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Research Article Open access CC BY 4.0

Beyond Affordability: The Determinants of Private Healthcare Utilisation in Rural Uttar Pradesh

Anjali Mishra

South Asian Journal of Social Studies and Economics · pp. 313–324 · Published 4 Feb 2026

10.9734/sajsse/2026/v23i11259

Abstract

Aims: This study examines the determinants of healthcare utilisation in rural Uttar Pradesh with a particular focus on the role of educational attainment in influencing the choice between private and government healthcare facilities. Study Design: Cross-sectional empirical study. Place and Duration of Study: Jarauli village, Fatehpur district, Uttar Pradesh; primary survey conducted during the reference period. Methodology: Primary household-level data were collected from 100 households using a structured questionnaire. Binary Logistic Regression models were employed to analyse the determinants of private healthcare utilisation, while Principal Component Analysis (PCA) was used to examine the multidimensional nature of financial accessibility and out-of-pocket expenditure (OOPE). Results: The findings reveal that educational attainment and household expenditure significantly increase the likelihood of utilising private healthcare services, whereas occupation is not statistically significant. The affordability-based model indicates that higher hospital expenses and reliance on out-of-pocket payments strongly influence private hospital choice. PCA results identify hospital cost burden, financial protection through schemes, awareness–utilisation gaps, and household coping mechanisms as key dimensions of financial accessibility. Conclusion: Education emerges as a critical driver of private healthcare utilisation beyond affordability constraints. Strengthening public healthcare quality and improving awareness and utilisation of government health schemes are essential to reduce financial burden and ensure equitable healthcare access in rural India.

Healthcare utilisation financial accessibility out-of-pocket expenditure education logistic regression principal component analysis

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