue, disturbances in appetite and sleep patterns, feelings of guilt and worthlessness, difficulties with concentration and decision-making, and suicidal ideation, all of\u0026nbsp;which\u0026nbsp;can significantly impair an older adult's physical, mental, social, and occupational functioning [2]. Mental health is significantly influenced by age. As individuals age, they face various challenges that affect their psychological and interpersonal health, alongside the physical health changes that occur with aging. Natural brain aging, deteriorating physical conditions, and brain diseases all play a role in the increased prevalence of mental and behavioral disorders. Consequently, older adults must navigate these difficulties while adjusting to the complexities of aging. Furthermore, elderly individuals face an elevated risk of developing mental and behavioral problems\u0026nbsp;owing\u0026nbsp;to factors such as disability stemming from diverse illnesses, social isolation, limited support from family members, diminished ability to make personal decisions,\u0026nbsp;and financial reliance [3–5]. Among all mental health conditions, older adults most frequently experience depression, which significantly affects their quality of life and independence. Depression in the elderly can have major clinical and social consequences, but early diagnosis, treatment, and intervention can improve\u0026nbsp;the\u0026nbsp;quality of life, prevent suffering and premature death, and maintain independence while also reducing healthcare costs and mortality [6,7].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch has suggested that depression is more prevalent among older females. Along with aging, several demographic factors contribute to this condition in the elderly\u0026nbsp;population. These include residing in rural areas, lacking formal education, having a lower socio-economic status, and being unemployed. Marital status is also a key factor as individuals who are single, divorced, widowed, or living alone are more likely to experience depression. In addition, beyond population characteristics, diverse social and psychological elements have been linked to depression in older adults. Feelings of loneliness, insufficient social and family support, and dependence on others are\u0026nbsp;the\u0026nbsp;major contributors. A lack of emotional bonds in the family, reduced interaction with offspring, and encountering challenging life circumstances further elevate risk. Moreover,\u0026nbsp;low perception of personal health, absence of spiritual involvement, and increased use of emotional coping methods have also been associated with depression in the elderly. Depression has been linked to lifestyle and dietary factors such as the absence of hobbies, irregular eating habits, substance use or smoking, and inadequate physical activity [6,8–10]. Approximately 900 million people worldwide, or 12% of the global population, are currently 60 years of age or older. It\u0026nbsp;is\u0026nbsp;anticipated that by 2050, the global elderly population will surpass two billion, with approximately 80% residing in developing nations, such as Bangladesh. The World Health Organization indicates that globally, 7% of older adults experience depression and 15% have some form of mental health disorder [11]. Elderly individuals often suffer from multiple chronic illnesses while\u0026nbsp;facing a lack of social networks and support. Additionally, owing to financial constraints, they frequently consume meals deficient in essential vitamins and minerals. These limitations often increase vulnerability to depression [12–14]. Depression contributes significantly to long-term disability in older adults. While numerous studies have explored depression in the elderly, including its symptoms, causes, and suicidal tendencies, they have yielded varying and unreliable results regarding its global prevalence [15–18]. The rate of depression in the elderly population of Bangladesh is growing alarmingly, with roughly half of those affected remaining undiagnosed. Along with unhealthy aging, financial hardship creates a double burden for the elderly in the country. Many older adults experience poor health, abuse, neglect, and exploitation. To address these issues, policymakers urgently need a sustainable public health strategy supported by data on elderly mental health and its socio-demographic determinants. This study aims to explore the prevalence of depression among the elderly population in Gopalganj District, Bangladesh, and to\u0026nbsp;identify\u0026nbsp;the social and demographic factors that increase the risk. The results of this study could offer important information to help policymakers and healthcare professionals create effective and focused programs. These programs could play a vital role in improving mental health support for older adults in Bangladesh.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSource and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStudy\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the population included\u0026nbsp;adults aged 55 years or older. While the typical cutoff age for seniors is 60 years, we adopted 55 years as the threshold, in line with the Bangladesh Association of Geriatrics. A field study was conducted between July 2023\u0026nbsp;and June 2024\u0026nbsp;to collect the data. Data and related information were obtained using a predefined questionnaire. The mental health of the participants was assessed using the 15-question Geriatric Depression Scale (GDS-15). Additionally, socio-demographic information and elderly status variables\u0026nbsp;were gathered. Well-trained field investigators conducted interviews with all\u0026nbsp;the\u0026nbsp;selected participants. 400 individuals were included in the study and participated in interviews. Trained personnel were recruited, and they worked under direct supervision to ensure accurate data collection.\u003c/p\u003e\n\u003cp\u003eThis cross-sectional descriptive\u0026nbsp;study was conducted at the household level. It employs a multi-indicator survey approach to explore a range of diverse health issues in the elderly population. The study took place in\u0026nbsp;Gopalganj Pourashava, comprising 15 administrative Wards. A two-stage random sampling method was employed. In the first stage, 7 out of the 15 wards were selected using simple random sampling.