Social networks and mental health: A study of elderly in rural Bihar, India

doi:10.21203/rs.3.rs-5206134/v1

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Social networks and mental health: A study of elderly in rural Bihar, India

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With record high population growth in India, the number of elderly has significantly increased. In Bihar (a state in India) with a high population density, the number of elderly is substantial, but they are under-studied. Moreover, there has not been any significant research on mental health concerns of the elderly, and none from rural Bihar. The elderly go through common mental health conditions like depression, which is often overlooked and neglected. In our study, the role of social networks on depression emerges as pivotal. This study used data from the Longitudinal Ageing Study in India (LASI) with a sample of 1255 older people (60 years or older) from rural Bihar. We used Berkman's conceptual model of how social relationships affect depression. The network was measured by Confirmatory Factor Analysis (CFA) given by Glass et al which shows that four types of social networks were formed- children's network, relative's network, friends’ network, and confidant network. Bivariate and multivariate analysis was done to understand the association and effect of specific networks on depression. The result establishes the importance of social networks for the community life of the elderly. It suggests that a strong social network with relatives and trusted confidence protected the elderly from depression in rural Bihar. This study highlighted the need for more social network centers for the elderly to engage with friends within or between communities and participate in group activities that promote good mental health.

Elderly

Mental Health

Social Networks

Depression

Bihar

LASI

The growing older population in any country creates enormous socioeconomic and public health issues. These include increased spending on pensions and healthcare, the need for social security changes, a shrinking workforce, and a shortage of energetic adults capable of aiding older dependents (Singh et al., 2016). India's population of individuals aged 60 and more, which is currently the world's second-largest after China, is predicted to increase from 8% in 2010 (approximately 80 million) to 19% (containing 323 million people) by 2050 (Singh et al., 2013; Hussain & Ghosh, 2011). In low- and middle-income nations such as India, the aging process has generated a new epidemiological issue characterized by a high frequency of chronic diseases that significantly increase disability and mortality. Depression is a common mental health disorder among the elderly that presents specific challenges, particularly in countries like India (Singh et al., 2016). Depression affects people's physical, cognitive, and social functioning (Kupferberg & Hasler, 2023). According to the World Health Organization (WHO), "depression is a common mental disorder, characterized by sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, feelings of tiredness, and poor concentration" (WHO, 2013). Because of the increased risk of suicide, self-neglect, and increased mortality rates (Blazer, 2003; Hasin et al., 2005) that result from this, late-life depression is a severe public health concern (Fiske et al., 2009).

According to data from the WHO Survey on Global Ageing and Adult Health (SAGE), which was carried out in India between 2007 and 2010, 19.3% of older persons in the country reported having depressive symptoms, with substantial differences seen between socioeconomic categories (IIPS, 2013). There has been some region-specific research on depression among elderly individuals in India. For instance, using the criteria established in the International Classification of Diseases, 10th revision (ICD-10), a study carried out in a rural community in South India discovered a 13% prevalence of depression among the elderly (Sherbiny et al., 2016). On the other hand, a cross-sectional research of people 60 years of age and older in rural West Bengal revealed a significantly greater prevalence of depression at 54%. The Geriatric Depression Scale and a pretested semi-structured routine were used to evaluate the study (Maulik & Dasgupta, 2012).

According to the WHO, several factors, including genetic predisposition, chronic illness and disability, pain, frustration with limitations in activities of daily living (ADL), specific personality traits (such as dependency, anxiety, or avoidance), unfavourable life events (like separation, divorce, bereavement, poverty, and social isolation), and a lack of social support, increase an older adult's risk of developing depression (WHO, 2013). These risk variables have been the subject of empirical research in several contexts, including developed and low- and middle-income countries (Peltzer & Phaswana, 2013; Zheng et al., 2012).

