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

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Názov:	Using Policy Learning to Inform Health Insurance Targeting: A Case Study of Indonesia.
Autori:	Shah V; Centre for Health Economics, University of York, York, UK., Jones AM; Department of Economics and Related Studies, University of York, York, UK., Malenica I; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA., Hidayat T; Center for Health Economics and Policy Studies, Universitas Indonesia, Depok, Indonesia.; Department of Economics, University of Sussex, Brighton, UK., Kreif N; The CHOICE Institure, School of Pharmacy, University of Washington, Seattle, Washington, USA.
Zdroj:	Health economics [Health Econ] 2025 Dec; Vol. 34 (12), pp. 2270-2296. Date of Electronic Publication: 2025 Sep 08.
Spôsob vydávania:	Journal Article
Jazyk:	English
Informácie o časopise:	Publisher: Wiley Country of Publication: England NLM ID: 9306780 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1099-1050 (Electronic) Linking ISSN: 10579230 NLM ISO Abbreviation: Health Econ Subsets: MEDLINE
Imprint Name(s):	Original Publication: Chichester ; New York : Wiley, c1992-
Výrazy zo slovníka MeSH:	Insurance, Health/economics , Health Policy, Indonesia ; Humans ; Health Expenditures/statistics & numerical data ; Machine Learning ; Female
Abstrakt:	This paper demonstrates how optimal policy learning can inform the targeted allocation of Indonesia's two subsidized health insurance programmes. Using national survey data, we develop policy rules aimed at minimizing "catastrophic health expenditure" among enrollees of APBD or APBN, the two government-funded schemes. Employing a super learner ensemble approach, we use regression and machine learning methods of varying complexity to estimate conditional average treatment effects and construct policy rules to optimize program benefits, both with and without budget constraints. We find that the financial impact of APBD enrollment over APBN differs with household characteristics, particularly demographic composition, socioeconomic status, and geography. Households assigned to APBD under the policy rule are typically urban-based with better facilities, whereas rural households with less accessible healthcare are assigned to APBN-a pattern intensified under budget constraints. Both constrained and unconstrained optimal policy assignments show lower expected catastrophic expenditure risk than the current assignment strategy. This study contributes to the literature on heterogeneous treatment effects, optimal policy leaning, and health financing in developing countries, showcasing data-driven solutions for more equitable resource allocation in public health insurance contexts. (© 2025 John Wiley & Sons Ltd.)
References:	Agustina, R., T. Dartanto, R. Sitompul, K. A. Susiloretni, S. Suparmi, E. L. Achadi, A. Taher, F. Wirawan, S. Sungkar, P. Sudarmono, A. H. Shankar, H. Thabrany, Indonesian Health Systems Group. 2019. “Universal Health Coverage in Indonesia: Concept, Progress, and Challenges.” Lancet 393, no. 10166: 75–102. https://doi.org/10.1016/s0140‐6736(18)31647‐7. Alatas, V., A. Banerjee, R. Hanna, B. A. Olken, R. Purnamasari, and M. Wai‐Poi. 2016. “Self‐Targeting: Evidence From a Field Experiment in Indonesia.” Journal of Political Economy 124, no. 2: 371–427. https://doi.org/10.1086/685299. Alatas, V., A. Banerjee, R. Hanna, B. A. Olken, and J. Tobias. 