Abstract
Background
The global population of older adults is rapidly growing, presenting significant challenges for health systems, particularly in low- and middle-income countries such as India. Age-based discrimination is an important social determinant of health; however, its relationship with healthcare utilization (HCU) among older adults in India remains underexplored. This study examines whether perceived age-based discrimination is associated with HCU through a sequential pathway involving probable depression and multimorbidity, and whether these associations differ between rural and urban settings.
Methods
We analysed cross-sectional data from 31,902 community-dwelling adults aged 60 and above from the nationally representative Longitudinal Ageing Study in India (LASI) Wave 1. Perceived age-based discrimination was a binary measure based on attributing unfair treatment to age. Probable depression was assessed using the Composite International Diagnostic Interview-Short Form (CIDI-SF), multimorbidity was defined as the coexistence of two or more self-reported chronic conditions, and HCU was measured as any service use in the past 12 months. Structural equation modelling with weighted least squares means and variance adjusted (WLSMV) estimator was used to examine direct and indirect pathways, with rural-urban differences assessed using a multi-group sequential model.
Results
Perceived age-based discrimination was positively associated with probable depression (β = 0.434, p < 0.001), which was associated with multimorbidity (β = 0.207, p < 0.001), which in turn was associated with HCU (β = 0.387, p < 0.001). A small but significant direct effect of discrimination on HCU was observed (β = 0.077, p = 0.005). The total indirect effect was significant (β = 0.035, p < 0.001), indicating a significant sequential indirect pathway via probable depression and multimorbidity. Multi-group analysis showed that the overall indirect effect did not differ significantly between rural and urban groups; however, the association between discrimination and probable depression was stronger in urban areas, whereas the direct association between discrimination and HCU was significant in rural areas but not in urban settings.
Conclusions
Perceived age-based discrimination is associated with increased healthcare utilization among older adults in India through both direct and indirect pathways, with the indirect effect operating via probable depression and subsequent multimorbidity. These findings highlight the importance of addressing discrimination and integrating mental health within primary care, while also accounting for rural-urban disparities, to promote equitable healthcare access and improve health outcomes in ageing populations.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-026-07548-x.
Keywords: Health inequities, Healthy aging, Psychosocial pathways, Rural–urban disparities, Social determinants of health
Introduction
The increase in life expectancy has led to rapid growth in the number of older adults worldwide. By 2050, the population aged 60 years and above is projected to exceed 2 billion, doubling from 1 billion in 2020 [1]. While this demographic transition reflects major improvements in public health and living conditions, it also poses significant challenges for health systems, particularly in low- and middle-income countries, where health and social infrastructures are often less prepared to support ageing populations [2]. India illustrates this transition clearly. By 2050, the country is expected to have nearly 345 million older adults (60 years and above), representing one-fifth of its total population [3]. This demographic shift is occurring in the context of persistent economic inequality, changing family structures, and rapid urbanization, all of which may influence how older adults experience health, access care, and navigate social systems [4]. Within this context, social determinants of health, including discrimination, are increasingly recognized as critical factors shaping health outcomes and healthcare access in later life [5, 6].
Age-based discrimination refers to unfair or biased treatment directed at individuals because of their age [7]. As a behavioural manifestation of ageism, it has been identified as an important but often overlooked social determinant of health. Evidence from multiple countries suggests that age-based discrimination is associated with poorer mental and physical health, lower quality of life, shorter life expectancy, and increased healthcare costs [8, 9]. In India, however, research on age-based discrimination remains limited despite the presence of strong social hierarchies, rapid intergenerational transitions, and economic disparities that may intensify its effects [6]. Data from a nationally representative survey of older adults (i.e., the Longitudinal Ageing Study in India) indicate that approximately 10% report experiencing age-based discrimination, with the highest prevalence observed among the oldest-old (aged 80 years and above) [10]. Other studies using this national-level data have identified several factors associated with perceived age-based discrimination, including place of residence, gender, socioeconomic status, functional ability, and psychosocial health [5, 11]. Although these studies provide important insights into the prevalence and associated factors of age-based discrimination, less is known about how such experiences influence patterns of healthcare utilization among older adults.
Healthcare utilization represents a critical indicator of both individual health management and the functioning of healthcare systems. In ageing populations, patterns of healthcare use reflect not only the burden of disease but also access to services, health-seeking behaviour, and structural inequalities within healthcare systems [12]. Understanding healthcare utilization among older adults is therefore important for both public health planning and resource allocation. Age-based discrimination may influence healthcare utilization through multiple mechanisms. On one hand, it can increase healthcare needs and costs by worsening mental and physical health [8, 13–15], or it can act as a barrier by discouraging older adults from seeking or accessing care in a timely manner [16–18]. Despite the potential importance of these mechanisms, the association between perceived age-based discrimination and healthcare utilization has not been extensively examined in the Indian context.
