Urban-rural and Gender Differential in Depressive Symptoms Among Older Adults in India

doi:10.21203/rs.3.rs-716532/v1

Background: To date, evidence remained inconclusive explaining rural-urban and male-female differential in depression. Unlike other previous research on the association of several risk factors with depressive symptoms among the elderly, this study focussed on the socio-economic status-related inequality in the prevalence of depression among the elderly along with focussing urban-rural and male-female gradients of depression among the elderly.

Methods: This study used data from Longitudinal Ageing Study in India (LASI) wave-I, 2017-18, survey. The outcome variable for this study was self-reported depression. Bivariate analysis was used to understand the prevalence by sociodemographic clusters. Fairlie decomposition analysis has been done to measures rural-urban inequalities for depression among older men and women.

Results: Results found that around 22 percent of urban elderly and 17 percent of rural elderly reported depression. A higher proportion of female elderly (22.6% vs. 18.4%) reported depression than male elderly. Almost one in every five elderly (20.6%) reported depression in India. The results found that a higher percentage of women in rural and urban areas reported depression than their male counterparts. While examining SES-related inequality in the prevalence of depression, education was a significant factor explaining the SES-related inequality in the prevalence of depression among female elderly and not in male elderly.

Conclusion: Given the large proportion of elderly reporting depression, this study highlights the need for improving health care services among the elderly. The increasing burden of depression in specific sub-populations also highlights the importance of understanding the broader consequences of depression among rural and female elderly.

Health Economics & Outcomes Research

Depression

urban-rural differential

Gender differential

Decomposition analysis

India

The proportion of the elderly population in India has increased significantly over time. The older population has increased from 24.71 million in 1961 to 43.98 million in 1981, an increment of almost 78 percent [1]. Furthermore, it has increased from 43.98 million in 1981 to 104 million in 2011, an increment of almost 136 percent [2]. 104 million elderly in 2011 roughly translated into 8 percent of India’s total population, and the proportion of the elderly population is expected to rise by 20 percent by 2050 [3]. With such unprecedented growth in the elderly population over time, there is an escalating demand for geriatric healthcare in India. With an increase in life expectancy and decline in fertility levels, ageing phenomenon has transpired into a significant health problem. Depression among the elderly is one such concern that is rising off lately in India.

Depression is a leading cause of disability and is a significant contributor to the overall global burden of disease [4], [5]. Globally, more than 264 million people of all ages suffer from depression [6]. Furthermore, depression has been ranked as the foremost contributor to non-fatal health loss, accounting for 7.5 percent of global years lived with disability (YLDs) and 2 percent of global disability-adjusted life years (DALYs) [7]. Depression can affect people from all backgrounds across the life course; however, it is more pronounced in older people [8]. The Elderly are more prone to psychological disturbances due to natural age-related decline in physiological functioning, leading to severe depression [9]. Currently, geriatric depression is a growing concern in India due to an increase in the proportion of the elderly population.

Previous studies have attributed depression among the elderly to certain risk factors. Studies have noted education [10], [11], place of residence [10], marital status [11], [12], age [10], [13], gender [10]–[12], wealth index [10], [11], physical activity [12], self-rated health [14], obesity [15], work status [10], functional impairment [10] as a risk factor of depression. Despite the abundance of literature on risk factors associated with depression, there is no conclusive evidence on urban-rural and male-female gradients on depression among the elderly.

The prevalence of depression among the elderly has been increasing over time. With an ever-increasing proportion of the older population, it is expected that a higher number of older people than ever would fall for depression. It will not be an exaggeration to state that older people would be more vulnerable to depression in the coming days, given the current disease burden. So, there is a need to study the prevalence and factors associated with depression among the elderly in India. Furthermore, it is also important to examine the factors contributing to the socio-economic status (SES) related to inequality in the prevalence of depression among the elderly. To date, evidence remained inconclusive explaining rural-urban and male-female differential in depression. Certain studies noted higher depression among female elderly [16]–[18], while other few noted higher depressive symptoms among male elderly [19], and some others stated inconclusiveness while depicting gender differential in depression among elderly [20]. Therefore, this study examines the rural-urban and male-female gradient of depression among the elderly in India. Furthermore, this study decomposes the factors explaining the SES-related inequality in the prevalence of depression among the elderly.

Data:

In this study, the first wave of the longitudinal study of ageing in India (LASI) was used as a data source which was conducted across all the 35 Indian states (exclude Sikkim) and union territories (UTs) in 2017-18. The LASI adopted a multistage stratified area probability cluster sampling to achieve the country’s representative sample. The survey has used a three-stage sampling design for rural areas and a four-stage sampling design for urban areas. The first wave of LASI included a sample of 72,250 individuals aged 45 years and above and their spouses; however, our study concerned 31,464 elderly aged 60 years and above, including 6,749 individuals aged 75 years above from 35 states and union territories.