\u0026nbsp;In\u0026nbsp;the second stage, a minimum sample size of 400 elderly individuals\u0026nbsp;was randomly chosen from these\u0026nbsp;selected wards,\u0026nbsp;with\u0026nbsp;at least 55 elderly individuals of both sexes randomly selected within each ward. The Ward Councilor\u0026apos;s office provided essential information to facilitate the data collection. Trained personnel collected socio-demographic information\u0026nbsp;using\u0026nbsp;a\u0026nbsp;structured questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size Determination\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA two-stage cluster sampling technique was employed to select a representative sample of elderly individuals (aged 55 years and above) from the Gopalganj Pourashava areas. The Pourashava comprises 15 administrative wards. Initially, seven out of the fifteen wards, representing approximately half, were chosen using a basic random selection process. To ensure a statistically robust study, a minimum sample size \u0026nbsp; was calculated. Where, n= sample size and \u0026nbsp; = utilized a two-sided 95% confidence interval (1.96), p = assumed population proportion (0.5 for unknown prevalence), and d = precision level of 0.05, with a maximum of 0.10. However, to account for potential complexities and to simplify data collection, approximately 400 elderly individuals were ultimately selected from the urban areas of the Gopalganj District. In the second stage, within each selected ward, at least 55 elderly individuals, encompassing both males and females, were randomly selected. This was performed to ensure a minimum sample size of 400 elderly individuals per ward.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Geriatric Depression Scale (GDS) is a depression assessment tool specifically designed for use with older individuals. It has been successfully translated into many languages (Chinese, Dutch, French, Greece, Hindi, etc.) and\u0026nbsp;has been\u0026nbsp;widely validated in a wide variety of elder populations and settings [19\u0026ndash;22]. The Geriatric Depression Scale (GDS) is a well-known and valid self-administered questionnaire used to evaluate depression among the elderly. The GDS initially consists\u0026nbsp;of 30 questions (Long Form).\u0026nbsp;It requires\u0026nbsp;yes/no responses about feelings over the past week [19,23]. This tool is frequently used and\u0026nbsp;is\u0026nbsp;simple to administer when checking for depression in the elderly. A shorter 15-item version (GDS-15) was later created,\u0026nbsp;with\u0026nbsp;questions most strongly linked to depression [23]. The Geriatric Depression Scale (GDS) assesses a range of symptoms, including emotional, cognitive, and behavioral indicators. In this scale, scores between 0 and 15 are used to measure depression, with higher scores representing increased severity. Ten questions were scored positively for depression, while a negative response to questions 1, 5, 7, 11, and 13 was indicative of depressive symptoms.\u003c/p\u003e\n\u003cp\u003eThe abbreviated 15-question version of the Geriatric Depression Scale (GDS-15) is particularly useful for older adults who are physically unwell, have mild to moderate dementia, or experience difficulty\u0026nbsp;in\u0026nbsp;focusing or fatigue. In this study, the GDS-15 was used to assess geriatric depression within the study area with data gathered through a 15-question survey. A simple \u0026quot;yes/no\u0026quot; response format was utilized by most administrations. The variables related to geriatric depression are presented in the following table\u003cstrong\u003e\u0026nbsp;[Table 1]\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eIndicators of geriatric depression were assessed by the scale (short form).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnswer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAre you basically satisfied with your life?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[\u003cstrong\u003eNo\u003c/strong\u003e=1, Yes=0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHave you dropped many of your activities and interests?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you feel that your life is empty?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you often get bored?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAre you in good spirits most of the time?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[\u003cstrong\u003eNo\u003c/strong\u003e=1, Yes=0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAre you afraid that something bad is going to happen to you?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you feel happy most of the time?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[\u003cstrong\u003eNo\u003c/strong\u003e=1, Yes=0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you often feel helpless?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you prefer to stay at home, rather than going out and doing new things?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you feel you have more problems with memory than most?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you think it is wonderful to be alive now?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[\u003cstrong\u003eNo\u003c/strong\u003e=1, Yes=0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you feel pretty worthless the way you are now?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you feel full of energy?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[\u003cstrong\u003eNo\u003c/strong\u003e=1, Yes=0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you feel that your situation is hopeless?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDo you think that most people are better off than you are?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[No=0, \u003cstrong\u003eYes\u003c/strong\u003e=1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e https://web.stanford.edu/~yesavage/GDS.html\u003c/p\u003e\n\u003cp\u003eIn the GDS-15, the responses marked in \u003cstrong\u003ebold\u003c/strong\u003e indicate depressive symptoms. One point was assigned to each of the bold answers.\u003c/p\u003e\n\u003cp\u003eA score of \u0026gt; 5 points suggests depression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA score of \u0026ge; 10 points is almost always indicative of depression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA score\u0026nbsp;of\u0026nbsp;\u0026gt; 5 points should warrant a follow-up comprehensive assessment.\u003c/p\u003e\n\u003cp\u003eTo determine an individual\u0026apos;s GDS score, the total number of points accumulated from the bold responses was calculated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 444px;\"\u003e\u003c/p\u003e\n\u003cp\u003eOnce the geriatric depression assessment was completed, we categorized the scores using the following scale:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eGeriatric depression cut points.