How migration and its types affect mental health in later life: a cross-sectional study among the older adults in India

Abstract

Background

Migration has extensive consequences on socioeconomic and health status among older adults at the place of destination; various factors in the migration process affect mental health, a prominent social determinant of health. However, no evidence exists of migration and health outcomes among India’s older adults. Thus, the current study investigates the association of individual migration history with depressive symptoms among older Indian adults.

Methods

This study used information on 64,340 older adults aged 45 and above from the Longitudinal Ageing Study in India (LASI) wave-1, 2017–18. Migration history was calculated, and categories were based on boundary, duration, stream, and age at migration in this study. The depressive symptoms were calculated using both the CES-D and CIDI-SF scores. Using logistic regression models, the association of selected covariates and domains of migration on depressive symptoms was estimated to assess the links between migration and depressive symptoms.

Results

More than half of the older adults (56.3%) had migrant status in India. The prevalence of depressive symptoms was significantly higher among migrants compared to non-migrants, as measured by both CES-D (30.6% vs. 25.2%) and CIDI-SF (9.3% vs. 6.5%). Multivariate logistic regression revealed that intra-state migrants had significantly higher odds of depression (AOR: 1.08 for CES-D; 1.40 for CIDI-SF) compared to non-migrants. Inter-state migrants also had elevated odds, particularly for CIDI-SF based depression (AOR: 1.38). Among migration streams, rural–to–rural migrants showed the highest odds of depression (AOR: 1.12 for CES-D; 1.39 for CIDI-SF). Duration of migration also influenced mental health: migrants with 25 + years of stay had significantly higher odds (AOR: 1.10 for CES-D; 1.36 for CIDI-SF). Regarding age at migration, individuals who migrated at age 60 or older had the highest odds of depression (AOR: 1.22 for CES-D; 1.42 for CIDI-SF), followed by those who migrated in early life (0–14 years). These findings underscore a strong association between migration history and late-life depression.

Conclusions

This study’s findings shed light on migration and its association with depression symptoms among older Indians. Older healthcare services should be expanded in breadth while also addressing migration, resulting in considerable improvements in older individuals’ mental health.

Introduction

Mental health, especially depression symptoms (a poor state of mental condition), is a common health problem among older adults and is becoming a major public health concern, particularly in developing countries [1, 2], including India [3,4,5]. According to the World Health Organization, depression is a common mental disorder characterized by sadness, loss of interest or pleasure, guilt or low self-worth, disturbed sleep, tiredness, and poor concentration [6]. Decreased physical, cognitive, and social functioning, greater self-neglect, and increased risk of suicide, all of which are in turn associated with increased mortality [7, 8] make late-life depression an important public health problem [1]. Depression is a significant public health concern leading to functional decline, physical disability, and increased healthcare usage [9]. It is negatively impacting on physical and psychological health and quality of life [10]. Migration and migration-related processes such as pre-migration, migration, and post-migration phases have been widely found to increase the risk of depression [11]. The complexity of migration often brings stress, strain, and risk factors such as poor medical care, separation of family and children, and other relatives. Post-migration brings Stress of adaptation, Discrimination, Economic/material difficulties, and Rootlessness. Such circumstances may increase the risk of late-life depression, which has been identified as a major cause of morbidity and mortality in later life [12]. Post-migration acculturation stress includes language barriers, unfair treatment by others, worries about legal status, and lack of social ties at the destination place [13], and is also associated with poor physical and mental health conditions among migrants [14,15,16].