To better understand this relationship, the present study draws on the Andersen Behavioural Model of Healthcare Utilization, which conceptualizes healthcare use as influenced by predisposing, enabling, and need-related factors [19]. Building on this framework, the present study examines specific pathways through which age-based discrimination may affect healthcare utilization, with particular attention to psychological and physical health-related mechanisms within a sequential model, as well as potential differences in these pathways by place of residence.
Depression as a psychological pathway
Experiences of age-based discrimination may generate psychological distress by fostering feelings of social exclusion, reduced self-worth, and perceived marginalization [19]. Depression is among the most commonly documented mental health outcomes associated with perceived discrimination [5, 20, 21], and global evidence consistently shows that exposure to age-based discrimination is linked to higher levels of depressive symptoms among older adults [8]. For example, Ayalon and Gum (2011) reported that greater exposure to perceived discrimination was associated with increased depressive symptoms among older adults across three racial and ethnic groups in the United States [22]. Similarly, in India, Pengpid and Peltzer (2021) found that older individuals experiencing discrimination had significantly higher depression scores even after adjusting for socioeconomic and health-related factors [23]. Beyond its psychological consequences, depression may also influence health behaviours and biological processes relevant to healthcare utilization, including reduced motivation to engage in preventive care, poorer adherence to treatment, and maladaptive coping behaviours [15, 24]. Over time, these behavioural changes may contribute to physiological dysregulation, such as heightened inflammatory responses, thereby increasing vulnerability to chronic disease [25, 26], linking psychological distress to broader physical health burdens in later life.
Multimorbidity as a physical health pathway
Multimorbidity, defined as the coexistence of two or more chronic conditions [27], represents a major health challenge in later life and is strongly associated with increased healthcare needs among older adults [13, 14]. Psychological distress, including depression, may contribute to the development or progression of multimorbidity through behavioural and biological mechanisms such as unhealthy coping behaviours, reduced engagement in health management, and chronic physiological stress responses [25, 26, 28]. Individuals living with multiple chronic conditions often require more frequent medical consultations, monitoring, and treatment, making multimorbidity an important driver of healthcare utilization among older adults [13, 14, 27, 29]. Within this context, experiences of age-based discrimination may contribute to depression, which in turn increase vulnerability to physical health burdens such as multimorbidity, thereby increasing healthcare needs and patterns of service use in later life. This pathway is central to the present study, which seeks to understand these relationships among older adults in India.
Differences by place of residence
The relationship between age-based discrimination, health outcomes (psychological and physical), and healthcare utilization is likely to vary by social context. In India, the rural-urban divide represents a fundamental axis of inequality that shapes healthcare access, social support, and exposure to stressors [30, 31]. Rural areas are characterized by limited healthcare infrastructure, fewer specialists, greater travel distances, and lower socioeconomic resources, which together may delay care-seeking and amplify the health consequences of discrimination [5, 30]. Urban environments, while offering better access to health services, may expose older adults to different stressors such as social isolation, competitive intergenerational dynamics, and institutional ageism that can heighten the psychological impact of discriminatory experiences [4, 32]. These contextual differences have direct implications for the pathways proposed in this study. Psychological distress resulting from age-based discrimination may be more pronounced in urban settings, where social hierarchies are more visible [23, 33]. However, the translation of such distress into physical health burdens and healthcare utilization may be more pronounced in rural areas due to systemic barriers and delays in accessing care [30, 31]. However, evidence examining whether the sequential pathways linking age-based discrimination to healthcare utilization through depression and multimorbidity differ between rural and urban older adults in India remains limited. Addressing this gap is essential for informing context-sensitive interventions and policies that account for the structural conditions shaping older adults’ experiences of discrimination and their pathways to care.
Study objective and conceptual model
Guided by the Andersen Behavioural Model of Healthcare Utilization [34], this study conceptualizes perceived age-based discrimination as a social determinant that may influence healthcare utilization through both psychological and physical health pathways (Fig. 1). Specifically, age-based discriminatory experiences may contribute to psychological distress, such as depression, which may increase vulnerability to physical health burdens, including multimorbidity. These health conditions may subsequently increase healthcare needs and patterns of service use. At the same time, contextual factors such as residential environment may influence how these processes unfold.
Fig. 1.
Conceptual model linking age discrimination, probable depression, multimorbidity, and healthcare utilization here
Accordingly, the objective of this study is to examine the association between perceived age-based discrimination and healthcare utilization among older adults in India and to investigate whether depressive symptoms and multimorbidity function as sequential mediators in this relationship. In addition, the study explores whether these pathways differ between rural and urban settings. By integrating psychological, physical, and structural determinants of health, this analysis aims to provide a more comprehensive understanding of how age-based discrimination may influence healthcare access and utilization in later life.
Methods
Study design and sample
This study uses data from the Longitudinal Ageing Study in India (LASI), Wave 1 (2017–2018). LASI is the first nationally representative study on ageing in India and one of the largest of its kind globally. The survey was implemented by the International Institute for Population Sciences (IIPS), Mumbai, in collaboration with the Harvard T. H. Chan School of Public Health (HSPH) and the University of Southern California (USC), USA. Its primary aim is to generate comprehensive evidence on the health, social, and economic conditions of older adults in India. The baseline sample comprised more than 73,000 respondents, including adults aged 45 years and above and their spouses, irrespective of age, thereby providing broad coverage of India’s ageing population. LASI achieved a high overall response rate of 87.3% at the individual level, strengthening the representativeness and reliability of the data.