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMild or Moderate Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSevere Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e https://web.stanford.edu/~yesavage/GDS.html\u003c/p\u003e\n\u003cp\u003eThe GDS results were then incorporated into the model using the following categorization:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examined the influence of ten independent variables on the outcome of interest. The study considered Age was grouped into seven intervals: 55\u0026ndash;59, 60\u0026ndash;64, 65\u0026ndash;69, 70\u0026ndash;74, 75\u0026ndash;79, 80\u0026ndash;84, and above. Education level was classified as Illiterate, Primary, Secondary, Higher Secondary, and Graduate. Occupation is categorized into four categories: government, private sector, business, and other occupations. Family income is grouped into three levels: lower, middle, and upper. This study considered family structure, which was defined as nuclear, joint, or extended. Additionally, marital status was recorded as married, widowed, or divorced. Smoking behavior was categorized as regular, frequent, or no habit. The accommodation type was classified as Kacha House, Semi-Paka House, or Paka House. Living arrangements were considered in terms of whether individuals lived alone (yes/no). Lastly, breadwinner status identifies whether the primary financial provider is the individual, their spouse, son, or daughter. Despite the country\u0026apos;s religious diversity, with four major faiths, this study\u0026apos;s sample only included individuals identifying as Muslim, Hindu, or Other. Family income served as a measure of socioeconomic status, classifying individuals into lower (less than 10,000 BDT), middle (10,000-20,000 BDT), or upper classes (over 20,000 BDT) [24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study focused on determining the prevalence of depression among the elderly population in the Gopalganj district and identified associated demographic\u0026nbsp;or\u0026nbsp;socio-economic risk factors. Frequency distribution was the primary method used to analyze the characteristics of the sample data. Bivariate analysis played a vital role in investigating the associations\u0026nbsp;among\u0026nbsp;the current health status of the elderly, their place of residence, and other socio-demographic characteristics. To determine the relationship between geriatric depression and different demographic and socioeconomic variables, a Chi-Square test was used. A binary logistic regression model was used to\u0026nbsp;identify\u0026nbsp;the most significant socio-demographic predictors.\u003c/p\u003e\n\u003cp\u003eSPSS (IBM SPSS Statistics 25) was used for data management and statistical analysis, whereas Python was employed to generate visual representations such as graphs and charts. The first phase of the analysis involved univariate analysis, employing frequency distributions to describe the socio-demographic profile of the elderly population, encompassing variables such as age, sex, religion, and occupation. During the second stage, the Chi-square test was applied to examine the association between the dependent and independent variables. The \u0026nbsp;null hypothesis was then considered as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e: There is no\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eassociation between geriatric depression\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand a specific factor.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe alternative hypothesis is,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cem\u003eH\u003csub\u003e1\u003c/sub\u003e\u003c/em\u003e:\u0026nbsp;\u003c/strong\u003eThere exists an\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eassociation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;between geriatric depression and a specific factor.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the third stage, binary logistic regression was used to analyze the relationship between response and explanatory variables. This statistical technique is commonly employed when the outcome variable has two categories, such as: \u0026apos;yes\u0026apos; or \u0026apos;no.\u0026apos; The analysis aimed to identify the most significant socio-demographic and socio-economic factors affecting the elderly population.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eBackground Characteristics of the Elderly\u003c/h2\u003e\n\u003cp\u003eThis study surveyed 400 elderly individuals, both male and female, from Gopalganj Pourashava, Bangladesh. Among them, 34.7% exhibited moderate depression, 13.8% experienced severe depression, and 51.5% were at normal levels \u003cstrong\u003e[Fig.\u0026nbsp;1A].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study sample consisted of a majority of male (62.7%) and Muslim (81.3%) elderly individuals, with 37.3% female participants, 18.3% Hindu, and a small fraction (0.3%) identified as other religions. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the demographic profiles of the elderly participants with a focus on gender and religious affiliation. The study included individuals aged 55 and above, with the largest segment (20.5%) falling within the 55\u0026ndash;59 age bracket. The subsequent age groups showed a gradual decline in the number of participants. Notably, within the 55\u0026ndash;59 age range, females outnumbered males (64.6% vs. 35.4%) and the majority were identified as Muslim (80.5%). The table also reveals that the 55\u0026ndash;59 age group contained the highest number of Muslim and Hindu participants. Conversely, the 55\u0026ndash;59 age group had the fewest male participants, while the 80\u0026ndash;84 age group had the fewest female participants. The 85\u0026thinsp;+\u0026thinsp;age group had the lowest representation of both Muslim and Hindu elderly individuals. These results were consistent with those reported by Rahman et al. (2017) [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the socio-economic profiles of the elderly participants. A small majority (51.6%) were literate, ranging from primary to graduate education, while 48.5% were illiterate. Among literate individuals, primary education was the most common level attained (27.8%), followed by higher secondary education (4.5%). Literate males exhibited a higher literacy rate than females, and Muslims formed the majority of the literate group. Regarding occupation, 4.8% were in government services, 16% were in business, and 78.3% were homemakers or engaged in other activities. Notably, females were more than twice as likely as males to work in government services (68.4% vs. 31.6%). Finally, 51% of the elderly were classified as middle-income, with Muslims comprising 77% and Hindus comprising 23% of this group. The housing situation of the elderly in the Gopalganj district varied, with 49% living in Paka houses, 39.75% living in Semi Paka houses, and 11.25% in Kacha houses. A significant majority of Muslim elderly individuals resided in Paka housing, whereas Hindu elderly were less represented in Kacha housing. Elderly individuals living alone were uncommon, with 99.3% living with their families. Financial dependence on sons was prevalent, affecting 66.75% of the elderly, with males showing a higher reliance. Self-reliance was more common among males and Muslims, while females typically relied on their spouses. Muslim elderly individuals were more likely to depend on their sons (78.7%), and Muslim female elderly participants were significantly more reliant on their spouses (81.6%) compared to Hindu female elderly participants. These findings highlight notable gender and religious differences in socio-demographic and socio-economic factors among elderly individuals in Gopalganj \u003cstrong\u003e[\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e]\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBackground characteristics of the elderly with respect to gender and religion.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eResponse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale (251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale (149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMuslim (326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHindu (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (78.