{ "abstract": "Objective: To apply an integrated causal-inference and welfare-optimization framework to Iranian Parkinson’s disease (PD) inpatient insurance claims, estimating how resource-allocation decisions relate to recovery, discharge risk, cost and financial burden; testing whether these relationships vary across patient subgroups; and assessing whether budget-neutral reallocation could improve efficiency and equity simultaneously. Setting: Six provinces of Iran, using de-identified administrative inpatient claims obtained from the Iran Health Insurance Organization for admissions with a primary or secondary diagnosis of PD during the 2023–2025 observation period. Design: Retrospective claims-based cohort study. Transaction-level records (n = 21,121) were aggregated into 1,813 hospitalization episodes and analyzed through a six-stage pipeline: gradient-boosted predictive models; double machine learning and augmented inverse-probability weighting for average treatment effects, restricted to an admission-time-only, pre-treatment severity adjustment; causal forests for subgroup heterogeneity; concentration-index decomposition for socioeconomic inequality; constrained integer-linear-programming optimization for reallocation scenarios; and bootstrap, E-value and design-effect sensitivity analyses. Results: Predictive models showed acceptable-to-strong discrimination (recovery, AUC-ROC 0.709; unsuccessful-discharge proxy, AUC-ROC 0.870) and explained 81.3% of variance in total cost. Prolonged length of stay, treated as an exploratory, within-episode exposure rather than a causal one because it is realized during the hospitalization itself, was associated with a 6.4-percentage-point lower discharge proxy and 200% higher cost (E-value limit 1.93–4.04). Higher insurance-coverage generosity, the study’s pre-treatment, policy-assignable exposure, was associated with an approximately 40% reduction in cost (E-value limit 2.19) and no significant change in the discharge proxy; effects were consistent across age, severity, hospital sector, rurality and socioeconomic rank, with an attenuated estimate in a small private-hospital subgroup. Recovery was concentrated among higher-socioeconomic episodes, while cost, out-of-pocket burden and length of stay were concentrated among lower-socioeconomic episodes; age and hospital ownership, not disease severity (which contributed only −0.8% to +6.2% of the observed gradients), were the leading correlates. A coverage-based, budget-neutral reallocation scenario reached 35.8% of the cohort within the observed budget. Ninety-eight percent of significant associations remained robust to plausible within-patient clustering, the principal limitation arising from the absence of a patient identifier in these claims data. Conclusion: Insurance-coverage generosity showed the strongest evidence of a causal, cost-reducing effect, while socioeconomic inequity in PD hospitalization outcomes was driven primarily by administrative rather than clinical-severity factors. The reallocation scenarios illustrate model-based, budget-neutral efficiency-equity gains and should be read as decision-support for phased piloting, not as empirically demonstrated policy effects. Patient-level claims linkage and validated clinical severity measures are needed to extend this framework toward genuine longitudinal outcomes.", "abstract_shown": true, "affiliations": [ "PhD in Health Economics, National Centre for Health Insurance Research, Tehran, Iran", "Health Services Research (concentration: Knowledge Discovery & Health Informatics) Department of Health Administration, Policy, and Informatics (HAPI) George Mason University Fairfax, Fairfax, Virginia, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 2, "authors": [ { "affs": [ 0 ], "name": "Shekoofeh Sadat Momahhed", "orcid": "0000-0002-5076-5517" }, { "affs": [ 1 ], "name": "Atefehsadat Haghighathoseini" } ], "bibtex": "https://research.draconitic.com/paper/2g4a0gqa5sn2p.bib", "cited_by": 0, "doi": "10.1371/journal.pone.0360944", "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13730354/", "id": "2g4a0gqa5sn2p", "issns": [ "1932-6203" ], "landing_url": "https://doi.org/10.1371/journal.pone.0360944", "language": "en", "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "heading", "Artificial Intelligence" ], [ "heading", "Computer and Information Sciences" ], [ "heading", "Economics" ], [ "heading", "Epidemiology" ], [ "heading", "Finance" ], [ "heading", "Health Care" ], [ "heading", "Health Care Facilities" ], [ "heading", "Health Economics" ], [ "heading", "Health Insurance" ], [ "heading", "Hospitals" ], [ "heading", "Inpatients" ], [ "heading", "Machine Learning" ], [ "heading", "Medical Conditions" ], [ "heading", "Medical Risk Factors" ], [ "heading", "Medicine and Health Sciences" ], [ "heading", "Movement Disorders" ], [ "heading", "Neurodegenerative Diseases" ], [ "heading", "Neurology" ], [ "heading", "Parkinson Disease" ], [ "heading", "Patients" ], [ "heading", "Public and Occupational Health" ], [ "heading", "Research Article" ], [ "heading", "Social Sciences" ], [ "heading", "Socioeconomic Aspects of Health" ], [ "openalex", "2002|Healthcare Policy and Management" ], [ "plos", "/Computer and information sciences/Artificial intelligence/Machine learning" ], [ "plos", "/Medicine and health sciences/Epidemiology/Medical risk factors" ], [ "plos", "/Medicine and health sciences/Health care/Health care facilities/Hospitals" ], [ "plos", "/Medicine and health sciences/Health care/Health economics/Health insurance" ], [ "plos", "/Medicine and health sciences/Health care/Patients/Inpatients" ], [ "plos", "/Medicine and health sciences/Health care/Socioeconomic aspects of health" ], [ "plos", "/Medicine and health sciences/Medical conditions/Neurodegenerative diseases/Movement disorders/Parkinson disease" ], [ "plos", "/Medicine and health sciences/Neurology/Neurodegenerative diseases/Movement disorders/Parkinson disease" ], [ "plos", "/Medicine and health sciences/Public and occupational health/Socioeconomic aspects of health" ], [ "plos", "/Social sciences/Economics/Finance" ], [ "plos", "/Social sciences/Economics/Health economics/Health insurance" ] ], "openalex": "W7225140934", "pdf_url": null, "pmcid": "PMC13730354", "pmid": "42976191", "published": "2026-11-03", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/2g4a0gqa5sn2p.ris", "sources": [ { "first_seen": "2026-11-04T12:20:43Z", "native_id": "10.1371/journal.pone.0360944", "source": "plos", "url": "https://doi.org/10.1371/journal.pone.0360944" }, { "first_seen": "2026-11-07T12:22:55Z", "native_id": "PMC13730354", "source": "pmc", "url": "https://doi.org/10.1371/journal.pone.0360944" } ], "subjects": [ { "field": "medicine", "origin": "plos:epidemiology", "subfield": "epidemiology-public-health" }, { "field": "medicine", "origin": "plos:neurology", "subfield": "neurology" } ], "title": "Resource allocation under uncertainty and equity constraints in Parkinson’s disease care: An econometrically grounded machine learning framework", "title_text": "Resource allocation under uncertainty and equity constraints in Parkinson’s disease care: An econometrically grounded machine learning framework", "type": "peer-reviewed", "updated_at": "2026-11-07T12:22:55Z", "url": "https://research.draconitic.com/paper/2g4a0gqa5sn2p", "venue": "PLOS One", "versions": [] }