The elderly have historically found a feeling of social security in the traditional family structure, which was usually joint or undivided, and they were treated with respect (Samanta et al., 2014). However, over the last 20 years, globalization has brought about visible social changes in India and many other Asian countries, including a sharp increase in urbanization and a large-scale exodus of young adults seeking better jobs and educational prospects (Knodel & Ofsedal,2003; Samanta et al., 2014). As a result, there has been a discernible shift away from intergenerational co-residence and towards nuclear family structures (Bongaarts & Zimmer, 2002; Adams, 2010). The societal changes expose the elderly to emotional, physical, and financial risks (Pilania et al., 2013). Depression disorders are more common in rural areas, where the elderly population has limited access to public healthcare, health insurance, and social security benefits (Rajan, 2007; Bloom et al., 2010). At the same time, studies in India have shown a link between depression and a variety of socioeconomic factors, such as age, low educational status, and occupation (Sengupa & Benjamin, 2015; Sati et al., 2013; Reddy et al., 2012). So, it is impossible to underestimate the significance of social networks for the mental health of older persons living in rural India, and there has been little emphasis on investigating the impact of social networks on depression among the elderly, particularly in rural areas.

2.1 Ethical Statement

Secondary data was utilized in this study, which is readily available in the public domain, and survey agencies that conducted the field survey for the data collection have received prior informed consent from the respondents before data collection. There was no information that could help identify the respondents. The Indian Council of Medical Research (ICMR), Delhi; the International Institute for Population Sciences (IIPS), Mumbai; the Harvard T.H. Chan School of Public Health (HSPH), Boston; the University of Southern California (USC), Los Angeles; the ICMR-National AIDS Research Institute (NARI), Pune; and the Indian Regional Geriatric Centers (RGCs), Ministry of Health and Family Welfare, Government of India were among the collaborating organizations from which ethical approvals were obtained. The Declaration of Helsinki's guidelines for human protection were followed during the study's execution.

2.3 Conceptual Frameworks

This study examined the impact of social networks on well-being in the Indian context using a modified version of Berkman's (2000) conceptual framework. The Berkman model proposed multiple mechanisms through which social networks may promote well-being. To thoroughly explain these events, Berkman's model starts with the "upstream forces," or the macro-social environment in which the social network functions. Socioeconomic status, culture, and societal change processes are some of the broader social and cultural factors that may influence social networks' size, form, nature, and structure (Berkman & Glass, 2000).

Furthermore, in Berkman's concept, social network structure and member interaction are called the "mezzo" level. The definition of a social network is shaped by structural elements, including size (the total number of members), the closeness of members, and interaction-related features like contact frequency (Berkman & Glass, 2000). This study investigates how older social networks impact people's well-being in rural locations at the mezzo level.

According to the framework, social networks can function at the "micro" level by giving people access to resources, tangible items, social support, influence, and participation. This "micro" level of social network function is then thought to affect many aspects of well-being via several closer-in channels (Berkman & Glass, 2000). These could include reactions to psychological stress, habits that harm one's health, like smoking, behaviors that promote health, including using medical services appropriately, following prescriptions, and exercising, and exposure to contagious and non-communicable diseases.

This study employs multidimensional models of social networks proposed by Glass et al. (1997), which are based on data from the New Haven site of the National Institute on Aging's Establishment of Populations for the Epidemiologic Study of the Elderly Program (EPESE). These researchers created a measurement model that included social networks involving children, family, friends, and confidants, as well as a total social network for each study participant (Glass et al, 1997). He argued that because diverse ties with different people can serve distinct societal roles, it is important to consider each network's strength independently. Summary scores were created for each distinct type of network in Glass' model based on the quantity and frequency of interactions with members of that network. In their model, for children's network construction, they use the number of children, reciprocity of children, the proximity of children, the intimacy of children, and frequency of children variables. For relative network construction, the proximity of relatives, frequency of contacts, and non-visual contacts with relatives. Similarly, for the friend’s network, the proximity of friends, frequency, and non-visual contact are used. For a confident network, proximity of confidence, frequency of contact, and non-visual contact variables were used. In this study, we utilize the same model with some different variables based on the availability of data in the LASI survey.

