Navigating Trust and Health in India: The Influence of Social Status and Neighbourhood Environment

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Addressing this gap, the study examines the association between interpersonal trust (both generalised and particularised) and health outcomes (self-rated health /SRH, and depression) among Indian adults, considering the moderating roles of social statuses (gender and caste) and macro-level factors like district-level income inequality. Methods The study draws on data from the World Health Organization's Study on global AGEing and adult health (SAGE) Wave-1, collected between 2007 and 2010. This dataset provides a comprehensive overview of health outcomes, including self-rated health (SRH) and depression, socio-cultural status of adults aged 18 and above in India. Additionally, district-level data on income inequality, quantified through the Gini index, were incorporated to examine the influence of contextual socioeconomic influence on the trust-health relationship. Multilevel regression analysis with interaction effects with social statuses and income inequality at district was employed in the analysis to investigate the intricate relationship between interpersonal trust (both generalised and particularised) and health outcomes. Results The study reveals that while generalised trust does not directly influence depression or SRH, particularised trust acts as a protective factor for both health outcomes. Gender-specific interaction effect shows that generalised trust reduces depression among males and improves SRH among females. Notably, caste does not significantly moderate the trust-health relationship. High district-level income inequality, however, modifies these associations: generalised trust is associated with improved SRH in areas of high inequality, whereas particularised trust correlates with increased depression in these districts. Conclusion The findings highlight the complex dynamics between interpersonal trust, social status, and income inequality in shaping health outcomes in India. Generalised trust emerges as a potential buffer against the health-detrimental effects of income inequality, providing crucial insights for developing targeted health interventions. These results offer valuable guidance for global health policymakers and practitioners in effectively allocating development aid to enhance health outcomes, especially among the most marginalised groups. Generalised trust particularised trust health India multilevel modelling social status macro-level factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The role of interpersonal trust in health and wellness has been a subject of expanding interest across various fields of social science (Szreter & Woolcock, 2004 ), public health (Kawachi, 2018 ; Kawachi et al., 2010 ) and epidemiology (Kawachi et al., 1997 ). Interpersonal trust is considered 'moral resource' that promotes mutual reciprocity within social networks and exerts a defensive impact on individuals' health and well-being. Trust might influence health through some possible mechanisms: providing social support, enforcing informal social control, production of collective efficacy, and the dispersion of health-related knowledge (Berkman et al., 2014 ; Kawachi et al., 2010 ). Previous theoretical studies have identified two distinct types of interpersonal trust, generalised trust and particularised trust, and highlighted their distinct role in individuals' lives (Yamagishi & Yamagishi, 1994 ). Generalised trust refers to a disposition to trust people in general, including strangers and individuals beyond one's immediate social circle. It allows individuals to trust and rely on others beyond their immediate social network and, can facilitate access to resources and support and has been found to contribute to a sense of control, provide health-related information, and promote healthy behaviours. On the other hand, particularised trust is developed within close social relationships, such as family, friends, neighbours, and colleagues, based on regular interactions and familiarity (Erickson, 2003 ). Due to such distinction, the scope of generalised and particularised trust differs, and their health-protective mechanisms are often exclusive to each other. Most of the research conducted in the Western context treated interpersonal trust as a monolithic unidimensional concept, typically measured as respondents' agreement that 'most people can be trusted' and thus overlook particularised trust or the trust in specific individuals like family, friends, and significant other. Due to the widespread emphasis on generalised trust, little is known about particularised trust and its influences on health. While many scholars have begun to highlight the theoretical differences between these two types of trusts, only a handful of studies have simultaneously examined the role of two widely discussed dimensions of interpersonal trust that are generalised and particularised (Glanville & Story, 2018 ; Kim, 2018 ). This study aims to distinguish generalised and particularised trust, concentrating on their possible impact on health. The research on the relationship between interpersonal trust and health has primarily focused on Western contexts, with limited attention given to non-Western developing countries. Recently, there has been a call to extend the assessment of interpersonal trust based on diverse health outcomes in low- and middle-income countries (LMICs). Particularly in the Indian context, only a few studies have investigated the role of interpersonal trust in relation to health (Samanta, 2014 ; Himanshu, 2019). In low- and middle-income countries (LMICs) such as India, the influence of interpersonal trust on health outcomes is especially critical as these countries often grapple with challenges like sparse social protection schemes and significant geographic and infrastructural barriers, limiting access to formal support services (Braveman & Tarimo, 2002 ; Peters et al., 2008). As a result, people commonly depend on their direct or indirect social networks, including friends of friends, for support and guidance on health-related decision making. The path to recovery from a health diagnosis involves a series of trust-dependent decisions, ranging from selecting reputable hospitals to ensuring adequate care post-surgery, and securing support in old age. Trust in one's social network becomes indispensable, particularly in the developing world, where these decisions are compounded by the inadequate reach of healthcare systems and the variability in the quality of healthcare services across different regions (Braveman and Tarimo, 2002 ; Peters and Muraleedharan, 2008 ). It is noteworthy that India has a public healthcare system, and the government hospitals are often free and highly subsidised otherwise. However, the healthcare system is marred with mismanagement, inefficiency, corruption, administrative failures, and overcrowding- to name a few (Peters et al., 2008; Kane et al., 2017). Against this background, health-related decision-making, particularly visiting a new doctor or a hospital for surgery, necessitates extensive background study and consultation from trustworthy sources. People prefer to visit physicians whom trustworthy neighbours suggest. Further, several operational decisions between the onset of symptoms to the final prognosis of a patient, primarily streamlined through health protocols in developed nations, are often outsourced to the patient/caregiver in India. In low-income countries, including India, informal caregivers, primarily family members, play a vital role in managing health emergencies and providing daily care for individuals with chronic illnesses and disabilities. This reliance on informal caregivers is more pronounced due to the absence of robust social security systems and formal long-term healthcare support, setting these regions apart from Western countries (Hannon et al., 2016 ; Thrush & Hyder, 2014). Unlike in developed countries, where comprehensive care services are often integrated into the public healthcare system, Indian hospitals primarily focus on acute conditions, leaving long-term care to be managed by families and informal networks (Bhattacharyya & Chatterjee, 2020; Narayan et al., 2015 ). Consequently, informal caregivers in India provide essential support without any financial or state-provided physical assistance, underscoring the critical role of interpersonal trust in navigating health emergencies and care particularised trustees fulfil health needs and take care health emergency situation. Interpersonal trust and health: Role of moderating factors Interpersonal trust, as an individual's network resource, is unevenly distributed across social groups, and this unequal access extends to the differential returns accrued from interpersonal trust (Lin, 2000 ). Women and ethnic minorities frequently engage in social networks based on shared social traits, promoting particularised trust. However, these networks may lack connections across diverse social backgrounds, resulting in reduced generalised trust. The uneven distribution of two forms of trust has the potential to exacerbate health disparities across social groups. The existing literature, which predominantly focuses on the Western context, reveals substantial variations in the association between interpersonal trust and health, particularly in relation to ethnic groups and gender (Engström et al., 2008; Eriksson et al., 2011; Kavanagh et al. 2006 ). These scholarly sources often argue that the psychosocial risk of poor health is higher among disadvantaged social groups due to factors such as material disadvantage, poor social integration and poor networks, including perceived and actual discriminatory life experiences. All these factors can influence health directly and indirectly by heightening the level of mistrust, aggression and pessimism towards others. Further, women and racial minorities may encounter additional challenges within tightly bonded networks due to their responsibility for emotional labour. Particularised trustees within close-knit networks may inadvertently contribute to psychological strain rather than stress reduction, especially for disadvantaged racial groups (Gaffey et al., 2019 ; Rhodes & Woods, 1995 ). The connection between interpersonal trust and health is further complicated by the broader socio cultural set up into which individuals are embedded. Although the evidence of a positive association between interpersonal trust and health status is strong, the strength and direction of the relationship vary greatly across broader socio-economic and cultural contexts (Hamamura, 2012 ; Islam et al., 2006 ). Ecological analyses focusing on developed countries revealed income inequality as one of the most prominent macrosocial characteristics that can modify the relationship between trust and health. According to the neighbourhood effects literature, income inequality imposes a detrimental effect on the quality of social relations and the level of generalised trust, leading to poor physical and mental health among its residents (Kawachi et al., 1997 ; Lynch et al., 2000 ; Marmot, 2002). Wilkinson's seminal study (1996) first linked higher income inequality in wealthier countries to lower life expectancy due to increased class conflict, deprivation, and reduced trust, leading to mortality. Kawachi et al. ( 1997 ) expanded this in a US-focused study, finding that aggregated trust mediates the relationship between income inequality and all-cause mortality. In a contrary perspective, Islam et al.'s ( 2006 ) systematic review underscores that social capital or generalised trust yields a beneficial influence on health in nations marked by pronounced income inequality, such as the United States. The review suggests social capital can mitigate the negative impact of income inequality on health by compensating with network-mediated forms of social support. This is particularly relevant in in less egalitarian countries where income inequality is high, health care is not equally accessible to achieve a decent level of health However, in more egalitarian nations like Canada and Sweden, characterized by robust welfare systems, the role of social capital or generalised trust in health is less pronounced, as the state ensures an equitable safeguarding of citizens' well-being. Furthermore, numerous studies have also highlighted that in communities characterised by income inequality and its associated disadvantages, such as corruption, high crime rates, and public sector failure, there is often a greater reliance on particularised or small group-based trust. This reliance serves as a way to compensate for limited resources and to safeguard close relationships from exploitation by outsiders or external social networks. It is evident that the intervention of reliable neighbours, good friends, and family members in low-income communities can help overcome (or partially compensate) financial hardship or medical emergencies (Brisson & Usher, 2007 ; Szreter & Woolcock, 2004 ). However, these communities lack the resources provided by expansive connections to larger networks, which could propel them forward beyond their current circumstances. (Woolcock and Narayan, 2000). With this background, this study aims to understand the effect of two types of interpersonal trust and their association with self-rated health and depression in the context of India using a large-scale, nationally representative sample (11230). Further, this study also examined the moderating role of individual social statuses pertinent to the Indian context and district-level income inequality in the relationship between trust and health outcomes. Hypotheses Micro-level factors: Social status Caste hierarchy and gender hierarchy are fundamental pillars of traditional Indian social structure, and they continue to be the dominant sources of socioeconomic inequality, health disparities, and equality of opportunity for upward social mobility. Gender is considered a symbol of inequality and disadvantage in India; research in India has highlighted the ways in which gender inequality and disadvantage intersect with biological, social, and cultural factors that impact women's health (Das Gupta et al., 2003 ; Dreze et al., 1999 ). Lower socioeconomic status, heavier reproductive roles, and gender-specific socialisation render women vulnerable to health issues. Persistent gender roles in family and work compound these challenges, disadvantaging women (Lastrapes & Rajaram, 2016 ; Oksuzyan et al., 2018 ). Existing research conducted in non-Indian contexts has highlighted the influence of gender stereotypes on networking opportunities. Traditional roles, limited public interaction, and gendered work expectations hinder broader social connections create challenges for women in building social connections beyond their immediate social circles. Consequently, women may experience lower levels of trust, resulting in a lack of social support that can adversely affect their health and well-being (Chua et al., 2016 ; McDonald & Day, 2010 ; Van Emmerik, 2006 ). Given India's deep gender disparities, considering gender is pivotal in understanding trust's health link. The caste system in India is a unique cultural context for understanding intergroup disparities and social relations between groups (Borooah et al., 2014 ; Deshpande, 2007 ). It originated from the ancient 'Varna' system, which divides society into four endogamous categories based on occupational groups, each granting different levels of power and prestige (Berreman, 1972 ). Caste is a birth-ascriptive social status and shares similar features, functions, and implications as race. (Berreman, 1967 ; Milner, 1994 ). Caste (like race in the United States) is a significant determinant of life opportunities in India, affecting the availability of network resources. The most socially and economically disadvantaged group is the scheduled castes (SCs), historically subjected to discrimination and oppression by upper caste groups in a variety of spheres of life, including in education, the jobs market, and the social justice system. Although the idea of caste and its impact on society have changed substantially, it remains a significant driver of disparities, creating social and political tension and inter-community distrust due to caste-based discrimination, marginalisation, and alienation (Fontaine & Yamada, 2014 ; Himanshu, 2018 ). The lack of trust and mutual reciprocity among social groups can increase anxiety and stress, affecting physical and mental health (Wilkinson & Pickett, 2009 ; Wilkinson et al., 1998). Caste-based social exclusion in India has a significant impact on health and increases the risk of illness for marginalised castes such as the scheduled caste. Health indicators for these groups have consistently lagged behind those of middle and upper castes due to poor living conditions, limited opportunities for social