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026ndash;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (97.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 and over\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (91.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194 (48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (96.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovt. Service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313 (78.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183 (58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (59.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157 (77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172 (43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155 (90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Family\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNuclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260 (80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtended\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e354 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287 (81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (82.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequently\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Habit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e329 (82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147 (44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264 (80.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccommodation Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKacha House\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (11.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSemi Paka House\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159 (39.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125 (78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaka House\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117 (59.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (40.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving Alone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e397 (99.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 (63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325 (81.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreadwinner\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (85.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (12.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e267 (66.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182 (68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210 (78.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaughter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003ePrevalence of Depression\u003c/h3\u003e\n\u003cp\u003eThe prevalence of depression among the elderly was as follows: 34.7% experienced moderate depression, 13.8% suffered from severe depression, and 51.5% were in the normal depression category \u003cstrong\u003e[Fig.\u0026nbsp;1A]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThis study found that depression is prevalent in the elderly population, with 48.6% exhibiting depressive symptoms and an average depression score of 9.056. Gender disparity was evident, with women showing a higher rate of depression (52.3%) than men (46.3%) \u003cstrong\u003e[\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cstrong\u003e].\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClassification of elderly based on GDS-15 scores (N\u0026thinsp;=\u0026thinsp;400).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMild or moderately depressed\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeverely depressed\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.1154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to \u003cstrong\u003eFig.\u0026nbsp;1B, 53\u003c/strong\u003e.8% of individuals with no smoking habit were observed to have a normal depression level, 30.1% experienced moderate depression, and 16.1% fell into the severe category. Among frequent smokers, 50.0% exhibit moderate depression, 45.8% are in the normal category, and 4.2% fall into the severe category. For regular smokers, 69.6% experience moderate depression, and 30.4% are in the normal category \u003cstrong\u003e[Fig.\u0026nbsp;1B].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;1C\u003c/strong\u003e suggests that 79.2% of lower-income individuals experience moderate depression, 16.7% are in the normal category, and 4.2% fall into the severe category. In the middle-income class, 41.2% have normal depression, 39.2% experience moderate depression, and 19.6% fall into the severe category. For the upper-income class, 68.6% are in the normal category, 23.3% experience moderate depression, and 8.1% fall into the severe category \u003cstrong\u003e[Fig.\u0026nbsp;1C].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to \u003cstrong\u003eFig.\u0026nbsp;1D, 88\u003c/strong\u003e.5% of graduates fall into the normal depression category, while 7.7% experience moderate depression and 3.8% have severe depression. Among individuals with higher secondary education, 66.7% are in the normal category, 33.3% have moderate depression. For those with secondary education, 68.6% are in the normal category, 23.5% experience moderate depression, and 7.8% fall into the severe category. In the primary education group, 45.9% are in the normal category, 33.3% have moderate depression, and 20.7% experience severe depression. Among illiterate individuals, 43.8% fall into the normal category, 42.3% have moderate depression, and 13.9% experience severe depression \u003cstrong\u003e[Fig.\u0026nbsp;1D].\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eRelationship Between Geriatric Depression with Socio-demographic and Socio-economic Variables\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e examines the relationship between geriatric depression and several socio-demographic and socioeconomic variables. Notably, age demonstrated a significant relationship with depression levels. The study population was distributed across various age brackets: 36.6% were aged 55\u0026ndash;59, 42.0% were 60\u0026ndash;64, 39.7% were 65\u0026ndash;69, 35.9% were 70\u0026ndash;74, 41.7% were 75\u0026ndash;79, 15.9% were 80\u0026ndash;84, and 20.0% were 85 and older. Within these age groups, a considerable proportion experienced mild to moderate depression. However, severe depression prevalence varied, with 4.9% in the 55\u0026ndash;59 age group, 10.1% in the 60\u0026ndash;64 age group, 12.1% in the 65\u0026ndash;69 age group, 12.5% in the 70\u0026ndash;74 age group, 8.3% in the 75\u0026ndash;79 age group, 31.8% in the 80\u0026ndash;84 age group, and 31.4% in the 85\u0026thinsp;+\u0026thinsp;age group exhibiting severe depressive symptoms. Statistical analysis revealed a strong association between age and geriatric depression, rejecting the null hypothesis (H\u003csub\u003e0\u003c/sub\u003e: there is no association between geriatric