2012. “Targeting the Poor: Evidence From a Field Experiment in Indonesia.” American Economic Review 102, no. 4: 1206–1240. https://doi.org/10.1257/aer.102.4.1206. Amram, M., J. Dunn, and Y. D. Zhuo. 2022. “Optimal Policy Trees.” Machine Learning 111, no. 7: 2741–2768. https://doi.org/10.1007/s10994‐022‐06128‐5. Athey, S., J. Tibshirani, and S. Wager. 2019. “Generalized Random Forests.” Annals of Statistics 47, no. 2: 1148–1178. https://doi.org/10.1214/18‐aos1709. Athey, S., and S. Wager. 2021. “Policy Learning With Observational Data.” Econometrica 89, no. 1: 133–161. https://doi.org/10.3982/ecta15732. Badan Pusat Statistik. 2017. “Survei Sosial Ekonomi Nasional (SUSENAS).”. Badan Pusat Statistik. 2018. “Village Potential Statistics (PODES).”. Bahamyirou, A., M. E. Schnitzer, E. H. Kennedy, L. Blais, and Y. Yang. 2022. “Doubly Robust Adaptive LASSO for Effect Modifier Discovery.” International Journal of Biostatistics 18, no. 2: 307–327. https://doi.org/10.1515/ijb‐2020‐0073. Bernal, N., M. A. Carpio, and T. J. Klein. 2017. “The Effects of Access to Health Insurance: Evidence From a Regression Discontinuity Design in Peru.” Journal of Public Economics 154: 122–136. https://doi.org/10.1016/j.jpubeco.2017.08.008. Bertsimas, D., J. Dunn, and N. Mundru. 2019. “Optimal Prescriptive Trees.” INFORMS Journal on Optimization 1, no. 2: 164–183. https://doi.org/10.1287/ijoo.2018.0005. Bhattacharya, D., and P. Dupas. 2012. “Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints.” Journal of Econometrics 167, no. 1: 168–196. https://doi.org/10.1016/j.jeconom.2011.11.007. Bookwalter, J. T., B. S. Fuller, and D. R. Dalenberg. 2006. “Do Household Heads Speak for the Household? A Research Note.” Social Indicators Research 79, no. 3: 405–419. https://doi.org/10.1007/s11205‐005‐4925‐9. Chernozhukov, V., D. Chetverikov, M. Demirer, et al. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” Econometrics Journal 21, no. 1: C1–C68. https://doi.org/10.1111/ectj.12097. Chernozhukov, V., M. Demirer, E. Duflo, and I. Fernández‐Val. 2018. Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India. Technical Report 24678. National Bureau of Economic Research. Coyle, J. R. 2017. Computational Considerations for Targeted Learning. Phd Thesis. University of California. Crump, R. K., V. J. Hotz, G. W. Imbens, and O. A. Mitnik. 2009. “Dealing With Limited Overlap in Estimation of Average Treatment Effects.” Biometrika 96, no. 1: 187–199. https://doi.org/10.1093/biomet/asn055. Dartanto, T., A. Halimatussadiah, J. F. Rezki, et al. 2020. “Why Do Informal Sector Workers Not Pay the Premium Regularly? Evidence From the National Health Insurance System in Indonesia.” Applied Health Economics and Health Policy 18, no. 1: 81–96. https://doi.org/10.1007/s40258‐019‐00518‐y. Dehejia, R. H. 2005. “Program Evaluation as a Decision Problem.” Journal of Econometrics 125, no. 1: 141–173. https://doi.org/10.1016/j.jeconom.2004.04.006. Dudoit, S., and M. J. van der Laan. 2005. “Asymptotics of Cross‐Validated Risk Estimation in Estimator Selection and Performance Assessment.” Statistical Methodology 2, no. 2: 131–154. https://doi.org/10.1016/j.stamet.2005.02.003. Dutta, A., K. Ward, E. Setiawan, and S. Prabhakaran. 2020. Fiscal Space for Health in Indonesia: Public Sector Opportunities and Constraints in Achieving the Goals of Indonesia’s Mid‐Term Development Plan (RPJMN) 2020–2024. Technical report. Kementerian PPN/Bappenas. El‐Sayed, A. M., D. Vail, and M. E. Kruk. 2018. “Ineffective Insurance in Lower and Middle Income Countries is an Obstacle to Universal Health Coverage.” Journal of Global Health 8, no. 2: 020402. https://doi.org/10.7189/jogh.08.020402. Erlangga, D., M. Suhrcke, S. Ali, and K. Bloor. 