In our study, we use the number of children, face-to-face contact with children, the proximity of children, and phone contact with children as variables to construct a children's network. For relative network construction, the number of relatives, face-to-face contact with relatives, and phone contact with relatives are variables. For friends’ network construction, the number of friends, face-to-face contact, and phone contact with friends are used. The number of existing confident spouses is considered a confident variable for the confident network. For data analysis purposes, these four networks are divided into upper, middle, and lower subcategories based on tertile value, which divides each network into three equal parts.

2.4 Sample and Study Area

The data for this study was obtained from the Longitudinal Ageing Study in India (LASI), Wave-1, a nationwide cross-sectional survey performed by the International Institute for Population Sciences (IIPS) Mumbai from 2017 to 2018 (IIPS, 2020). For our present study, we examined a cohort of 1255 persons aged 60 years and above, including males and females living in rural parts of Bihar and having at least one living child. The map below (see figure 1) shows the location of the study area.

Figure 1: Location map of the study area (prepared by authors using ArcGIS 10.5, India, 2024)

[Figure 1 here]

To understand the impact of certain social networks and social participation networks on depression, we evaluated the relationship between depression and socioeconomic and demographic factors in the older population of rural Bihar. To reiterate, the rationale is that in the LASI survey, the second largest sample of the elderly population was taken from Bihar (1808 sample). Within Bihar, almost 90% of the sample is from rural areas (IIPS, 2020)

2.5 Studying Elderly Social Network

Glass et al. (1997) suggested multidimensional models of social networks that incorporated ties with children, relatives, friends, and confidants for each study participant, using Confirmatory Factor Analysis (CFA) (Glass et al., 1997). This study also uses CFA to construct social networks. There are two methods of factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). EFA is used when nothing is known about the factor of a constructed (latent) variable, as it is used for exploratory purposes (Mahdavi et al., 2023). In this study, we know the underlying factors of each specific network component using the Glass et al. measurement model. The purpose of using CFA in this study is to confirm and validate the factors of each network component. To construct the social network, the quantity, and regularity of interactions with members of each network category are considered to produce summary scores for each category (e.g., Friends, Family, Confident, and Children).

In the LASI survey, the following questions from the individual questionnaire were asked of the respondents: (1) How many children do you have? (2) Children are alive or decrease? (3) Children is household member or not? (4) Father alive or decrease? (5) Mother alive or decrease? (6) Number of siblings do you have? (7) Among family members, with whom you feel close relationship? (8) Number of friends do you have? (9) How often do you meet up with friends? (10) How often do you speak on the phone or mail/e-mail with friends? With whom do you share most of your matters? (IIPS, 2020). These questions were asked of each network component, including children, family, friends, and confidants. A confidant is a "person with whom you share your matters." To determine contact with the network component, (1) Daily. (2) At least once in a week. (3) At least once in a month. (4) At least once in a year. (5) Never (IIPS, 2020).

We calculated the number, proximity, and contact frequency of network members (Children, relatives, friends, and Confidence) from the responses to the above questions. In addition, we calculated the composite reliability of the constructed network variables (Children, Relatives, Friends, and Confident network).

2.6 Studying Depression Among the Elderly

Self-administered questionnaires, such as the Patient Health Questionnaire-9, the Beck Depression Inventory, and the Centre for Epidemiological Studies Depression (CESD) scale, are the primary tools for assessing depression. These scales are well-liked because of their affordability, ease of use, and user-friendliness. They typically include a series of emotional symptoms the respondent indicates as present or absent over a specified period (Fisher et al., 2007). This study uses the CESD-10 scale of 10 questions to assess depression. The total score of this scale is 30, with 10 as the cut-off point; a score above 10 is considered depressed (Andresen et al., 1994). This study used 10 questions from the LASI individual questionnaire. These questions are: (1) How often did you have trouble concentrating? (2) How often did you feel depressed? (3) How often did you feel tired or low in energy? (4) How often were you afraid of something? (5) How often did you feel you were overall satisfied? (6) How often did you feel alone? (7) How often were you bothered by things that do not usually bother you? (8) How often did you feel that everything you did was an effort? (9) How often did you feel hopeful about the future? (10) How often did you feel happy? (IIPS, 2020).