mobility, and discrimination in healthcare services (Acharya, 2007 ; Borooah, 2010 ; Nayar et al., 2007 ). This paper explores the relationship between interpersonal trust and health through the lens of caste, recognising that group differences involve distinct amounts and types of trust that can differentially impact health. Motivated by the discussion presented above, I propose the following three hypotheses: the impact of gender and caste on the relationship between interpersonal trust and health. H1 Men will have higher health benefits from generalised and particularised trust than women. H2 Scheduled caste members will have lower health benefits from the generalised and particularised trust than non-scheduled caste members. Macro-level factor Income inequality Despite experiencing significant economic growth, India has witnessed a rise in economic inequality at the national level and within and between states (Sen & Himanshu, 2004; Bandyopadhyay, 2021 ). Such differences are partly due to the growing divergences of income and non-inclusive economic development within the country that has existed since independence (Himanshu, 2018 ). India provides an excellent case study due to its distinct extremes and also for the fact that the distance between the richest and poorest states has increased substantially over the post-independence period (Bandyopadhyay, 2021 ). The Indian context exemplifies significant disparities and highlights the contrasting fortunes of states with evidence of divergence, polarisation, and the formation of distinct economic clubs. While some northern and western states (Haryana, Maharashtra, Punjab, and Gujarat) have enjoyed sustained prosperity, several southern states (such as Karnataka, Kerala, and Tamil Nadu) have experienced notable economic growth. However, a disconcerting pattern of persistent poverty remains in certain states like Assam, Bihar, Odisha, Madhya Pradesh, and Rajasthan over an extended period (Bandyopadhyay, 2021 ). Further, Intra-state inequality indices tend to be higher in districts with higher levels of living and development than in districts with lower levels of development (Mohanty et al., 2016 ). Analyses of the role of inequality in public health tend to be lower in the poorest countries, specially at the subnational level. This research sheds light on the importance of interpersonal trust in the context of income disparities at districts and its potential impact on the health of adults. In the context of India, districts stand as the most basic administrative entities where elected district councils formulate plans for infrastructure, development, and various essential services (Mohanty et al., 2019 ). District-level analysis can play a crucial role in guiding decentralised planning and ensuring the success of health intervention programs aimed at reducing inequities in the country. In high-income inequality districts, individuals may experience negative emotions such as distrust, shame, and exclusion due to their heightened awareness of their comparative socioeconomic standing and relative isolation from the rest of the population. These negative emotions may lead to chronic stress and subsequently affect their health negatively. Therefore, the study proposes H3a. The health benefit of interpersonal trust (generalised and particularised trust ) on health status SRH and depression will be reduced in districts with a higher level of income inequality. On the other hand, in highly unequal districts, people may be compelled to rely on their social networks and interpersonal trust to access basic medical services or receive adequate care. In such cases, little amount of trust becomes particularly crucial as a "substitute" for the lack of formal infrastructure and health care services. Therefore the study proposes H3b . The health benefit of interpersonal trust (generalised trust and particularised trust ) on health status SRH and depression will enhance in districts with a higher level of income inequality. Data and methods The study used data from two sources: individual-level data from the WHO Study on global AGEing and adult health (SAGE) wave 1 (2007) for India. The SAGE Wave 1 India survey included 11,230 completed interviews from six states (Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh, and West Bengal), with 4,670 interviews with individuals aged 18–49 and 6,560 interviews with individuals aged 50 and over. The district level (contextual level) estimates of income inequality were taken from the published source (Mohanty et. al., 2016 ). Dependent variables Self-rated health is an indicator of subjective wellbeing. In the survey, respondents were asked “how would you rate your health today?” and answers are measured in a five-point scale 1 = very good 2 = good 3 = moderate 4 = bad 5 = very bad. I combined moderate, bad and very bad (3,4,5) as “bad health” and good and very good (1,2) as “good health” as ‘good health’. Hence the final version of SRH is measured as 0 = bad health 1 = good health. Mental health Mental illness is measured by diagnosed depression and symptom-based algorithms (Appendix). Respondents were asked if they were ever diagnosed with depression and if they had encountered depression and anxiety in the 12 months. The International Classification of Diseases, 10th Edition (ICD-10-DCR), was used to confirm the diagnosis of depression based on the symptoms reported. Depression is measured as dichotomous variable 0 = no depression, 1 = having depression. Interpersonal trust Generalised trust is measured by whether respondents have trust for generalised others in society Q1. “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people” ? Particularised is measured by whether respondents have someone they can confide in, Q1. “Do you have someone you can trust and confide in?” In the statistical analysis, both of the trust variables are treated as binary response: 0 = Not having trust, 1 = Having trust Social statuses Gender (1 = male) and Caste (1 = Scheduled caste) are dummy variables. Other socioeconomic variables Among demographic variables, age is measured as a continuous variable. Religion is measured in three categories ‘Hindu’, ‘Muslim’ and a dummy variable is created for each category. Marital status is also a dummy variable (1 = currently married) place of residence (urban = 1) is also a dummy variable. The respondent’s education level and household wealth quintile, have been taken as indicators of socio-economic status. Educational level is measured by respondents’ highest level of completed education and counted as 0= ‘no formal education’, 1= ‘less than primary school’, 2= ‘Primary school completed’, 3= ‘Secondary school completed’, 4= ‘High school (or equivalent) completed’, 5= ‘College/university completed’, 6= ‘Post-graduate degree completed’. These categories are further broadly grouped as 0= ‘less than high school’ and 1=‘high school and above’. The second indicator of SES are measured by household wealth quintile to capture the relative inequalities in income at households. A statistical division of sample households into five equal parts, based on wealth (assets). Quintile 1 contains the poorest households, and quintile 5, the richest households. We categorized wealth quintile into two groups 1–3 = ‘low income’ 4–5= ‘high income’. The final version of household income is 0 = Low income 1 = High income. Contextual level factor: Income inequality Income inequality for 136 districts is measured by Gini index. The district-level Gini index were obtained from the published source by Mohanty et al. ( 2016 ). Mohanty et al. ( 2016 ) utilized data from the 66th and 68th rounds of consumption expenditure surveys conducted in 2009–2010 and 2011–2012, respectively. Using this data, they calculated the Gini index at the district level to capture the extent of income disparities within each district. Analytical strategy: In this study, a two-level logistic regression model examines binary health variables. The initial model, called the null model, lacks predictors but features a random intercept. This intercept gauges how much the individual variability across districts contributes to odds in dependent variables. The null model's main goal is calculating the Intra-class correlation (ICC). This measures how much dependent variable variance traces back to district grouping. An ICC above zero suggests intra district outcome similarities, warranting a multi-level model. logit( π ij ) = α + u j (1) where πij represents the probability of an event ( P(Yi = 1) ) for individual i in district j , α is the predicted value of the outcome variable when x = 0 (i.e the intercept) District level effect is measured by the random intercept α j ( j....J ) a linear combination of a grand mean ( α ) and a deviation of ( u j ). u j is normally distributed var( u 0 j ) is the random intercept variance. A higher value indicates higher variation between districts ; (π2/3) ≈ 3.29 is the value standard logistic distribution. In subsequent models, both individual-level (L1) predictors and district-level (L2) predictors are included: logit(πij) = α + uj + βxij Here, βxij represents the coefficient values for the individual-level predictor xij. The terms α and β are the fixed effects, while (uj) represents the district-level random effect. logit(πij) = α + γzj + uj + βxij In addition to individual-level predictors, this model includes a district-level fixed effect γzj, where zj represents the district-level attribute. logit(πij) = α + γzj + uj + βxij + θjxij This model further introduces an interaction term (θjxij) between the district-level attribute zj and the individual-level predictor xij. Marginal effect In non-linear models, understanding the significance and interpretation of interaction terms can be challenging by solely examining the coefficient's magnitude and direction. To address this, we adopted a more accessible approach by calculating and visualizing the marginal effects of the interaction terms using the Stata 14 margin command. To further enhance the comprehension of the interaction effect, we plotted the marginal effects, enabling us to visualize the relationships between predictors and the outcome variable, taking into account their interactions. Table 1 Descriptive statistics for the variables used in the study Variables Description Mean/ Percentage (weighted) SD N Range /Categories Health indicators SRH 38.96 11,227 0 = not good 1 = good Mental health (Depression) 15.44 11,230 0 = not depressed 1 = depressed Trust Generalised trust 55.56 11,218 0 = no trust = yes Particularised trust 83.14 11,191 0 = no trust = yes SES and demographics Age 50.07 16.6 11,230 18–106 Male 42 11,230 0 = female 1 = male Urban 26.98 11,230 0 = rural 1 = urban Married 79.04 11,230 0 = never married 1 = married High school and above 18.95 11,229 0 = less than high school 1 = high school and above High income 47.08 11,159 0 = low income 1 = high income Scheduled caste 23.32 11,160 0 = non-scheduled caste 1 = scheduled caste Hindu 83.85 11,230 0 = non Hindu 1 = Hindu Muslim 12.30 11,230 0 = non-Muslim 1 = Muslim District level factor Gini index 0.28 0.06 11,225 0.17–0.47 Results The estimates by multilevel modelling for good SRH are shown in Table 2 . Model 1 is null and consists of only a constant term (β= -0.41, p = 0.000) with a district-level random parameter, which accounts for the variation in good self-rated health across districts (district level variance 0.18). The ICC for model 1 denotes that 5% of the variability in individuals' SRH is due to district-level. differences. In model 2, generalised trust does not significantly impact individuals' good SRH; particularised trust has shown a significant positive impact (β = 0.36, p = 0.000) on good SRH. Model 3 incorporates all individual levels, including trust variables. The association with particularised trust remains significant after adding individuals' socio-economic demographic characteristics; nonetheless, the strength of association drops from β = 0.36 to β = 0.21. Table 2 Mixed effect logistic regression models showing beta coefficient of SRH (good SRH = 1) with generalised trust, particularised trust, micro & macro level predictors and interaction effect. SRH SRH SRH SRH SRH SRH Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Individual/ micro level factors (level 1) Generalized trust (yes = 1) 0.06 0.09* 0.09* -0.45* 0.29 (0.04) (0.05) (0.05) (0.25) (0.42) Particularized trust (yes = 1) 0.36*** 0.21*** 0.23*** -0.38 -0.07 (0.06) (0.06) (0.06) (0.34) (0.50) Age -0.05*** -0.05*** -0.05*** -0.05*** (0.00) (0.00) (0.00) (0.00) Male (female = 0) 0.48*** 0.48*** 0.56** 0.40*** (0.05) (0.05) (0.17) (0.12) Scheduled caste (non-scheduled caste = 0) -0.05 -0.05 -0.25 -0.1 (0.06) (0.06) (0.19) (0.13) Hindu (yes1) 0.01 0.01 0.02 0.01 (0.13) (0.13) (0.13) (0.13) Muslim(yes = 1) -0.34 -0.35* -0.33* -0.34* (0.15) (0.15) (0.15) (0.15) Currently married (yes = 1) -0.05 -0.05 -0.05 -0.05 (0.06) (0.06) (0.06) (0.06) High school and above (less than high school = 0) 0.51*** 0.51*** 0.51*** 0.51*** (0.06) (0.06) (0.06) (0.06) High income (low income = 0) 0.21*** 0.22*** 0.22*** 0.22*** (0.05) (0.05) (0.05) (0.05) Urban(rural = 0) 0.23*** 0.23*** 0.23*** 0.24*** (0.07) (0.07) (0.07) (0.07) Generalized trust*male -0.18* -0.17* (0.09) (0.09) Generalized trust*scheduled caste 0.13 0.11 (0.1) (0.11) Particularized trust*male 0.24* 0.23* (0.13) (0.13) Particularized trust*scheduled caste 0 0 (0.14) (0.14) District/ macro level factors (Level2) Gini 0.17 -2.02* (0.84) (1.23) micro*macro interaction Generalised trust*gini 2.71*** (0.71) Particularised trust*gini 0.85 (1.03) Intercept -0.41*** -0.79*** 0.97*** 2.12*** 2.28*** 2.18*** (0.04) (0.08) (0.29) (0.46) (0.51) (0.61) Dist level variance 0.18 0.18 0.25 0.25 0.25 0.24 ICC 0.05 0.05 0.07 0.07 0.07 0.07 N 11222 11184 11042 11042 11042 11042 *p < 0.05 **p < 0.01 ***p < 0.001 Standard Error in parentheses In Model 4, after controlling for other individual and district-level predictors, the beta coefficient for generalised trust is 0.09 with a significance level (p < 0.05). This model additionally reveals a substantial increase in the district-level variance (from 0.18 to 0.25), implying that the SRH of individuals has strong contextual (district-level) components that reflect even after controlling individual-level and district-level income inequality. District-level Gini index does not show any association with good SRH after controlling individual-level trust and other individual-level factors. Model 5 incorporated interaction effect of social statuses (gender and caste), with generalised and particularised trust. The interactive effect of generalised trust with gender shows a significant negative effect (β=-0.18, p < 0.05) on good SRH for males compared to females. The interaction effect suggests that the health benefit of generalised trust is more pronounced for females than males (Fig. 1 a). The interactive effect of particularised trust on gender shows that compared to women, men will have good SRH (Fig. 1 b). Model 6 incorporated the interaction effect of district-level income inequality (Gini index) with individual level interpersonal trust. The interaction effect between Gini and generalised trust on SRH generalised trustor will have a higher benefit of good SRH in the context of high-income inequality rather than low-income inequality. Figures (2) demonstrates a notable spike in good SRH among highly generalised trustors versus non-trustors when income inequality (Gini) is at its peak (+ 1 SD), as opposed to moderate or low Gini levels. Table 3 provides the estimates for depression by multilevel modelling. Model 1 is null and consists of only a constant term (β= -1.77, p = 0.000) with a district-level random parameter, which accounts for the variation in having depression across districts (district-level variance 0.69). The ICC for model 1 denotes that 17% of the variability in individuals’ depression is due to district-level differences. In model 2, generalised trust doesn’t show any significant impact on having depression; particularised trust has shown a significant negative impact (β= -0.39 p = 0.000) on having depression. In Model 3, social statuses like gender and caste don’t have any significant direct influence on depression. In Model 4, district-level Gini index does not exhibit any link with depression, even after accounting for individual-level trust and other factors. In Model 5, the interaction between generalised trust and gender indicates a significant negative impact (β=-0.26, p < 0.05) on male depression compared to females. In simpler terms, this interaction highlights that men are less likely to experience depression than women in the context of higher generalised trust. Figures (3a) illustrates a notable decline in the predicted likelihood of depression for males among generalised trustors versus non-trustors, while for women, the likelihood of depression is higher among trustors. Model 6 shows that when income