Study Variables

Outcome variable

The outcome variable for this study is “depression,” which was self-reported. The question has been asked, “During the last 12 months, was there ever a time when you felt sad, blue, or depressed for two weeks or more in a row” and responses recorded in dichotomous for as “yes” and “no.” Another outcome variable for this study is ‘thinking’ or ‘perception’ about depression. The question has been asked for thinking of depression as “did you lose interest in most things?”, “did you ever feel more tired out or low in energy than is usual for you?”, “did you lose your appetite?”, During the same two-week period, did you have a lot more trouble concentrating than usual? “people sometimes feel down on themselves, and no good or worthless, during those two weeks, did you feel this way?”, “did you think a lot about death – either your own, someone else’s, or death in general – during those two weeks?” and “did you have more trouble falling asleep than you usually do during those two weeks?”. All this question has been asked in the form of either ‘yes’ or ‘no.’

Covariates

Covariates for this study considered as age (60–69 and 70 years and above); gender (male and female); marital status (currently married, never married, Divorced/Separated/Deserted/Widowhood); living arrangements (living alone, with spouse and with others); religion (Hindu, Muslim and others); education (No education, below primary, primary, secondary, and higher); place of residence (rural and urban); wealth index (poorest, poorer, middle, richer and richest); currently working (yes and no); self-rated health (poor and good); ); tobacco use (no and yes). Activity of daily diving (ADL) has been constructed using five parameters: bathing, dressing, mobility, feeding, and toileting. Similarly, instrumental activity of daily living (IADL) has created using seven parameters: preparing a hot meal (cooking and serving), shopping for groceries, making telephone calls, taking medications, doing work around the house or garden, managing money, such as paying bills and keeping track of expenses and getting around or finding an address in an unfamiliar place. Further, ADL and IADL disability classified into “severe,” “moderate,” and “no disability.”

Statistical Measures

Data were analyzed using STATA version 16. Bivariate analysis was used to understand the prevalence and thinking of depression among sociodemographic clusters. The prevalence of depression has been presented separately for men and women and for rural and urban areas, respectively. Fairly decomposition analysis has been used to measure rural-urban inequalities for depression among older men and women. Fairlie decomposition is a very simple technique used to estimate from a logit or probit model, first described by Fairlie in 1999 [21]. The decomposition results have been presented in terms of coefficient and percent contribution by a group of sociodemographic variables. The equation for fairlie decomposition can be written as:

$${\text{Y}̄}^{U}-{\stackrel{̄}{\text{Y}}}^{R}=\left[\sum _{i=1}^{{N}^{U}}\frac{F\left({X}_{i}^{U}{\widehat{\beta }}^{U}\right)}{{N}^{U}}-\sum _{i=1}^{{N}^{R}}\frac{F({X}_{i}^{R}{\widehat{\beta }}^{R}}{{N}^{R}}\right]+\left[\sum _{i=1}^{{N}^{R}}\frac{F\left({X}_{i}^{R}{\widehat{\beta }}^{U}\right)}{{N}^{R}}-\sum _{i=1}^{{N}^{R}}\frac{F({X}_{i}^{R}{\widehat{\beta }}^{R}}{{N}^{R}}\right]$$

Where N^U and N^R is the sample size for urban and rural respectively,${\text{Y}̄}^{U}$ and ${\stackrel{̄}{\text{Y}}}^{R}$ are the average probability of a binary outcome of interest for group urban and rural, F is the cumulative distribution function from the logistic distribution, distribution, ${X}_{i}^{R}$ and ${X}_{i}^{U}$ are the set of the average value of the independent variable and ${\widehat{\beta }}^{U}$ and ${\widehat{\beta }}^{R}$ are the coefficient estimates for the urban and rural respectively.

Table 1 depicts the sociodemographic profile of the elderly population in India. Around three-fifths (58.5%) of the elderly belonged to the 60–69 years of age group, and the remaining (41.5%) belonged to 70 + years. The distribution of elderly by gender was nearly equal as 47.5 percent of the elderly were male, and the remaining 52.6 percent of elderly were female. Around three-fifths (62.1%) of the elderly were currently married. More than half of the elderly (56.5%) had no education, and only 4.1 percent of the elderly had higher education. Nearly 70 percent of the elderly belonged to rural areas, and the remaining 30 percent belonged to urban areas. Almost two-fifths (41.8%) of the elderly were working. Nearly 15 percent elderly reported poor self-rated health.

Table 1

Sociodemographic profile of elderly population aged 60 years and above in India
	N	%
Age
60–69	18,410	58.5
70+	13,054	41.5
Gender
Male	14,931	47.5
Female	16,533	52.6
Marital status
Currently married	19,536	62.1
never married	225	0.7
divorced/separated/deserted/widowhood	11,703	37.2
Living arrangements
Living alone	1,787	5.7
With spouse	19,176	60.9
With others	10,501	33.4
Religion
Hindu	25,871	82.2
Muslim	3,548	11.3
Others	2,045	6.5
Education
No Education	17,782	56.5
Below primary	3,598	11.4
Primary	3,520	11.2
Secondary	5,285	16.8
Higher	1,278	4.1
Place of residence
Rural	22,196	70.6
Urban	9,268	29.5
Wealth Index
Poorest	6,829	21.7
Poorer	6,831	21.7
Middle	6,590	21.0
Richer	6,038	19.2
Richest	5,175	16.5
Currently working
Yes	9,483	41.8
No	13,197	58.2
Self-rated health
Poor	4,630	15.0
Good	26,181	85.0
Tobacco use
No	18,665	59.8
Yes	12,539	40.2
ADL disability
Severe ADL	999	3.2
Moderate ADL	6,045	19.3
No ADL	24,291	77.5
IADL disability
Severe IADL	1,859	5.9
Moderate IADL	13,281	42.4
No IADL	16,164	51.6
Total	31,464	100

Figure 1 shows the prevalence of depression among the elderly by their residence and sex. Results found that around 22 percent of urban elderly and 17 percent of rural elderly reported depression. A higher proportion of female elderly (22.6% vs. 18.4%) reported depression than male elderly. Almost one in every five elderly (20.6%) reported depression in India.