For the present study, the sample was restricted to older adults aged 60 years and above. The final analytic sample consisted of 31,902 participants, with a mean age of 68.89 ± 7.52 years. As shown in Supplementary Material S1, the majority of participants were aged 60–69 years (58.5%), and women slightly outnumbered men (52.5% vs. 47.5%). Most participants lived with family members (94.3%) and resided in rural areas (70.5%). Educational attainment was generally low, with more than two-thirds (68.0%) having less than primary education. Participants were categorized into five Monthly Per Capita Expenditure (MPCE) quintiles, representing household economic status, ranging from the lowest to the highest economic group.
Measures
Perceived age-based discrimination
Perceived age-based discrimination in LASI Wave 1 was assessed using items adapted from established measures of everyday discrimination [35]. Respondents were first asked whether, in the past 12 months, they had been treated with less courtesy or respect than others in their daily lives. Those who reported such experiences were then asked to identify the reason for this treatment. Response options included age, gender, caste, religion, economic status, place of residence, and other social characteristics. For the present study, respondents who selected “age” as one of the reasons for discrimination were classified as having experienced perceived age-based discrimination (1 = yes). All other respondents, including those who reported no discrimination or cited reasons other than age, were classified as not experiencing age-based discrimination (0 = no). This binary operationalization has been applied in previous analyses of LASI data examining age-based discrimination among older adults in India [5, 10].
Depression
Probable depression was assessed in LASI Wave 1 using the Composite International Diagnostic Interview-Short Form (CIDI-SF) [35], a screening instrument designed to identify probable major depression based on self-reported symptomatology. Participants were first screened with three questions assessing whether they had experienced persistent sadness or loss of interest in activities for at least two weeks during the past 12 months, as well as the duration and frequency of these feelings. Respondents who met the screening criteria were subsequently asked seven additional symptom-based questions covering fatigue, appetite loss, sleep disturbances, difficulty concentrating, feelings of worthlessness, thoughts of death, and loss of interest in usual activities. Each symptom contributed to the total CIDI-SF score (range: 0–10). Following the standard scoring protocol commonly used in population-based surveys, individuals reporting depressed mood or anhedonia along with sufficient additional symptoms to produce a score of ≥ 3 were classified as having probable major depression. For the present analysis, a binary variable was constructed indicating the presence (1 = yes) or absence (0 = no) of depression. Similar operationalizations using the CIDI-SF have been applied in previous studies based on LASI data and other large-scale ageing surveys [35, 36]. The full sequence of CIDI-SF questions is presented in Supplementary Material S2.
Multimorbidity
Multimorbidity was defined as the coexistence of two or more chronic conditions [27]. Information on physician-diagnosed diseases was obtained through self-reports in LASI Wave 1. Respondents were asked whether a health professional had ever diagnosed them with any of the following conditions: hypertension, diabetes, cancer, chronic lung disease, chronic heart disease, stroke, arthritis, neurological disorders, psychiatric conditions, and high cholesterol. For the present study, a binary variable was constructed to indicate multimorbidity, coded as 0 for fewer than two chronic conditions (0 = no) and 1 for two or more chronic conditions (1 = yes). This operational definition has been widely used in epidemiological studies and allows comparability with previous research on multimorbidity in India and internationally [27, 37].
Healthcare utilization
Healthcare utilization was assessed using self-reported information on whether respondents had visited any healthcare facility or received a visit from a health professional during the past 12 months. The LASI survey captures service use across several providers, including public health facilities (e.g., sub-centers, primary health centers, community health centers, district hospitals), private healthcare facilities (e.g., private hospitals, nursing homes, and outpatient clinics), and other sources such as health camps, mobile health units, pharmacies, or home visits by healthcare providers. For the present analysis, a binary indicator of healthcare utilization was constructed, coded as 1 if the respondent reported at least one healthcare contact during the reference period (1 = yes) and 0 otherwise (0 = no). This measure captures both preventive and curative healthcare use across different types of providers. However, the indicator does not distinguish between types or intensity of healthcare encounters (e.g., preventive vs. acute care or inpatient vs. outpatient services), and therefore reflects overall engagement with healthcare services during the reference period, consistent with approaches used in previous studies [12, 19].
Place of residence
Place of residence (rural/urban) was included as a key contextual variable in the association between age-based discrimination, depression, multimorbidity, and healthcare utilization. LASI records whether respondents live in rural or urban areas according to the definitions used by the Census of India [35]. Accordingly, residence was coded as a binary variable (0 = urban, 1 = rural).