inequality is high, the effectiveness of particularised trust in reducing depression diminishes compared to situations of low-income inequality. This trend is reflected in Figure (3b), where having particularised trust is beneficial for depression reduction across varying income inequality levels. However, when the Gini index of inequality is -1 SD, trustors of particularised trust experience a significant decrease in depression levels. Table 3 Mixed effect logistic regression models showing beta coefficient of depression (having depression = 1) with generalised trust, particularised trust, micro & macro level predictors and interaction effect. Depression Depression Depression Depression Depression Depression Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Individual/ micro level factors (level 1) Generalized trust (yes = 1) 0.05 0.02 0.01 0.56* -0.3 (0.06) (0.06) (0.06) (0.32) (0.52) Particularized trust (yes = 1) -0.39*** -0.28*** -0.27*** -1.10* -0.59 (0.07) (0.07) (0.07) (0.43) (0.59) Age 0.03*** 0.03*** 0.03*** 0.03*** (0.00) (0.00) (0.00) (0.00) Male (female = 0) -0.09 -0.09 0.24 -0.07 (0.06) (0.06) (0.21) (0.14) Scheduled caste (non-scheduled caste = 0) -0.04 -0.04 -0.25 -0.28* (0.07) (0.07) (0.15) (0.16) Hindu (yes1) 0.22 0.22 0.22 0.24 (0.19) (0.19) (0.19) (0.19) Muslim(yes = 1) 0.45* 0.45* 0.45* 0.47* (0.21) (0.21) (0.21) (0.21) Currently married (yes1) -0.07 -0.07 -0.07 -0.07 (0.07) (0.07) (0.07) (0.07) High school and above (less than high school = 0) -0.35*** -0.35*** -0.35*** -0.35*** (0.10) (0.10) (0.10) (0.10) High income (low income = 0) -0.33*** -0.32*** -0.33*** -0.32*** (0.06) (0.06) (0.06) (0.06) Urban(rural = 0) -0.22* -0.22* -0.22* -0.22* (0.09) (0.09) (0.09) (0.09) Generalized trust*male -0.26* -0.27* (0.12) (0.12) Generalized trust*scheduled caste 0.27 0.07 (0.16) (0.14) Particularized trust*male 0.11 0.14 (0.15) (0.15) Particularized trust*scheduled caste 0.25 0.25 (0.17) (0.17) District/ macro level factors (Level2) Gini 0.73 -1.13 (1.39) (1.77) micro*macro interaction Generalised trust*Gini -0.69 (0.97) Particularised trust *Gini 2.67* (1.31) Intercept -1.77*** -1.53*** -3.18*** -3.30*** -3.37*** -2.81*** (0.08) (0.12) (0.45) (0.74) (0.70) (0.86) Dist level variance 0.69 0.69 0.76 0.74 0.76 0.75 ICC 0.17 0.17 0.19 0.18 0.19 0.18 N 11225 11185 11043 11043 11043 11043 *p < 0.05 **p < 0.01 ***p < 0.001 Standard Error in parentheses Discussion Overall, the results from the multilevel regression analysis showed that the association between interpersonal trust and health is mixed and depends on the type of trust (generalised and particularised) in India. Surprisingly, generalised trust does not exhibit a significant direct association with self-rated health (SRH) or depression. In contrast, particularised trust demonstrates a health-protective effect on both SRH and depression. These findings address two important gaps in existing research: firstly, they underscore the distinct impacts of generalised and particularised trust on the health status of Indian adults, and secondly, they highlight the heightened relevance of particularised trust as a predictor for SRH and depression, surpassing the influence of generalised trust. Although limited research exists on the differences between these two forms of trust and their health implications, two cross-national studies using World Values Survey (WVS) data across more than 50 countries have shown the protective effect of particularised trust on SRH, surpassing generalised trust (Glanville & Story, 2018 ; Kim, 2018 ). Similarly, a study in the Chinese context identified the distinct impact of bridging and bonding trust (equivalent to generalised and particularised trust) on SRH (Meng & Chen, 2014 ). Meng and Chen ( 2014 ) found that bonding trust positively influenced good SRH in urban and rural areas, while bridging trust only predicted good SRH in urban China. Further, particularised trust has a more robust connection in developing countries than in developed countries, specially for mental illness. Harpham et al. ( 2004 ), in a study based in Cali, Columbia, found that mental illness is negatively associated with thin/generalised trust (measured by respondents' agreement on the majority of people trustworthy in the neighbourhood) as opposed to thick /particularised trust (measured by the trust in friends and close neighbours). Similarly, few studies in the African context in Ghana and South Africa have pointed out the negative influence of individual level generalised trust on depressive symptoms (Adjaye Gbewonyo et al., 2018, 2019; Amegbor et al., 2020 ). In summary, these studies collectively suggest that the impact of trust on health outcomes is nuanced and can vary across different populations and contexts. Caste does not seem to play a significant role as a moderator in trust-health relationships (Fig. 4 ). However, gender does exert a noteworthy moderating impact on the association between generalised trust and depression and on both generalised and particularised trust with self-rated health (SRH). The gender-based moderation effect on the trust-health relationship further indicates that men derive a greater health advantage from having particularised trust to enhance SRH and generalised trust to alleviate depression. On the other hand, women benefit more (than men) from particularised trust concerning SRH improvement. Gender disparities in the health impacts of interpersonal trust can be linked to distinct roles men and women play in trusting connections. Women typically possess higher levels of particularised trust due to their active participation in close social circles. Despite similar social ties, notable gender differences in health outcomes persist. Interestingly, women often serve as essential "nodes" in these networks, offering vital support to other members (Kavanagh et al., 2006 ). Conversely, men's networks are broader but less intense, often relying heavily on their spouses for primary support. Notably, gender differences extend to trust use in marital relationships, consistently highlighting men's greater benefits from these bonds. Men's health gains from marriage often result from their spouses' active promotion of healthy habits and emotional backing. In contrast, women receive less care and support from husbands, leaning more on external confidants (Shumaker & Hill, 1991 ; Thoits, 1992 ; Umberson, 1987 ). The findings further validate that generalised trust positively impacts men's mental health more than women's. This discrepancy is partly due to women encountering difficulties in forming social networks beyond their immediate circles, leading to lower levels of accessible generalised trust. Since men tend to have more ready access to generalised trust, they naturally enjoy more substantial benefits (Antonucci & Akiyama, 1987 ). The interaction between generalised trust and gender favours women, especially concerning self-rated health (SRH). This implies that even though women might initially have limited access to generalised trust, once established, its impact on health becomes more prominent for them compared to men. In essence, women tend to derive stronger health benefits from generalised trust compared to men. The cross-level interaction among individual level trust and district-level gini index reveals some interesting findings (Fig. 4 ). Specifically, when generalised trust interacts with income inequality, there is a significant positive effect on good self-rated health (SRH). In essence, while generalised trust might not directly lead to a health-protective outcome in terms of SRH (Table 2 ), individuals who possess generalised trust can reap benefits if they reside in areas marked by high-income inequality. One way to interpret this finding is that within neighbourhoods or districts marked by pronounced inequality, social capital—particularly in the form of generalised trust—functions as a compensatory mechanism. This mechanism helps counterbalance infrastructural limitations and contributes to enhancing overall health. Prior literature suggests that generalised trust mitigates the negative impact of income inequality on health, particularly in the absence of sufficient government services, as individuals rely on interpersonal trust to navigate insecurity and vulnerability (Islam et al., 2006 ). While particularised trust has demonstrated a significant health protecting impact on depression, its interaction with district-level Gini suggests a contrasting outcome – a positive effect on having depression. Prior research has explored regions with high income inequality; economically disadvantaged communities often find themselves confined within tight-knit, insular social networks. Such close-knit, impoverished communities possess a wealth of particularised trust that aids them in coping, but they lack the resources of expansive bridging networks necessary for advancement (Woolcock & Narayan, 2000).While having particularised trust or a reliable support system can enhance health by offering informal assistance, it can also contribute to mental distress, particularly among caregivers. In a nutshell, particularised trust, especially concerning socioeconomic disadvantage, can have health-damaging consequences, as observed in some studies (Friedrichs & Blasius, 2003 ; Mitchell & LaGory, 2002 ). This phenomenon highlights the complex relationship between particularised trust, socioeconomic disadvantage, and its potential health consequences. Conclusion While a considerable body of research in India has primarily focused on systemic challenges like inadequate healthcare infrastructure, imbalanced resource distribution, and socioeconomic disparities, this study notably breaks new ground by highlighting the importance of investigating psychosocial resources as potential determinants of health. The findings of this study underscore the potential significance of interpersonal trust as a determinant of both self-rated health (SRH) and mental well-being indicated by depression. While direct effects of generalised trust on these health outcomes were not evident, the analysis reveals a strong health protective impact between particularised trust and the health outcomes. Besides the direct effects on health, both generalised and particularised interact with their social contexts and individual social statuses to generate various variants that influence health in numerous ways. In other words, the combined effect of interpersonal trust with social status and broader social factor significantly predict a higher/ lower risk of poor health. Although this study does not find any direct effect district-level income inequality, some interesting findings emerge from the interactive effect. Notably, the interactive effect of district level Gini index on generalised and particularised trust appear to be in line with previous studies showing the risks of relying too much on particularised trustees or bonding social networks may have a detrimental effect on mental health specially in district where, inequality is high (Friedrichs & Blasius, 2003 ). Further it confirms the protective effect of generalised trust or diverse social networks for SRH as they may help buffer against the detrimental influences of neighbourhood income inequality (Erickson, 2003 ). The buffering effect of generalised trust in high-income inequality districts indicates that communities that rely on networks to fulfil various needs can harness the power of these networks to mitigate poor health. In other words, if properly harnessed, interpersonal trust, specially generalised, can be a predominant avenue for Indians to maintain better health statuses. Further, this study suggests that the amount of trust available in the community may determine whether trust is helpful or harmful for depressive symptoms and SRH. In societies characterised by pervasive distrust, higher levels of distrust may correlate with poorer mental health and heightened suffering in relation to placing trust in others. Consequently, it becomes imperative to strengthen interpersonal trust both at the individual and contextual levels. This finding may aid in the development and tailoring of effective interventions for social trust and health in different communities. These findings could inform global health policymakers and practitioners in strategizing the more efficient and impactful allocation of limited development aid, aiming to enhance health outcomes for a wider populace or specifically targeting the most deprived groups. Exploring innovative ways to leverage network resources like health knowledge through creative interventions is critical in resource-limited settings, amid intense competition for development funds. To enhance the level of trust, the state can facilitate implementing robust social security measures, strengthening local institutions, ensuring resident safety, and providing comprehensive healthcare, insurance, and unemployment support. These efforts can reduce disparities, enhancing both trust and health outcomes. To further bolster social trust, government initiatives can focus on neighborhood-based programs, fostering community integration and empowerment. Adequate local resources like schools, clubs, or community activities can contribute to trust-building. Encouraging interactions beyond face-to-face encounters also shows potential for nurturing broader societal trust. Declarations Ethics approval and consent to participate The SAGE study was approved by research review board at the International Institute for Population Sciences, Mumbai and by the WHO, Geneva Ethical Review committee. Consent for publication Not applicable Availability of data and materials The study utilizes a secondary data which is available on formal request from the World Health Organization Multi-Country Studies Data Archive through its online platform (http://apps.who.int/ healthinfo/systems/surveydata/index.php/catalog) for researchers who meet the criteria for access to confidential data. Competing interests The author declare that they have no competing interests Funding Author did not receive any specific funding for this research Authors' contributions Conceptualization: Shrestha Saha, Study design: Shrestha Saha, Data curation and data analysis: Shrestha Saha Writing – original draft: Shrestha Saha Review & editing: Shrestha Saha Acknowledgements NA References Acharya, S. (2007). Health care utilization among Dalit children-understanding social discrimination and exclusion: A study in selected villages of Gujarat and Rajasthan. UNICEF-IIDS Study, Indian Institute of Dalit Studies. Adjaye-Gbewonyo, D., Rebok, G. W., Gross, A. L., Gallo, J. J., and Underwood, C. R. (2019). Assessing urban-rural differences in the relationship between social capital and depression among Ghanaian and south African older adults. PLoS One, 14(6): e0218620. Adjaye-Gbewonyo, K., Kawachi, I., Subramanian, S., and Avendano, M. (2018). High social trust associated with increased depressive symptoms in a longitudinal south African sample. Social Science & Medicine, 197:127–135. Amegbor, P. M., Braimah, J. A., Adjaye-Gbewonyo, D., Rosenberg, M. W., and Sabel, C. E. (2020). Effect of cognitive and structural social capital on depression among older adults in Ghana: A multilevel cross-sectional analysis. Archives of gerontology and geriatrics, 89:104045. Antonucci, T. C. and Akiyama, H. (1987). An examination of sex differences in social support among older men and women. Sex roles, 17(11):737–749. Bandyopadhyay, S. (2021). The persistence of inequality across Indian states: a time series approach. Review of Development Economics, 25(3):1150–1171. Berkman, L. F., Kawachi, I., and Glymour, M. M. (2014). Social epidemiology. Oxford University Press. Berreman, G. D. (1967). Caste as social process. Southwestern Journal of Anthropology, 23(4):351–370. Berreman, G. D. (1972). Race, caste, and other invidious distinctions in social stratification. Race,385–414. Bhattacharyya, T. and Chopra Chatterjee, S. (2020). Experience of family caregivers of hospitalized older people in kolkata, india. The International Journal of Health Planning and Management, 35(1):e81–e95. Borooah, V. (2010). Inequality in health outcomes in India: the role of caste and religion. Borooah, V. K., Diwakar, D., Mishra, V. K., Naik, A. K., and Sabharwal, N. S. (2014). Caste, inequality, and poverty in india: a re-assessment. Development Studies Research. An Open Access Journal, 1(1):279–294. Braveman, P. and Tarimo, E. (2002). Social inequalities in health within countries: not only an issue for affluent nations. Social science & medicine, 54(11):1621–1635. Brisson, D. and Usher, C. L. (2007). Conceptualizing and measuring bonding social capital in low-income neighborhoods. Journal of social service research, 34(1):1–11. Chua, V., Mathews, M., and Loh, Y. C. (2016). Social capital in Singapore: Gender differences, ethnic hierarchies, and their intersection. Social Networks, 47:138–150. Das Gupta, M., Zhenghua, J., Bohua, L., Zhenming, X., Chung, W., and