Table 2 depicts the gender-wise prevalence of self-reported depression among the elderly in rural and urban areas. The results found that a higher percentage of women in rural and urban areas reported depression than their male counterparts. Also, depression was more prevalent among the elderly in rural areas than in urban areas. Almost one-fifth (19.13%) of the rural men reported depression, and almost one-fourth (23.9%) of rural women reported depression. Similarly, around 15 percent of urban men reported depression, and 18 percent of urban women reported depression. A higher percentage of rural men with higher education (21.4% vs. 18.1%) reported depression than uneducated rural men, whereas a lower percentage of rural women with higher education (15.3% vs. 24%) reported depression than uneducated rural women. Results found that the reporting of depression was higher among the richest men and women from rural and urban areas than their poorest counterparts. Depression was lower among those who were currently working than their non-working counterparts. The prevalence of depression was higher among elderly men and women who reported poor self-rated health, severe ADL, and IADL disability.

Table 2

Gender-wise prevalence of self-reported depression among elderly in rural and urban in last 12 months for two or more weeks by sociodemographic characteristics
	Rural Men		Rural Women		Urban Men		Urban Women
Age	%	N	%	N	%	N	%	N
60–69	20.0	5,865	24.2	6,274	13.8	2,855	18.9	3,408
70+	17.8	4,212	23.4	4,374	14.9	2,166	16.6	2,310
Marital status
Currently married	19.0	8,168	22.3	4,938	12.6	4,189	17.1	2,278
Never married	17.2	119	7.0	22	15.6	39	25.1	47
divorced/separated/deserted/widowhood	19.7	1,790	25.3	5,688	22.9	793	18.5	3,393
Living arrangements
Living alone	20.0	272	30.0	1,037	29.3	103	27.1	333
With spouse	18.9	8,052	22.3	4,825	12.5	4,102	16.8	2,229
With others	19.7	1,753	24.0	4,786	21.5	815	17.8	3,155
Religion
Hindu	19.4	8,433	23.7	8,876	15.4	3,906	18.7	4,587
Muslim	17.4	993	24.0	1,028	11.5	829	16.8	780
Others	17.0	651	25.3	744	6.8	286	11.4	351
Education
No Education	18.1	4,635	24.0	9,009	16.1	978	22.4	2,630
Below primary	21.4	1,592	25.3	739	15.9	563	18.7	714
Primary	20.8	1,539	22.4	533	16.9	682	17.9	827
Secondary	17.8	2,001	20.9	351	13.8	1,942	9.6	1,300
Higher	21.4	308	15.3	17	9.9	856	12.7	246
Wealth Index
Poorest	18.7	2,034	22.8	2,411	15.1	1,129	23.0	1,268
Poorer	16.2	2,213	23.0	2,395	14.6	989	18.2	1,206
Middle	21.3	2,114	23.7	2,261	11.1	1,166	13.3	1,051
Richer	18.8	1,962	24.4	1,971	14.0	933	15.6	1,180
Richest	20.8	1,754	26.4	1,610	17.7	804	19.1	1,014
Currently working
Yes	17.9	4,817	25.5	2,370	12.4	1,581	21.2	562
No	20.1	4,734	27.1	3,821	15.1	3,207	27.3	1,588
Self-rated health
Poor	34.8	1,510	37.4	1,773	32.6	553	31.3	730
Good	16.7	8,361	21.7	8,651	12.8	4,360	16.4	4,873
Tobacco use
Yes	16.8	3,433	23.6	7,786	12.4	2,723	16.8	4,909
No	20.5	6,569	24.6	2,809	18.1	2,216	27.1	759
ADL disability
Severe ADL	33.0	285	28.2	390	33.4	140	27.4	180
Moderate ADL	26.6	1,781	31.9	2,351	26.3	704	26.7	1,155
No ADL	17.0	7,974	21.3	7,886	12.2	4,130	15.4	4,362
IADL disability
Severe IADL	27.6	475	30.8	922	27.4	143	23.3	268
Moderate IADL	22.2	3,784	27.8	5,456	23.6	1,308	23.4	2,552
No IADL	16.5	5,770	17.2	4,229	11.1	3,522	12.8	2,874
Total	19.13	10,077	23.9	10,648	14.9	5,021	18.1	5,718
Note- prevalence of depression has been calculated based on self-reporting asked for last 12 months for two or more weeks in a row

Figure 2 depicts the experience of depression among the elderly by various characteristics. Results found that a higher percentage of those with severe ADL and IADL reported depression all day long. More than two-fifths of the elderly with severe ADL (43.87%) and severe IADL (41.44%) reported depression all day long. A higher percentage of elderly living alone reported depression all day long (29.66% vs. 27.01%) than elderly living with a spouse. Similarly, a higher percentage of urban elderly than rural elderly (29.16% vs. 27.24%) reported depression all day long. A higher percentage of female elderly than male elderly (28.65% vs. 26.39%) reported depression all day long.