Covariates
Several key sociodemographic and socioeconomic characteristics were included as covariates to account for potential confounding factors. In line with the Andersen Behavioural Model of Healthcare Utilization [34], a parsimonious set of covariates was selected to represent core predisposing and enabling characteristics while avoiding overadjustment. Age was categorized into three groups: 60–69 years, 70–79 years, and 80 years and above. Gender was coded dichotomously (male/female) according to LASI classifications. Educational attainment was grouped into less than primary, primary, and secondary or higher education. Living arrangement was categorized as living alone or living with family members (spouse, children, or others). Household economic status was measured using the Monthly Per Capita Expenditure (MPCE) quintiles provided in the LASI dataset. MPCE is calculated by dividing total household consumption expenditure by the number of household members and is widely used as a proxy indicator of economic status in large-scale Indian surveys [35]. It is considered a reliable indicator of long-term economic well-being in the Indian context because it captures actual consumption patterns rather than self-reported income, which may be irregular or underreported among older adults. For the present analysis, participants were classified into five quintiles ranging from the lowest to the highest economic group.
Statistical analysis
Statistical analyses were conducted using R (version 4.5.1). Descriptive statistics were first computed for the analytic sample, stratified by rural-urban residence and healthcare utilization status, including demographic, socioeconomic, and health characteristics. Group differences were examined using chi-square tests, given the categorical nature of the variables. Weighted percentages were reported to account for the complex survey design of LASI Wave 1.
To examine the hypothesized relationships, structural equation modelling (SEM) was applied in three stages aligned with the study objectives. First, path coefficients were estimated to assess whether perceived age-based discrimination was associated with probable depression, multimorbidity, and healthcare utilization. All models were adjusted for age group, gender, education, living arrangement, and household economic status. Second, indirect and total effects were examined by specifying a sequential mediation pathway, in which age-based discrimination associated probable depression, which in turn associated with multimorbidity, which was subsequently associated with healthcare utilization. Indirect effects were calculated as the product of path coefficients, with delta-method standard errors and 95% confidence intervals (CIs). Third, differences by place of residence were examined using a multi-group SEM approach, in which the sequential mediation model was estimated separately for rural and urban older adults. Differences in path coefficients between groups were evaluated using a scaled chi-square difference test comparing models with freely estimated versus constrained regression parameters.
Because the key study variables (age-based discrimination, probable depression, multimorbidity, and healthcare utilization) were binary indicators, models were estimated using the weighted least squares mean and variance adjusted (WLSMV) estimator with a probit link. Standardized path coefficients and explained variance (R²) for each endogenous variable are reported, and statistical significance was defined as p < 0.05 (two-tailed).
Results
Demographic, socioeconomic, and health characteristics
Table 1 presents the demographic, socioeconomic, and health characteristics of the study population by residence status. The distribution of age groups was broadly similar across residence, but rural older adults were significantly more likely to live alone (6.3% vs. 4.1%), report age-based discrimination (11.4% vs. 7.9%), and experience probable depression (9.4% vs. 6.1%) than urban counterparts. The most noticeable disparities emerged in education and health status, as urban older adults were four times more likely to have attained secondary education or higher (30.2% vs. 7.5%), and also higher rates of multimorbidity (35.4% vs. 19.1%). Economic status did not differ significantly by residence, yet healthcare utilization was slightly higher among urban older adults (75.7% vs. 74.7%). Moreover, these findings underscore a pattern where rural older adults face compounded social and health vulnerabilities despite representing the majority of India’s aging population [35].
Table 1.
Socio-demographic and health characteristics of older adults by place of residence (urban vs. rural), LASI Wave 1
| Urban (n = 10817, 33.9%) | Rural (n = 21085, 66.1%) | χ 2 test | p-value | |||
|---|---|---|---|---|---|---|
| n (%) | Weighted% | n (%) | Weighted% | |||
