
==== Front
Cureus
Cureus
2168-8184
Cureus
2168-8184
Cureus Palo Alto (CA)

10.7759/cureus.66987
Psychiatry
Public Health
Geriatrics
Unraveling the Tapestry of Depression: A Cross-Sectional Study
Muacevic Alexander
Adler John R
Gandhi Rohankumar 1
Kotecha Ilesh 1
Damor Kaushikkumar R 2
Murugan Yogesh 3
1 Community and Family Medicine, Shri Meghaji Pethraj (MP) Shah Government Medical College, Jamnagar, IND
2 Community Medicine, Gujarat Medical Education and Research Society (GMERS) Medical College, Rajpipla, IND
3 Family Medicine, Guru Gobind Singh Government Hospital, Jamnagar, IND
Yogesh Murugan psmresidentnov2022@gmail.com
16 8 2024
8 2024
16 8 e6698716 8 2024
Copyright © 2024, Gandhi et al.
2024
Gandhi et al.
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This article is available from https://www.cureus.com/articles/285347-unraveling-the-tapestry-of-depression-a-cross-sectional-study
Background: An often-occurring and severely disabling mental illness that mostly affects older people living in urban slums is depression. Developing successful therapies requires an understanding of the complex interactions between the different factors that contribute to depression in this susceptible population.

Objectives: This study aimed to find the prevalence of depression and identify the factors associated with depression in the geriatric population aged ≥60 years in the study area during the study period in Gujarat, India.

Methods: This study was carried out among 450 participants aged ≥60 years. Face-to-face interviews and standardized assessment tools, including the Geriatric Depression Scale (GDS) for depression and the Mini-Cog test for cognitive impairment, were used to collect data on depression levels, sociodemographic characteristics, behavioral factors, medical conditions, life events, and psychiatric history. Statistical analyses, including chi-squared tests, were performed to assess the associations.

Results: Significant associations were found between various factors and depression levels, which were lower education (11.11% severe depression among non-literate vs. 2.11% among literate, p<0.001) and widowhood (11.56% severe depression among widowed vs. 4.53% among married, p<0.001), which were linked to higher depression severity. Behavioral risk factors like short sleep duration (<6 hours at night: 21.71% severe depression, p<0.001), tobacco snuffing (16.24% severe depression, p<0.001), and lack of physical activity (28.71% severe depression, p<0.001) were strongly associated with increased depression. Medical conditions such as hypertension (10.36% severe depression, p<0.001) and stressful life events like family conflicts (16.67% severe depression, p<0.001) exhibited strong associations. A personal history of depression (38.82% severe depression, p<0.001) was a potent predictor.

Conclusions: The study highlights the multifaceted nature of depression in the geriatric population of the study area, underscoring the necessity of all-encompassing measures to tackle the recognized possible risk factors. The results provide valuable insights for developing targeted prevention strategies, healthcare policies, and support systems to enhance the mental well-being of this vulnerable population.

sociodemographic factors
mental health
risk factors
urban slums
elderly
depression
==== Body
pmcIntroduction

Depression affects millions of people globally, especially the elderly, and is a common and crippling mental health issue. A person's aging process can be exacerbated or precipitated by a variety of obstacles, such as deteriorating physical health, losing autonomy, a lack of social interaction, and life-changing events. Given its links to greater rates of illness and death, worse quality of life, and increased functional impairment, the burden of depression among the elderly is especially worrisome [1,2].

Globally, the depression prevalence among the elderly population varies widely, with estimates ranging from 4.7% to 16% in community-dwelling older adults [1,2]. However, because of the cumulative impact of socioeconomic status, suffering, inadequate access to healthcare services, an increased level of stress and adversity, and living in slum communities or financially disadvantaged areas, some subgroups of the elderly population may be more susceptible to depression.

Elderly depression has been linked to a number of causes. Age, gender, marital status, degree of education, and socioeconomic status are sociodemographic characteristics that have been repeatedly linked to the occurrence and severity of depression [3,4]. Furthermore, a higher incidence of depression in older persons has been associated with behavioral risk factors, such as inactivity, substance abuse (alcohol and tobacco), and sleep disorders [5-7].

Chronic medical illnesses, including diabetes, musculoskeletal disorders, cardiovascular diseases, and hypertension, have been demonstrated to have a reciprocal association with depression, whereby each condition may exacerbate the other [8,9]. In addition, important life events that occur in the elderly population, like the death of a loved one, financial hardships, or substantial life changes, can trigger depressive episodes [3,10].

Significantly, it has been found that a history of depression or other mental conditions in oneself or one's family can strongly predict the presence of depressive symptoms in an older adult [10,11]. This emphasizes how crucial it is to evaluate and treat depression in the geriatric population while taking into account the person's past mental health history as well as any possible hereditary or environmental predispositions.

Despite the expanding corpus of studies on depression in the senior citizen population, there remains a need for comprehensive studies that examine the interplay of various sociodemographic, behavioral, medical, and psychosocial factors in diverse settings, particularly among vulnerable populations residing in urban slums or low-resource areas. In order to improve the mental health and general quality of life of the senior population, specific measures and healthcare strategies can be informed by an understanding of the individual risk factors and factors that contribute to the onset of depression.