Figure 3 depicts the depression-related perception of the elderly. Almost four-fifths of the elderly male (82.15%) and female (80.24%) reported trouble falling asleep during the depression. More than half of the elderly males (56.06%) and females (57.31%) reported that they thought a lot about death during the depression. More than four-fifths of the elderly aged 60–69 years (86.26%) and elderly aged 70 + years (84.38%) had trouble concentrating during the depression. A similar percentage of elderly males (86.06%) and females (84.49%) lost appetite during the depression.

Table 3 depicts the results of the rural-urban decomposition of depression among male and female elderly. Results found that self-rated health and IADL disability explained nearly 40 percent of the urban-rural inequality in the prevalence of depression among male elderly. Tobacco use is another predictor that explained nearly 23 percent of the inequality in the prevalence of depression among the elderly. Education, self-rated health, and IADL disability were the significant predictors explaining inequality in the prevalence of depression among female elderly. Education and IADL disability explained nearly 41 percent and 47 percent of the inequality in the prevalence of depression among female elderly, respectively.

Table 3

Estimates of urban-rural decomposition analysis for contribution of explanatory variables for depression among male and female elderly in India
	Male			Female
Variables	Coefficient	Standard Error	Percent Contribution	Coefficient	Standard Error	Percent Contribution
Age	-0.0000646	0.000538	0.18	0.0074326	0.0046823	-13.83
Marital status	-0.0019447	0.002243	5.41	0.002432	0.0030675	-4.53
Living arrangements	-0.0003437	0.001472	0.96	-0.0081049	0.00611	15.08
Religion**	-0.0016708	0.000727	4.65	-0.0021447	0.0015572	3.99
Education	-0.0075153	0.007643	20.91	-0.0219335	0.0111695	40.82
Wealth Index	-0.0006336	0.000708	1.76	-0.0009391	0.0023607	1.75
Currently working	0.000433	0.003147	-1.20	0.0059097	0.0057995	-11.00
Self-rated health***	-0.0061494	0.001572	17.11	-0.0036714	0.0018623	6.83
Tobacco use**	-0.0084155	0.003362	23.41	-0.0075328	0.0062308	14.02
ADL disability	-0.001176	0.000793	3.27	0.0000112	0.0008997	-0.02
IADL disability**	-0.0084601	0.003464	23.53	-0.0252492	0.0084644	46.99
Significant at 95 percent CI and *Significant at 99% CI

Depression is a significant public health concern that is yet to be recognized as an important public health challenge. This study is very timely and significant in bringing the issue of depression among the elderly to the forefront as it is based on the nationally representative survey data. The study found that almost one-fifth (20.6%) of the elderly reported depressive symptoms. Based on 56 community-based studies in India, a meta-analysis found that the pooled prevalence of depression was 34.4 percent among elderly aged 60 + years from 1997 to 2016. A study in Bangladesh noted a higher prevalence of depression among the elderly, where almost 37 percent of the elderly reported depression [22]. Countries where the higher prevalence of depression among the elderly was reported include; 42.5 percent in Indonesia [23], 44.4 percent in Egypt [24], 23.7 percent in Thailand [25], whereas lower depression among elderly was reported in Brazil (3.8%) [26], 10.5 percent in China [27], 18.5 percent in Turkey [11]. A study based on World Health Organization (WHO) Study on Global Ageing and Adult Health (SAGE) data survey noticed that the prevalence of depression was higher in the Indian elderly than elderly in Mexico, Ghana, China, Russia, and South Africa [28]. The difference in the prevalence of depression among the elderly could be attributed to cultural differences, genetics, environmental factors, or even methodological or sampling differences [29]. However, taken together, all these studies reinforce an argument for placing greater importance on the mental health issues of older people.

This study revealed evidence of gender differences in depression among elderly people in India. The results found that the prevalence of depression was higher among elderly women than in elderly men. This finding is corroborated with the previous studies in different settings [17]. However, few studies noted an otherwise result where male elderly had a higher prevalence of depression than female elderly [19]. Worth mentioning, a few studies failed to notice any gender differences in depressive symptoms among older people [20]. Furthermore, gender differences persist with various background characteristics, disfavouring older women. The female disadvantages in depression were observed in age, living arrangement, education, wealth index, working status, self-rated health, and IADL disability, which are similar to the findings of another study in the Indian context [17]. Studies worldwide noticed that women tend to live longer than men, however, with worse health [30]–[33]. The fact that a larger share of Indian women than men never attended school may also partially explain the gender differences in depression [17]. Various studies have noted education as a significant predictor of depression and poor health among the elderly and other sub-populations [1], [2], [34]–[36].