| Age group | ||||||
| 60–69 | 6559 (60.6) | 58.4 | 12,652 (60.0) | 58.6 | 20.249 | < 0.001 |
| 70–79 | 3207 (29.6) | 31.2 | 6043 (28.7) | 29.8 | ||
| ≥ 80 | 1051 (9.7) | 10.4 | 2390 (11.3) | 11.7 | ||
| Gender | ||||||
| Male | 5063 (46.8) | 45.0 | 10,277 (48.7) | 48.5 | 10.722 | 0.001 |
| Female | 5754 (53.2) | 55.0 | 10,808 (51.3) | 51.5 | ||
| Living arrangement | ||||||
| With family and others | 10,347(95.7) | 95.9 | 19,925 (94.5) | 93.7 | 19.723 | < 0.001 |
| Alone | 470 (4.3) | 4.1 | 1160 (5.5) | 6.3 | ||
| Education | ||||||
| ≥Secondary | 3068 (28.4) | 30.2 | 1743 (8.3) | 7.5 | 3026.184 | < 0.001 |
| Primary | 2637 (24.4) | 23.8 | 3428 (16.3) | 15.3 | ||
| < Primary | 5112 (47.3) | 46.0 | 15,914 (75.5) | 77.1 | ||
| Economic status | ||||||
| Poorest | 2269 (21.0) | 22.3 | 4311 (20.4) | 21.5 | 2.709 | 0.608 |
| Poor | 2247 (20.8) | 20.5 | 4326 (20.5) | 22.2 | ||
| Middle | 2167 (20.0) | 20.6 | 4335 (20.6) | 21.1 | ||
| Richer | 2096 (19.4) | 19.7 | 4163 (19.7) | 19.0 | ||
| Richest | 2038 (18.8) | 17.0 | 3950 (18.7) | 16.2 | ||
| Age-based discrimination | ||||||
| No | 9951 (92.0) | 92.1 | 18,992 (89.6) | 88.6 | 47.297 | < 0.001 |
| Yes | 806 (8.0) | 7.9 | 2193 (10.4) | 11.4 | ||
| Probable depression | ||||||
| No | 10,215 (94.4) | 93.9 | 19,494 (92.5) | 90.6 | 43.797 | < 0.001 |
| Yes | 602 (5.6) | 6.1 | 1591 (7.5) | 9.4 | ||
| Multimorbidity | ||||||
| No | 6976 (54.5) | 64.6 | 17,026(80.7) | 80.9 | 1014.314 | < 0.001 |
| Yes | 3841 (35.5) | 35.4 | 4059 (19.3) | 19.1 | ||
| Healthcare utilization | ||||||
| No | 3000 (27.7) | 24.3 | 6431 (30.5) | 25.3 | 26.272 | < 0.001 |
| Yes | 7817 (72.3) | 75.7 | 14,654 (69.5) | 74.7 | ||
Bivariate associations with healthcare utilization
Table 2 presents the distribution of key psychosocial and health variables by healthcare utilization status. Among older adults, 70.4% reported utilizing healthcare services in the past 12 months. Healthcare service users were more likely than non-users to report experiences of age discrimination (10.6% vs. 9.4%) and probable depression (9.6% vs. 5.2%). Multimorbidity was also substantially higher among those who accessed healthcare, with 28.0% of users reporting two or more chronic conditions compared with only 11.7% of non-users. Prior to the path and mediation analysis, a logistic regression model was estimated to examine the direct association between perceived age-based discrimination and healthcare utilization (Supplementary Material S3).
Table 2.
Distribution of age-based discrimination, probable depression, and multimorbidity by health care utilization status among older adults, LASI Wave 1
| Health Care Utilization | ||||||
|---|---|---|---|---|---|---|
| No (n = 9431, 29.6%) | Yes (n = 22471, 70.4%) | χ 2 test | p-value | |||
| n (%) | Weighted % | n (%) | Weighted % | |||
| Age-based discrimination | ||||||
| No | 8614 (91.3) | 90.6 | 20,229 (90.0) | 89.4 | 13.238 | < 0.001 |
| Yes | 817 (8.7) | 9.4 | 2242 (10.0) | 10.6 | ||
| Probable depression | ||||||
| No | 9058 (96.0) | 94.8 | 20,651 (91.9) | 90.4 | 178.226 | < 0.001 |
| Yes | 373 (4.0) | 5.2 | 1820 (8.1) | 9.6 | ||
| Multimorbidity | ||||||
| No | 8300 (88.0) | 88.3 | 15,702 (69.9) | 72.0 | 1172.094 | < 0.001 |
| Yes | 1131 (12.0) | 11.7 | 6769 (30.1) | 28.0 | ||
Path and mediation analysis
The results of the hypothesized sequential path model are presented in Tables 3 and 4. The path analysis (see Table 3) demonstrated that perceived age-based discrimination was significantly and positively associated with probable depression (β = 0.434, p < 0.001), which in turn was associated with higher multimorbidity (β = 0.207, p < 0.001). Multimorbidity was strongly associated with increased healthcare utilization (β = 0.387, p < 0.001). A small but statistically significant direct association between age-based discrimination and healthcare utilization was also observed (β = 0.077, p = 0.005). The model explained 3.0% of the variance in probable depression, 14.2% in multimorbidity, and 15.4% in healthcare utilization. Consistent with these findings, the total effect of age-based discrimination on healthcare utilization (see Table 4) was statistically significant (β = 0.112, p < 0.001). This association was partly mediated through the sequential pathway involving probable depression and multimorbidity. Specifically, the indirect effect through probable depression and subsequent multimorbidity was significant (β = 0.035, p < 0.001), indicating that experiences of age-based discrimination are linked to increased healthcare utilization through both psychological (probable depression) and physical (multimorbidity) health pathways. Overall, these findings support the hypothesized model in which perceived age-based discrimination associated with healthcare utilization both directly and indirectly via its impact on mental and physical health.
Table 3.