Several studies from both developing and developed countries have found that migrants are more likely to experience dementia and depression compared to non-migrants. One study examines the long-term mental health effects of the Great Migration on African American descendants and finds that both those who migrated to the North and those who remained in the North exhibited higher risks of lifetime mental health issues, including mood, anxiety, and substance use disorders, compared to those who stayed in the South. The study suggests that the migration experience, combined with subsequent exposure to discrimination, may have contributed to adverse mental health outcomes among descendants of African American migrants [17]. Another study found that dementia and comorbidities were more prevalent among Caribbean migrants living in the United Kingdom [18]. Marin et al. [19] investigate the relationship between migration status and late-life depression across Europe using data from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) cohort. Their findings show that poor healthy ageing is more strongly associated with depressive symptoms among migrants than non-migrants, highlighting the compounded mental health challenges faced by older migrants [19]. Zhang et al. [20] explore gender differences in mental health outcomes among rural-to-urban migrants in China and find that depression is significantly associated with migration trajectories, settlement status, and socioeconomic factors [20]. Similarly, Paul and Mandal [21] examine the impact of rural-to-urban migration on the mental health of India’s elderly population. Their study reveals that elderly individuals who migrated after the age of 50 are more likely to experience depression than their urban-native counterparts [21].

India is experiencing a demographic transition wherein increased life expectancy and a decline in the fertility rate have raised the proportion of older adults in the country [22, 23]. According to the census of India 2011, 103 million people are 60 and above age, which has increased from 5.6% in 1961 to 8.6% in 2011 to the total population of India. With the increase in the old age population, the share of older migrants also increased in India, and 53 million people aged 60 + were migrants, which is 51% of the total older population. From the 2001 census to 2011, the number of elderly migrants changed from 34.6 million to 53.8 million, a 55.2% increase between the census periods [24]. Studies in the Indian context have demonstrated the relationship between depression and various socioeconomic factors, which were advanced age, low education, poverty, and manual occupation [3, 25,26,27]. Other factors impacting older individuals include social exclusion, physical limitation, and living arrangements [28,29,30]. However, due to limited data sets and researchers' reduced focus on migration studies from an ageing perspective, the direct effect of migration on depression conditions remains largely unexplored. With migration, how is depression associated with later life? Internal and international migration have a role to play in older adults’ depression conditions. This question must be examined in the Indian context because more than half of the older population are migrants’ characteristics, and depression conditions are critical for older adults' positive health and psychological well-being. This paper focuses on the level and pattern of older adults’ migration and the distribution of depression levels among older migrants in India. Moreover, examines depression associated with migration. The paper argued that the migration pattern (distance, duration, age at migration, and stream) affects older adults' mental condition in later life. In the present study, the depressive symptom was considered for mental health status and adopted the shortened version of the Centre for Epidemiological Studies- Depression (CES-D) score developed by Andersen in 1994 [31] and also using the Composite International Diagnostic Interview short-form (CIDI-SF) scale. Persons aged 45 years and above were considered as older adults in the present study.

Data and methodology

Data Source

A cross-sectional study design was adopted for this study. Data for the analysis were drawn from the Longitudinal Ageing Study in India (LASI) wave one, collected during 2017–18. It is a nationally representative survey of 73,396 individuals, 31,135 male and female, 42,261 aged 45 and above years, and their spouses (regardless of age) across all states of India, according to the Census of India, 2011. The survey’s main objective was to study the health status and socioeconomic well-being of older adults in India. The LASI adopted the multistage stratified area probability cluster sampling design to arrive at the eventual observation units: older adults age 45 and above and their spouses, irrespective of age. The present study was conducted on respondents aged 45 years and above andfinal sample size was 64,340older adults selected. The sampling frame of the final sample and the exclusion and inclusion criteria of the sample are shown in Fig. 1.

Fig. 1

Sample size for the study

Methodology

Outcome variable

In the present study, depressive symptom was considered for mental health status. Radlof (1977) initially developed a screening tool, a short self-report score comprising 20 items to calculate the depressive symptoms. However, in this study, we adopted the shortened version of the Centre for Epidemiological Studies- Depression (CES-D) score developed by Anderson et al. (1994) [31], also used in the LASI to measure depressive symptoms. The CES-D by Anderson (1994) comprised seven negative symptoms, i.e., fear of something, low energy, trouble concentrating, feeling alone, feeling depressed, bothered by things, and everything is an effort, while three positive symptoms included feeling happy, hopeful, and satisfied. For all these ten symptoms, individuals had responded rarely or never, i.e., < 1 day; sometimes, i.e., 1 or 2 days; often, i.e., 3 or 4 days; and most or all of the time, i.e., 5–7 days in a week before the interview in the LASI. For the negative symptoms, rarely or never were sometimes scored zero, while often and most or all of the time categories were scored one. At the same time, the scoring was reversed for three positive symptoms. The overall score varies from 0 to 10, and a score of four or more was considered to calculate the prevalence of depressive symptoms.