2019. “The Impact of Public Health Insurance on Health Care Utilisation, Financial Protection and Health Status in Low‐ and Middle‐Income Countries: A Systematic Review.” PLoS One 14, no. 11: e0225237. https://doi.org/10.1371/journal.pone.0219731. Fink, G., P. J. Robyn, A. Sié, and R. Sauerborn. 2013. “Does Health Insurance Improve Health?: Evidence From a Randomized Community‐Based Insurance Rollout in Rural Burkina Faso.” Journal of Health Economics 32, no. 6: 1043–1056. https://doi.org/10.1016/j.jhealeco.2013.08.003. Finkelstein, A., and M. J. Notowidigdo. 2019. “Take‐Up and Targeting: Experimental Evidence From SNAP.” Quarterly Journal of Economics 134, no. 3: 1505–1556. https://doi.org/10.1093/qje/qjz013. Grogger, J., T. Arnold, A. S. León, and A. Ome. 2015. “Heterogeneity in the Effect of Public Health Insurance on Catastrophic Out‐of‐Pocket Health Expenditures: The Case of Mexico.” Health Policy and Planning 30, no. 5: 593–599. https://doi.org/10.1093/heapol/czu037. Hahn, R. P., J. S. Murray, and C. M. Carvalho. 2020. “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (With Discussion).” Bayesian Analysis 15, no. 3: 965–1056. https://doi.org/10.1214/19‐ba1195. Hanna, R., and B. A. Olken. 2018. “Universal Basic Incomes Versus Targeted Transfers: Anti‐Poverty Programs in Developing Countries.” Journal of Economic Perspectives 32, no. 4: 201–226. https://doi.org/10.1257/jep.32.4.201. Harimurti, P., E. Pambudi, A. Pigazzini, and A. Tandon. 2013. The Nuts and Bolts of Jamkesmas: Indonesia’s Government‐Financed Health Coverage Program for the Poor and Near‐Poor. Technical report. World Bank. J. Hatamyar and N. Kreif 2023. “Policy Learning With Rare Outcomes.” Preprint, arXiv [econ.EM]. Johar, M., P. Soewondo, R. Pujisubekti, H. K. Satrio, and A. Adji. 2018. “Inequality in Access to Health Care, Health Insurance and the Role of Supply Factors.” Social Science & Medicine 213: 134–145. https://doi.org/10.1016/j.socscimed.2018.07.044. Künzel S. R., J. S. Sekhon, P. J. Bickel, and B. Yu. 2019. “Metalearners for Estimating Heterogeneous Treatment Effects Using Machine Learning.” Proceedings of the National Academy of Sciences of the United States of America 116, no. 10: 4156–4165. https://doi.org/10.1073/pnas.1804597116. Kennedy, E. H. 2023. “Towards Optimal Doubly Robust Estimation of Heterogeneous Causal Effects.” Electronic Journal of Statistics 17, no. 2. https://doi.org/10.1214/23‐ejs2157. Kitagawa, T., and A. Tetenov. 2018. “Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice.” Econometrica 86, no. 2: 591–616. https://doi.org/10.3982/ecta13288. Kruse, I., M. Pradhan, and R. Sparrow. 2012. “Marginal Benefit Incidence of Public Health Spending: Evidence From Indonesian Sub‐National Data.” Journal of Health Economics 31, no. 1: 147–157. https://doi.org/10.1016/j.jhealeco.2011.09.003. Kube, A., S. Das, and P. J. Fowler. 2019. “Allocating Interventions Based on Predicted Outcomes: A Case Study on Homelessness Services.” Proceedings of the AAAI Conference on Artificial Intelligence 33, no. 1: 622–629. https://doi.org/10.1609/aaai.v33i01.3301622. Li, F., L. E. Thomas, and F. Li. 2019. “Addressing Extreme Propensity Scores Via the Overlap Weights.” American Journal of Epidemiology 188, no. 1: 250–257. https://doi.org/10.1093/aje/kwy201. Limwattananon, S., V. Tangcharoensathien, K. Tisayaticom, T. Boonyapaisarncharoen, and P. Prakongsai. 