Hwa-Ok, B. (2003). Why is son preference so persistent in east and south Asia? a cross-country study of China, India and the republic of Korea. The Journal of Development Studies, 40(2):153–187. Deshpande, A. (2007). Overlapping identities under liberalization: Gender and caste in India. Economic Development and Cultural Change, 55(4):735–760. Dreze, J., Sen, A., et al. (1999). India: Economic development and social opportunity. OUP Catalogue. EngstrÅNom, K., Mattsson, F., JÅNarleborg, A., and Hallqvist, J. (2008). Contextual social capital as a risk factor for poor self-rated health: a multilevel analysis. Social Science & Medicine, 66(11):2268–2280. Erickson, B. (2003). Social networks: The value of variety. Contexts, 2(1):25–31. Fontaine, X. and Yamada, K. (2014). Caste comparisons in India: Evidence from subjective well-being data. World Development, 64:407–419. Friedrichs, J. r. and Blasius, J. r. (2003). Social norms in distressed neighbourhoods: testing the Wilson hypothesis. Housing Studies, 18(6):807–826. Fukuyama, F. (1995). Trust: The social virtues and the creation of prosperity, volume 99. Free press New York. Fukuyama, F. (2001). Social capital, civil society and development. Third world quarterly, 22(1):7–20. Gaffey, A. E., Aranda, F., Burns, J. W., Purim-Shem-Tov, Y. A., Burgess, H. J., Beckham, J. C., Bruehl, S., and Hobfoll, S. E. (2019). Race, psychosocial vulnerability and social support differences in inner-city women’s symptoms of posttraumatic stress disorder. Anxiety, Stress, & Coping, 32(1):18–31. Glanville, J. L. and Story, W. T. (2018). Social capital and self-rated health: Clarifying the role of trust. Social Science Research, 71:98–108. Hamamura, T. (2012). Social class predicts generalized trust but only in wealthy societies. Journal of Cross-Cultural Psychology, 43(3):498–509. Hannon, B., Zimmermann, C., Knaul, F. M., Powell, R. A., Mwangi-Powell, F. N., and Rodin, G. (2016). Provision of palliative care in low-and middle-income countries: overcoming obstacles for effective treatment delivery. J Clin Oncol, 34(1):62–8. Harpham, T., Grant, E., and Rodriguez, C. (2004). Mental health and social capital in Cali, Colombia. Social science & medicine, 58(11):2267–2277. Himanshu (2018). India inequality report 2018: Widening gaps. Himanshu, H., Arokiasamy, P., and Talukdar, B. (2019). Illustrative effects of social capital on health and quality of life among older adult in India: Results from who-sage India. Archives of gerontology and geriatrics, 82:15–21. Islam, M. K., Merlo, J., Kawachi, I., LindstrÅNom, M., and Gerdtham, U.-G. (2006). Social capital and health: does egalitarianism matter? a literature review. International journal for equity in health, 5(1):1–28. Kane, S. and Calnan, M. (2017). Erosion of trust in the medical profession in india: time for doctors to act. International journal of health policy and management, 6(1):5. Kavanagh, A. M., Bentley, R., Turrell, G., Broom, D. H., and Subramanian, S. (2006). Does gender modify associations between self rated health and the social and economic characteristics of local environments? Journal of Epidemiology & Community Health, 60(6):490–495. Kawachi, I. (2018). Trust and population health. The Oxford handbook of social and political trust, pages 447–470. Kawachi, I., Bird, C., Fremont, A., and Zimmermans, S. (2010). Handbook of medical sociology. Handbook of medical sociology, 2010:18–32. Kawachi, I., Kennedy, B. P., Lochner, K., and Prothrow-Stith, D. (1997). Social capital, income inequality, and mortality. American journal of public health, 87(9):1491–1498. Kim, H.-S. (2018). Particularized trust, generalized trust, and immigrant self-rated health: cross national analysis of world values survey. Public health, 158:93–101. Lastrapes, W. D. and Rajaram, R. (2016). Gender, caste and poverty in India: Evidence from the national family health survey. Eurasian Economic Review, 6(2):153–171. Lin, N. (2000). Inequality in social capital. Contemporary sociology, 29(6):785–795. Lynch, J.W., Smith, G. D., Kaplan, G. A., and House, J. S. (2000). Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions. Bmj 320(7243):1200–1204. Marmot, M. and Wilkinson, R. G. (2001). Psychosocial and material pathways in the relation between income and health: a response to lynch et al. Bmj, 322(7296):1233–1236. McDonald, S. and Day, J. C. (2010). Race, gender, and the invisible hand of social capital. Sociology Compass, 4(7):532–543. Meng, T. and Chen, H. (2014). A multilevel analysis of social capital and self-rated health: evidence from China. Health & place, 27:38–44. Milner, M. (1994). Status and sacredness: A general theory of status relations and an analysis of Indian culture. Oxford University Press on Demand. Mitchell, C. U. and LaGory, M. (2002). Social capital and mental distress in an impoverished community. City & Community, 1(2):199–222. Mohanty, S. K., Govil, D., Chauhan, R. K., Kim, R., and Subramanian, S. (2016). Estimates of poverty and inequality in the districts of India, 2011–2012. Journal of Development Policy and Practice, 1(2):142–202. Mohanty, S. K., Mishra, U. S., and Chauhan, R. K. (2019). Contextualising demographic and development divide in districts of india. In The Demographic and Development Divide in India , pages 1–15. Springer. Narayan, S. M., Varghese, M., Hepburn, K., Lewis, M., Paul, I., and Bhimani, R. (2015). Caregiving experiences of family members of persons with dementia in south india. American Journal of Alzheimer’s Disease & Other Dementias®, 30(5):508–516. Nayar, K. et al. (2007). Social exclusion, caste & health: a review based on the social determinants framework. Indian Journal of Medical Research, 126(4):355. Oksuzyan, A., Singh, P. K., Christensen, K., and Jasilionis, D. (2018). A cross-national study of the gender gap in health among older adults in India and China: Similarities and disparities. The Gerontologist, 58(6):1156–1165. Peters, D. H. and Muraleedharan, V. (2008). Regulating india’s health services: to what end? what future? Social science & medicine , 66(10):2133–2144. Rhodes, J. E. and Woods, M. (1995). Comfort and conflict in the relationships of pregnant, minority adolescents: Social support as a moderator of social strain. Journal of Community Psychology, 23(1):74–84. Samanta, T. (2014). Household context, social capital and wellbeing of older adults in India. Technical report, BKPAI Working Paper Series II. Sen, A. and Himanshu (2004). Poverty and inequality in india: Ii: Widening disparities during the 1990s. Economic and Political Weekly, pages 4361–4375. Shumaker, S. A. and Hill, D. R. (1991). Gender differences in social support and physical health. Health psychology, 10(2):102. Son, J. (2012). Social capital and institutional constraints: A comparative analysis of China, Taiwan and the US. Routledge. Szreter, S. and Woolcock, M. (2004). Health by association? social capital, social theory, and the political economy of public health. International journal of epidemiology, 33(4):650–667. Thoits, P. A. (1992). Identity structures and psychological well-being: Gender and marital status comparisons. Social psychology quarterly, pages 236–256. Umberson, D. (1987). Family status and health behaviors: Social control as a dimension of social integration. Journal of health and social behavior, pages 306–319. Van Emmerik, I. H. (2006). Gender differences in the creation of different types of social capital: A multilevel study. Social networks, 28(1):24–37. Wilkinson, R. (1996). Unhealthy societies: The afflictions of inequality. Routledge. Wilkinson, R. G. and Pickett, K. E. (2009). Income inequality and social dysfunction. Annual review of sociology, 35:493–511. Yamagishi, T. and Yamagishi, M. (1994). Trust and commitment in the United States and Japan. Motivation and emotion, 18 (2):129–166. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3947827","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273661924,"identity":"c5bc1eb1-e969-4090-92dc-656ad3ac977c","order_by":0,"name":"Shrestha Saha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACCTB5gIEfLnKAKC0JBxgkG0jWYgBXSUiL/Ozehw9//rgjb3wjge0xzx8GOb4bCfi1GNw5bmzMk/DMcNuNBHZj3jYGY0mCWiTS2KQZEg4zArWwSfM2MCRuIKRFfkYa+88fCYftN88AagE6rJ6gFoYbaWwMPAmHEzdIgLSwMSQYEHTYjTRmaZ60w8kzzjxsk5zbJmE488wDgg5j/PjD5rBtf3vyMYk3f2zk+Y4TchgCMDYwwBLDKBgFo2AUjAIKAQBrgUS2XKRIpQAAAABJRU5ErkJggg==","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":true,"prefix":"","firstName":"Shrestha","middleName":"","lastName":"Saha","suffix":""}],"badges":[],"createdAt":"2024-02-11 07:15:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3947827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3947827/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-19826-7","type":"published","date":"2024-10-01T15:58:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51381719,"identity":"997777f5-d61e-4877-8efd-0e47089e7f9a","added_by":"auto","created_at":"2024-02-20 16:07:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131053,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction effect between (a) generalised trust \u0026amp; gender on SRH (b) particularised trust \u0026amp; gender on SRH\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3947827/v1/8857f735c459a68e890d95ed.png"},{"id":51381715,"identity":"049caeed-09b8-424f-a28d-5d0ff55dd31a","added_by":"auto","created_at":"2024-02-20 16:07:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction effect between district level Gini index and generalised trust on SRH\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3947827/v1/5d2f4cd7e6540578fe097e4c.png"},{"id":51381718,"identity":"7cc43cc3-c5d2-4fff-9684-fb131d9ed137","added_by":"auto","created_at":"2024-02-20 16:07:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction effect between (a) generalised trust and gender on depression (b) district level Gini index and particularised trust on depression\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3947827/v1/a73fcd92ef561623ba0372ec.png"},{"id":51381720,"identity":"071ebdc1-18de-4cde-8025-d4049aafc7f5","added_by":"auto","created_at":"2024-02-20 16:07:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106135,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDepicting the interactive effects of individual level and contextual level factors on trust-health relationships. The β values indicate significant interactive effects.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3947827/v1/6dc93f572097829a4755d195.png"},{"id":66096991,"identity":"a8551319-f43e-4cd8-88e1-3bcee5d42868","added_by":"auto","created_at":"2024-10-07 16:12:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1486076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3947827/v1/7b7702bb-97e5-414e-be83-a8f61e227933.pdf"},{"id":51381716,"identity":"81f15d32-282c-4daa-b13d-7704f564aa78","added_by":"auto","created_at":"2024-02-20 16:07:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18250,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixSSM.docx","url":"https://assets-eu.researchsquare.com/files/rs-3947827/v1/93eda46c3b918a6546ef9a36.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Navigating Trust and Health in India: The Influence of Social Status and Neighbourhood Environment","fulltext":[{"header":"Background","content":"\u003cp\u003eThe role of interpersonal trust in health and wellness has been a subject of expanding interest across various fields of social science (Szreter \u0026amp; Woolcock, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), public health (Kawachi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kawachi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and epidemiology (Kawachi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Interpersonal trust is considered 'moral resource' that promotes mutual reciprocity within social networks and exerts a defensive impact on individuals' health and well-being. Trust might influence health through some possible mechanisms: providing social support, enforcing informal social control, production of collective efficacy, and the dispersion of health-related knowledge (Berkman et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kawachi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Previous theoretical studies have identified two distinct types of interpersonal trust, generalised trust and particularised trust, and highlighted their distinct role in individuals' lives (Yamagishi \u0026amp; Yamagishi, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Generalised trust refers to a disposition to trust people in general, including strangers and individuals beyond one's immediate social circle. It allows individuals to trust and rely on others beyond their immediate social network and, can facilitate access to resources and support and has been found to contribute to a sense of control, provide health-related information, and promote healthy behaviours. On the other hand, particularised trust is developed within close social relationships, such as family, friends, neighbours, and colleagues, based on regular interactions and familiarity (Erickson, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Due to such distinction, the scope of generalised and particularised trust differs, and their health-protective mechanisms are often exclusive to each other. Most of the research conducted in the Western context treated interpersonal trust as a monolithic unidimensional concept, typically measured as respondents' agreement that 'most people can be trusted' and thus overlook particularised trust or the trust in specific individuals like family, friends, and significant other. Due to the widespread emphasis on generalised trust, little is known about particularised trust and its influences on health. While many scholars have begun to highlight the theoretical differences between these two types of trusts, only a handful of studies have simultaneously examined the role of two widely discussed dimensions of interpersonal trust that are generalised and particularised (Glanville \u0026amp; Story, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kim, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study aims to distinguish generalised and particularised trust, concentrating on their possible impact on health.\u003c/p\u003e \u003cp\u003eThe research on the relationship between interpersonal trust and health has primarily focused on Western contexts, with limited attention given to non-Western developing countries. Recently, there has been a call to extend the assessment of interpersonal trust based on diverse health outcomes in low- and middle-income countries (LMICs). Particularly in the Indian context, only a few studies have investigated the role of interpersonal trust in relation to health (Samanta, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Himanshu, 2019). In low- and middle-income countries (LMICs) such as India, the influence of interpersonal trust on health outcomes is especially critical as these countries often grapple with challenges like sparse social protection schemes and significant geographic and infrastructural barriers, limiting access to formal support services (Braveman \u0026amp; Tarimo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Peters et al., 2008). As a result, people commonly depend on their direct or indirect social networks, including friends of friends, for support and guidance on health-related decision making. The path to recovery from a health diagnosis involves a series of trust-dependent decisions, ranging from selecting reputable hospitals to ensuring adequate care post-surgery, and securing support in old age. Trust in one's social network becomes indispensable, particularly in the developing world, where these decisions are compounded by the inadequate reach of healthcare systems and the variability in the quality of healthcare services across different regions (Braveman and Tarimo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Peters and Muraleedharan, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It is noteworthy that India has a public healthcare system, and the government hospitals are often free and highly subsidised otherwise. However, the healthcare system is marred with mismanagement, inefficiency, corruption, administrative failures, and overcrowding- to name a few (Peters et al., 2008; Kane et al., 2017). Against this background, health-related decision-making, particularly visiting a new doctor or a hospital for surgery, necessitates extensive background study and consultation from trustworthy sources. People prefer to visit physicians whom trustworthy neighbours suggest.