Evidence map›Paper›PMID 42108437›Full record

ArticleBMC geriatrics2026

Age-based discrimination and healthcare utilization among older adults in India: a sequential mediation model with rural-urban differences.

Sayani Das, Liat Ayalon

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Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Sayani DasLouis and Gabi Weisfeld School of Social Work, Liat Ayalon, Bar-Ilan University, Ramat Gan, 5290002, Israel. das.sayani6@gmail.com.
Liat AyalonLouis and Gabi Weisfeld School of Social Work, Liat Ayalon, Bar-Ilan University, Ramat Gan, 5290002, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsWe 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.

resultsPerceived 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.

conclusionsPerceived 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.

Indexed as

AgeismPatient Acceptance of Health CareRural PopulationUrban PopulationAgedAged, 80 and overCross-Sectional StudiesDepressionFemaleHumansIndiaLongitudinal StudiesMaleMiddle AgedHealth inequitiesHealthy agingPsychosocial pathwaysRural–urban disparitiesSocial determinants of health

Identifiers

PMID42108437
PMCPMC13326142

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.