depression and age of the elderly). The relationship was highly significant, with a p-value below 0.001 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Many other studies strongly support the statement that the association between chronological age and geriatric depression is statistically significant [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. The study demonstrated a significant association between education level and depression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), revealing that illiteracy is a strong predictor of depressive symptoms. Specifically, over half (56.28%) of illiterate participants experienced depression, a substantially higher rate compared to the 43% of literate individuals who reported depressive symptoms. Regarding the educational attainment profile of the literate elderly, 33.3% had completed primary-level education, 23.5% had attained secondary-level education, 33.3% had achieved higher secondary-level education, and 7.7% had graduated. Within these distinct educational cohorts, the prevalence of mild to moderate depressive symptomatology was observed. Additionally, the severe depression rates were 20.7% in the primary education group, 7.8% in the secondary education group, and 3.8% in the graduate education group.\u003c/p\u003e\n \u003cp\u003eOccupation significantly influences geriatric depression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): 55% of depressed individuals were housewives or in \u0026quot;others\u0026quot; occupations, while rates were substantially lower for business (29.7%), private service (25%), and government employees (10.6%). This highlights a strong link between occupational status and depression risk. The data revealed that the majority of elderly respondents (83.4%) were experiencing depression, and these individuals predominantly belonged to lower Family Income families. In contrast, a significant level of depression was found among elderly persons from middle-class (58.8%) and upper-class (31.4%) income backgrounds also. The analysis concluded that an elderly individual\u0026apos;s family income level is highly associated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with their likelihood of developing geriatric depression. The study\u0026apos;s results establish a clear connection between marital status and the prevalence of geriatric depression. The majority of elderly respondents who were widowed (81.8%) were found to be depressive, whereas only around 44.4% of married elderly individuals were depressive. These results suggest that married elder people are less likely to experience depression compared to widowed elderly individuals. The strong statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) supports the conclusion that marital status is a key factor linked to geriatric depression. Most of the studies on elderly health agreed that married elder people are less depressed than unmarried ones [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe findings revealed a significant association (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between elderly individuals\u0026apos; smoking behaviors and geriatric depression. Notably, 46.2% of non-smoking respondents were identified as depressive, in contrast to substantially higher rates observed among those with frequent (54.2%) and regular (69.6%) smoking habits. The study indicates that smoking is a notable risk factor for depression in older adults. Additionally, accommodation type significantly impacts geriatric depression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): 84.5% of those in \u0026quot;kacha\u0026quot; houses reported depression, compared to 62.2% in \u0026quot;semi-paka\u0026quot; and 29% in \u0026quot;paka\u0026quot; houses. Finally, a significant association (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exists between depression and the elderly respondent\u0026apos;s status as the family breadwinner. The majority of elderly individuals (83.3%) were identified as depressive when the primary breadwinner in the household was the daughter. Furthermore, substantial proportions of elderly respondents were also found to be depressive in families where the breadwinner was the daughter (83.3%), the son (55.0%), the spouse (32.6%), or the elderly individuals themselves (33.3%). However, the analysis revealed no statistically significant relationships between geriatric depression and variables such as the elderly respondents\u0026apos; gender, type of family, religious affiliation, and living alone status \u003cstrong\u003e[\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cstrong\u003e].\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation of depression with socio-demographic and socio-economic variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMild or Moderate Depression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSevere Depression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026ndash;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 and over\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex/ Gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovt. Service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousewives or Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Family\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNuclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtended\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Behavior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequently\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Habit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHindu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccommodation Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKacha House\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSemi Paka House\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaka House\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving Alone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreadwinner\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaughter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eResults on Binary Logistic Regression Analysis of Depression and Socio-demographic and Socio-economic Variables\u003c/h3\u003e\n\u003cp\u003eA binary logistic regression analysis was conducted to identify key socio-demographic factors for geriatric depression. The Geriatric Depression Scale (GDS) was used as the binary dependent variable, while various sociodemographic factors served as independent variables. The findings, summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, indicate that age, marital status, occupation, family income, breadwinner status, smoking habits, and accommodation type have a significant association with depression within the entire group studied.