Each question has four responses- (1) Rarely or never (less than 1 day). (2) Sometimes (1 or 2 days). (3) Often (3 or 4 days). (4) Most of the time (5 - 7 days). For negative questions (questions no.1 to 4 and 5 to 8), we have followed the same coding as the LASI individual questionnaire provided (IIPS, 2020). We followed reverse coding for positive questions (questions 5 and 9 to 10). Higher scores indicate a higher level of depression. In this study, Cronbach’s alpha is 0.73, which indicates an acceptable reliability level in the measurement of depression.

2.7 Useful Statistical Tools

The four dimensions of the social network construct were evaluated for validity and reliability using Confirmatory Factor Analysis (CFA), which, as a statistical method, examines the connections between constructed (latent) and observable variables to assess a suggested measurement model. In the current study, the social networks of the children, relatives, friends, and confidants had composite reliabilities of 0.81, 0.70, 0.87, and 0.72, respectively, of the constructed variables., which indicates an acceptable level of reliabilities of the four constructed network variables.

Both bivariate and multivariate analyses were conducted to investigate the relationship between social networks and depression. The association of depression with socioeconomic and demographic background variables was examined using a chi-square test. This statistical test determines whether the difference between the observed and expected values is statistically significant. Logistic regression was used in multivariate analyses to evaluate the overall impact of social networks on depression. All four types of social networks of children, relatives, friends, and confidants were examined for their impact using two multivariate models. The first model shows the unadjusted effects of four types of social networks, whereas the second model represents the effects of four types of social networks adjusted for background variables such as age, sex, education, employment status, caste, wealth index, and living arrangement. The binary logistic regression model incorporated all variables that the chi-square test indicated to be significant in the bivariate analysis. The results' estimated odds ratios with 95% confidence intervals (CIs) were displayed.

Table 1 presents a detailed snapshot of the demographic characteristics of the elderly in the sample, providing valuable insights into their age distribution, marital status, educational background, employment status, living arrangements, and cultural affiliations.

Table 1: Background characteristics of the study population

[Table 1 here]

The data shows that a significant majority (61.5%) of the elderly individuals in the study fell within the 60–69 age groups. This age group's higher representation suggests it is the most prevalent among the elderly participants, which could impact various aspects of their socioeconomic status and health outcomes. Marital status among the elderly is also prominently featured in the table, with nearly three-quarters of them reported as currently married. Of this group, the majority were males (71.87%). This finding highlights the prevalence of marriage among older adults, which can influence their support networks and overall well-being in later life. Educational attainment among the elderly reveals that a significant proportion (73%) had never attended a formal educational institution. This finding underscores the potential disparities in access to education across generations and its implications for lifelong learning opportunities and socio-economic mobility in old age. Employment status data indicates that nearly half (49.4%) of the elderly individuals were still working. This finding challenges stereotypes of labour force participation of the elderly and underscores the diverse economic contributions and roles elderly individuals play in society, especially in rural areas of India. Living arrangements among the elderly varied widely, with approximately 44% living with their spouse and children. In contrast, a smaller percentage (3%) lived alone. These figures reflect the diverse household compositions among older adults and highlight the importance of familial support structures in aging populations in the study area. Ethnic and religious affiliations were also notable aspects of the study findings. Around half of the elderly participants identified as belonging to Other Backward Castes (OBCs), while Hinduism was the predominant religion among the sample, with approximately 80% identifying as Hindu. These cultural dimensions provide context for understanding the study's socio-cultural diversity and practices among older individuals.

3.1 Bivariate Analysis

The bivariate analysis of depression among elderly individuals, examining various socioeconomic and demographic factors, is represented in Table 2. The findings reveal significant variations across different categories, shedding light on the complex interplay between these factors and mental health outcomes in older adults. The data highlights a notable gender disparity in depression rates, with 38.4% of females reporting depression compared to their male counterparts. This suggests that gender may be a critical factor influencing susceptibility to depression among elderly populations, possibly related to differences in social roles, life experiences, and biological factors. Marital status also emerges as a significant determinant of depression among the elderly. Individuals who were not currently married (including widowed, divorced, or separated) showed a higher prevalence of depression at 39.9%, compared to 32.4% among those who were married. This disparity emphasises the importance of social support and companionship in mitigating depressive symptoms among the elderly population. Educational attainment is another factor linked to depression among the elderly. The data indicates that 37% of elderly individuals who had never attended any formal school reported experiencing depression.