The current study sought to determine whether different risk factors for behavioral problems, medical diseases, life events, historical history of depression or psychiatric issues, and other sociodemographic variables were associated with higher levels of depression in older people living in urban slums. In order to provide important insights into the intricate interactions between determinants that lead to depression in this susceptible population, this study examined all of these variables in a community-based setting. The findings will ultimately help develop early identification, mitigation, and efficient management strategies for depressive disorders in the elderly.

Materials and methods

Study design and setting

The research was planned to be a cross-sectional survey done in urban slums within the study area, using a community-based approach in Gujarat, India. The study duration spanned from November 2021 to December 2022.

Eligibility criteria

Inclusion Criteria

The geriatric population aged ≥60 years, residing in the study area for the past year, and providing consent to participate in the study were included.

Exclusion Criteria

Those individuals who did not give consent, those with a Mini-Cog test score <3 (suggestive of cognitive impairment or dementia), and those with pre-diagnosed psychiatric illnesses other than depression (such as anxiety, paranoid disorders, delusions, hallucinations, schizophrenia, etc.) were excluded. These exclusions were made to ensure reliable data collection and assessment of depression.

Sampling technique

At a 95% confidence level and a 5% absolute allowed error, the sample size was determined using an anticipated prevalence rate of 31% for geriatric depression. An estimated 342 individuals would be the required minimum sample size; however, to consider a 20% non-response rate, the sample size was expanded to 410 subjects [12].

The sampling technique involved a simple random sampling method, with the house as the primary sampling unit. All 27 urban slums in the study area were included, and from each slum, 16 elderly individuals were selected. The slums were divided into four quadrants, and in each quadrant, houses were chosen at random using a random number table. Using the Mini-Cog test, all eligible elderly people in the chosen homes were evaluated for cognitive impairment and, if they satisfied the eligibility requirements, were enrolled in the study.

Sampling method

In-person interviews with the qualified senior participants were done at their residences as part of the data-gathering procedure. Prior to starting the interviews, topic specialists' opinions were sought in order to determine the questionnaire's content validity. Additionally, a pilot study was conducted in an area other than the main study area to standardize the questionnaire and identify potential operational difficulties.

The questionnaire was rendered into the regional language, Gujarati, and then back into English to guarantee that the questions' meanings stayed the same. The participants' literacy status was questioned during the interviews, and literate individuals were defined as those who could read and write with understanding in any language.

Data collection tool

The data collection tool was a pre-tested, semi-structured questionnaire designed to gather information from the elderly participants. The questionnaire consisted of the following components: (1) sociodemographic factors such as age, sex, religion, education, occupation, marital status, income, socioeconomic status (modified Brahm Govind (BG) Prasad classification) [13], house ownership (own/rented), family size, number of children, type of family, financial dependency, and health insurance status; (2) assessment scales, namely, (a) Mini-Cog test to assess cognitive impairment [14] and (b) GDS-30 Scale (Geriatric Depression Scale using 30 items) to assess depression levels (score: 0-9: no depression; 10-19: mild depression; and 20-30: severe depression) [15]; (3) behavioral factors such as sleep duration, alcohol consumption, tobacco use (chewing and snuffing), smoking habits, and physical activity levels; (4) medical conditions such as information on chronic diseases or conditions the participant was suffering from; (5) life events such as significant life events experienced by the participant in the past year; and (6) history of depression such as information on the participants and their family members' history of depression or other psychiatric problems.

Data analysis

Microsoft Excel was used for data entry, and IBM SPSS Statistics for Windows, V. 22.0 (IBM Corp., Armonk, NY) was used for statistical analysis. To analyze the data, appropriate statistical tests were applied. For finding the association between various sociodemographic variables, behavioral risk factors, medical conditions, life events, and depression levels, the chi-squared test was used. The significance of the associations was determined based on the calculated p-values. The data was presented and summarized using frequencies and percentages. A significance level of p-value <0.05 was applied.

Ethical consideration

Ethical considerations were duly addressed, such as obtaining approval from the Institutional Ethics Committee of Shri Meghaji Pethraj (MP) Shah Government Medical College and Guru Gobind Singh Government Hospital, Jamnagar (approval number: 123/05/2021), ensuring voluntary participation, maintaining confidentiality, and obtaining informed consent.