The study noted male-female gradient in depressive symptoms among the elderly, where a higher proportion of elderly females reported depression than their male counterparts. However, while examining SES-related inequality in the prevalence of depression, education was a significant factor explaining the SES-related inequality in the prevalence of depression among female elderly and not in male elderly. This signifies that education predicts SES-related inequality in the prevalence of depression only among elderly females. Results also noted that a higher proportion of uneducated elderly women in rural and urban areas reported depression than highly educated elderly women in rural and urban areas. Previously available literature suggests that male-female difference in various cognition-related parameters could be controlled by education status among the elderly [33]. Given that education inequality disfavours women in India, the study findings are not surprising at all. Better schooling during early years promote the development of brain reserve capacity, which could be a plausible explanation of higher depression level among uneducated female elderly [37]. Various studies agree with the brain reserve hypothesis and found educational attainment to be associated with various mental health issues [38], [39].

Consistent with several studies in India [17], [28], [29], [40]–[42], this study noted a higher proportion of depression among rural elderly than in urban elderly. Poor education attainment among rural people than their urban counterparts could explain the paradox of higher depression among rural elderly in parts. Better educated people tend to attain a greater sense of control, which further facilitates their adaptive strategies for coping with mental health [43]. Lack of social support in the rural areas due to rural to urban migration of the younger population could also be a probable reason for higher depression among rural elderly in India [40]. Lack of professional health services in rural areas could also be another important factor of higher depression among rural elderly. Moreover, an underqualified and untrained medical professional in rural areas [44] could also be a significant factor for higher depression levels in rural elderly. Also, treatment-seeking is relatively lower in rural areas than in urban areas [45], which could further be attributed to higher reporting of depressive symptoms among rural elderly.

The study noted that depressive symptoms were higher among those with severe ADL and IADL disability and reported poor self-rated health. Furthermore, the study reported a significantly high SES-related inequality in the prevalence of depression among the elderly attributed to IADL disability and self-rated health. It could be inferred that IADL disability and self-rated health are the two important predictors explaining depression among the elderly. Previous research agrees with the above findings [17], [46], [47]. Those with poor self-rated health might perceive their health negatively, which could be a plausible explanation of higher depression. Difficulties of IADL make it tough to perform social roles and a reduced sense of mastery among older people and interrupt their independent living; all this could lead to higher depression among elderly with severe IADL [48], [49].

This study has important theoretical implications in that it extends the field of research on the elderly and depression while explaining the socio-economic inequalities in the prevalence of depression. However, the study has few significant limitations. Firstly, depression was not clinically diagnosed, and it was self-reported. However, several previous studies have examined depression as a self-reported entity [22], [46], [50]. The self-reporting of depression could lead to overestimating the prevalence of depression and can be misleading [28]. The cross-sectional nature of data limits our understanding of causality. The reporting of depression could have been affected by the recall bias as it categorizes depression for the last 12 months. Furthermore, self-reported health can also affect the study estimates in examining the inequalities in the prevalence of depression as it was self-reported. Furthermore, cross-sectional data could have limited our understanding of causal inferences. Despite these limitations, the study has specific strengths too. The study findings are based on a nationally representative survey released recently, thereby providing current and reliable estimates on depression among the elderly in India.

Unlike other previous research on the association of several risk factors with depressive symptoms among the elderly, this study focussed on the socio-economic status-related inequality in the prevalence of depression among the elderly along with focussing urban-rural and male-female gradients of depression among the elderly. The study found that almost one in every five elderly reported depression, which was higher among rural elderly and female elderly. Furthermore, self-rated health and IADL disability were the two important factors explaining the SES-related inequality in the prevalence of depression among elderly men. However, education, IADL disability, and self-rated health were three factors explaining the SES-related inequality in the prevalence of depression among elderly women.

Given the large proportion of elderly reporting depression, this study highlights the need for improving health care services among the elderly. The increasing burden of depression in specific sub-populations also highlights the importance of understanding the broader consequences of depression among rural and female elderly. Preventive measures to tackle depression among the elderly shall be considered as an integral part of public health, specifically at Primary Health centers, which have a wider reach to the rural population. Furthermore, training the medical officer to identify the psychiatric problems and strengthening the national mental health program in primary health care set-up would help in early identification and appropriate management of depression among the elderly.

ADL: Activity of daily diving

CEBs: Census Enumeration Blocks

DALY: Disability-Adjusted Life Years

HRS: Health and Retirement Study

IADL: Instrumental Activity of Daily Living

LASI: Longitudinal Ageing Study in India

MoHFW: Ministry of Health and Family Welfare

NIA: National Institute on Aging

SES: Socio-economic Status

UNFPA: United Nations Population Fund

UTs: Union Territories

YLD: Years Lived with Disability

Ethics approval and consent to participate: Authors used secondary source of data which is available to anyone and therefore there is no need for ethical approval and consent to participate from author’s side. However, the survey agencies that conducted the field survey for the data collection have collected a prior written consent from the respondent. The Indian Council of Medical Research (ICMR) extended the necessary guidance and ethical approval for conducting the LASI. The more information on ethical approval and consent to participate can be found at: https://www.iipsindia.ac.in/sites/default/files/LASI_India_Report_2020_compressed.pdf

Consent for publication: Not applicable

Availability of data and materials: The datasets generated and/or analysed during the current study are available with the International Institute for Population Sciences, Mumbai, India repository and could be accessed from the following link: https://iipsindia.ac.in/sites/default/files/LASI_DataRequestForm_0.pdf. Those who wish to download the data have to follow the above link. This link leads to a data request form designed by International Institute for Population Sciences. After completing the form, it should be mailed to: [email protected] for further processing. After successfully sending the mail, individual will receive the data in a reasonable time.