\u003c/p\u003e\n\u003cp\u003eThe results reveal that, older adults in age groups 60\u0026ndash;64 (OR\u0026thinsp;=\u0026thinsp;1.769, 95% CI: 1.383\u0026ndash;2.196, p\u0026thinsp;=\u0026thinsp;0.037), 65\u0026ndash;69 (OR\u0026thinsp;=\u0026thinsp;1.579, 95% CI: 1.295\u0026ndash;2.056, p\u0026thinsp;=\u0026thinsp;0.024), and 75\u0026ndash;79 (OR\u0026thinsp;=\u0026thinsp;1.403, 95% CI: 1.128\u0026ndash;1.275, p\u0026thinsp;=\u0026thinsp;0.012) had a higher likelihood of experiencing depression compared to those aged 55\u0026ndash;59. Marital status emerged as a critical factor, with widowed individuals showing a significant risk of depression (OR\u0026thinsp;=\u0026thinsp;9.113, 95% CI: 3.44\u0026ndash;24.09, p\u0026thinsp;=\u0026thinsp;0.000) than their married counterparts. Additionally, occupation was identified as an important determinant of depression among the elderly.\u003c/p\u003e\n\u003cp\u003eThe logistic regression analysis identified several occupational categories associated with increased depression risk compared to government service employees. Individuals working in private service showed 1.901 times increase in the odds of depression (p\u0026thinsp;=\u0026thinsp;0.048), while those in business had 2.181 times increase (p\u0026thinsp;=\u0026thinsp;0.014). Notably, individuals classified within \u0026quot;other\u0026quot; occupations exhibited the highest risk, with 3.612 times increase in the odds of depression (p\u0026thinsp;=\u0026thinsp;0.003). Specifically, housewives or those categorized as \u0026quot;others\u0026quot; were 3.61 times (95% CI: 1.597\u0026ndash;5.656, p\u0026thinsp;=\u0026thinsp;0.003) more likely to experience depression than elderly government job holders. Similarly, elderly business owners were 2.18 times (95% CI: 1.382\u0026ndash;6.459, p\u0026thinsp;=\u0026thinsp;0.014) more likely to experience depression compared to government job holders.\u003c/p\u003e\n\u003cp\u003eFurthermore, family income demonstrated a significant association with geriatric depression. Elderly individuals from upper-income families were significantly less likely to be depressed (OR\u0026thinsp;=\u0026thinsp;0.330, 95% CI 0.081\u0026ndash;0.983, p\u0026thinsp;=\u0026thinsp;0.000) than those from lower-income families. Likewise, elderly individuals from middle-income families also exhibited lower odds of depression (OR\u0026thinsp;=\u0026thinsp;0.352, 95% CI 0.095\u0026ndash;0.837, p\u0026thinsp;=\u0026thinsp;0.017) compared to those from lower-income families. Religious affiliation was found to be an important determinant, with Hindu elderly individuals experiencing lower depression compared to the Muslim elderly (OR\u0026thinsp;=\u0026thinsp;0.968, 95% CI: 0.496\u0026ndash;0.973, p\u0026thinsp;=\u0026thinsp;0.023).\u003c/p\u003e\n\u003cp\u003eThe role of the family\u0026rsquo;s primary breadwinner also had a significant influence on depression risk. Elderly individuals living in households where the son was the main breadwinner were 3.77 times more likely to experience depression (95% CI: 1.335\u0026ndash;10.665, p\u0026thinsp;=\u0026thinsp;0.012) compared to those who were financially independent. Smoking habits further influenced depression prevalence among the elderly. Individuals who did not smoke had a significantly lower chance of being depressed (OR\u0026thinsp;=\u0026thinsp;0.125, 95% CI: 0.029\u0026ndash;0.538, p\u0026thinsp;=\u0026thinsp;0.005) compared to regular smokers. Additionally, living conditions played a crucial role in mental health outcomes. Elderly individuals residing in semi-pucca houses had a lower likelihood of depression (OR\u0026thinsp;=\u0026thinsp;0.284, 95% CI: 0.106\u0026ndash;0.760, p\u0026thinsp;=\u0026thinsp;0.012) compared to those in kachcha houses. The risk was even lower for those living in pucca houses (OR\u0026thinsp;=\u0026thinsp;0.086, 95% CI: 0.029\u0026ndash;0.252, p\u0026thinsp;=\u0026thinsp;0.000). The study identifies key risk factors for geriatric depression, including older age, lower family income, unmarried or widowed status, unemployment, Muslim religious affiliation, dependence on a son as the main breadwinner, and living in kachcha houses. In contrast, education, gender, living alone, and the type of family structure did not show a significant relationship with depression. [Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cstrong\u003e]\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBinary logistic regression analysis of depression with socio-demographic and socio-economic variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eAdjusted OR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e95% CI for OR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;59 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026ndash;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eService (govt.) (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eService (private)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousewives or Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Family\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNuclear (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtended\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Habit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequently\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Habit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMuslim (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHindu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccommodation Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKacha House (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSemi Paka House\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaka House\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving Alone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreadwinner\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaughter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to examine the factors contributing to depressive symptoms within the geriatric population in the Gopalganj district of Bangladesh. The present study found a GD prevalence rate of 48.6%, including 34.8% and 13.8% moderately and severely depressed, respectively. Studies conducted in rural Bangladeshi communities revealed a high depression rate of 84.3% in two southern regions of the country [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], across multiple villages in the Dhamrai Upazila, Dhaka, the rate was 60% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and 59% prevalence in rural Mirzapur, Tangail, including 42% with mild and 17% with severe depressive symptoms [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies conducted in Indian communities show depression rates between 8.9% and 62.16% [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Research across ten European nations, focusing on those 50 and older, showed depression prevalence between 18% and 37% [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The observed variations in depression prevalence across studies are likely attributable to diverse geographical locations, cultural contexts, variations in study instruments, varied study environments, different sample sizes, and diverse selection methods. Despite these differences, this study's findings align with research conducted in other countries and settings. It was confirmed that depression is universally related to older age, since this research also demonstrated a significant association between older age and a higher probability of depression. Some studies