Results

Various sociodemographic risk factors and depression were described in Table 1. It shows the association between various sociodemographic risk factors and depression among the study participants. Age was found to have a statistically significant association (p=0.04) with depression; those aged 75-84 years (15.25%) and ≥85 years (11.11%) were more likely to experience severe depression than those aged 60-74 years (5.77%). Depression and education level were also significantly associated (p<0.001), with a higher incidence of severe depression among illiterate people (11.11%) than among literate people (2.11%). Marital status showed a highly significant association with depression (p<0.001), with the highest proportion of severe depression observed among unmarried, separated, or divorced individuals (18.75%), followed by widows and widowers (11.56%) and married individuals (4.53%). The study found a significant association between depression and the monthly income of the family (p<0.001) as well as socioeconomic level (p=0.024). Notably, individuals with lower incomes and socioeconomic positions were more likely to experience severe depression. A higher percentage of people who live in rented homes (18.92%) than in homes they own (6.29%) experience severe depression. The ownership status of a home was also substantially correlated with depression (p=0.0067). Depression had a significant association with the number of children (p=0.0267), with a greater rate of severe depression (12.33%) among those without children than among those with 1-2 children (6.49%) or more than two children (5.80%). Furthermore, a higher number of those without health insurance (10.21%) experienced severe depression compared to those with health insurance (4.18%), and having health insurance was substantially associated with lower levels of depression (p=0.03).

Table 1 Association between sociodemographic characteristics and depression

p<0.05: significant; p<0.001: highly significant; BG: Brahm Govind

Variables	Category	Depression	Total 	Chi-squared (ꭓ2)value	P-value (p)	
No depression 	Mild depression 	Severe depression 	
Age (in years)	60-74	245 (67.31%)	98 (26.92%)	21 (5.77%)	364 (80.89%)	ꭓ2=12.387	p=0.04	
75-84	30 (50.85%)	20 (33.90%)	9 (15.25%)	59 (13.11%)	
≥85	13 (48.15%)	11 (40.74%)	3 (11.11%)	27 (6.00%)	
Gender	Male	126 (63.96%)	61 (30.96%)	10 (5.08%)	197 (43.78%)	ꭓ2=3.08	p=0.21	
Female	162 (64.03%)	68 (26.88%)	23 (9.09%)	253 (56.22%)	
Religion	Hindu	212 (65.84%)	85 (26.40%)	25 (7.76%)	322 (71.55%)	ꭓ2=3.657 (with Yates' correction)	p=0.45	
Muslim	74 (61.16%)	40 (33.06%)	7 (5.78%)	121 (26.89%)	
Christian	2 (28.57%)	4 (57.14%)	1 (14.28%)	7 (1.56%)	
Education (literacy)	Literate	137 (72.49%)	48 (25.40%)	4 (2.11%)	189 (42.00%)	ꭓ2=16.976	p<0.001	
Not literate	151 (57.86%)	81 (31.03%)	29 (11.11%)	261 (58.00%)	
Occupation (employment)	Employed	109 (69.40%)	42 (26.12%)	7 (4.48%)	158 (35.11%)	ꭓ2=4.113	p=0.13	
Unemployed	179 (61.30%)	87 (29.79%)	26 (8.91%)	292 (64.89%)	
Marital status	Married	213 (74.22%)	61 (21.25%)	13 (4.53%)	287 (63.78%)	ꭓ2=39.878 (with Yates' correction)	p<0.001	
Widow/widower	73 (49.66%)	57 (38.78%)	17 (11.56%)	147 (32.67%)	
Unmarried/separated/divorced	2 (12.50%)	11 (68.75%)	3 (18.75%)	16 (3.55%)	
Family monthly income (₹=in rupees)	≤₹ 10,000	167 (58.19%)	91 (31.71%)	29 (10.10%)	287 (63.78%)	ꭓ2=15.035	p<0.001	
>₹ 10,000	121 (74.23%)	68 (23.31%)	4 (2.46%)	163 (36.22%)	
Socioeconomic status (modified BG Prasad's classification 2022)	I (upper)	14 (87.50%)	2 (12.50%)	0 (0.00%)	16 (3.55%)	ꭓ2=17.64 (with Yates' correction)	p=0.024	
II (upper middle)	31 (77.50%)	6 (15.00%)	3 (7.50%)	40 (8.89%)	
III (middle)	78 (72.90%)	24 (22.43%)	5 (4.67%)	107 (23.78%)	
IV (lower middle)	90 (60.40%)	51 (34.23%)	8 (5.37%)	149 (33.11%)	
V (lower)	75 (54.35%)	46 (33.33%)	17 (12.32%)	138 (30.67%)	
House ownership status	Own	271 (65.62%)	116 (28.09%)	26 (6.29%)	413 (91.78%)	ꭓ2=10.021	p=0.007	
Rented	17 (45.95%)	13 (35.13%)	7 (18.92%)	37 (8.22%)	
Family size	1-5	161 (65.72%)	64 (26.12%)	20 (8.16%)	245 (54.45%)	ꭓ2=3.417	p=0.69	
6-10	105 (62.50%)	51 (30.36%)	12 (7.14%)	168 (37.33%)	
>10	22 (59.46%)	14 (37.84%)	1 (2.70%)	37 (8.22%)	
No. of children	0	39 (53.42%)	25 (34.25%)	9 (12.33%)	73 (16.22%)	ꭓ2=10.987	p=0.03	
1-2	211 (68.51%)	77 (25.00%)	20 (6.49%)	308 (68.45%)	
>2	38 (55.07%)	27 (39.13%)	4 (5.80%)	69 (15.33%)	
Type of family	Nuclear	102 (62.96%)	47 (29.01%)	13 (8.03%)	162 (36.00%)	ꭓ2=2.497	p=0.65	
Joint	89 (63.57%)	44 (31.43%)	7 (5.00%)	140 (31.11%)	
Three generation	97 (65.54%)	38 (25.68%)	13 (8.78%)	148 (32.89%)	
Extent of financial dependency	Independent	49 (68.05%)	20 (27.78%)	3 (4.17%)	72 (16.00%)	ꭓ2=2.753	p=0.59	
Partially dependent	71 (65.14%)	32 (29.36%)	6 (5.50%)	109 (24.22%)	
Totally dependent	168 (62.45%)	77 (28.63%)	24 (8.92%)	269 (59.78%)	
Type (on whom) of financial dependency	Self	49 (68.05%)	20 (27.78%)	3 (4.17%)	72 (16.00%)	ꭓ2=7.241 (with Yates' correction)	p=0.29	
Spouse	21 (70.00%)	6 (20.00%)	3 (10.00%)	30 (6.67%)	
Children	209 (64.51%)	92 (28.40%)	23 (7.09%)	324 (72.00%)	
Distant family members	9 (37.50%)	11 (45.83%)	4 (16.67%)	24 (5.33%)	
Health insurance	With health insurance	146 (67.91%)	60 (27.91%)	9 (4.18%)	215 (47.78%)	ꭓ2=6.626	p=0.03	
No health insurance	142 (60.43%)	69 (29.36%)	24 (10.21%)	235 (52.22%)	