Path analysis estimates for the sequential mediation model linking age-based discrimination, probable depression, multimorbidity, and health care use
| Variables | Estimate | SE | Z-value | P(>|z|) | ci.lower | ci.upper |
|---|---|---|---|---|---|---|
| Age-based discrimination → probable depression | 0.434 | 0.030 | 14.257 | < 0.001 | 0.375 | 0.494 |
| Probable depression→ multimorbidity | 0.207 | 0.015 | 14.102 | < 0.001 | 0.178 | 0.235 |
| Multimorbidity → HCU | 0.387 | 0.012 | 32.357 | < 0.001 | 0.364 | 0.411 |
| Age-based discrimination → HCU | 0.077 | 0.027 | 2.797 | 0.005 | 0.023 | 0.131 |
| R square | ||||||
| Probable depression | 0.030 | |||||
| Multi-morbidity | 0.142 | |||||
| Healthcare use | 0.154 |
Note: All effects are adjusted for age group, gender, residence, living arrangement, economic status, education
HCU Healthcare utilization
Table 4.
Direct, indirect, and total effects of age-based discrimination on healthcare utilization via the sequential pathway
| Effects | Variables | Estimate | SE | Z-value | P(>|z|) | ci.lower | ci.upper |
|---|---|---|---|---|---|---|---|
| Total effect | Age-based discrimination→ HCU (direct + indirect) | 0.112 | 0.028 | 4.040 | < 0.001 | 0.057 | 0.166 |
| Direct effect | Age-based discrimination → HCU | 0.077 | 0.027 | 2.797 | 0.005 | 0.023 | 0.131 |
| Total indirect effect | Age-based discrimination → HCU (via probable depression and multimorbidity) | 0.035 | 0.004 | 9.879 | < 0.001 | 0.028 | 0.042 |
Note: All effects are adjusted for age group, gender, residence, living arrangement, economic status, education
HCU Healthcare utilization
Differences by place of residence
The multi-group SEM results examining differences between rural and urban older adults are presented in Table 5. The association between age-based discrimination and probable depression was significant in both rural (β = 0.394, p < 0.001) and urban (β = 0.546, p < 0.001) groups. A formal comparison indicated that this association was significantly weaker among rural older adults than among their urban counterparts (β difference = − 0.152, p = 0.025). The remaining pathways showed similar patterns across residence groups. Probable depression was positively associated with multimorbidity in both rural (β = 0.215, p < 0.001) and urban (β = 0.191, p < 0.001) areas, and multimorbidity was strongly associated with healthcare utilization in both contexts (rural: β = 0.374, p < 0.001; urban: β = 0.406, p < 0.001). The direct association between age-based discrimination and healthcare utilization was significant in rural areas (β = 0.111, p = 0.001) but not in urban areas (β = −0.006, p = 0.905). The sequential indirect effect linking age-based discrimination to healthcare utilization through probable depression and multimorbidity was significant in both rural (β = 0.032, p < 0.001) and urban (β = 0.042, p < 0.001) groups. Therefore, the difference between these indirect effects was not statistically significant (β difference = − 0.011, p = 0.190). Overall, the model comparison test indicated that the regression structure differed significantly between rural and urban groups (Δχ²(34) = 74.85, p < 0.001).
Table 5.
Differences in the sequential mediation model by place of residence (urban vs. rural)
| Rural | Urban | |||||||
|---|---|---|---|---|---|---|---|---|
| Estimate | P(>|z|) | ci.lower | ci.upper | Estimate | P(>|z|) | ci.lower | ci.upper | |
| Age-based discrimination→ probable depression | 0.394 | < 0.001 | 0.323 | 0.465 | 0.546 | < 0.001 | 0.433 | 0.658 |
| Probable depression→ multimorbidity | 0.215 | < 0.001 | 0.180 | 0.250 | 0.191 | < 0.001 | 0.142 | 0.241 |
| Multimorbidity → HCU | 0.374 | < 0.001 | 0.344 | 0.404 | 0.406 | < 0.001 | 0.369 | 0.444 |
| Age-based discrimination → HCU | 0.111 | 0.001 | 0.047 | 0.174 | -0.006 | 0.905 | -0.107 | 0.095 |
| Indirect effect | 0.032 | < 0.001 | 0.024 | 0.040 | 0.042 | < 0.001 | 0.028 | 0.056 |
| Total effect | 0.142 | < 0.001 | 0.078 | 0.206 | 0.036 | 0.484 | -0.065 | 0.138 |
| Difference (Rural-Urban) | ||||||||
| Age-based discrimination→ probable depression | -0.152 | 0.025 | -0.284 | -0.019 | ||||
| Probable depression→ multimorbidity | 0.024 | 0.448 | -0.037 | 0.084 | ||||
| Multimorbidity → HCU | -0.032 | 0.190 | -0.080 | 0.016 | ||||
| Age-based discrimination → HCU | 0.117 | 0.055 | -0.002 | 0.236 | ||||
| Indirect effect | -0.011 | 0.190 | -0.027 | 0.005 | ||||
| Total effect | 0.106 | 0.084 | -0.014 | 0.226 | ||||
| Model comparison | ||||||||
| χ² difference test (free vs. equal) | Δχ²(34) = 74.85 | < 0.001 | ||||||
Note: Estimates are adjusted for age group, gender, living arrangement, economic status, and education. Multi-group SEM was estimated separately for rural and urban older adults. A scaled chi-square difference test showed that the overall regression structure differed significantly by residence (p < 0.001)
HCU Healthcare utilization
Discussion
This study examined the association between perceived age-based discrimination, probable depression, multimorbidity, and healthcare utilization among community-dwelling older adults in India using nationally representative LASI Wave 1 data. The findings support the hypothesized sequential mediation pathway, whereby age-based discrimination is associated with increased psychological distress, which in turn is linked to poorer physical health and greater healthcare utilization. In addition, the results show that while the overall indirect pathway was similar across rural and urban settings, some individual associations differed between the two groups, indicating variation in specific relationships within the broader model.