2012. “Why Has the Universal Coverage Scheme in Thailand Achieved a Pro‐Poor Public Subsidy for Health Care?” Supplement, BMC Public Health 12, no. S1: S6. https://doi.org/10.1186/1471‐2458‐12‐s1‐s6. Luedtke, A. R., and M. J. van der Laan. 2016a. “Super‐Learning of an Optimal Dynamic Treatment Rule.” International Journal of Biostatistics 12, no. 1: 305–332. https://doi.org/10.1515/ijb‐2015‐0052. Luedtke, A. R., and M. J. van der Laan. 2016b. “Optimal Individualized Treatments in Resource‐Limited Settings.” International Journal of Biostatistics 12, no. 1: 283–303. https://doi.org/10.1515/ijb‐2015‐0007. Mahendradhata, Y., L. Trisnantoro, S. Listyadewi, et al. 2017. The Republic of Indonesia Health System Review. Technical report. WHO Regional Office for South‐East Asia. Manski, C. F. 2004. “Statistical Treatment Rules for Heterogeneous Populations.” Econometrica 72, no. 4: 1221–1246. https://doi.org/10.1111/j.1468‐0262.2004.00530.x. Mardiansjah, F. H., P. Rahayu, and D. Rukmana. 2021. “New Patterns of Urbanization in Indonesia: Emergence of Non‐statutory Towns and New Extended Urban Regions.” Environment and Urbanization ASIA 12, no. 1: 11–26. https://doi.org/10.1177/0975425321990384. Maulana, N., P. Soewondo, N. Adani, P. Limasalle, and A. Pattnaik. 2022. “How Jaminan Kesehatan Nasional (JKN) Coverage Influences Out‐of‐Pocket (OOP) Payments by Vulnerable Populations in Indonesia.” PLOS Global Public Health 2, no. 7: e0000203. https://doi.org/10.1371/journal.pgph.0000203. Montoya, L., J. Skeem, M. J. van der Laan, and M. Petersen. 2021. “Performance and Application of Estimators for the Value of an Optimal Dynamic Treatment Rule.” https://arxiv.org/abs/2101.12333. Montoya, L. M., M. J. van der Laan, A. R. Luedtke, J. L. Skeem, J. R. Coyle, and M. L. Petersen. 2023. “The Optimal Dynamic Treatment Rule Superlearner: Considerations, Performance, and Application to Criminal Justice Interventions.” International Journal of Biostatistics 19, no. 1: 217–238. https://doi.org/10.1515/ijb‐2020‐0127. National Team for the Acceleration of Poverty Reduction (Indonesia). 2015. “The Road to National Health Insurance (JKN).” Technical report. Newey, W. K., and J. R. Robins. 2018. “Cross‐Fitting and Fast Remainder Rates for Semiparametric Estimation.” https://arxiv.org/abs/1801.09138. Nie, X., and S. Wager. 2021. “Quasi‐Oracle Estimation of Heterogeneous Treatment Effects.” Biometrika 108, no. 2: 299–319. https://doi.org/10.1093/biomet/asaa076. Panch, T., H. Mattie, and R. Atun. 2019. “Artificial Intelligence and Algorithmic Bias: Implications for Health Systems.” Journal of Global Health 9, no. 2: 010318. https://doi.org/10.7189/jogh.09.020318. Phillips, R. V., M. J. van der Laan, H. Lee, and S. Gruber. 2022. “Practical Considerations for Specifying a Super Learner.” https://arxiv.org/abs/2204.06139. Polley, E. C., and M. J. van der Laan. 2010. Super Learner in Prediction. Technical report. University of California. Prabhakaran, S., A. Dutta, T. Fagan, and M. Ginivan. 2019. Financial Sustainability of Indonesia’s Jaminan Kesehatan Nasional: Performance, Prospects, and Policy Options. Technical report: Health Policy Plus, National Team for the Acceleration of Poverty Reduction (TNP2K). Pratiwi, A. B., H. Setiyaningsih, M. O. Kok, T. Hoekstra, A. G. Mukti, and E. Pisani. 