Table 2 shows the various behavioral risk factors and depression. The associations between several behavioral risk variables and study participants' levels of depression are analyzed in this table. Sleep duration, both during the day and at night, showed a highly significant association with depression (p<0.001 for both). A higher proportion of individuals with sleep durations of less than one hour during the day (13.82%) and less than six hours at night (21.71%) experienced severe depression compared to those with longer sleep durations. Also, there was a significant association (p<0.001) between tobacco addiction and depression. Among those who snuffed tobacco, the prevalence of severe depression was highest (16.24%), followed by chewers (7.19%) and non-users (1.20%). On the other hand, there was a stronger correlation between smoking addiction and depression (p<0.001) among smokers, with 11.84% reporting severe depression compared to 6.42% among non-smokers. Physical activity/exercise for at least 30 minutes per day showed a highly significant association with depression (p<0.001). Serious depression was more common in those who did not exercise (28.71%) than in those who worked out three times a week (3.75%) or every day (0.37%). Depression and alcohol addiction were also significantly associated (p<0.001) with a larger percentage of alcohol addicts (11.25%) reporting severe depression than non-addicts (6.49%).

Table 2 Various behavioral risk factors and depression

p<0.05: significant; p<0.001: highly significant

Behavioral risk factors	Category	Depression	Total 	Chi-squared (ꭓ2) value	P-value (p)	
No depression 	Mild depression 	Severe depression 	
Sleep duration (in hours) in a daytime	<1 hr	71 (32.72%)	116 (53.46%)	30 (13.82%)	217 (48.22%)	ꭓ2=171.762 (with Yates' correction)	p<0.001	
1-3 hrs	168 (92.82%)	10 (5.52%)	3 (1.66%)	181 (40.22%)	
>3 hrs	49 (94.23%)	3 (5.77%)	0 (0.00%)	52 (11.56%)	
Sleep duration (in hours) in a nighttime	<6 hrs	4 (3.10%)	97 (75.19%)	28 (21.71%)	129 (28.67%)	ꭓ2=299.156 (with Yates' correction)	p<0.001	
6-8 hrs	25 (62.50%)	12 (30.00%)	3 (7.50%)	40 (8.89%)	
>8 hrs	259 (92.17%)	20 (7.12%)	2 (0.71%)	281 (62.44%)	
Tobacco addiction	Chewing	92 (55.09%)	63 (37.72%)	12 (7.19%)	167 (37.11%)	ꭓ2=99.551	p<0.001	
Snuffing	44 (37.61%)	54 (46.15%)	19 (16.24%)	117 (26.00%)	
No	152 (91.57%)	12 (7.23%)	2 (1.20%)	166 (36.89%)	
Smoking addiction	Yes	28 (36.84%)	39 (51.32%)	9 (11.84%)	76 (16.89%)	ꭓ2=29.437	p<0.001	
No	260 (69.52%)	90 (24.06%)	24 (6.42%)	374 (83.11%)	
Physical activity/exercise for at least 30 minutes/day	Daily	252 (93.68%)	16 (5.95%)	1 (0.37%)	269 (59.78%)	ꭓ2=296.458 (with Yates' correction)	p<0.001	
Thrice weekly	32 (40.00%)	45 (56.25%)	3 (3.75%)	80 (17.78%)	
No	4 (3.96%)	68 (67.33%)	29 (28.71%)	101 (22.44%)	
Alcohol addiction	Yes	18 (22.50%)	53 (66.25%)	9 (11.25%)	80 (17.78%)	ꭓ2=76.16	p<0.001	
No	270 (72.97%)	76 (20.54%)	24 (6.49%)	370 (82.22%)	