Age-based discrimination as a social determinant of healthcare utilization
A key finding of this study is that perceived age-based discrimination was associated with healthcare utilization through both direct and indirect pathways, with the indirect effect operating sequentially via probable depression and multimorbidity and accounting for a meaningful proportion of the total association. The observed positive direct association between age-based discrimination and healthcare utilization represents an important contribution to the literature. Previous studies have reported mixed findings, with both positive and negative associations observed across different country contexts and healthcare systems [8, 13–18]. The present findings extend this body of evidence by demonstrating a predominantly positive association among older adults in India and by highlighting the underlying mechanisms through a sequential mediation framework. Guided by the Andersen Behavioural Model of Healthcare Utilization [34], the results suggest that healthcare use is largely driven by need-related factors, including both psychological and physical health conditions. In this context, age-based discrimination can be understood as a psychosocial stressor that is associated with higher levels of probable depression [22, 23], which in turn is associated with a higher likelihood of multimorbidity [25, 28, 37, 38], and subsequently with increased healthcare utilization [15, 16, 18].
Moreover, the sequential pathway identified in this study suggests that the effects of discrimination on healthcare utilization are not solely direct but may reflect a cumulative process through worsening mental and physical health. The presence of a modest direct effect alongside a more substantial indirect pathway further supports the conceptualization of discrimination as a broader social determinant of health, influencing healthcare utilization primarily through its association with psychological distress and subsequent physical health burdens in later life.
Psychological consequences of age-based discrimination
The finding that perceived age-based discrimination was positively associated with depression is consistent with both global and Indian literature. Global evidence has shown that age-based discrimination is associated to adverse mental health outcomes, especially depression [8, 9, 22]. Studies in India have similarly reported that older adults who experience discrimination are more likely to report depression, even after accounting for socioeconomic and health-related factors [10, 23]. The present study extends this literature by demonstrating that depression is not only associated with discrimination but also functions as an intermediary mechanism linking discriminatory experiences to later health burdens and healthcare utilization. This pattern may be especially relevant in the Indian context, where older adults often navigate social hierarchies, changing intergenerational relations, and persistent economic inequalities [4, 6]. These structural and social conditions may intensify the emotional consequences of age-based discrimination, increasing vulnerability to depression. In turn, depression may reduce motivation for self-care, weaken adherence to treatment, and contribute to biological dysregulation, all of which may increase later healthcare needs [15, 24–26].
Depression and the accumulation of multimorbidity
The study also found that depression was positively associated with multimorbidity, which was subsequently associated with increased healthcare utilization. This finding is important, as much of the existing literature in the Indian context has primarily examined the reverse relationship, identifying depression as a risk factor for multimorbidity [39, 40]. The present results suggest that this relationship may also operate in the opposite direction, with depression contributing to the accumulation or progression of chronic conditions. This interpretation is consistent with a growing body of evidence indicating that psychological distress can adversely affect physical health through both behavioural pathways, such as poor treatment adherence and unhealthy coping behaviours, and biological pathways, including chronic stress and inflammatory processes [25, 26]. The present findings therefore suggest the effects of age-based discrimination may extend beyond psychological distress to shape broader physical health trajectories in later life. The observed pathway linking discrimination to depression, multimorbidity, and healthcare utilization highlights a cumulative process through which social disadvantage becomes embodied in worsening health and increased need for care.
Rural-urban differences in the pathways linking discrimination and healthcare utilization
An important contribution of this study is the identification of rural-urban differences in the pathways linking age-based discrimination and healthcare utilization. Although the overall indirect effect did not differ significantly between groups, individual pathways varied, indicating that the underlying mechanisms operate differently across contexts.
The multi-group analysis showed that the association between age-based discrimination and probable depression was significant in both groups but was stronger among urban older adults. One possible explanation is that urban environments may expose older adults to a wider range of institutional and interpersonal settings in which discriminatory experiences become more visible or psychologically salient [4, 41, 42]. Greater exposure to bureaucratic systems, service institutions, and rapid intergenerational social change in urban areas may heighten awareness of age-based disadvantage and intensify its mental health consequences [43].