In this study the depression was also measured using the CIDI-SF scale. Major depressive disorder was assessed using the Composite International Diagnostic Interview short-form (CIDI-SF) scale, widely used to diagnose psychiatric depression [32, 33].

The LASI CIDI-SF questionnaire included the following 10 questions: (1) During the last 12 months, was there ever a time when you felt sad, blue, or depressed for two weeks or more in a row? (2) Please think of the two weeks during the last 12 months when these feelings were worst. During that time, did the feelings of being sad, blue, or depressed usually last all day long, most of the day, about half the day, or less than half the day? (3) During those 2 weeks, did you feel this way every day, almost every day, or less often than that? (4) During those 2 weeks, did you lose interest in most things? (5) During those 2 weeks, did you ever feel more tired out or low in energy than is usual? (6) During those 2 weeks, did you lose your appetite? (7) During those 2 weeks, did you have a lot more trouble concentrating than usual? (8) During those two weeks, did you feel down on yourself and worthless? (9) During those 2 weeks, did you think about your death or someone else’s in general? (10) During those 2 weeks, did you have more trouble falling asleep than usual?

The response for all the items (except items “2” and “3”) was binary, i.e., in “No” (coded as 0) and “Yes” (coded as 1). Individuals who felt sad, blue or depressed “all day long” or “most of the day” were coded as “Yes”; else, they were coded as “No.” Similarly, individuals who felt sad, blue or depressed “every day” or “almost every day” were coded as “Yes”; else, they were coded as “No”. The ten items had scores ranging from 0 to 10. We classified older adults with a 5 + score—as “Depressed” and those with a score of 4 and below as “Not depressed [33, 34].

Explanatory variables

Migration Status: Persons are classified as migrants based on the question “Place of last residence (POLR).” According to this, if a person’s place of last residence is different from the current place, then the person is considered a migrant; otherwise, the person is a non-migrant [35]. In this study, the migration duration is classified with the question, “How many years have you continuously lived in this place?” If the person answers since birth, then the person is considered a non-migrant. Otherwise, migrants and calculate the migration duration.

Migration types are defined by migration duration, which is categorized as 0 to 9 years, 10 to 24 years, and 25 and above years; migration by the boundary (Intra-state, Inter-state, and Immigration), migration stream (Rural to Rural, Rural to Urban, Urban to Rural and Urban to Urban) and age at migration (0–14, 15–44, 45–59, and 60 and above) [36]. These dimensions of migration capture distinct aspects of the migration experience that may affect mental health through different mechanisms [37]. Age at migration is a key life-course variable—migrating at an earlier or later life stage may influence one’s ability to adapt socially, economically, and emotionally. Duration of stay reflects the level of integration and adjustment; shorter durations may be associated with instability or lack of social support. Migration boundary (e.g., intra-state vs. inter-state) captures the extent of geographic and cultural disruption, which may affect stress levels and access to familiar support networks. The migration stream (rural-rural, rural–urban, etc.) reflects socio-spatial transitions that may shape exposure to environmental, social, and health service contexts [36, 37].

Other covariates

The other socio-economic and health variables are categorized as follows: Age (60–69, 70–79, and 80 +); sex (male and female); place of residence (rural and urban); marital status (currently married, widowhood, and others), religion (Hindu, Muslim, and others); Cast category (Scheduled caste, Scheduled tribes, Other Backward Class and Others); education (No education, primary, secondary and higher, and graduate and above); currently working (working, not working and never worked); MPCE quintile (Poorest, poorer, poor, richer, richest); Regions (north, east, northeast west, and south); and self-rated health (good, poor) based on previous studies [21, 28, 29, 33].