Table 3 shows the known cases of various medical conditions and depression. This table examines the association between known cases of various medical conditions and depression levels among the study participants. Depression was significantly associated with hypertension (p<0.001), asthma (p<0.001), arthritis/musculoskeletal issues (p<0.001), vision impairment (p<0.001), any cardiac illness (p<0.001), chronic constipation (p<0.001), and other unidentified conditions (p<0.001). For these conditions, a higher proportion of individuals with the condition experienced mild or severe depression compared to those without the condition. As an illustration, among individuals with hypertension, the percentages of mild and severe depression were 39.84% and 10.36%, respectively, whereas among those without hypertension, they were lower. Furthermore, the absence of any comorbidities was found to be substantially linked with lower levels of depression (p<0.001), as all individuals in this group reported not experiencing any depression.

Table 3 Known cases of various medical conditions and depression

p<0.05: significant; p<0.001: highly significant

Known cases of medical conditions (multiple responses)	Depression	Total 	Chi-squared (ꭓ2)value	P-value (p)	
No depression	Mild depression	Severe depression	
Diabetes	42 (54.55%)	28 (36.36%)	7 (9.09%)	77/450 (17.11%)	ꭓ2=3.609	p=0.16	
Hypertension	125 (49.80%)	100 (39.84%)	26 (10.36%)	251/450 (55.78%)	ꭓ2=49.685	p<0.001	
Asthma	5 (21.74%)	15 (65.22%)	3 (13.04%)	23/450 (5.11%)	ꭓ2=19.138 (with Yates' correction)	p<0.001	
Arthritis/musculoskeletal problems	166 (52.70%)	119 (37.78%)	30 (9.52%)	315/450 (70.00%)	ꭓ2=58.231	p<0.001	
Visually impaired	8 (25.00%)	19 (59.38%)	5 (15.62%)	32/450 (7.11%)	ꭓ2=19.88	p<0.001	
Hearing impaired	15 (60.00%)	6 (24.00%)	4 (16.00%)	25/450 (5.56%)	ꭓ2=2.979	p=0.23	
Any cardiac disease	5 (13.16%)	22 (57.89%)	11 (28.95%)	38/450 (8.44%)	ꭓ2=50.506	p<0.001	
Chronic constipation	4 (25.00%)	10 (62.50%)	2 (12.50%)	16/450 (3.56%)	ꭓ2=8.888 (with Yates' correction)	p<0.001	
Communicable disease (TB/HIV, etc.)	9 (50.00%)	9 (50.00%)	0 (0.00%)	18/450 (4.00%)	ꭓ2=3.152 (with Yates' correction)	p=0.21	
Any other	13 (21.31%)	35 (57.38%)	13 (21.31%)	61/450 (13.56%)	ꭓ2=59.184	p<0.001	
No comorbidities	80 (100.00%)	0 (0.00%)	0 (0.00%)	80/450 (17.78%)	ꭓ2=50.506 (with Yates' correction)	p<0.001	
Comorbidities	208 (56.22%)	129 (34.86%)	33 (8.92%)	370/450 (82.22%)	

Table 4 shows the various life events in the past one year and depression. This table shows the relationship between the research participants' depression levels and several life events that occurred within the last year. Each of the listed life events, family conflicts (p<0.001), unemployment of self or children (p<0.001), illness (p<0.001), illness of family members (p<0.001), family member or close relative death (p<0.001), big purchases or home construction (p<0.001), and financial problems or losses (p<0.001), was significantly linked to depression. In comparison to those who did not experience the event, there is a greater percentage of people who did report mild or severe depression for each of these life events. For instance, 57.78% of people with mild depression and 16.67% of people with severe depression were among those who encountered family conflicts; the equivalent percentages were lower among those who did not. Overall, having any of the life events on the list during the previous year was substantially linked to greater levels of depression (p<0.001), whereas having none of these events was linked to lower levels of depression.

Table 4 Various life events in the past year and depression

p<0.05: significant; p<0.001: highly significant

Various life events in the past one year (multiple responses)	Depression	Total	Chi-squared (ꭓ2)value	P-value (p)	
No depression 	Mild depression 	Severe depression 	
Conflicts in family	23 (25.55%)	52 (57.78%)	15 (16.67%)	90/450 (20.00%)	ꭓ2=72.601	p<0.001	
Unemployment of self or children	6 (20.00%)	16 (53.33%)	8 (26.67%)	30/450 (6.67%)	ꭓ2=32.929	p<0.001	
Illness of self	52 (30.59%)	88 (51.76%)	30 (17.65%)	170/450 (37.78%)	ꭓ2=133.786 (with Yates' correction)	p<0.001	
Illness of family members	69 (53.08%)	43 (33.08%)	18 (13.84%)	130/450 (28.89%)	ꭓ2=15.223	p<0.001	
Death of family members	15 (31.92%)	24 (51.06%)	8 (17.02%)	47/450 (10.44%)	ꭓ2=24.342	p<0.001	
Death of close relatives	8 (36.36%)	10 (45.46%)	4 (18.18%)	22/450 (4.89%)	ꭓ2=8.747 (with Yates' correction)	p=0.013	
Big purchase or construction of home	68 (43.31%)	67 (42.68%)	22 (14.01%)	157/450 (34.89%)	ꭓ2=47.301	p<0.001	
Financial problems or loss	185 (55.56%)	116 (34.83%)	32 (9.61%)	333/450 (74.00%)	ꭓ2=40.318 (with Yates' correction)	p<0.001	
Any of the above	241 (60.40%)	126 (31.58%)	32 (8.02%)	399/450 (97.11%)	ꭓ2=17.69 (with Yates' correction)	p<0.001	
None of the above	47 (92.16%)	3 (5.88%)	1 (1.96%)	51/450 (2.89%)	