Competing Interest: The authors declare that they have no competing interests.

Funding: Authors did not received any funding to carry out this research.

Author’s Contribution: The concept was drafted by SK and SC. SK contributed to the analysis design. SC advised on the paper and assisted in paper conceptualization. SC, RP, and MK contributed in the comprehensive writing of the article. All authors read and approved the final manuscript.

Acknowledgement: Not applicable.

S. Srivastava, S. Chauhan, and R. Patel, “Socio-economic inequalities in the prevalence of poor self-rated health among older adults in India from 2004 to 2014: A decomposition analysis,” Ageing International, pp. 1–18, 2020.
R. Patel, S. Chauhan, D. Chaurasiya, S. Kumar, and B. Paswan, “Role and Impact of Social Capital on Health of Older Adult in India,” Indian Journal of Social Research, vol. 60, no. 2, pp. 279–305, 2019.
A. Singh and B. M. Purohit, “Addressing geriatric oral health concerns through national oral health policy in India,” Int J Health Policy Manag, vol. 4, no. 1, pp. 39–42, Nov. 2014, doi: 10.15171/ijhpm.2014.126.
C. O. Akosile, U. G. Mgbeojedo, F. A. Maruf, E. C. Okoye, I. C. Umeonwuka, and A. Ogunniyi, “Depression, functional disability and quality of life among Nigerian older adults: Prevalences and relationships,” Archives of Gerontology and Geriatrics, vol. 74, pp. 39–43, Jan. 2018, doi: 10.1016/j.archger.2017.08.011.
M. J. Friedrich, “Depression Is the Leading Cause of Disability Around the World,” JAMA, vol. 317, no. 15, pp. 1517–1517, Apr. 2017, doi: 10.1001/jama.2017.3826.
WHO, “Key facts and information on types and symptoms, contributing factors, diagnosis and treatment of depression,” Jan. 2020. Accessed: May 23, 2021. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/depression
K. Taibanganba Meitei and H. Singh, “Prevalence of Depression Among Indian Population,” Online Journal of Health and Allied Sciences, vol. 18, Feb. 2020.
A. R. Assariparambil et al., “Depression among older adults: a systematic review of South Asian countries,” Psychogeriatrics, vol. 21, no. 2, pp. 201–219, 2021, doi: https://doi.org/10.1111/psyg.12644.
S. V. Thittamaranahalli, P. P. Yannick, M. Madhusudan, R. N R, and G. B, “Depression and its associated factors among elderly patients attending rural primary health care setting,” International Journal of Community Medicine and Public Health, vol. 4, no. 2, pp. 471–476, Feb. 2017, doi: 10.18203/2394-6040.ijcmph20170275.
P. Sengupta and A. I. Benjamin, “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 Journal of Public Health, vol. 59, no. 1, p. 3, Jan. 2015, doi: 10.4103/0019-557X.152845.
E. Yaka, P. Keskinoglu, R. Ucku, G. G. Yener, and Z. Tunca, “Prevalence and risk factors of depression among community dwelling elderly,” Archives of Gerontology and Geriatrics, vol. 59, no. 1, pp. 150–154, Jul. 2014, doi: 10.1016/j.archger.2014.03.014.
M. Gorska-Ciebiada, M. Saryusz-Wolska, M. Ciebiada, and J. Loba, “Mild Cognitive Impairment and Depressive Symptoms in Elderly Patients with Diabetes: Prevalence, Risk Factors, and Comorbidity,” Journal of Diabetes Research, vol. 2014, p. e179648, Nov. 2014, doi: 10.1155/2014/179648.
E. Stordal, A. Mykletun, and A. A. Dahl, “The association between age and depression in the general population: a multivariate examination,” Acta Psychiatrica Scandinavica, vol. 107, no. 2, pp. 132–141, 2003, doi: https://doi.org/10.1034/j.1600-0447.2003.02056.x.
K.-M. Han, Y.-H. Ko, H.-K. Yoon, C. Han, B.-J. Ham, and Y.-K. Kim, “Relationship of depression, chronic disease, self-rated health, and gender with health care utilization among community-living elderly,” Journal of Affective Disorders, vol. 241, pp. 402–410, Dec. 2018, doi: 10.1016/j.jad.2018.08.044.
R. E. Roberts, S. Deleger, W. J. Strawbridge, and G. A. Kaplan, “Prospective association between obesity and depression: evidence from the Alameda County Study,” International Journal of Obesity, vol. 27, no. 4, Art. no. 4, Apr. 2003, doi: 10.1038/sj.ijo.0802204.
M. G. Cole and N. Dendukuri, “Risk Factors for Depression Among Elderly Community Subjects: A Systematic Review and Meta-Analysis,” AJP, vol. 160, no. 6, pp. 1147–1156, Jun. 2003, doi: 10.1176/appi.ajp.160.6.1147.