Statistical analysis

The study participants' general characteristics and distribution were determined using descriptive analysis. The preliminary study used descriptive statistics and bivariate analysis to examine migration levels, patterns, and other independent variables' characteristics with depression. Aside from that, the findings of the association of depression with migration status and other independent variables were carved out using logistic regression analysis. A logistic regression model can be written as follows:

$$\text{log}\left(odds\right)=logit \left(p\right)=\text{ln}\left(\frac{p}{1-p}\right)= {\beta }_{0}+{\beta }_{1}{x}_{1}+{\beta }_{2}{x}_{2}+\dots +{\beta }_{k}{x}_{k}$$

where p is the expected probability of the outcome variable, and ${x}_{1}, {x}_{2}, {x}_{3}, \dots . , {x}_{k}$ is the set of explanatory variables, and β_1, β_2, β_{3, ––––} β_k are the regression coefficients to be estimated in the model (Ryan, 2008). The statistical package STATA for Windows version was used for all statistical analyses. The proper individual-level sampling weights were used to make the results representative.

Results

Migration level and pattern among older adults in India

Figure 2 depicts the migration proportion of older adults in India by regions, and it shows that 57.2% of the total older adults were migrants in India. Furthermore, the proportion of older adults migration showed that the highest concentration of migrants was from the South (60.6%), North (60.4%), and East (58.7%) regions, and the lowest concentration of migrants was from the West (56.5%), Central (52.4%), Northeast (45.5%) regions, respectively.

Fig. 2

Proportion of older adults migration in India by regions, LASI wave-1, 2017–18

Table 1 depicts the socioeconomic characteristics of older adults stratified by their migration status and types of migration among older adults in India. Among the total sample, 50.3% belong to the 45–59 age group, which is higher than other age groups among both migrants and non-migrants. The sample has a higher proportion of females (54.4%), which is a higher proportion among migrants than non-migrants. The residence characteristics of the sample show that the rural have a higher proportion, 69.0%, than the urban. The marital status of the sample shows that more than seventy percent (73.6%) were currently married. The educational background of the sample depicts that 50.7% of older adults have no education, and only 5.7% have higher education. The other backward class shows a higher proportion (45.5%) than other caste categories among the sample, and Hindu was the dominant religion in the sample, which comprised 82.2% of the total sample. 46.7% of the sample was currently working, and 21.0% was the poorest monthly per capita expenditure. The South region had the highest proportion of the sample. Most of the sample was reported poor health (59.3%).

Table 1 Respondent characteristics stratified by migration status, LASI wave-1, 2017–18

The table described the migration types based on migration duration, stream, age at migration, and distance measured by administrative boundary. 80.5% of total migrants migrated 25 years back, and only 5.8% migrated in the last 0-to-9-year duration. The migration status by boundary shows that 90% of total migrants migrated within the state, 9.2% migrated from one state to another, and only 1.3% migrated from another country. Migration by stream shows that 61.7% of total migrants followed the rural-to-rural stream, then rural-to-urban (21.3%), urban-urban (14.2%), and urban–rural (2.8%) streams. Most importantly, the migration at age shows that 72.1% of the total migrants migrated at their age between 0 to 14 years, 18.5% migrated at their age 15 to 44, and only 2.8% migrated after their age 60 and above..

Prevalence of depression among older adults with migration status in India

Figure 3 shows the prevalence of depressive symptoms, measured by CES-D and CIDI-SF, among older adults by migration status. Among total migrants, the prevalence was 30.6% for CES-D and 9.3% for CIDI-SF, whereas among total non-migrants, it was 25.2% and 6.5%, respectively. Similarly, both male and female migrants exhibited a higher prevalence of depressive symptoms based on CES-D and CIDI-SF compared to their non-migrant counterparts.