2021. “Is Indonesia Achieving Universal Health Coverage? Secondary Analysis of National Data on Insurance Coverage, Health Spending and Service Availability.” BMJ Open 11, no. 10: e050565. https://doi.org/10.1136/bmjopen‐2021‐050565. Sambodo, N. P., E. Van Doorslaer, M. Pradhan, and R. Sparrow. 2021. “Does Geographic Spending Variation Exacerbate Healthcare Benefit Inequality? A Benefit Incidence Analysis for Indonesia.” Health Policy and Planning 36, no. 7: 1129–1139. https://doi.org/10.1093/heapol/czab015. Shalit, U., F. D. Johansson, and D. Sontag. 2017. “Estimating Individual Treatment Effect: Generalization Bounds and Algorithms.” In Proceedings of the 34th International Conference on Machine Learning, Volume 70 of Proceedings of Machine Learning Research, edited by D. Precup and Y. W. Teh, 3076–3085. PMLR. Sparrow, R., S. Budiyati, A. Yumna, N. Warda, A. Suryahadi, and A. S. Bedi. 2017. “Sub‐National Health Care Financing Reforms in Indonesia.” Health Policy and Planning 32, no. 1: 91–101. https://doi.org/10.1093/heapol/czw101. Stürmer, T., M. Webster‐Clark, J. L. Lund, et al. 2021. “Propensity Score Weighting and Trimming Strategies for Reducing Variance and Bias of Treatment Effect Estimates: A Simulation Study.” American Journal of Epidemiology 190, no. 8: 1659–1670. https://doi.org/10.1093/aje/kwab041. Sverdrup, E., A. Kanodia, Z. Zhou, S. Athey, and S. Wager. 2020. “Policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees.” Journal of Open Source Software 5, no. 50: 2232. https://doi.org/10.21105/joss.02232. van der Laan, M. J., and S. Dudoit. 2003. “Unified cross‐validation Methodology for Selection Among Estimators and a General Cross‐Validated Adaptive Epsilon‐Net Estimator: Finite Sample Oracle Inequalities and Examples.” U.C. Berkeley Division of Biostatistics Working Paper Series. van der Laan, M. J., S. Dudoit, and A. W. van der Vaart. 2006. “The Cross‐Validated Adaptive Epsilon‐Net Estimator.” Statistics & Decisions 24, no. 3: 373–395. https://doi.org/10.1524/stnd.2006.24.3.373. van der Laan, M. J., and A. R. Luedtke. 2014. “Targeted Learning of an Optimal Dynamic Treatment, and Statistical Inference for Its Mean Outcome.” U.C. Berkeley Division of Biostatistics Working Paper Series. van der Laan, M. J., and A. R. Luedtke. 2015. “Targeted Learning of the Mean Outcome Under an Optimal Dynamic Treatment Rule.” Journal of Causal Inference 3, no. 1: 61–95. https://doi.org/10.1515/jci‐2013‐0022. van der Laan, M. J., E. C. Polley, and A. E. Hubbard. 2007. “Super Learner.” Statistical Applications in Genetics and Molecular Biology 6, no. 1: Article25. https://doi.org/10.2202/1544‐6115.1309. van der Laan, M. J., and S. Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Series in Statistics. Springer Publishing. van der Laan, M. J., and S. Rose. 2018. Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies. Springer Series in Statistics. Springer Publishing. van der Vaart, A. W., S. Dudoit, and M. J. van der Laan. 2006. “Oracle Inequalities for Multi‐Fold Cross Validation.” Statistics & Decisions 24, no. 3: 351–371. https://doi.org/10.1524/stnd.2006.24.3.351. Vilcu, I., L. Probst, B. Dorjsuren, and I. Mathauer. 2016. “Subsidized Health Insurance Coverage of People in the Informal Sector and Vulnerable Population Groups: Trends in Institutional Design in Asia.” International Journal for Equity in Health 15, no. 1: 165. https://doi.org/10.1186/s12939‐016‐0436‐3. Wagstaff, A., G. Flores, J. Hsu, et al. 2018. “Progress on Catastrophic Health Spending in 133 Countries: A Retrospective Observational Study.” Lancet Global Health 6, no. 2: e169–e179. https://doi.org/10.1016/s2214‐109x(17)30429‐1. Wagstaff, A., and M. Lindelow. 