\u003c/p\u003e \u003cp\u003eFurther, several operational decisions between the onset of symptoms to the final prognosis of a patient, primarily streamlined through health protocols in developed nations, are often outsourced to the patient/caregiver in India. In low-income countries, including India, informal caregivers, primarily family members, play a vital role in managing health emergencies and providing daily care for individuals with chronic illnesses and disabilities. This reliance on informal caregivers is more pronounced due to the absence of robust social security systems and formal long-term healthcare support, setting these regions apart from Western countries (Hannon et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Thrush \u0026amp; Hyder, 2014). Unlike in developed countries, where comprehensive care services are often integrated into the public healthcare system, Indian hospitals primarily focus on acute conditions, leaving long-term care to be managed by families and informal networks (Bhattacharyya \u0026amp; Chatterjee, 2020; Narayan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consequently, informal caregivers in India provide essential support without any financial or state-provided physical assistance, underscoring the critical role of interpersonal trust in navigating health emergencies and care particularised trustees fulfil health needs and take care health emergency situation.\u003c/p\u003e \u003cp\u003eInterpersonal trust and health: Role of moderating factors\u003c/p\u003e \u003cp\u003eInterpersonal trust, as an individual's network resource, is unevenly distributed across social groups, and this unequal access extends to the differential returns accrued from interpersonal trust (Lin, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Women and ethnic minorities frequently engage in social networks based on shared social traits, promoting particularised trust. However, these networks may lack connections across diverse social backgrounds, resulting in reduced generalised trust. The uneven distribution of two forms of trust has the potential to exacerbate health disparities across social groups. The existing literature, which predominantly focuses on the Western context, reveals substantial variations in the association between interpersonal trust and health, particularly in relation to ethnic groups and gender (Engström et al., 2008; Eriksson et al., 2011; Kavanagh et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). These scholarly sources often argue that the psychosocial risk of poor health is higher among disadvantaged social groups due to factors such as material disadvantage, poor social integration and poor networks, including perceived and actual discriminatory life experiences. All these factors can influence health directly and indirectly by heightening the level of mistrust, aggression and pessimism towards others. Further, women and racial minorities may encounter additional challenges within tightly bonded networks due to their responsibility for emotional labour. Particularised trustees within close-knit networks may inadvertently contribute to psychological strain rather than stress reduction, especially for disadvantaged racial groups (Gaffey et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rhodes \u0026amp; Woods, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe connection between interpersonal trust and health is further complicated by the broader socio cultural set up into which individuals are embedded. Although the evidence of a positive association between interpersonal trust and health status is strong, the strength and direction of the relationship vary greatly across broader socio-economic and cultural contexts (Hamamura, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Ecological analyses focusing on developed countries revealed income inequality as one of the most prominent macrosocial characteristics that can modify the relationship between trust and health. According to the neighbourhood effects literature, income inequality imposes a detrimental effect on the quality of social relations and the level of generalised trust, leading to poor physical and mental health among its residents (Kawachi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Lynch et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Marmot, 2002). Wilkinson's seminal study (1996) first linked higher income inequality in wealthier countries to lower life expectancy due to increased class conflict, deprivation, and reduced trust, leading to mortality. Kawachi et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) expanded this in a US-focused study, finding that aggregated trust mediates the relationship between income inequality and all-cause mortality.\u003c/p\u003e \u003cp\u003eIn a contrary perspective, Islam et al.'s (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) systematic review underscores that social capital or generalised trust yields a beneficial influence on health in nations marked by pronounced income inequality, such as the United States. The review suggests social capital can mitigate the negative impact of income inequality on health by compensating with network-mediated forms of social support. This is particularly relevant in in less egalitarian countries where income inequality is high, health care is not equally accessible to achieve a decent level of health However, in more egalitarian nations like Canada and Sweden, characterized by robust welfare systems, the role of social capital or generalised trust in health is less pronounced, as the state ensures an equitable safeguarding of citizens' well-being. Furthermore, numerous studies have also highlighted that in communities characterised by income inequality and its associated disadvantages, such as corruption, high crime rates, and public sector failure, there is often a greater reliance on particularised or small group-based trust. This reliance serves as a way to compensate for limited resources and to safeguard close relationships from exploitation by outsiders or external social networks. It is evident that the intervention of reliable neighbours, good friends, and family members in low-income communities can help overcome (or partially compensate) financial hardship or medical emergencies (Brisson \u0026amp; Usher, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Szreter \u0026amp; Woolcock, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, these communities lack the resources provided by expansive connections to larger networks, which could propel them forward beyond their current circumstances. (Woolcock and Narayan, 2000).\u003c/p\u003e \u003cp\u003eWith this background, this study aims to understand the effect of two types of interpersonal trust and their association with self-rated health and depression in the context of India using a large-scale, nationally representative sample (11230). Further, this study also examined the moderating role of individual social statuses pertinent to the Indian context and district-level income inequality in the relationship between trust and health outcomes.\u003c/p\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003cp\u003eMicro-level factors:\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eSocial status\u003c/h2\u003e \u003cp\u003eCaste hierarchy and gender hierarchy are fundamental pillars of traditional Indian social structure, and they continue to be the dominant sources of socioeconomic inequality, health disparities, and equality of opportunity for upward social mobility.\u003c/p\u003e \u003cp\u003eGender is considered a symbol of inequality and disadvantage in India; research in India has highlighted the ways in which gender inequality and disadvantage intersect with biological, social, and cultural factors that impact women's health (Das Gupta et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Dreze et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Lower socioeconomic status, heavier reproductive roles, and gender-specific socialisation render women vulnerable to health issues. Persistent gender roles in family and work compound these challenges, disadvantaging women (Lastrapes \u0026amp; Rajaram, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oksuzyan et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExisting research conducted in non-Indian contexts has highlighted the influence of gender stereotypes on networking opportunities. Traditional roles, limited public interaction, and gendered work expectations hinder broader social connections create challenges for women in building social connections beyond their immediate social circles. Consequently, women may experience lower levels of trust, resulting in a lack of social support that can adversely affect their health and well-being (Chua et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; McDonald \u0026amp; Day, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Van Emmerik, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Given India's deep gender disparities, considering gender is pivotal in understanding trust's health link.\u003c/p\u003e \u003cp\u003eThe caste system in India is a unique cultural context for understanding intergroup disparities and social relations between groups (Borooah et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Deshpande, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). It originated from the ancient 'Varna' system, which divides society into four endogamous categories based on occupational groups, each granting different levels of power and prestige (Berreman, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Caste is a birth-ascriptive social status and shares similar features, functions, and implications as race. (Berreman, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Milner, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Caste (like race in the United States) is a significant determinant of life opportunities in India, affecting the availability of network resources. The most socially and economically disadvantaged group is the scheduled castes (SCs), historically subjected to discrimination and oppression by upper caste groups in a variety of spheres of life, including in education, the jobs market, and the social justice system.\u003c/p\u003e \u003cp\u003eAlthough the idea of caste and its impact on society have changed substantially, it remains a significant driver of disparities, creating social and political tension and inter-community distrust due to caste-based discrimination, marginalisation, and alienation (Fontaine \u0026amp; Yamada, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Himanshu, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The lack of trust and mutual reciprocity among social groups can increase anxiety and stress, affecting physical and mental health (Wilkinson \u0026amp; Pickett, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wilkinson et al., 1998). Caste-based social exclusion in India has a significant impact on health and increases the risk of illness for marginalised castes such as the scheduled caste. Health indicators for these groups have consistently lagged behind those of middle and upper castes due to poor living conditions, limited opportunities for social mobility, and discrimination in healthcare services (Acharya, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Borooah, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nayar et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This paper explores the relationship between interpersonal trust and health through the lens of caste, recognising that group differences involve distinct amounts and types of trust that can differentially impact health.\u003c/p\u003e \u003cp\u003eMotivated by the discussion presented above, I propose the following three hypotheses: the impact of gender and caste on the relationship between interpersonal trust and health.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eMen will have higher health benefits from generalised and particularised trust than women.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eScheduled caste members will have lower health benefits from the generalised and particularised trust than non-scheduled caste members.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eMacro-level factor\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIncome inequality\u003c/h3\u003e\n\u003cp\u003eDespite experiencing significant economic growth, India has witnessed a rise in economic inequality at the national level and within and between states (Sen \u0026amp; Himanshu, 2004; Bandyopadhyay, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such differences are partly due to the growing divergences of income and non-inclusive economic development within the country that has existed since independence (Himanshu, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). India provides an excellent case study due to its distinct extremes and also for the fact that the distance between the richest and poorest states has increased substantially over the post-independence period (Bandyopadhyay, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Indian context exemplifies significant disparities and highlights the contrasting fortunes of states with evidence of divergence, polarisation, and the formation of distinct economic clubs. While some northern and western states (Haryana, Maharashtra, Punjab, and Gujarat) have enjoyed sustained prosperity, several southern states (such as Karnataka, Kerala, and Tamil Nadu) have experienced notable economic growth. However, a disconcerting pattern of persistent poverty remains in certain states like Assam, Bihar, Odisha, Madhya Pradesh, and Rajasthan over an extended period (Bandyopadhyay, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further, Intra-state inequality indices tend to be higher in districts with higher levels of living and development than in districts with lower levels of development (Mohanty et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Analyses of the role of inequality in public health tend to be lower in the poorest countries, specially at the subnational level. This research sheds light on the importance of interpersonal trust in the context of income disparities at districts and its potential impact on the health of adults. In the context of India, districts stand as the most basic administrative entities where elected district councils formulate plans for infrastructure, development, and various essential services (Mohanty et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). District-level analysis can play a crucial role in guiding decentralised planning and ensuring the success of health intervention programs aimed at reducing inequities in the country. In high-income inequality districts, individuals may experience negative emotions such as distrust, shame, and exclusion due to their heightened awareness of their comparative socioeconomic standing and relative isolation from the rest of the population. These negative emotions may lead to chronic stress and subsequently affect their health negatively. Therefore, the study proposes\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3a.\u003c/b\u003e The health benefit of interpersonal trust (generalised and particularised trust ) on health status SRH and depression will be reduced in districts with a higher level of income inequality.\u003c/p\u003e \u003cp\u003eOn the other hand, in highly unequal districts, people may be compelled to rely on their social networks and interpersonal trust to access basic medical services or receive adequate care. In such cases, little amount of trust becomes particularly crucial as a \"substitute\" for the lack of formal infrastructure and health care services. Therefore the study proposes\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3b\u003c/b\u003e. The health benefit of interpersonal trust (generalised trust and particularised trust ) on health status SRH and depression will enhance in districts with a higher level of income inequality.\u003c/p\u003e "},{"header":"Data and methods","content":"\u003cp\u003eThe study used data from two sources: individual-level data from the WHO Study on global AGEing and adult health (SAGE) wave 1 (2007) for India. The SAGE Wave 1 India survey included 11,230 completed interviews from six states (Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh, and West Bengal), with 4,670 interviews with individuals aged 18–49 and 6,560 interviews with individuals aged 50 and over. The district level (contextual level) estimates of income inequality were taken from the published source (Mohanty et. al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDependent variables\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eSelf-rated health\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eis an indicator of subjective wellbeing. In the survey, respondents were asked “how would you rate your health today?” and answers are measured in a five-point scale 1 = very good 2 = good 3 = moderate 4 = bad 5 = very bad. I combined moderate, bad and very bad (3,4,5) as “bad health” and good and very good (1,2) as “good health” as ‘good health’. Hence the final version of SRH is measured as 0 = bad health 1 = good health.