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Using Policy Learning to Inform Health Insurance Targeting: A Case Study of Indonesia.

Vishalie Shah1, Andrew M Jones2, Ivana Malenica3

  • 1Centre for Health Economics, University of York, York, UK.

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Summary
This summary is machine-generated.

Optimal policy learning can improve Indonesia's health insurance allocation. Machine learning identifies better benefit distribution for the regional (APBD) and national (APBN) schemes, reducing catastrophic health spending for households.

Keywords:
Indonesiaglobal healthhealth insuranceheterogeneous treatment effectsoptimal policy learning

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Area of Science:

  • Health Economics
  • Public Policy
  • Machine Learning

Background:

  • Indonesia operates two subsidized health insurance programs: APBD (regional) and APBN (national).
  • Targeted allocation is crucial for minimizing catastrophic health expenditure among enrollees.
  • Existing allocation strategies may not be optimal for diverse household needs.

Purpose of the Study:

  • To develop optimal policy rules for allocating enrollees to Indonesia's APBD and APBN health insurance programs.
  • To minimize catastrophic health expenditure using data-driven approaches.
  • To explore the impact of budget constraints on optimal policy allocation.

Main Methods:

  • Utilized national survey data for analysis.
  • Employed a super learner ensemble approach combining regression and machine learning.
  • Estimated conditional average treatment effects to inform policy rules.

Main Results:

  • Financial impacts of APBD versus APBN enrollment vary significantly by household characteristics (demographics, socioeconomic status, geography).
  • Optimal policy rules assigned urban households to APBD and rural households to APBN, a pattern amplified under budget constraints.
  • Both optimal allocation strategies (constrained and unconstrained) reduced expected catastrophic expenditure risk compared to current methods.

Conclusions:

  • Optimal policy learning offers a data-driven solution for equitable resource allocation in public health insurance.
  • Machine learning can effectively identify heterogeneous treatment effects for targeted health policy.
  • Findings inform more efficient health financing strategies in developing countries.