R. Patel, S. P. Marbaniang, S. Srivastava, P. Kumar, S. Chauhan, and D. J. Simon, “Gender differential in low psychological health and low subjective well-being among older adults in India: With special focus on childless older adults,” PLOS ONE, vol. 16, no. 3, p. e0247943, Mar. 2021, doi: 10.1371/journal.pone.0247943.
M. V. Zunzunegui, F. Béland, A. Llácer, and V. León, “Gender differences in depressive symptoms among Spanish elderly,” Soc Psychiatry Psychiatr Epidemiol, vol. 33, no. 5, pp. 195–205, Apr. 1998, doi: 10.1007/s001270050043.
R. Patel and S. Chauhan, “Gender differential in health care utilisation in India,” Clinical Epidemiology and Global Health, vol. 8, no. 2, pp. 526–530, 2020.
B. Zhang and J. Li, “Gender and marital status differences in depressive symptoms among elderly adults: The roles of family support and friend support,” Aging & Mental Health, vol. 15, no. 7, pp. 844–854, Sep. 2011, doi: 10.1080/13607863.2011.569481.
T. K. Bauer and M. Sinning, “An extension of the Blinder–Oaxaca decomposition to nonlinear models,” AStA, vol. 92, no. 2, pp. 197–206, May 2008, doi: 10.1007/s10182-008-0056-3.
T. R. Disu, N. J. Anne, M. D. Griffiths, and M. A. Mamun, “Risk factors of geriatric depression among elderly Bangladeshi people: A pilot interview study,” Asian Journal of Psychiatry, vol. 44, pp. 163–169, Aug. 2019, doi: 10.1016/j.ajp.2019.07.050.
B. A. Pramesona and S. Taneepanichskul, “Prevalence and risk factors of depression among Indonesian elderly: A nursing home-based cross-sectional study,” Neurology, Psychiatry and Brain Research, vol. 30, pp. 22–27, Dec. 2018, doi: 10.1016/j.npbr.2018.04.004.
A.-H. El-Gilany, G. O. Elkhawaga, and B. B. Sarraf, “Depression and its associated factors among elderly: A community-based study in Egypt,” Archives of Gerontology and Geriatrics, vol. 77, pp. 103–107, Jul. 2018, doi: 10.1016/j.archger.2018.04.011.
N. Wongpakaran et al., “Prevalence, clinical and psychosocial variables of depression, anxiety and suicidality in geriatric tertiary care settings,” Asian Journal of Psychiatry, vol. 41, pp. 38–44, Mar. 2019, doi: 10.1016/j.ajp.2018.10.007.
R. Barcelos-Ferreira, E. Yoshio Nakano, D. C. Steffens, and C. M. C. Bottino, “Quality of life and physical activity associated to lower prevalence of depression in community-dwelling elderly subjects from Sao Paulo,” Journal of Affective Disorders, vol. 150, no. 2, pp. 616–622, Sep. 2013, doi: 10.1016/j.jad.2013.02.024.
L. Cong, P. Dou, D. Chen, and L. Cai, “Depression and Associated Factors in the Elderly Cadres in Fuzhou, China: A Community-based Study,” International Journal of Gerontology, vol. 9, no. 1, pp. 29–33, Mar. 2015, doi: 10.1016/j.ijge.2015.02.001.
A. Anand, “Understanding Depression among Older Adults in Six Low-Middle Income Countries using WHO-SAGE Survey,” Behavioral Health, vol. 1, Jan. 2015.
M. Pilania et al., “Prevalence of depression among the elderly (60 years and above) population in India, 1997–2016: a systematic review and meta-analysis,” BMC public health, vol. 19, no. 1, pp. 1–18, 2019.
A. Barford, D. Dorling, G. D. Smith, and M. Shaw, “Life expectancy: women now on top everywhere,” BMJ, vol. 332, no. 7545, p. 808, Apr. 2006, doi: 10.1136/bmj.332.7545.808.
A. Oksuzyan, K. Juel, J. W. Vaupel, and K. Christensen, “Men: good health and high mortality. Sex differences in health and aging,” Aging Clin Exp Res, vol. 20, no. 2, pp. 91–102, Apr. 2008, doi: 10.1007/BF03324754.
A. Oksuzyan, P. K. Singh, K. Christensen, and D. Jasilionis, “A Cross-National Study of the Gender Gap in Health Among Older Adults in India and China: Similarities and Disparities,” Gerontologist, vol. 58, no. 6, pp. 1156–1165, Nov. 2018, doi: 10.1093/geront/gnx111.
P. K. Singh, D. Jasilionis, and A. Oksuzyan, “Gender difference in cognitive health among older Indian adults: A cross-sectional multilevel analysis,” SSM - Population Health, vol. 5, pp. 180–187, Aug. 2018, doi: 10.1016/j.ssmph.2018.06.008.
S. Chauhan, S. Srivastava, P. Kumar, R. Patel, M. T, and P. Dhillon, “Interaction of substance use with physical activity and its effect on depressive symptoms among adolescents,” Journal of Substance Use, vol. 0, no. 0, pp. 1–7, Dec. 2020, doi: 10.1080/14659891.2020.1851411.