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eMental health\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eMental illness is measured by diagnosed depression and symptom-based algorithms (Appendix). Respondents were asked if they were ever diagnosed with depression and if they had encountered depression and anxiety in the 12 months. The International Classification of Diseases, 10th Edition (ICD-10-DCR), was used to confirm the diagnosis of depression based on the symptoms reported. Depression is measured as dichotomous variable 0 = no depression, 1 = having depression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eInterpersonal trust\u003c/h2\u003e\u003cp\u003eGeneralised trust is measured by whether respondents have trust for generalised others in society Q1. \u003cem\u003e“Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people”\u003c/em\u003e? Particularised is measured by whether respondents have someone they can confide in, Q1. \u003cem\u003e“Do you have someone you can trust and confide in?”\u003c/em\u003e In the statistical analysis, both of the trust variables are treated as binary response: 0 = Not having trust, 1 = Having trust\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eSocial statuses\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eGender (1 = male) and Caste (1 = Scheduled caste) are dummy variables.\u003c/p\u003e\u003ch3\u003eOther socioeconomic variables\u003c/h3\u003e\u003cp\u003eAmong demographic variables, age is measured as a continuous variable. Religion is measured in three categories ‘Hindu’, ‘Muslim’ and a dummy variable is created for each category. Marital status is also a dummy variable (1 = currently married) place of residence (urban = 1) is also a dummy variable. The respondent’s education level and household wealth quintile, have been taken as indicators of socio-economic status. Educational level is measured by respondents’ highest level of completed education and counted as 0= ‘no formal education’, 1= ‘less than primary school’, 2= ‘Primary school completed’, 3= ‘Secondary school completed’, 4= ‘High school (or equivalent) completed’, 5= ‘College/university completed’, 6= ‘Post-graduate degree completed’. These categories are further broadly grouped as 0= ‘less than high school’ and 1=‘high school and above’. The second indicator of SES are measured by household wealth quintile to capture the relative inequalities in income at households. A statistical division of sample households into five equal parts, based on wealth (assets). Quintile 1 contains the poorest households, and quintile 5, the richest households. We categorized wealth quintile into two groups 1–3 = ‘low income’ 4–5= ‘high income’. The final version of household income is 0 = Low income 1 = High income.\u003c/p\u003e\u003ch2\u003eContextual level factor:\u003c/h2\u003e\u003cp\u003e \u003cstrong\u003eIncome inequality\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eIncome inequality for 136 districts is measured by Gini index. The district-level Gini index were obtained from the published source by Mohanty et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Mohanty et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) utilized data from the 66th and 68th rounds of consumption expenditure surveys conducted in 2009–2010 and 2011–2012, respectively. Using this data, they calculated the Gini index at the district level to capture the extent of income disparities within each district.\u003c/p\u003e\u003cp\u003eAnalytical strategy:\u003c/p\u003e\u003cp\u003eIn this study, a two-level logistic regression model examines binary health variables. The initial model, called the null model, lacks predictors but features a random intercept. This intercept gauges how much the individual variability across districts contributes to odds in dependent variables.\u003c/p\u003e\u003cp\u003eThe null model's main goal is calculating the Intra-class correlation (ICC). This measures how much dependent variable variance traces back to district grouping. An ICC above zero suggests intra district outcome similarities, warranting a multi-level model.\u003c/p\u003e\u003cp\u003elogit(\u003cem\u003eπ\u003c/em\u003e\u003csub\u003eij\u003c/sub\u003e) = \u003cem\u003eα\u003c/em\u003e + \u003cem\u003eu\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e (1)\u003c/p\u003e\u003cp\u003ewhere πij represents the probability of an event ( P(Yi = 1) ) for individual i in district \u003cem\u003ej\u003c/em\u003e, \u003cem\u003eα\u003c/em\u003e is the predicted value of the outcome variable when \u003cem\u003ex\u003c/em\u003e = 0 (i.e the intercept) District level effect is measured by the random intercept \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003ej....J\u003c/em\u003e) a linear combination of a grand mean (\u003cem\u003eα\u003c/em\u003e) and a deviation of (\u003cem\u003eu\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e). \u003cem\u003eu\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is normally distributed\u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003evar(\u003cem\u003eu\u003c/em\u003e0\u003cem\u003ej\u003c/em\u003e) is the random intercept variance. A higher value indicates higher variation between districts ;\u003c/p\u003e\u003cp\u003e(π2/3) ≈ 3.29\u003c/p\u003e\u003cp\u003eis the value standard logistic distribution.\u003c/p\u003e\u003cp\u003eIn subsequent models, both individual-level (L1) predictors and district-level (L2) predictors are included:\u003c/p\u003e\u003cp\u003elogit(πij) = α + uj + βxij\u003c/p\u003e\u003cp\u003eHere, βxij represents the coefficient values for the individual-level predictor xij. The terms α and β are the fixed effects, while (uj) represents the district-level random effect.\u003c/p\u003e\u003cp\u003elogit(πij) = α + γzj + uj + βxij\u003c/p\u003e\u003cp\u003eIn addition to individual-level predictors, this model includes a district-level fixed effect γzj, where zj represents the district-level attribute.\u003c/p\u003e\u003cp\u003elogit(πij) = α + γzj + uj + βxij + θjxij\u003c/p\u003e\u003cp\u003eThis model further introduces an interaction term (θjxij) between the district-level attribute zj and the individual-level predictor xij.\u003c/p\u003e\u003ch2\u003eMarginal effect\u003c/h2\u003e\u003cp\u003eIn non-linear models, understanding the significance and interpretation of interaction terms can be challenging by solely examining the coefficient's magnitude and direction. To address this, we adopted a more accessible approach by calculating and visualizing the marginal effects of the interaction terms using the Stata 14 margin command. To further enhance the comprehension of the interaction effect, we plotted the marginal effects, enabling us to visualize the relationships between predictors and the outcome variable, taking into account their interactions.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for the variables used in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean/ Percentage (weighted)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRange /Categories\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth indicators\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,227\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = not good 1 = good\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMental health (Depression)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = not depressed 1 = depressed\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrust\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneralised trust\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,218\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = no trust = yes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticularised trust\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,191\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = no trust = yes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eSES and demographics\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18–106\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = female 1 = male\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = rural 1 = urban\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = never married 1 = married\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school and above\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,229\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = less than high school 1 = high school and above\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,159\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = low income 1 = high income\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScheduled caste\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,160\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = non-scheduled caste 1 = scheduled caste\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = non Hindu 1 = Hindu\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 = non-Muslim 1 = Muslim\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict level factor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGini index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,225\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17–0.47\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe estimates by multilevel modelling for good SRH are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Model 1 is null and consists of only a constant term (β= -0.41, p\u0026thinsp;=\u0026thinsp;0.000) with a district-level random parameter, which accounts for the variation in good self-rated health across districts (district level variance 0.18).\u003c/p\u003e \u003cp\u003eThe ICC for model 1 denotes that 5% of the variability in individuals' SRH is due to district-level. differences. In model 2, generalised trust does not significantly impact individuals' good SRH; particularised trust has shown a significant positive impact (β\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;=\u0026thinsp;0.000) on good SRH.\u003c/p\u003e \u003cp\u003eModel 3 incorporates all individual levels, including trust variables. The association with particularised trust remains significant after adding individuals' socio-economic demographic characteristics; nonetheless, the strength of association drops from β\u0026thinsp;=\u0026thinsp;0.36 to β\u0026thinsp;=\u0026thinsp;0.21.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed effect logistic regression models showing beta coefficient of SRH (good SRH\u0026thinsp;=\u0026thinsp;1) with generalised trust, particularised trust, micro \u0026amp; macro level predictors and interaction effect.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual/ micro level factors (level 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralized trust (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.45*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticularized trust (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (female\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.40***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScheduled caste (non-scheduled caste\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu (yes1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim(yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.35*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.33*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.34*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently married (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school and above (less than high school\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.51***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh income (low income\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban(rural\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralized trust*male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.18*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.17*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralized trust*scheduled caste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticularized trust*male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticularized trust*scheduled caste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict/ macro level factors (Level2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emicro*macro interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralised trust*gini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.71***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticularised trust*gini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.41***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.79***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.28***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.18***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDist level variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 Standard Error in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Model 4, after controlling for other individual and district-level predictors, the beta coefficient for generalised trust is 0.09 with a significance level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This model additionally reveals a substantial increase in the district-level variance (from 0.18 to 0.25), implying that the SRH of individuals has strong contextual (district-level) components that reflect even after controlling individual-level and district-level income inequality. District-level Gini index does not show any association with good SRH after controlling individual-level trust and other individual-level factors. Model 5 incorporated interaction effect of social statuses (gender and caste), with generalised and particularised trust. The interactive effect of generalised trust with gender shows a significant negative effect (β=-0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on good SRH for males compared to females. The interaction effect suggests that the health benefit of generalised trust is more pronounced for females than males (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The interactive effect of particularised trust on gender shows that compared to women, men will have good SRH (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Model 6 incorporated the interaction effect of district-level income inequality (Gini index) with individual level interpersonal trust. The interaction effect between Gini and generalised trust on SRH generalised trustor will have a higher benefit of good SRH in the context of high-income inequality rather than low-income inequality. Figures\u0026nbsp;(2) demonstrates a notable spike in good SRH among highly generalised trustors versus non-trustors when income inequality (Gini) is at its peak (+\u0026thinsp;1 SD), as opposed to moderate or low Gini levels.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides the estimates for depression by multilevel modelling. Model 1 is null and consists of only a constant term (β= -1.77, p\u0026thinsp;=\u0026thinsp;0.000) with a district-level random parameter, which accounts for the variation in having depression across districts (district-level variance 0.69). The ICC for model 1 denotes that 17% of the variability in individuals\u0026rsquo; depression is due to district-level differences. In model 2, generalised trust doesn\u0026rsquo;t show any significant impact on having depression; particularised trust has shown a significant negative impact (β= -0.39 p\u0026thinsp;=\u0026thinsp;0.000) on having depression. In Model 3, social statuses like gender and caste don\u0026rsquo;t have any significant direct influence on depression. In Model 4, district-level Gini index does not exhibit any link with depression, even after accounting for individual-level trust and other factors. In Model 5, the interaction between generalised trust and gender indicates a significant negative impact (β=-0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on male depression compared to females. In simpler terms, this interaction highlights that men are less likely to experience depression than women in the context of higher generalised trust. Figures\u0026nbsp;(3a) illustrates a notable decline in the predicted likelihood of depression for males among generalised trustors versus non-trustors, while for women, the likelihood of depression is higher among trustors. Model 6 shows that when income inequality is high, the effectiveness of particularised trust in reducing depression diminishes compared to situations of low-income inequality. This trend is reflected in Figure (3b), where having particularised trust is beneficial for depression reduction across varying income inequality levels. However, when the Gini index of inequality is -1 SD, trustors of particularised trust experience a significant decrease in depression levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed effect logistic regression models showing beta coefficient of depression (having depression\u0026thinsp;=\u0026thinsp;1) with generalised trust, particularised trust, micro \u0026amp; macro level predictors and interaction effect.