Resource allocation under uncertainty and equity constraints in Parkinson’s disease care: An econometrically grounded machine learning framework
- 1PhD in Health Economics, National Centre for Health Insurance Research, Tehran, Iran
- 2Health 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
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.
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BibTeX
@article{momahhed2026resource,
title = {{Resource allocation under uncertainty and equity constraints in Parkinson’s disease care: An econometrically grounded machine learning framework}},
author = {Shekoofeh Sadat Momahhed and Atefehsadat Haghighathoseini},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0360944},
url = {https://doi.org/10.1371/journal.pone.0360944},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Resource allocation under uncertainty and equity constraints in Parkinson’s disease care: An econometrically grounded machine learning framework
AU - Shekoofeh Sadat Momahhed
AU - Atefehsadat Haghighathoseini
PY - 2026
DA - 2026/11/03
JO - PLOS One
DO - 10.1371/journal.pone.0360944
UR - https://doi.org/10.1371/journal.pone.0360944
AB - 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.
SN - 1932-6203
ER -