Table 5 shows past history of depression/psychiatric problems and depression. This table examines the relationship between research participants' or their families' past history with depression or other mental issues and depression. A past history of depression (p<0.001), receiving treatment for depression in the past (p<0.001), any other psychiatric problem in the past (p<0.001), and having parents, siblings, or children who suffered from depression in the past (p<0.001) were all highly significantly associated with current depression levels. Individuals who had a positive history of any of these characteristics were more likely than those who had not to report having mild to severe depression. For instance, among individuals who had previously experienced depression, 60% had mild depression and 38.82% had severe depression; in contrast, the comparable percentages were significantly lower among those who had never had depression. In general, higher levels of current depression were substantially (p<0.001) associated with any of the listed factors related to a past history of depression or psychiatric issues, whereas lower levels of depression were connected with none of these factors.

Table 5 Past history of depression/psychiatric problems and depression

p<0.05: significant; p<0.001: highly significant

History of depression/psychiatric problems (multiple responses)	Depression	Total 	Chi-squared (ꭓ2)value	P-value (p)	
No depression 	Mild depression 	Severe depression 	
Did you suffer from depression in the past?	1 (1.18%)	51 (60.00%)	33 (38.82%)	85/450 (18.89%)	ꭓ2=234.437 (with Yates' correction)	p<0.001	
Did you take treatment for depression in the past?	0 (0.00%)	6 (50.00%)	6 (50.00%)	12/450 (2.67%)	ꭓ2=33.083 (with Yates' correction)	p<0.001	
Any other psychiatric problems in the past?	0 (0.00%)	8 (38.10%)	13 (61.90%)	21/450 (4.67%)	ꭓ2=95.269 (with Yates' correction)	p<0.001	
Did your parents, siblings, or children suffer from depression in the past?	0 (0.00%)	10 (55.56%)	8 (44.44%)	18/450 (4.00%)	ꭓ2=44.923 (with Yates' correction)	p<0.001	
Any of the above	1 (1.15%)	53 (60.92%)	33 (37.93%)	87/450 (19.33%)	ꭓ2=235.676 (with Yates' correction)	p<0.001	
None of the above	287 (79.06%)	76 (20.94%)	0 (0.00%)	363/450 (80.67%)	

Discussion

This study examined the relationships between a range of sociodemographic risk factors, behavioral risk factors, medical conditions, events in life, and a previous history of depression or psychiatric issues with the depression levels experienced by older people living in the study area. The findings showed a significant relationship between several risk factors and the geriatric population's prevalence of depression in the aforementioned study area.

Sociodemographic factors and depression

In line with earlier research, older people had a higher prevalence of depression [16], and in the geriatric population, there was a strong relationship found between greater levels of depression and older age, lesser educational attainment, widowhood/divorce, lower income, and lower socioeconomic status [17,18]. These findings highlight the vulnerability of older adults, particularly those with limited resources and social support, to developing depression. Additionally, the lack of health insurance coverage was linked to increased severity of depression, which aligns with the findings of Langa et al. [19] and emphasizes the importance of access to healthcare services in mitigating mental health issues among the elderly.

Behavioral risk factors and depression

The study found a significant association between depression levels and a variety of behavioral risk factors. Lower levels of depression had a significant association with shorter sleep durations, both at night and during the day, which supports the findings of earlier research [20,21]. Tobacco and smoking addictions were also linked to increased depression severity, consistent with previous research [22,23]. In line with the conclusions of Daskalopoulou et al., a substantial risk factor for severe depression was shown to be a lack of physical activity [24], emphasizing the importance of promoting an active lifestyle among the elderly [25]. Alcohol addiction was another notable risk factor for depression, as reported by previous studies [20,21,26].

Medical conditions and depression

Higher levels of depression were significantly associated with a number of medical illnesses, including chronic constipation, asthma, heart disease, arthritis/musculoskeletal issues, visual impairment, and hypertension. These results align with earlier research [27-29] and draw attention to the reciprocal relationship that exists between elderly people's physical and mental health. It is even more crucial to address and manage chronic illnesses in this age group because the lack of comorbidities was linked to lower levels of depression.

Life events and depression

The research findings indicate a noteworthy association between a range of life events that occurred within the last year and increased levels of depression. An increased risk of depression has been associated with family conflicts, unemployment, illness (oneself or family members), death of family members or close relatives, big purchases or construction, and financial problems. These results are consistent with earlier research [30] and highlight how difficult life circumstances affect older people's mental health.