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual/ micro level factors (level 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralized trust (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticularized trust (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.39***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.28***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.10*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (female\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScheduled caste (non-scheduled caste\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.28*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu (yes1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim(yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently married (yes1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school and above (less than high school\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.35***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh income (low income\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.32***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban(rural\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.22*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralized trust*male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.26*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.27*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralized trust*scheduled caste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticularized trust*male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticularized trust*scheduled caste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict/ macro level factors (Level2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emicro*macro interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralised trust*Gini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticularised trust *Gini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.67*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.77***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.37***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.81***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDist level variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 Standard Error in parentheses\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOverall, the results from the multilevel regression analysis showed that the association between interpersonal trust and health is mixed and depends on the type of trust (generalised and particularised) in India. Surprisingly, generalised trust does not exhibit a significant direct association with self-rated health (SRH) or depression. In contrast, particularised trust demonstrates a health-protective effect on both SRH and depression. These findings address two important gaps in existing research: firstly, they underscore the distinct impacts of generalised and particularised trust on the health status of Indian adults, and secondly, they highlight the heightened relevance of particularised trust as a predictor for SRH and depression, surpassing the influence of generalised trust.\u003c/p\u003e \u003cp\u003eAlthough limited research exists on the differences between these two forms of trust and their health implications, two cross-national studies using World Values Survey (WVS) data across more than 50 countries have shown the protective effect of particularised trust on SRH, surpassing generalised trust (Glanville \u0026amp; Story, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kim, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, a study in the Chinese context identified the distinct impact of bridging and bonding trust (equivalent to generalised and particularised trust) on SRH (Meng \u0026amp; Chen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Meng and Chen (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that bonding trust positively influenced good SRH in urban and rural areas, while bridging trust only predicted good SRH in urban China. Further, particularised trust has a more robust connection in developing countries than in developed countries, specially for mental illness. Harpham et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), in a study based in Cali, Columbia, found that mental illness is negatively associated with thin/generalised trust (measured by respondents' agreement on the majority of people trustworthy in the neighbourhood) as opposed to thick /particularised trust (measured by the trust in friends and close neighbours). Similarly, few studies in the African context in Ghana and South Africa have pointed out the negative influence of individual level generalised trust on depressive symptoms (Adjaye Gbewonyo et al., 2018, 2019; Amegbor et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In summary, these studies collectively suggest that the impact of trust on health outcomes is nuanced and can vary across different populations and contexts.\u003c/p\u003e \u003cp\u003eCaste does not seem to play a significant role as a moderator in trust-health relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, gender does exert a noteworthy moderating impact on the association between generalised trust and depression and on both generalised and particularised trust with self-rated health (SRH). The gender-based moderation effect on the trust-health relationship further indicates that men derive a greater health advantage from having particularised trust to enhance SRH and generalised trust to alleviate depression. On the other hand, women benefit more (than men) from particularised trust concerning SRH improvement. Gender disparities in the health impacts of interpersonal trust can be linked to distinct roles men and women play in trusting connections. Women typically possess higher levels of particularised trust due to their active participation in close social circles. Despite similar social ties, notable gender differences in health outcomes persist. Interestingly, women often serve as essential \"nodes\" in these networks, offering vital support to other members (Kavanagh et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Conversely, men's networks are broader but less intense, often relying heavily on their spouses for primary support. Notably, gender differences extend to trust use in marital relationships, consistently highlighting men's greater benefits from these bonds. Men's health gains from marriage often result from their spouses' active promotion of healthy habits and emotional backing. In contrast, women receive less care and support from husbands, leaning more on external confidants (Shumaker \u0026amp; Hill, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Thoits, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Umberson, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe findings further validate that generalised trust positively impacts men's mental health more than women's. This discrepancy is partly due to women encountering difficulties in forming social networks beyond their immediate circles, leading to lower levels of accessible generalised trust. Since men tend to have more ready access to generalised trust, they naturally enjoy more substantial benefits (Antonucci \u0026amp; Akiyama, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). The interaction between generalised trust and gender favours women, especially concerning self-rated health (SRH). This implies that even though women might initially have limited access to generalised trust, once established, its impact on health becomes more prominent for them compared to men. In essence, women tend to derive stronger health benefits from generalised trust compared to men.\u003c/p\u003e \u003cp\u003eThe cross-level interaction among individual level trust and district-level gini index reveals some interesting findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, when generalised trust interacts with income inequality, there is a significant positive effect on good self-rated health (SRH). In essence, while generalised trust might not directly lead to a health-protective outcome in terms of SRH (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), individuals who possess generalised trust can reap benefits if they reside in areas marked by high-income inequality. One way to interpret this finding is that within neighbourhoods or districts marked by pronounced inequality, social capital\u0026mdash;particularly in the form of generalised trust\u0026mdash;functions as a compensatory mechanism. This mechanism helps counterbalance infrastructural limitations and contributes to enhancing overall health. Prior literature suggests that generalised trust mitigates the negative impact of income inequality on health, particularly in the absence of sufficient government services, as individuals rely on interpersonal trust to navigate insecurity and vulnerability (Islam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile particularised trust has demonstrated a significant health protecting impact on depression, its interaction with district-level Gini suggests a contrasting outcome \u0026ndash; a positive effect on having depression. Prior research has explored regions with high income inequality; economically disadvantaged communities often find themselves confined within tight-knit, insular social networks. Such close-knit, impoverished communities possess a wealth of particularised trust that aids them in coping, but they lack the resources of expansive bridging networks necessary for advancement (Woolcock \u0026amp; Narayan, 2000).While having particularised trust or a reliable support system can enhance health by offering informal assistance, it can also contribute to mental distress, particularly among caregivers. In a nutshell, particularised trust, especially concerning socioeconomic disadvantage, can have health-damaging consequences, as observed in some studies (Friedrichs \u0026amp; Blasius, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Mitchell \u0026amp; LaGory, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This phenomenon highlights the complex relationship between particularised trust, socioeconomic disadvantage, and its potential health consequences.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhile a considerable body of research in India has primarily focused on systemic challenges like inadequate healthcare infrastructure, imbalanced resource distribution, and socioeconomic disparities, this study notably breaks new ground by highlighting the importance of investigating psychosocial resources as potential determinants of health.\u003c/p\u003e \u003cp\u003eThe findings of this study underscore the potential significance of interpersonal trust as a determinant of both self-rated health (SRH) and mental well-being indicated by depression. While direct effects of generalised trust on these health outcomes were not evident, the analysis reveals a strong health protective impact between particularised trust and the health outcomes. Besides the direct effects on health, both generalised and particularised interact with their social contexts and individual social statuses to generate various variants that influence health in numerous ways. In other words, the combined effect of interpersonal trust with social status and broader social factor significantly predict a higher/ lower risk of poor health.\u003c/p\u003e \u003cp\u003eAlthough this study does not find any direct effect district-level income inequality, some interesting findings emerge from the interactive effect. Notably, the interactive effect of district level Gini index on generalised and particularised trust appear to be in line with previous studies showing the risks of relying too much on particularised trustees or bonding social networks may have a detrimental effect on mental health specially in district where, inequality is high (Friedrichs \u0026amp; Blasius, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Further it confirms the protective effect of generalised trust or diverse social networks for SRH as they may help buffer against the detrimental influences of neighbourhood income inequality (Erickson, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The buffering effect of generalised trust in high-income inequality districts indicates that communities that rely on networks to fulfil various needs can harness the power of these networks to mitigate poor health. In other words, if properly harnessed, interpersonal trust, specially generalised, can be a predominant avenue for Indians to maintain better health statuses. Further, this study suggests that the amount of trust available in the community may determine whether trust is helpful or harmful for depressive symptoms and SRH. In societies characterised by pervasive distrust, higher levels of distrust may correlate with poorer mental health and heightened suffering in relation to placing trust in others. Consequently, it becomes imperative to strengthen interpersonal trust both at the individual and contextual levels.\u003c/p\u003e \u003cp\u003eThis finding may aid in the development and tailoring of effective interventions for social trust and health in different communities. These findings could inform global health policymakers and practitioners in strategizing the more efficient and impactful allocation of limited development aid, aiming to enhance health outcomes for a wider populace or specifically targeting the most deprived groups. Exploring innovative ways to leverage network resources like health knowledge through creative interventions is critical in resource-limited settings, amid intense competition for development funds. To enhance the level of trust, the state can facilitate implementing robust social security measures, strengthening local institutions, ensuring resident safety, and providing comprehensive healthcare, insurance, and unemployment support. These efforts can reduce disparities, enhancing both trust and health outcomes. To further bolster social trust, government initiatives can focus on neighborhood-based programs, fostering community integration and empowerment. Adequate local resources like schools, clubs, or community activities can contribute to trust-building. Encouraging interactions beyond face-to-face encounters also shows potential for nurturing broader societal trust.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SAGE study was approved by research review board at the International Institute for Population Sciences, Mumbai and by the WHO, Geneva Ethical Review committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilizes a secondary data which is available on formal request from \u0026nbsp;the World Health Organization Multi-Country Studies Data Archive through its online platform (http://apps.who.int/ healthinfo/systems/surveydata/index.php/catalog) for researchers who meet the criteria for access to confidential data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor did not receive any specific funding for this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Shrestha Saha,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy design: Shrestha Saha,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData curation and data analysis: Shrestha Saha\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: Shrestha Saha\u003c/p\u003e\n\u003cp\u003eReview \u0026amp; editing: Shrestha Saha\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcharya, S. (2007). Health care utilization among Dalit children-understanding social discrimination and exclusion: A study in selected villages of Gujarat and Rajasthan. UNICEF-IIDS Study, Indian Institute of Dalit Studies.\u003c/li\u003e\n\u003cli\u003eAdjaye-Gbewonyo, D., Rebok, G. W., Gross, A. L., Gallo, J. J., and Underwood, C. R. (2019). Assessing urban-rural differences in the relationship between social capital and depression among Ghanaian and south African older adults. PLoS One, 14(6): e0218620.\u003c/li\u003e\n\u003cli\u003eAdjaye-Gbewonyo, K., Kawachi, I., Subramanian, S., and Avendano, M. (2018). High social trust associated with increased depressive symptoms in a longitudinal south African sample. Social Science \u0026amp; Medicine, 197:127\u0026ndash;135.\u003c/li\u003e\n\u003cli\u003eAmegbor, P. M., Braimah, J. A., Adjaye-Gbewonyo, D., Rosenberg, M. W., and Sabel, C. E. (2020). Effect of cognitive and structural social capital on depression among older adults in Ghana: A multilevel cross-sectional analysis. Archives of gerontology and geriatrics, 89:104045.\u003c/li\u003e\n\u003cli\u003eAntonucci, T. C. and Akiyama, H. (1987). An examination of sex differences in social support among older men and women. Sex roles, 17(11):737\u0026ndash;749.\u003c/li\u003e\n\u003cli\u003eBandyopadhyay, S. (2021). The persistence of inequality across Indian states: a time series approach. Review of Development Economics, 25(3):1150\u0026ndash;1171.