At the same time, the direct association between age-based discrimination and healthcare utilization was significant only in rural older adults. Although the difference in indirect effects between rural and urban groups was not statistically significant, this rural-specific direct pathway suggests that discrimination may interact with structural vulnerability in distinct ways [30, 44]. Rural older adults in India often face poorer healthcare infrastructure, longer travel distances, fewer services, and greater financial and logistical barriers to care [30, 31]. In such contexts, experiences of age-based discrimination may be more likely to contribute to delayed care-seeking and unmet healthcare needs, which can result in more advanced illness by the time care is accessed. As a result, when healthcare is eventually utilized, it may reflect a more immediate and intensified need for care. This may help explain why, despite a stronger psychological pathway in urban areas, a direct association between discrimination and healthcare utilization was observed among rural older adults.
These findings underscore that social context matters. Rural-urban residence should not be understood only as a demographic characteristic, but as a structural condition that shapes how discrimination is experienced and how its consequences unfold. This interpretation strengthens the contribution of the study by locating age-based discrimination within the broader organization of health systems and inequalities in India.
Strengths and limitations section
Strengths of this study include the use of a large nationally representative dataset, the integration of psychosocial and physical health pathways within a single analytical framework, and the application of structural equation modelling to examine the complex associations linking age-based discrimination, depression, multimorbidity, and healthcare utilization. The study also contributes to the literature by examining contextual differences between rural and urban settings, which are often overlooked in research on ageing and discrimination.
However, several limitations should be considered when interpreting the findings. First, the cross-sectional design limits causal inference, as the temporal ordering of age-based discrimination, probable depression, multimorbidity, and healthcare utilization cannot be established. Therefore, the identified mediation pathways should be interpreted as associations consistent with the conceptual model rather than definitive causal effects. Second, several key variables were based on self-reported measures, which may be subject to reporting bias. Perceptions of age-based discrimination may be influenced by psychological states such as probable depression, and recall of chronic conditions and healthcare use may introduce measurement error. These biases may affect the magnitude of associations, although such measures are widely used in population-based ageing studies. Third, healthcare utilization was measured as a binary indicator of any healthcare contact in the past 12 months, which does not capture differences in type, frequency, or intensity of care. This may obscure variations in healthcare use and partly explain the variance observed in the model. To avoid additional analytical complexity, the present study adopted this binary measure as a baseline approach; future studies may build on this by examining more detailed patterns of healthcare utilization. Finally, although the analytic sample was large and nationally representative, the study focused on a limited set of mediating pathways. Other potential mechanisms, including social support, functional limitations, healthcare accessibility, and structural inequalities such as caste or regional disparities, were not examined. Future longitudinal research using multiple waves of LASI or other ageing surveys would be valuable to clarify the temporal dynamics between discrimination, mental health, chronic disease accumulation, and healthcare utilization.
Conclusion
This study demonstrates that age-based discrimination is an important social determinant of healthcare utilization among older adults in India, operating through both direct and indirect pathways. The findings highlight a sequential mechanism in which discriminatory experiences associated with probable depression, which in turn associated with multimorbidity and subsequent healthcare use. While the overall pathway was consistent across rural and urban settings, variations in specific associations underscore the importance of contextual factors in shaping these relationships. Addressing age-based discrimination and its psychological and physical health consequences is essential for promoting equitable healthcare access and improving health outcomes in ageing populations.
Supplementary Information
Acknowledgements
The authors acknowledge the International Institute for Population Sciences (IIPS) for providing access to the secondary data used in this study, which was obtained through a formal data request (Data Request No. RQ00001198, dated 02 December 2024) and used solely for research purposes. No external funding was received for this study.
Abbreviations
- CIDI-SF
Composite International Diagnostic Interview-Short Form
- LASI
Longitudinal Ageing Study in India
- MPCE
Monthly Per Capita Expenditure
- SEM
Structural Equation Modelling
- WLSMV
Weighted Least Squares Mean and Variance Adjusted
Authors’ contributions
SD and LA conceptualized the idea and jointly drafted the manuscript; SD conducted the statistical analysis. SD and LA critically reviewed and made contributions to the initial manuscript. The final manuscript has been read and approved by all authors.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The data used in this study are publicly available from the Longitudinal Aging Study in India (LASI) Wave 1, which can be accessed through the International Institute for Population Sciences (IIPS), Mumbai, India, upon request at https://www.iipsindia.ac.in/content/lasi-wave-i.
Declarations
Ethics approval and consent to participate
The Longitudinal Aging Study in India (LASI) Wave 1 (2017–2018) received ethical approval from the Ethics Review Board of the Indian Council of Medical Research (ICMR) and the International Institute for Population Sciences (IIPS), Mumbai, India. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the survey. LASI followed rigorous protocols to ensure voluntary participation, privacy, and confidentiality of respondent information. The present analysis used the publicly available, de-identified LASI Wave 1 dataset. Therefore, no additional ethical approval was required for this secondary data analysis.
Consent for publication
Not applicable for this study.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data used in this study are publicly available from the Longitudinal Aging Study in India (LASI) Wave 1, which can be accessed through the International Institute for Population Sciences (IIPS), Mumbai, India, upon request at https://www.iipsindia.ac.in/content/lasi-wave-i.