Table 2: Prevalence of Depression among Elderly with Depression by Demographic and Socioeconomic Characteristics.

[Table 2 here]

This suggests that lower levels of education may contribute to higher vulnerability to mental health issues in later life, potentially due to reduced cognitive stimulation and fewer opportunities for social interaction. Socioeconomic status also plays a significant role in depression prevalence among the elderly. Living arrangement, for instance, has a clear effect with living alone being very difficult for the elderly. Data suggests that prevalence of depression among ST elderly is more. of A clear gradient is observed across wealth quintiles, with approximately two-thirds of elderly individuals in the poorest (38.18%) and middle (39.85%) quintiles reporting depression, compared to 27.86% in the richest quintile. This gradient emphasizes the impact of economic factors on mental health outcomes, with lower socioeconomic status correlating with higher rates of depression.

3.2 Measurement of Social Networks using Confirmatory Factor Analyses (CFA)

Table 3 shows the factor loadings for the measurement model. The findings imply that each observable variable's coefficient of determination was moderate. The constructed variables representing friends, family, confidants, and children had standardized values of factor loadings comparable to those reported by Glass et al. (1997). Furthermore, the constructed variables in our study had the following composite reliabilities: 0.81 for the social network of children, 0.70 for relatives, 0.87 for friends, and 0.72 for the social network of confidants, which indicates an acceptable level of reliability.

Table 3: Factor loadings and composite reliability for children, relatives, friends, and confident network

[Table 3 here]

By combining the observable factors that made up each latent variable, Glass et al. (1997) were able to generate their particular network variables. Likewise, a similar methodology was adopted in our study to build the latent variables (children network, relative network, friend network, and confidant network).

Table 4 presents insights into the relationship between depression prevalence and various social networks among the elderly in rural Bihar. The study reveals distinct patterns across different types of social connections. A notable inverse association was found between depression and the family network. Elderly individuals with stronger family ties exhibited lower rates of depression, contrasting with the highest prevalence (38%) observed among those with a limited relatives network. This underscores the protective role of familial support in mitigating depressive symptoms among the elderly in rural settings, where family bonds often play a central role in social and emotional well-being. Interestingly, the study did not identify a significant link between depression and the children's network, suggesting that while family support is crucial, the specific influence of adult children on depressive symptoms may be less pronounced or variable among elderly populations. Moreover, the analysis highlighted the impact of friendships on mental health outcomes. Elderly individuals with a smaller network of friends reported higher rates of depression (35%) compared to those with a more extensive circle of friends (19%). This underscores the importance of social connections beyond immediate family in buffering against depressive symptoms in rural communities, where social isolation can exacerbate mental health issues.

Table 4: Percentage of Depression among Elderly by Specific Networks.

[Table 4 here]

Regarding confidants, the study noted a modest decrease in depression among those with a stronger confidant network (33%) compared to those with fewer confidants (35%). While the protective effect of confidants was evident, it was somewhat less pronounced compared to family and friends networks, suggesting varying degrees of influence depending on the type and depth of social relationships. Based on the p-value, relative networks are most significant in lowering depression among the elderly in rural Bihar.

3.3 Multivariate Analysis

Results from the multivariate analysis (Table 5) assessing the adjusted impacts of different social networks on depression among elderly individuals. The findings highlight varying degrees of influence across different types of social connections. Initially, the analysis indicates that the network involving children did not show a significant impact on depression, suggesting that relationships with adult children may not strongly influence depressive symptoms among elderly individuals in this context. This may be because of the migration trend of children and setting up their own families. However, significant protective effects against depression were observed for networks involving friends, family, and confidants. In Model 1, which assesses these relationships without adjusting for other factors, elderly individuals with stronger networks of friends, family, and confidants were less likely to experience depression.