2008. “Can Insurance Increase Financial Risk? The Curious Case of Health Insurance in China.” Journal of Health Economics 27, no. 4: 990–1005. https://doi.org/10.1016/j.jhealeco.2008.02.002. Wagstaff, A., O. O’Donnell, E. van Doorslaer, and M. Lindelow. 2007. Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and Their Implementation. World Bank Publications. Wagstaff, A., and E. van Doorslaer. 2003. “Catastrophe and Impoverishment in Paying for Health Care: With Applications to Vietnam 1993‐1998.” Health Economics 12, no. 11: 921–934. https://doi.org/10.1002/hec.776. World Health Organization. 2010. Health Systems Financing: The Path to Universal Coverage. Technical report. World Health Organization. Xu, K., D. B. Evans, K. Kawabata, R. Zeramdini, J. Klavus, and C. J. L. Murray. 2003. “Household Catastrophic Health Expenditure: A Multicountry Analysis.” Lancet 362, no. 9378: 111–117. https://doi.org/10.1016/s0140‐6736(03)13861‐5. Yadlowsky, S., S. Fleming, N. Shah, E. Brunskill, and S. Wager. 2021. “Evaluating Treatment Prioritization Rules via rank‐weighted Average Treatment Effects.” https://arxiv.org/abs/2111.07966. Zhou, Z., S. Athey, and S. Wager. 2022. “Offline Multi‐Action Policy Learning: Generalization and Optimization.” Operations Research 71, no. 1: 148–183. https://doi.org/10.1287/opre.2022.2271. Zou, H. 2006. “The Adaptive Lasso and Its Oracle Properties.” Journal of the American Statistical Association 101, no. 476: 1418–1429. https://doi.org/10.1198/016214506000000735.
Grant Information:	MR/T04487X/1 United Kingdom MRC_ Medical Research Council
Contributed Indexing:	Keywords: Indonesia; global health; health insurance; heterogeneous treatment effects; optimal policy learning
Entry Date(s):	Date Created: 20250908 Date Completed: 20251101 Latest Revision: 20251101
Update Code:	20251102
DOI:	10.1002/hec.70031
PMID:	40922046
Databáza:	MEDLINE

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Abstrakt:	This paper demonstrates how optimal policy learning can inform the targeted allocation of Indonesia's two subsidized health insurance programmes. Using national survey data, we develop policy rules aimed at minimizing "catastrophic health expenditure" among enrollees of APBD or APBN, the two government-funded schemes. Employing a super learner ensemble approach, we use regression and machine learning methods of varying complexity to estimate conditional average treatment effects and construct policy rules to optimize program benefits, both with and without budget constraints. We find that the financial impact of APBD enrollment over APBN differs with household characteristics, particularly demographic composition, socioeconomic status, and geography. Households assigned to APBD under the policy rule are typically urban-based with better facilities, whereas rural households with less accessible healthcare are assigned to APBN-a pattern intensified under budget constraints. Both constrained and unconstrained optimal policy assignments show lower expected catastrophic expenditure risk than the current assignment strategy. This study contributes to the literature on heterogeneous treatment effects, optimal policy leaning, and health financing in developing countries, showcasing data-driven solutions for more equitable resource allocation in public health insurance contexts.<br /> (© 2025 John Wiley & Sons Ltd.)
ISSN:	1099-1050
DOI:	10.1002/hec.70031