Limitations

Even though this study offers insightful information about the risk factors linked to depression in older people living in urban slums, there are several limitations that must be acknowledged.

Firstly, the study's cross-sectional design makes it impossible to demonstrate a causal association between the variables under investigation and depression severity. To investigate the temporal correlations and any reciprocal interactions between these variables and depressed symptoms over time, longitudinal research is necessary.

Secondly, self-reported data, on which the study relied on, may be prone to recall bias, especially for older people who may have cognitive impairment or who have had important previous life events. The accuracy of the data gathered may be improved by the use of clinical examinations and standardized assessment instruments.

Additionally, the study's exclusive focus on urban slum communities may have limited the findings' applicability to other senior groups living in other socioeconomic or geographic contexts. To investigate the possible differences in risk variables and their influence on depression in other population groups, more research is required.

Another drawback is the possible impact of unmeasured confounding factors, which could contribute to the onset and course of depression in older people. Examples of these factors include social support systems, coping strategies, and religious or cultural views. To obtain a fuller knowledge of the multidimensional complexity of depression within this subsection of the population, future studies could take these aspects into account.

Recommendations

Numerous suggestions for additional research, medical practice, and governmental initiatives can be made in light of the research's limitations and findings. First, it is important to carry out prospective and longitudinal research to determine the causal links among the identified risk variables and the initial or ongoing development of depression in elderly individuals. Second, comprehensive assessments that incorporate clinical evaluations, objective measures, and validated screening tools should be employed to enhance the accuracy of data collection and minimize potential biases. Third, research efforts should be expanded to include diverse elderly populations from various socioeconomic and geographic settings to improve the generalizability of findings and identify potential variations in risk factors and their impact on depression. Fourth, future research should examine the impact of undetected confounding factors on depression in older adults, including coping strategies, networks for social support, and religious or cultural views. Fifth, the modifiable factors influencing risk found in this study should be addressed by interventions and preventive measures, such as encouraging physical exercise, treating substance misuse, and offering assistance in managing long-term medical issues and major life events. Sixth, healthcare policies and programs should prioritize mental health services for the elderly, with a particular focus on vulnerable populations residing in urban slums or low-resource areas, ensuring access to screening, early intervention, and appropriate treatment options. Lastly, collaborations between researchers, healthcare professionals, policymakers, and community organizations should be fostered to develop comprehensive and culturally sensitive approaches to addressing depression in the elderly population.

Conclusions

The current study contributes valuable insights into the factors associated with depression among elderly residents in urban slums, highlighting significant relationships between various sociodemographic, behavioral, medical, and historical factors and the prevalence and severity of depression in this population. The findings underscore the importance of addressing depression in older adults through a multifaceted approach, considering the complex interplay of risk factors. While limitations such as the cross-sectional design and reliance on self-reported data should be acknowledged, the results provide a foundation for developing targeted interventions, preventive strategies, and healthcare policies aimed at improving the mental well-being of vulnerable elderly populations. Future research should focus on longitudinal studies to establish causal relationships, explore potential confounding factors, and examine diverse elderly populations to enhance the generalizability and depth of understanding in this crucial area of public health.

Disclosures

Author Contributions

Human subjects: Consent was obtained or waived by all participants in this study. Institutional Ethics Committee of Shri Meghaji Pethraj (MP) Shah Government Medical College and Guru Gobind Singh Government Hospital, Jamnagar issued approval 123/05/2021.

Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following:

Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work.

Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work.

Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Concept and design:  Rohankumar Gandhi, Ilesh Kotecha, Kaushikkumar R. Damor, Yogesh Murugan

Acquisition, analysis, or interpretation of data:  Rohankumar Gandhi, Ilesh Kotecha, Kaushikkumar R. Damor, Yogesh Murugan

Drafting of the manuscript:  Rohankumar Gandhi, Ilesh Kotecha, Kaushikkumar R. Damor, Yogesh Murugan

Critical review of the manuscript for important intellectual content:  Rohankumar Gandhi, Ilesh Kotecha, Kaushikkumar R. Damor, Yogesh Murugan

Supervision:  Rohankumar Gandhi, Ilesh Kotecha, Kaushikkumar R. Damor, Yogesh Murugan
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References