Specifically, Model 1 findings indicate that elderly individuals with higher relative networks had lower odds of depression (OR = 0.753; 95% CI = 0.604–1.732) compared to those with lower relative networks. Similarly, those with moderate (middle) friends’ networks showed reduced odds of depression (OR = 0.861; 95% CI = 0.679–1.910) compared to those with lower friends networks. Likewise, higher friends’ networks were associated with lower odds of depression (OR = 0.634; 95% CI = 0.162–1.489) than lower friends networks. For confidant networks, having middle and higher levels were associated with 14% (OR = 0.863; 95% CI = 0.709–1.854) and 21% (OR = 0.792; 95% CI = 0.424–1.346) lower odds of depression, respectively, compared to lower levels. In Model 2, after controlling for specific socioeconomic and demographic factors, the protective effects of relatives and confidant networks slightly decreased but remained statistically significant. Elderly individuals with higher relative networks continued to show lower odds of depression (OR = 0.792; 95% CI = 0.683–1.834) compared to those with lower networks. Similarly, those with higher confidant networks had about 30% lower odds of depression (OR = 0.704; 95% CI = 0.424–1.346) compared to those with lower confidant networks.

Table 5: Binary Logistic Regression Model to Estimate the Odds Ratio (OR) with 95% Confidence Intervals (95% CI) for the Effect of Four Specific Social Networks on Depression among the Elderly population in rural Bihar.

[Table 5 here]

These findings underscore the importance of social networks, especially friends, family, and confidants, in buffering against depression among elderly populations. Strengthening these social connections through community-based interventions could improve mental health outcomes and well-being in aging populations.

The outcomes of this study indicate the high prevalence of depression among elderly people in rural Bihar, India. The findings show that there are notable differences in depression rates among the elderly in which several socioeconomic factors, including housing arrangements, marital status, educational attainment, and household economic position, have an effect. Significant gender differences in depression were found in this study among the rural older individuals. This result, however, might be at odds with earlier research done in Northern India and other locations, which found no gender differences in depression among older persons. This study is both needed and appropriate because social networks are becoming more crucial for the health and well-being of older people.

This study's findings show that stronger social networks involving relatives and trusted confidence had statistically significant protective benefits against depression in elderly living in rural areas. This emphasizes the need for strong kin networks for the elderly. This finding's strength stems from the study's new approach, which, for the first time in a rural Indian environment, used specialized social networks of older persons, considering criteria such as network members, proximity, and frequency of contacts.

The cross-sectional design of the study limits its ability to demonstrate causal links. People who cultivate pleasant and useful interactions or who feel less melancholy may have a more diverse network. In conclusion, the current research makes it impossible to identify whether specific network types or relationships "cause" outstanding health or whether good health encourages specific interactions, partnerships, or networks. While longitudinal studies may provide further insight into these possibilities, the relationship is likely bidirectional.

In conclusion, we say that our study found that strong social networks involving family and trusted confidants can protect rural older persons against depression, even after controlling for numerous socioeconomic and demographic variables. These findings highlight the need to adopt programs promoting community collaboration and building network centers for older people. Such efforts could encourage greater engagement among friends within or between communities, create chances for elders to participate in group activities, and promote physical activity, recreation, and knowledge-sharing to improve general health and well-being. The scarcity of studies on social networks and their impact on the depression of the elderly in India provides an excellent opportunity to investigate additional dimensions of health and well-being. Further research could provide useful insights for establishing targeted interventions and policies to improve the health and quality of life of older individuals in India.

Ethics approval and consent to participate

The present study used secondary data available in the public domain. The dataset has no identifiable information about the survey participants. Therefore, no ethical approval is required to conduct this study.

Funding statement

This study received no specific funding for this work from any public, commercial, or nonprofit funding organizations.

Consent for publication

Not applicable.

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Table 1 to 5 are available in the Supplementary Files section.

No competing interests reported.

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Social networks and mental health: A study of elderly in rural Bihar, India

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Abstract

1 Introduction

2 Methods

3 Results

4 Discussion and conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

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