Fig. 3

Depression Symptoms among older adults with migration status in India, LASI wave-1, 2017–18

Table 2 depicts the prevalence of depression symptoms among older adults with migration status in India. The analysis of depressive symptoms among older adults, based on CES-D and CIDI-SF measures, reveals a consistently higher prevalence among migrants compared to non-migrants across most sociodemographic and economic groups. Overall, 30.6% of migrants reported CES-D depressive symptoms compared to 25.2% of non-migrants, while CIDI-SF-based depression was 9.3% among migrants versus 6.5% among non-migrants. The prevalence increased with age, and was higher among females, rural residents, the widowed, and those with no education—especially among migrants. Regional variations showed particularly high depression levels among migrants in the Central region. Socioeconomic gradients were evident, though not linear, with depression common among both the poorest and richest groups. Among migration-specific characteristics, intra-state migrants, recent migrants (0–9 years), those who migrated at older ages (45–59), and those in rural-to-rural and urban-to-rural streams exhibited higher levels of depression in both CES-D and CIDI-SF measures, respectively.

Table 2 Prevalence of depression among older adults with migration status in India, LASI wave-1, 2017–18

Association of depression with migration pattern

Table 3 depicts the association between migration boundary and the likelihood of experiencing depressive symptoms among older adults, using both CES-D and CIDI-SF measures. Compared to non-migrants, intra-state migrants had significantly higher odds of depression in both unadjusted and adjusted models. For CES-D, the adjusted odds ratio (AOR) was 1.08 [CI: 1.04–1.13], and for CIDI-SF, it was 1.40 [CI:1.29–1.51], indicating a consistent and significant association. Inter-state migrants did not show a significant association in the unadjusted CES-D model (OR: 1.01), but after adjustment, the odds increased slightly [AOR: 1.07; CI:1.00–1.16]. For CIDI-SF, both unadjusted [OR: 1.19; CI:1.05–1.34] and adjusted models [AOR: 1.38; CI:1.20–1.58] showed a significant association with depression. Immigrants had the highest unadjusted odds of CES-D depression [OR: 1.46; CI:1.22–1.76], which remained significant in the adjusted model [AOR: 1.33; CI:1.10–1.61]. However, for CIDI-SF, the association was not statistically significant in either model.

Table 3 Association of depression symptoms with migration boundary among older adults in India, LASI wave-1, 2017–18

Table 4 depicts the association between the duration of migration and depressive symptoms among older adults, using CES-D and CIDI-SF depression measures. When compared to non-migrants, older adults who had migrated 25 years or more ago showed significantly higher odds of depression in both measures. For CES-D depression, the adjusted odds ratio (AOR) was 1.10 [CI: 1.05–1.15], and for CIDI-SF depression, it was 1.36 [CI: 1.26–1.48], suggesting a strong and consistent association with long-term migration. Those who migrated 10–24 years ago also had significantly higher odds of CIDI-SF depression [AOR: 1.48; CI:1.31–1.68], though the association with CES-D was not statistically significant after adjustment [AOR: 1.04; CI:0.97–1.12]. Interestingly, recent migrants (0–9 years) had the highest adjusted odds for CIDI-SF depression [AOR: 1.46; CI:1.23–1.73], even though the association with CES-D remained non-significant [AOR: 1.08; CI:0.98–1.19].

Table 4 Association of depression symptoms with migration duration among older adults in India, LASI wave-1, 2017–18

Table 5 shows the relationship between different migration streams and the likelihood of depressive symptoms among older adults, using CES-D and CIDI-SF measures. Compared to non-migrants, those who migrated from rural-to-rural areas had significantly higher odds of experiencing depression. The adjusted odds ratios (AOR) were 1.12 [CI: 1.06–1.18] for CES-D and 1.39 [CI: 1.27–1.52] for CIDI-SF, indicating a robust association. Urban-to-rural migrants also had elevated odds of depression, particularly with the CIDI-SF measure [AOR: 1.46; CI:1.15–1.86], while their CES-D results showed no significant association. Similarly, urban-to-urban migrants had significantly higher odds of CIDI-SF depression [AOR: 1.56; CI:1.33–1.84] and marginally increased odds for CES-D [AOR: 1.08; CI:0.99–1.17]. Interestingly, rural-to-urban migrants, who are often seen as economically motivated movers, did not exhibit a significant association with CES-D depression [AOR: 0.95; CI:0.89–1.03], but had a higher likelihood of CIDI-SF depression [AOR: 1.28; CI:1.10–1.49].