1 Prevalence of minor depression in elderly persons with and without mild cognitive impairment: a systematic review J Affect Disord Polyakova M Sonnabend N Sander C Mergl R Schroeter ML Schroeder J Schönknecht P 28 38 152-154 2014 24103852
2 Prevalence and predictors of depression in populations of elderly: a review Acta Psychiatr Scand Djernes JK 372 387 113 2006 16603029
3 Depression and its socio-demographic correlates among urban slum dwellers of North India: a cross-sectional study J Family Med Prim Care Pawar N Kumar N Vikram A Sembiah S Rajawat G 2369 2376 11 2022 36119324
4 Relation between depression and sociodemographic factors Int J Ment Health Syst Akhtar-Danesh N Landeen J 4 1 2007 18271976
5 The burden of loneliness: implications of the social determinants of health during COVID-19 Psychiatry Res McQuaid RJ Cox SM Ogunlana A Jaworska N 113648 296 2021 33348199
6 Comparative assessment of psychosocial status of elderly in urban and rural areas, Karnataka, India J Family Med Prim Care Akila GV Arvind BA Isaac A 2870 2876 8 2019 31681658
7 The association of cigarette smoking with depression and anxiety: a systematic review Nicotine Tob Res Fluharty M Taylor AE Grabski M Munafò MR 3 13 19 2017 27199385
8 Risk factors for depression among elderly community subjects: a systematic review and meta-analysis Am J Psychiatry Cole MG Dendukuri N 1147 1156 160 2003 12777274
9 Prevalence of depression among the elderly (60 years and above) population in India, 1997-2016: a systematic review and meta-analysis BMC Public Health Pilania M Yadav V Bairwa M 832 19 2019 31248394
10 Prevalence and predictors of depression in community-dwelling elderly in rural Haryana, India Indian J Community Med Pilania M Bairwa M Khurana H Kumar N 13 18 42 2017 28331248
11 Prevalence of geriatric depression in the Kavre district, Nepal: findings from a cross sectional community survey BMC Psychiatry Manandhar K Risal A Shrestha O Manandhar N Kunwar D Koju R Holen A 271 19 2019 31481037
12 Prevalence and determinants of depression among old age: a systematic review and meta-analysis Ann Gen Psychiatry Zenebe Y Akele B W/Selassie M Necho M 55 20 2021 34922595
13 Updated BG Prasad's socioeconomic status classification for the year 2023 Indian J Community Med Ghodke M 934 936 48 2023 38249702
14 Mini-Cog for the detection of dementia within a primary care setting Cochrane Database Syst Rev Seitz DP Chan CC Newton HT 0 7 2021
15 How to try this: the Geriatric Depression Scale: short form Am J Nurs Greenberg SA 60 69 107 2007
16 A study on prevalence and factors associated with depression among elderly residing in tenements under resettlement scheme, Kancheepuram District, Tamil Nadu J Midlife Health Kumar BM Raja TK Liaquathali F Maruthupandian J Raja PV 137 143 12 2021 34526749
17 The impact of multimorbidity on adult physical and mental health in low- and middle-income countries: what does the study on global ageing and adult health (SAGE) reveal? BMC Med Arokiasamy P Uttamacharya U Jain K 178 13 2015 26239481
18 Depression in an older adult rural population in India MEDICC Rev Sinha SP Shrivastava SR Ramasamy J 41 44 15 2013 24253350
19 Extent and cost of informal caregiving for older Americans with symptoms of depression Am J Psychiatry Langa KM Valenstein MA Fendrick AM Kabeto MU Vijan S 857 863 161 2004 15121651
20 Association between depressive symptoms and sleep disturbances in community-dwelling older men J Am Geriatr Soc Paudel ML Taylor BC Diem SJ Stone KL Ancoli-Israel S Redline S Ensrud KE 1228 1235 56 2008 18482297
21 Prevalence of depression and associated risk factors among the elderly in urban and rural field practice areas of a tertiary care institution in Ludhiana Indian J Public Health Sengupta P Benjamin AI 3 8 59 2015 25758724
22 An overview of Indian research in depression Indian J Psychiatry Grover S Dutt A Avasthi A 0 88 52 2010
23 Investigating the possible causal association of smoking with depression and anxiety using Mendelian randomisation meta-analysis: the CARTA consortium BMJ Open Taylor AE Fluharty ME Bjørngaard JH 0 4 2014
24 Physical activity and healthy ageing: a systematic review and meta-analysis of longitudinal cohort studies Ageing Res Rev Daskalopoulou C Stubbs B Kralj C Koukounari A Prince M Prina AM 6 17 38 2017 28648951
25 Association between physical activity and risk of depression: a systematic review and meta-analysis JAMA Psychiatry Pearce M Garcia L Abbas A 550 559 79 2022 35416941
26 The prevalence of depression among the elderly people living in rural Wardha Ind Psychiatry J Goswami S Deshmukh PR 90 95 30 2021 34483530
27 Prevalence of depression and anxiety among elderly primary care patients in Palestine Front Psychiatry Maraqa BN Nazzal Z Hamshari S Alutt B Rishmawi E Qawasmeh A 1291829 14 2023 38312914
28 Contribution of chronic diseases to disability in elderly people in countries with low and middle incomes: a 10/66 Dementia Research Group population-based survey Lancet Sousa RM Ferri CP Acosta D 1821 1830 374 2009 19944863
29 Chronic diseases and risk for depression in old age: a meta-analysis of published literature Ageing Res Rev Huang CQ Dong BR Lu ZC Yue JR Liu QX 131 141 9 2010 19524072
30 Prevalence and socioeconomic impact of depressive disorders in India: multisite population-based cross-sectional study BMJ Open Arvind BA Gururaj G Loganathan S 0 9 2019