S. Srivastava, T. Anwar, R. Patel, and S. Chauhan, “Dynamics of chronic diseases in metro and non-metro regions of India: evidence from India Human Development Survey I and II,” International Journal, vol. 6, no. 8, p. 322, 2020.
S. Srivastava et al., “Older adults’ psychological and subjective well-being as a function of household decision making role: Evidence from cross-sectional survey in India,” Clinical Epidemiology and Global Health, vol. 10, p. 100676, 2021.
J. Lee, H. Park, and J. Chey, “Education as a Protective Factor Moderating the Effect of Depression on Memory Impairment in Elderly Women,” Psychiatry Investig, vol. 15, no. 1, pp. 70–77, Jan. 2018, doi: 10.4306/pi.2018.15.1.70.
C. B. Hall, C. Derby, A. LeValley, M. J. Katz, J. Verghese, and R. B. Lipton, “Education delays accelerated decline on a memory test in persons who develop dementia,” Neurology, vol. 69, no. 17, pp. 1657–1664, Oct. 2007, doi: 10.1212/01.wnl.0000278163.82636.30.
Y. Stern, B. Gurland, T. K. Tatemichi, M. X. Tang, D. Wilder, and R. Mayeux, “Influence of Education and Occupation on the Incidence of Alzheimer’s Disease,” JAMA, vol. 271, no. 13, pp. 1004–1010, Apr. 1994, doi: 10.1001/jama.1994.03510370056032.
G. V. Akila, B. A. Arvind, and A. Isaac, “Comparative assessment of psychosocial status of elderly in urban and rural areas, Karnataka, India,” J Family Med Prim Care, vol. 8, no. 9, pp. 2870–2876, Sep. 2019, doi: 10.4103/jfmpc.jfmpc_536_19.
B. Arumugam, S. Nagalingam, and R. Nivetha, “Geriatric depression among rural and urban slum community in Chennai–a cross sectional study,” Journal of Evolution of Medical and Dental Sciences, vol. 2, no. 7, pp. 795–802, Feb. 2013.
M. Sundru and K. Goru, “Epidemiological study of depression among population above 60 years in Visakhapatnam, India,” International Journal of Medical Science and Public Health, vol. 2, p. 1, Jan. 2013, doi: 10.5455/ijmsph.2013.030520133.
L. W. Li, J. Liu, H. Xu, and Z. Zhang, “Understanding Rural–Urban Differences in Depressive Symptoms Among Older Adults in China,” J Aging Health, vol. 28, no. 2, pp. 341–362, Mar. 2016, doi: 10.1177/0898264315591003.
J. Das, A. Holla, V. Das, M. Mohanan, D. Tabak, and B. Chan, “In Urban And Rural India, A Standardized Patient Study Showed Low Levels Of Provider Training And Huge Quality Gaps,” Health Affairs, vol. 31, no. 12, pp. 2774–2784, Dec. 2012, doi: 10.1377/hlthaff.2011.1356.
G. Agrawal and P. Arokiasamy, “Morbidity Prevalence and Health Care Utilization Among Older Adults in India,” J Appl Gerontol, vol. 29, no. 2, pp. 155–179, Apr. 2010, doi: 10.1177/0733464809339622.
G. Bishwajit, D. P. O’Leary, S. Ghosh, S. Yaya, T. Shangfeng, and Z. Feng, “Physical inactivity and self-reported depression among middle-and older-aged population in South Asia: World health survey,” BMC geriatrics, vol. 17, no. 1, pp. 1–8, 2017.
P. R. Konda, P. K. Sharma, E. Ganguly, and A. R. Gandhi, “Correlates of geriatric depression among urban South Indian elders,” Journal of the Neurological Sciences, vol. 405, p. 94, Oct. 2019, doi: 10.1016/j.jns.2019.10.1741.
B. J. Kim, C. C. Sangalang, and T. Kihl, “Effects of acculturation and social network support on depression among elderly Korean immigrants,” Aging & Mental Health, vol. 16, no. 6, pp. 787–794, Aug. 2012, doi: 10.1080/13607863.2012.660622.
B. J. Kim, L. Liu, S. Nakaoka, S. Jang, and C. Browne, “Depression among older Japanese Americans: The impact of functional (ADL & IADL) and cognitive status,” Social Work in Health Care, vol. 57, no. 2, pp. 109–125, Feb. 2018, doi: 10.1080/00981389.2017.1397588.
K. B. Laksham, R. Selvaraj, and C. Kameshvell, “Depression and its determinants among elderly in selected villages of Puducherry – A community-based cross-sectional study,” J Family Med Prim Care, vol. 8, no. 1, pp. 141–144, Jan. 2019, doi: 10.4103/jfmpc.jfmpc_235_18.

No competing interests reported.

Urban-rural and Gender Differential in Depressive Symptoms Among Older Adults in India

Status:

Version 1

Abstract

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Background

Methods

Outcome variable

Covariates

Results

Discussion

Limitations And Strengths

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References

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Status:

Version 1