Table 5 Association of depression symptoms with migration stream among older adults in India, LASI wave-1, 2017–18

Table 6 depicts the association between age at migration and depression among older adults, as measured by CES-D and CIDI-SF measures. Compared to non-migrants, those who migrated at a younger age (0–14 years) had significantly higher odds of depression, with adjusted odds ratios (AOR) of 1.08 [CI: 1.04–1.13] for CES-D and 1.36 [CI: 1.25–1.47] for CIDI-SF, indicating elevated psychological vulnerability among early-life migrants. For those who migrated during their prime working ages (15–45 years), the CES-D results showed a slight increase in risk [AOR: 1.07; CI:1.00–1.14], while the CIDI-SF findings indicated a stronger and more significant association [AOR: 1.48; CI: 1.32–1.67]. Older adults who migrated at middle age (45–59 years) also had significantly higher odds of depression per the CIDI-SF measure [AOR: 1.45; CI: 1.23–1.71], though the CES-D association was marginal and not statistically significant after adjustment. Notably, those who migrated at age 60 and above experienced the highest risk of depression, according to both CES-D [AOR: 1.22; CI: 1.06–1.40] and CIDI-SF [AOR: 1.42; CI: 1.11–1.82] metrics, suggesting that late-life migration may be particularly distressing.

Table 6 Association of depression symptoms with age at migration among older adults in India, LASI wave-1, 2017–18

Discussions

The current research aims to examine the relationships between migration and health outcomes in terms of depression among older adults in India by using nationally representative data. As India is experiencing a swift demographic transition due to increasing life expectancy, a proportionate rise in the population aged 45 years or older, and the economic, social, and psychological factors linked with ageing, the findings reveal distinct associations between different migration dimensions and depressive symptoms. We find that depressive symptoms are a significant aspect of socioeconomic status and are more common in those aged 80 and above, women, rural residents, unmarried persons, those currently not working, those in the poorest monthly per capita quintile, and those without a formal education. Poor self-rated health shows a higher prevalence of depression than good health. In the Indian context, the prevalence of depression with selected characteristics supports the previous studies [25, 28, 29, 38, 39]. While focusing on the principal study objective, assessing migration and depression symptoms, our research highlights that the prevalence of depressive symptoms is higher among migrants’ older adult migrants who migrated from another country, long duration of migration, rural to a rural-to-rural stream, and migrated after age 60 and above. The regression analysis indicates that the likelihood of depression among older adults is likely to increase even after controlling for demographic and socioeconomic characteristics when migration status is significantly associated.

The study shows migrants from another country (Immigrants) are more likely to have depression than interstate and intrastate migrants compared to non-migrants. These results demonstrate that migration to a region with social and cultural differences significantly affects deteriorating health conditions due to assimilating to new surroundings. Moreover, cultural practices can lead to considerable levels of acculturative stress [40], which in turn has been linked with psychiatric disorders [41, 42]. Studies related to migration literature suggest that immigrants have lots of factors in new places that are associated with depression, such as discrimination, lack of social support, stressful work, and lack of access to healthcare [43, 44]. Internal migrants in India were also found to be more susceptible to depression [45], possibly due to the sociocultural disparities associated with migration from one state to another, resulting in acculturation stress and discrimination behavior among migrants [46,47,48]