\u003c/li\u003e\n\u003cli\u003eBerkman, L. F., Kawachi, I., and Glymour, M. M. (2014). Social epidemiology. Oxford University Press.\u003c/li\u003e\n\u003cli\u003eBerreman, G. D. (1967). Caste as social process. Southwestern Journal of Anthropology, 23(4):351\u0026ndash;370.\u003c/li\u003e\n\u003cli\u003eBerreman, G. D. (1972). Race, caste, and other invidious distinctions in social stratification. Race,385\u0026ndash;414.\u003c/li\u003e\n\u003cli\u003eBhattacharyya, T. and Chopra Chatterjee, S. (2020). Experience of family caregivers of hospitalized older people in kolkata, india. The International Journal of Health Planning and Management, 35(1):e81\u0026ndash;e95.\u003c/li\u003e\n\u003cli\u003eBorooah, V. (2010). Inequality in health outcomes in India: the role of caste and religion.\u003c/li\u003e\n\u003cli\u003eBorooah, V. K., Diwakar, D., Mishra, V. K., Naik, A. K., and Sabharwal, N. S. (2014). Caste, inequality, and poverty in india: a re-assessment. Development Studies Research. An Open Access Journal, 1(1):279\u0026ndash;294.\u003c/li\u003e\n\u003cli\u003eBraveman, P. and Tarimo, E. (2002). Social inequalities in health within countries: not only an issue for affluent nations. Social science \u0026amp; medicine, 54(11):1621\u0026ndash;1635.\u003c/li\u003e\n\u003cli\u003eBrisson, D. and Usher, C. L. (2007). Conceptualizing and measuring bonding social capital in low-income neighborhoods. Journal of social service research, 34(1):1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eChua, V., Mathews, M., and Loh, Y. C. (2016). Social capital in Singapore: Gender differences, ethnic hierarchies, and their intersection. Social Networks, 47:138\u0026ndash;150.\u003c/li\u003e\n\u003cli\u003eDas Gupta, M., Zhenghua, J., Bohua, L., Zhenming, X., Chung, W., and Hwa-Ok, B. (2003). Why is son preference so persistent in east and south Asia? a cross-country study of China, India and the republic of Korea. The Journal of Development Studies, 40(2):153\u0026ndash;187.\u003c/li\u003e\n\u003cli\u003eDeshpande, A. (2007). Overlapping identities under liberalization: Gender and caste in India. Economic Development and Cultural Change, 55(4):735\u0026ndash;760.\u003c/li\u003e\n\u003cli\u003eDreze, J., Sen, A., et al. (1999). India: Economic development and social opportunity. OUP Catalogue.\u003c/li\u003e\n\u003cli\u003eEngstr\u0026Aring;Nom, K., Mattsson, F., J\u0026Aring;Narleborg, A., and Hallqvist, J. (2008). Contextual social capital as a risk factor for poor self-rated health: a multilevel analysis. Social Science \u0026amp; Medicine, 66(11):2268\u0026ndash;2280.\u003c/li\u003e\n\u003cli\u003eErickson, B. (2003). Social networks: The value of variety. Contexts, 2(1):25\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eFontaine, X. and Yamada, K. (2014). Caste comparisons in India: Evidence from subjective well-being data. World Development, 64:407\u0026ndash;419.\u003c/li\u003e\n\u003cli\u003eFriedrichs, J. r. and Blasius, J. r. (2003). Social norms in distressed neighbourhoods: testing the Wilson hypothesis. Housing Studies, 18(6):807\u0026ndash;826.\u003c/li\u003e\n\u003cli\u003eFukuyama, F. (1995). Trust: The social virtues and the creation of prosperity, volume 99. Free press New York.\u003c/li\u003e\n\u003cli\u003eFukuyama, F. (2001). Social capital, civil society and development. Third world quarterly, 22(1):7\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eGaffey, A. E., Aranda, F., Burns, J. W., Purim-Shem-Tov, Y. A., Burgess, H. J., Beckham, J. C., Bruehl, S., and Hobfoll, S. E. (2019). Race, psychosocial vulnerability and social support differences in inner-city women\u0026rsquo;s symptoms of posttraumatic stress disorder. Anxiety, Stress, \u0026amp; Coping, 32(1):18\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eGlanville, J. L. and Story, W. T. (2018). Social capital and self-rated health: Clarifying the role of trust. Social Science Research, 71:98\u0026ndash;108.\u003c/li\u003e\n\u003cli\u003eHamamura, T. (2012). Social class predicts generalized trust but only in wealthy societies. Journal of Cross-Cultural Psychology, 43(3):498\u0026ndash;509.\u003c/li\u003e\n\u003cli\u003eHannon, B., Zimmermann, C., Knaul, F. M., Powell, R. A., Mwangi-Powell, F. N., and Rodin, G. (2016). Provision of palliative care in low-and middle-income countries: overcoming obstacles for effective treatment delivery. J Clin Oncol, 34(1):62\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHarpham, T., Grant, E., and Rodriguez, C. (2004). Mental health and social capital in Cali, Colombia. Social science \u0026amp; medicine, 58(11):2267\u0026ndash;2277.\u003c/li\u003e\n\u003cli\u003eHimanshu (2018). India inequality report 2018: Widening gaps.\u003c/li\u003e\n\u003cli\u003eHimanshu, H., Arokiasamy, P., and Talukdar, B. (2019). Illustrative effects of social capital on health and quality of life among older adult in India: Results from who-sage India. Archives of gerontology and geriatrics, 82:15\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eIslam, M. K., Merlo, J., Kawachi, I., Lindstr\u0026Aring;Nom, M., and Gerdtham, U.-G. (2006). Social capital and health: does egalitarianism matter? a literature review. International journal for equity in health, 5(1):1\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eKane, S. and Calnan, M. (2017). Erosion of trust in the medical profession in india: time for doctors to act. International journal of health policy and management, 6(1):5.\u003c/li\u003e\n\u003cli\u003eKavanagh, A. M., Bentley, R., Turrell, G., Broom, D. H., and Subramanian, S. (2006). Does gender modify associations between self rated health and the social and economic characteristics of local environments? Journal of Epidemiology \u0026amp; Community Health, 60(6):490\u0026ndash;495.\u003c/li\u003e\n\u003cli\u003eKawachi, I. (2018). Trust and population health. The Oxford handbook of social and political trust, pages 447\u0026ndash;470.\u003c/li\u003e\n\u003cli\u003eKawachi, I., Bird, C., Fremont, A., and Zimmermans, S. (2010). Handbook of medical sociology. Handbook of medical sociology, 2010:18\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eKawachi, I., Kennedy, B. P., Lochner, K., and Prothrow-Stith, D. (1997). Social capital, income inequality, and mortality. American journal of public health, 87(9):1491\u0026ndash;1498.\u003c/li\u003e\n\u003cli\u003eKim, H.-S. (2018). Particularized trust, generalized trust, and immigrant self-rated health: cross national analysis of world values survey. Public health, 158:93\u0026ndash;101.\u003c/li\u003e\n\u003cli\u003eLastrapes, W. D. and Rajaram, R. (2016). Gender, caste and poverty in India: Evidence from the national family health survey. Eurasian Economic Review, 6(2):153\u0026ndash;171.\u003c/li\u003e\n\u003cli\u003eLin, N. (2000). Inequality in social capital. Contemporary sociology, 29(6):785\u0026ndash;795.\u003c/li\u003e\n\u003cli\u003eLynch, J.W., Smith, G. D., Kaplan, G. A., and House, J. S. (2000). Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions. Bmj 320(7243):1200\u0026ndash;1204.\u003c/li\u003e\n\u003cli\u003eMarmot, M. and Wilkinson, R. G. (2001). Psychosocial and material pathways in the relation between income and health: a response to lynch et al. Bmj, 322(7296):1233\u0026ndash;1236.\u003c/li\u003e\n\u003cli\u003eMcDonald, S. and Day, J. C. (2010). Race, gender, and the invisible hand of social capital. Sociology Compass, 4(7):532\u0026ndash;543.\u003c/li\u003e\n\u003cli\u003eMeng, T. and Chen, H. (2014). A multilevel analysis of social capital and self-rated health: evidence from China. Health \u0026amp; place, 27:38\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eMilner, M. (1994). Status and sacredness: A general theory of status relations and an analysis of Indian culture. Oxford University Press on Demand.\u003c/li\u003e\n\u003cli\u003eMitchell, C. U. and LaGory, M. (2002). Social capital and mental distress in an impoverished community. City \u0026amp; Community, 1(2):199\u0026ndash;222.\u003c/li\u003e\n\u003cli\u003eMohanty, S. K., Govil, D., Chauhan, R. K., Kim, R., and Subramanian, S. (2016). Estimates of poverty and inequality in the districts of India, 2011\u0026ndash;2012. Journal of Development Policy and Practice, 1(2):142\u0026ndash;202.\u003c/li\u003e\n\u003cli\u003eMohanty, S. K., Mishra, U. S., and Chauhan, R. K. (2019). Contextualising demographic and development divide in districts of india. In \u003cem\u003eThe Demographic and Development Divide in India\u003c/em\u003e, pages 1\u0026ndash;15. Springer.\u003c/li\u003e\n\u003cli\u003eNarayan, S. M., Varghese, M., Hepburn, K., Lewis, M., Paul, I., and Bhimani, R. (2015). Caregiving experiences of family members of persons with dementia in south india. American Journal of Alzheimer\u0026rsquo;s Disease \u0026amp; Other Dementias\u0026reg;, 30(5):508\u0026ndash;516.\u003c/li\u003e\n\u003cli\u003eNayar, K. et al. (2007). Social exclusion, caste \u0026amp; health: a review based on the social determinants framework. Indian Journal of Medical Research, 126(4):355.\u003c/li\u003e\n\u003cli\u003eOksuzyan, A., Singh, P. K., Christensen, K., and Jasilionis, D. (2018). A cross-national study of the gender gap in health among older adults in India and China: Similarities and disparities. The Gerontologist, 58(6):1156\u0026ndash;1165.\u003c/li\u003e\n\u003cli\u003ePeters, D. H. and Muraleedharan, V. (2008). Regulating india\u0026rsquo;s health services: to what end? what future? \u003cem\u003eSocial science \u0026amp; medicine\u003c/em\u003e, 66(10):2133\u0026ndash;2144.\u003c/li\u003e\n\u003cli\u003eRhodes, J. E. and Woods, M. (1995). Comfort and conflict in the relationships of pregnant, minority adolescents: Social support as a moderator of social strain. Journal of Community Psychology, 23(1):74\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eSamanta, T. (2014). Household context, social capital and wellbeing of older adults in India. Technical report, BKPAI Working Paper Series II.\u003c/li\u003e\n\u003cli\u003eSen, A. and Himanshu (2004). Poverty and inequality in india: Ii: Widening disparities during the 1990s. Economic and Political Weekly, pages 4361\u0026ndash;4375.\u003c/li\u003e\n\u003cli\u003eShumaker, S. A. and Hill, D. R. (1991). Gender differences in social support and physical health. Health psychology, 10(2):102.\u003c/li\u003e\n\u003cli\u003eSon, J. (2012). Social capital and institutional constraints: A comparative analysis of China, Taiwan and the US. Routledge.\u003c/li\u003e\n\u003cli\u003eSzreter, S. and Woolcock, M. (2004). Health by association? social capital, social theory, and the political economy of public health. International journal of epidemiology, 33(4):650\u0026ndash;667.\u003c/li\u003e\n\u003cli\u003eThoits, P. A. (1992). Identity structures and psychological well-being: Gender and marital status comparisons. Social psychology quarterly, pages 236\u0026ndash;256.\u003c/li\u003e\n\u003cli\u003eUmberson, D. (1987). Family status and health behaviors: Social control as a dimension of social integration. Journal of health and social behavior, pages 306\u0026ndash;319.\u003c/li\u003e\n\u003cli\u003eVan Emmerik, I. H. (2006). Gender differences in the creation of different types of social capital: A multilevel study. Social networks, 28(1):24\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eWilkinson, R. (1996). Unhealthy societies: The afflictions of inequality. Routledge.\u003c/li\u003e\n\u003cli\u003eWilkinson, R. G. and Pickett, K. E. (2009). Income inequality and social dysfunction. Annual review of sociology, 35:493\u0026ndash;511.\u003c/li\u003e\n\u003cli\u003eYamagishi, T. and Yamagishi, M. (1994). Trust and commitment in the United States and Japan. Motivation and emotion, 18 (2):129\u0026ndash;166.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Generalised trust, particularised trust, health, India, multilevel modelling, social status, macro-level factors","lastPublishedDoi":"10.21203/rs.3.rs-3947827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3947827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe research on the relationship between interpersonal trust and health has primarily focused on Western contexts, with scarce attention in developing contexts. Addressing this gap, the study examines the association between interpersonal trust (both generalised and particularised) and health outcomes (self-rated health /SRH, and depression) among Indian adults, considering the moderating roles of social statuses (gender and caste) and macro-level factors like district-level income inequality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study draws on data from the World Health Organization's Study on global AGEing and adult health (SAGE) Wave-1, collected between 2007 and 2010. This dataset provides a comprehensive overview of health outcomes, including self-rated health (SRH) and depression, socio-cultural status of adults aged 18 and above in India. Additionally, district-level data on income inequality, quantified through the Gini index, were incorporated to examine the influence of contextual socioeconomic influence on the trust-health relationship. Multilevel regression analysis with interaction effects with social statuses and income inequality at district was employed in the analysis to investigate the intricate relationship between interpersonal trust (both generalised and particularised) and health outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study reveals that while generalised trust does not directly influence depression or SRH, particularised trust acts as a protective factor for both health outcomes. Gender-specific interaction effect shows that generalised trust reduces depression among males and improves SRH among females. Notably, caste does not significantly moderate the trust-health relationship. High district-level income inequality, however, modifies these associations: generalised trust is associated with improved SRH in areas of high inequality, whereas particularised trust correlates with increased depression in these districts.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings highlight the complex dynamics between interpersonal trust, social status, and income inequality in shaping health outcomes in India. Generalised trust emerges as a potential buffer against the health-detrimental effects of income inequality, providing crucial insights for developing targeted health interventions. These results offer valuable guidance for global health policymakers and practitioners in effectively allocating development aid to enhance health outcomes, especially among the most marginalised groups.\u003c/p\u003e","manuscriptTitle":"Navigating Trust and Health in India: The Influence of Social Status and Neighbourhood Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 16:07:26","doi":"10.21203/rs.3.rs-3947827/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-15T10:23:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-11T21:35:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-08T02:45:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-29T23:49:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"eb5e4055-2f30-49a8-9968-3cf30b734c7b","date":"2024-03-19T11:24:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52ae6d95-64a4-4736-9524-589176c3a86e","date":"2024-03-19T10:48:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"d8d1ddba-826d-444f-9263-71a9abf18fd6","date":"2024-03-19T09:34:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-23T13:21:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15e5fe1e-d8bc-44a1-887d-dcc9d82044dc","date":"2024-02-19T11:24:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3e67dc5b-c76b-4a1a-8a98-6fa74105e253","date":"2024-02-18T14:18:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2864ba09-0452-4694-bedc-34c939532228","date":"2024-02-17T18:56:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"673ab978-4252-4836-8285-cd7366999e3f","date":"2024-02-17T18:32:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-17T18:28:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-17T18:26:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-17T11:17:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-17T11:15:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-02-11T07:03:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89c82596-6687-451b-9b1c-26857a2cd105","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-07T16:05:19+00:00","versionOfRecord":{"articleIdentity":"rs-3947827","link":"https://doi.org/10.1186/s12889-024-19826-7","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-10-01 15:58:15","publishedOnDateReadable":"October 1st, 2024"},"versionCreatedAt":"2024-02-20 16:07:26","video":"","vorDoi":"10.1186/s12889-024-19826-7","vorDoiUrl":"https://doi.org/10.1186/s12889-024-19826-7","workflowStages":[]},"version":"v1","identity":"rs-3947827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3947827","identity":"rs-3947827","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}