support our findings [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition to age, studies from Mexico, South Africa, India, and other low- and middle-income nations consistently show that women are more prone to depression than men [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In this study, no significant association is found between depression and gender. However, the authors did not find a contact link between gender and depression [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study's results suggest that higher education levels correlate with a reduced prevalence of depressive symptoms in elderly individuals. While univariate analysis demonstrated a significant association between increased education and lower depression rates, this relationship was not confirmed in the binary logistic regression analysis. This finding aligns with previous research, which has consistently demonstrated a link between low educational attainment and a higher likelihood of depressive symptoms [\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In Bangladesh, individuals with limited education often face financial hardship due to restricted access to well-paying jobs or positions offering retirement benefits. Thus, they endure poverty in their elderly years, and this is known to correlate with a higher incidence of depression [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To mitigate the high prevalence of depression related symptoms among elderly individuals in Bangladesh, sustained focus on increasing educational attainment is essential. Furthermore, unemployment was found to be a key factor in predicting geriatric depression in the binary logistic regression analysis. Specifically, elderly individuals those without formal employment, such as housewives or those in \"other\" occupations, experienced higher rates of depression compared to those who were employed or actively serving. This result is similar with previous research. Being unemployed causes economic uncertainty and financial dependence, making individuals more vulnerable to psychological problems such as depression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Within the Bangladeshi context, elderly individuals are often relegated to unemployment and dependence on family members, leading to financial strain and limited access to nutritious food, healthy living conditions, and necessary healthcare. The current study found that family income was significantly associated with GD. Notably, the elderly from higher-income families exhibited lower rates of depressive symptoms compared to those from lower-income families. Previous studies also supports our results [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The traditional Bangladeshi joint family structure is widely recognized as a beneficial support system for individuals who are emotionally vulnerable, financially insecure, unemployed, or elderly and infirm. Contrary to this, individuals residing in nuclear family settings have been observed to experience higher rates of depression. Our study's findings corroborate this, demonstrating a statistically significant association between living in a nuclear family and increased depression risk compared to living in a joint family. This can be attributed to the comprehensive support network provided by relatives in joint families, where responsibilities are shared across various aspects of life. Distributing responsibilities in this way improves the financial and social well-being of older family members. Prior research has consistently shown a link between family structure and depression [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study revealed that married individuals exhibited a lower likelihood of experiencing depression compared to those who were unmarried, separated, divorced, or widowed. This finding is aligned with the results of previous studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and inconsistent with the other [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A study comparing depression across Europe showed that unmarried, widowed, or divorced individuals reported higher levels of depressive symptoms than married individuals [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This result is consistent with the results of a study in China, where there was a significant increase in depression among widows (9.2% vs 4.9% married and 4.5% unmarried), never to be married or divorced again [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The death or divorce of a spouse in later life can lead to mental health issues like depression, due to the profound sense of loss and emptiness. Elderly individuals who are widowed or divorced require targeted support and monitoring to mitigate adverse health outcomes, including depression. Additionally, our study demonstrates that non-smoking elderly individuals experience lower rates of depressive symptoms compared to those who smoke. Similar to our findings, a community-based study done in India reported that depression was more common among those who were not using tobacco [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Elderly individuals who are not Muslim exhibited lower rates of depression compared to Muslim elderly, which aligns with previous research [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Depression rates among the elderly appear to be influenced by their living situation. In this study, it was observed that elderly individuals residing in basic, unrefined \"kacha\" houses had higher rates of depression compared to those living in more substantial \"semi-paka\" or \"paka\" houses. The quality of an elderly person's living environment seems to be a significant factor affecting their risk of developing depressive symptoms [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The findings indicate an association between the main breadwinner role and depression in elderly individuals, suggesting that this role is associated with poorer mental health [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The study reveals a significant prevalence of depression, particularly mild depression, among the elderly population in the Gopalganj district of Bangladesh. These findings underscore the need for targeted policies and programs designed to enhance the mental well-being of older adults in Bangladesh.\u003c/p\u003e\n\u003ch3\u003ePolicy Recommendations\u003c/h3\u003e\n\u003cp\u003eThis study emphasizes the critical need for policy interventions aimed at enhancing the mental health and general well-being of Bangladesh's elderly population, especially in urban regions like Gopalganj. A significant association was observed between depression and socio-demographic factors, such as age, education level, socioeconomic status, and occupation. Policymakers should develop targeted interventions for the well-being of the elderly population. The government can integrate mental health services into primary healthcare systems. Which ensures easy access to counseling and psychological support for the elderly population. This will benefit individuals from disadvantaged economic backgrounds and those with minimal formal education. Community-based programs can be introduced to promote social engagement. Participating in social programs, such as elderly clubs, support networks, and awareness initiatives, can help reduce isolation. Additionally, financial support systems, such as pension schemes for elderly individuals, particularly widowed and dependent women should be expanded. This will help address the economic factors contributing to depression. Public health policies must address lifestyle factors, such as smoking habits and chronic illnesses.