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<p>The Pradhan Mantri Jan Arogya Yojana (PM-JAY), a health scheme by the government of India, is one of the world’s largest publicly funded health insurance schemes, aimed at providing equitable access to healthcare to economically vulnerable populations in India. Although most existing evaluations are based on statistical surveys or administrative data summaries, this study introduces a new mathematical approach using persistent homology, a topological data analysis (TDA) tool, to investigate spatial disparities in the utilization of the scheme. We analyze district-level hospitalization data from 14 Indian states and two union territories, using a carefully constructed weight normalization to account for demographic disparities. Our persistence diagram highlights many districts of Maharashtra that exhibit strong topological features, interpreted as significant “gaps” or underutilized zones within the coverage data. Our study demonstrates the potential of TDA techniques in public policy analysis and uncovers areas where targeted interventions could enhance PM-JAY’s reach and equity.</p>

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Detecting spatial disparities in PM-JAY implementation via persistent homology

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Krishnendu Gongopadhyay,
  • Ramveer Singh

摘要

The Pradhan Mantri Jan Arogya Yojana (PM-JAY), a health scheme by the government of India, is one of the world’s largest publicly funded health insurance schemes, aimed at providing equitable access to healthcare to economically vulnerable populations in India. Although most existing evaluations are based on statistical surveys or administrative data summaries, this study introduces a new mathematical approach using persistent homology, a topological data analysis (TDA) tool, to investigate spatial disparities in the utilization of the scheme. We analyze district-level hospitalization data from 14 Indian states and two union territories, using a carefully constructed weight normalization to account for demographic disparities. Our persistence diagram highlights many districts of Maharashtra that exhibit strong topological features, interpreted as significant “gaps” or underutilized zones within the coverage data. Our study demonstrates the potential of TDA techniques in public policy analysis and uncovers areas where targeted interventions could enhance PM-JAY’s reach and equity.