Further, the study result shows that migrants with more years of migration duration are more likely to have depression. This result aligns with previous studies suggesting that the new migrants may initially have good health conditions but are at greater risk of developing depression over time due to the stress of adjusting to a new life, feelings of alienation, discrimination, and lack of support system [49]. The migration stream did not show a significant association with depression. However, studies indicate that migrants from rural to rural and rural to urban are more likely to experience depression after residing in the new location for some time [45, 48, 49]. The age at migration shows that the migration at earli childhood and after the age of 60 are more likely to depression than migrants before the age of 60 as compared to non-migrants. The risk factors for psychological distress among newly arrived older migrants include female sex, less education, unemployment, poor self-rated health, widowhood or divorce, and lack of social contacts [50]. When an older person joins an already settled family, issues that could affect the mental disorder can include learning a new language and acculturation; separation from family, peers, and familiar surroundings; decreased social support and isolation because extended family and community networks are lost; increased dependency on others because of language and mobility difficulties; fewer opportunities for meaningful work and productivity; and loss of status as a respected elder in the new cultural context [51]. Overall, the study's findings suggest that migration and its types have a significant association with depression symptoms. Various types of migration, such as migration distance, duration, and age at migration, are related to depressive symptoms.

These findings have important implications for public health and social policy in India. As the country becomes both older and more mobile, targeted mental health strategies for older migrants are essential. Interventions must address not only age-related vulnerabilities but also migration-specific challenges such as cultural assimilation, loss of community, and discrimination. Community-based mental health services, peer support groups, and culturally tailored counseling could help address the psychological needs of older migrants. Including migration history in health assessments and geriatric care guidelines would also facilitate the early identification of at-risk individuals. Although this study presents robust evidence, it does not fully explore gender differences in the relationship between migration and depression—a limitation that future research should address. A gendered lens would help uncover additional layers of vulnerability and resilience among older migrant men and women.

In conclusion, this study highlights that migration history—defined by distance, duration, and timing—plays a significant role in shaping depression risk among older adults in India. Understanding these associations is vital to designing inclusive health and social care policies that promote healthy ageing for both migrants and non-migrants.

Limitations

The study has some potential limitations. The symptoms of depression were recorded on the CES-D and CIDI-SF scales and were self-reported. Since depression was not diagnosed, it could lead to under or overestimation of depression among older adults. Also, the cross-sectional nature of data limits our understanding of causal inference. The absence of data on the reasons for migration limits the ability to understand the specific factors driving migration and their association with depression. The study also does not address gender differences in the relationship between migration and depression, which limits the overall understanding. Future research should aim to explore this aspect in greater depth. Despite the above limitations, the study has various strengths, too. This study adds valuable information to the existing knowledge of migration and depression relations. The study is based on the latest data source and provides current and relevant estimates for older adults in India.

Conclusions

The findings of this study reveal a significant association between migration history and depressive symptoms among older adults in India. More than half of the older population in the dataset had a migration background, emphasizing the need to understand migration as a critical social determinant of mental health in later life.

The higher prevalence of depression among migrants—evident across both CES-D and CIDI-SF measures- suggests that the experience of migration may have long-term psychological consequences. Notably, intra- and inter-state migrants both showed increased odds of depression, with inter-state migrants exhibiting particularly elevated risks on clinical diagnostic criteria. These results may reflect the cumulative stress of relocation, social disconnection, and adaptation challenges over time, especially when migration involves crossing state boundaries with linguistic and cultural differences. The study further identifies specific vulnerable subgroups among older migrants. Rural-to-rural migrants, often overlooked in policy discourse, showed the highest odds of depression, indicating that even within similar geographic settings, migration can disrupt traditional support systems. Moreover, individuals who migrated at age 60 or older were particularly vulnerable to depression—likely due to loss of community ties, limited employment opportunities, and weaker integration into new environments. Importantly, the duration of stay at the destination was also linked with mental health outcomes. Migrants with 25 or more years of residence still exhibited elevated depression risks, suggesting that time alone does not necessarily mitigate the psychological burden of migration in the absence of adequate social and structural support.

These findings underscore the need to integrate migration-sensitive mental health strategies into policies targeting older adults. Social support networks, culturally appropriate health services, and community engagement initiatives should be tailored to meet the needs of elderly migrants. Moreover, special attention must be given to late-life migrants and rural-to-rural migrants, who may face heightened isolation and reduced access to formal care systems.

Future research should address the existing data gaps, particularly regarding reasons for migration and gender differentials in mental health outcomes, to better inform targeted interventions and promote healthy ageing for all.