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study's findings are subject to several limitations. Firstly, the results are specific to the Gopalganj district and may not be generalizable to the broader population of Bangladesh. Secondly, as this study used a cross-sectional approach, we cannot determine cause-and-effect relationships. Therefore, future research should prioritize longitudinal studies with sufficiently large, nationally representative samples to provide a more comprehensive understanding of geriatric depression and its associated risk factors in the elderly population in Bangladesh.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to determine the prevalence of geriatric depression and identify the socio-demographic factors associated with it among elderly individuals. The study revealed a substantial prevalence of depressive symptoms within this population in Bangladesh. Significant determinants for the development of these symptoms included older age, education level, smoking habits, family income, residence type, occupation, marital status, and breadwinner status. The results suggest that approximately half of the geriatric population in the study area reported experiencing depression, with the oldest age group (60\u0026ndash;64 years) at the highest risk. The study also indicates that mild or moderate depression is common in this population. The substantial prevalence of depression observed among elderly individuals in this study indicates a concerning rise in depression within the Bangladeshi community. Understanding the identified key factors associated with geriatric depression can inform the development of comprehensive, multi-faceted strategies aimed at reducing depression and enhancing the quality of life for this population. However, to establish a causal relationship between these risk factors and depression, further longitudinal research is necessary.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely acknowledge the elderly individuals and their families for their cooperation in data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM. K. H.:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Supervision, Writing \u0026ndash; original draft, Funding acquisition; \u003cstrong\u003eM. R. H.:\u003c/strong\u003e Data curation, Formal analysis, Software, Visualization, Writing \u0026ndash; original draft; \u003cstrong\u003eM. S. H.:\u003c/strong\u003e Data curation, Formal analysis, Software, Visualization, Writing-original draft; \u003cstrong\u003eM. H. A.:\u003c/strong\u003e Data curation, Supervision, Visualization, Validation, Writing \u0026ndash; review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the GSTU Research Cell, administered by Gopalganj Science and Technology University (GSTU), Bangladesh, under the Research and Innovation Grant from the People\u0026apos;s Republic of Bangladesh. The funding covered data collection and staff remuneration but did not include publication costs. No funding organization was involved in the research activities of this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethical Review Board of Gopalganj Science and Technology University, Bangladesh. \u0026nbsp;Under the Declaration of Helsinki for research involving human subjects, we obtained informed consent (written or verbal) from every participant by explaining the study design, purpose, procedure, risk, and benefits before the interview.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript does not contain any individual person\u0026rsquo;s data in any form (including individual details, images, or videos). Therefore, consent for publication is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Statistics, Gopalganj Science and Technology University, Gopalganj-8100, Bangladesh.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Statistics, slal University of Science and Technology, Sylhet-3114, Bangladesh.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoss RE, VanDerwerker CJ, Saladin ME, Gregory CM. 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Int J Adv Res (Indore). 2018;6:238\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geriatric depression, Elderly, Mental health, Depression, Bangladesh, Geriatric depression scale (GDS)","lastPublishedDoi":"10.21203/rs.3.rs-6336673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6336673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGeriatric depression has become a significant concern. This study addresses the growing concern regarding geriatric depression by investigating its prevalence and associated socio-demographic and socioeconomic factors among elderly individuals in the Gopalganj District of Bangladesh.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis cross-sectional study employed a multi-indicator survey to investigate various health issues among elderly individuals. The study utilized a two-stage cluster sampling method to select a sample of 400 elderly participants of both sexes. Depressive symptoms were measured using the 15-item Geriatric Depression Scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe results indicated a high prevalence of geriatric\u003cstrong\u003e \u003c/strong\u003edepression among elderly individuals, with approximately 48.8% experiencing moderate to severe depression. This study highlights high depression rates among elderly individuals, particularly among women, lower-income groups, the illiterate, and the unemployed. Education, occupation, income, smoking habits, age, and family structure play a significant role in geriatric depression. Additionally, the study revealed that social support, marital status, and living arrangements significantly influence the mental well-being of elderly individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe results of this study emphasize that policymakers must urgently address the mental health of elderly individuals, with a specific focus on women and those financially reliant on their families, especially sons.\u003c/p\u003e","manuscriptTitle":"Prevalence and Determinants of Geriatric Depression in Gopalganj, Bangladesh: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-07 09:57:37","doi":"10.21203/rs.3.rs-6336673/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2143931-d0e1-4f84-9d83-cdc2fd129d73","owner":[],"postedDate":"April 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-07T11:23:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-07 09:57:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6336673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6336673","identity":"rs-6336673","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}