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Promoting sustainability for micro health insurance: a risk-adjusted subsidy approach for maternal healthcare service

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Abstract

Micro health insurance is an important way to finance health expenditure for low-income people, and maternity care is a key component of relevant coverage. We propose a risk-adjusted subsidy provided by the government to microinsurers as a method to enhance micro health insurance for maternity benefits. Using a large data set from a micro health insurance programme in Pakistan, we apply various econometric models to predict maternity-related expenses and to calculate an appropriate risk-adjusted subsidy from the government to microinsurer. This allows us to further simulate the microinsurers’ financial results. We find that the risk-adjusted subsidy could significantly improve the loss ratio by almost 40%, and the Ordinary Least Squares model is preferred among the four model forms we test. We contribute to the literature by demonstrating that this method is feasible, and further, by illustrating the potential effect of such a subsidy on micro health insurer outcomes. If successful, such a payment model could improve efficiency and extend affordable maternity care to low-income women in developing regions.

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Notes

  1. Source WHO (2018) Maternal Mortality Fact Sheet, available at http://who.int/mediacentre/factsheets/fs348/en/.

  2. WHO, UNICEF, UNFPA, The World Bank, and the United Nations Population Division (2014).

  3. Source https://afro.who.int/health-topics/maternal-health.

  4. A number of developing countries including Uganda, Liberia, Senegal, Burundi, Kenya and Niger have begun to provide free delivery services. With the exemption in national health insurance premiums for pregnant woman, Ghana has seen an increased enrolment rate among the target population (Frimpong et al. 2014).

  5. Risk-adjusted subsidy is the subsidy that government pays to a microinsurer using risk-adjusted expenditure of insureds.

  6. The flat premium of PKR 350 per person was set in November 2007. It increased to PKR 400 for participants enrolled in November 2008, July 2009, and November 2009. It increased again to PKR 450 for new participants in two LSOs (ZADO and DANYORE) in July 2010, but remained at PKR 400 for all the other insureds.

  7. The coverage increases to PKR 30,000 for renewed customers as an encouragement to stay in the programme.

  8. Source WHO Indicator Metadata Registry, available at http://apps.who.int/gho/indicatorregistry/App_Main/view_indicator.aspx?iid=4668.

  9. Source Average Household Social Statistics, Bureau of Statistics, Pakistan, available at http://www.pbs.gov.pk/sites/default/files/social_statistics/publications/hies07_08/Table1.pdf.

  10. Alternatively, we could use age at enrolment as the control variable instead of an age band of 5 years. We ran the regression as a robustness check, and the results were similar to those reported in Table 3. The prediction and simulation results are also very close to those reported in Table 5. Detailed results are available on request.

  11. The pseudo R squared of Tobit model is based on MLE estimation and is not suitable for comparing with R squared directly. The direct comparison of AIC among the four models is also not appropriate, given that the sample size of TPM is much smaller.

  12. Additional test for the model fit is also performed. We apply a Monte Carlo simulation for the Tobit model, and the results are presented in Table 9 in Appendix, which shows that the average of Monte Carlo simulated MAPEs and MSEs are both comparable to the level of the original Tobit regression results. Therefore, we regard the original Tobit regression results as being reliable.

  13. We present the pattern separately for the two parts because there are around 78% of participants with no claims; therefore, the prediction pattern by deciles will be affected significantly by the 10th decile alone if we use pooled data.

  14. The total budget for the Northern Areas was PKR 12 billion, and the budget for public health service was PKR 942 million in fiscal year 2016–2017. Source Sector-Wise Summary of Annual Development Programme of Gilgit-Baltisan for the Year 2016–2017, available at http://www.gilgitbaltistan.gov.pk/DownloadFiles/ADPS/ADP2016-17.pdf.

References

  • Alexander, D. 2016. How do doctors respond to incentives? Unintended consequences of paying doctors to reduce costs. Working Paper. http://scholar.princeton.edu/sites/default/files/dalexand/files/alexander_jmp.pdf.

  • Ankrah, O.E., P. Akweongo, B. Yankah, F. Asenso-Boadi, and I. Agyepong. 2013. Sustainability of recurrent expenditure on public social welfare programmes: Expenditure analysis of the free maternal care programme of the Ghana National Health Insurance Scheme. Health Policy and Planning 29 (3): 271–279.

    Google Scholar 

  • Basu, A., and W.G. Manning. 2009. Issues for the next generation of health care cost analyses. Medical Care 47 (7): 109–114.

    Google Scholar 

  • Beck, K., M. Trottmann, and P. Zweifel. 2010. Risk adjustment in health insurance and its long-term effectiveness. Journal of Health Economics 29 (4): 489–498.

    Google Scholar 

  • Biener, C., and M. Eling. 2011. The performance of microinsurance programs: A data envelopment analysis. The Journal of Risk and Insurance 78 (1): 83–115.

    Google Scholar 

  • Brown, J., M. Duggan, I. Kuziemko, and W. Woolston. 2014. How does risk selection respond to risk adjustment? New evidence from the Medicare Advantage program. American Economic Review 104 (10): 3335–3364.

    Google Scholar 

  • Buntin, M.B., and A.M. Zaslavsky. 2004. Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures. Journal of Health Economics 23 (3): 525–542.

    Google Scholar 

  • Clement, O.N. 2009. Asymmetry information problem of moral hazard and adverse selection in a national health insurance: The case of Ghana national health insurance. Management Science and Engineering 3 (3): 101–106.

    Google Scholar 

  • Cole, S. 2015. Overcoming barriers to microinsurance adoption: Evidence from the field. The Geneva Papers on Risk and Insurance—Issues and Practice 40 (4): 720–740.

    Google Scholar 

  • Dafny, L.S. 2005. How do hospitals respond to price changes? American Economic Review 95 (5): 1525–1547.

    Google Scholar 

  • Desai, S., T. Sinha, and A. Mahal. 2011. Prevalence of hysterectomy among rural and urban women with and without health insurance in Gujarat, India. Reproductive Health Matters 19 (37): 42–51.

    Google Scholar 

  • Duan, N., W.G. Manning, C.N. Morris, and J.P. Newhouse. 1983. A comparison of alternative models for the demand for medical care. Journal of Business & Economic Statistics 1 (2): 115–126.

    Google Scholar 

  • Duggan, M. 2004. Does contracting out increase the efficiency of government programs? Evidence from Medicaid HMOs. Journal of Public Economics 88 (12): 2549–2572.

    Google Scholar 

  • Dumont, A., P. Fournier, M. Abrahamowicz, M. Traoré, S. Haddad, and W.D. Fraser. 2013. Quality of care, risk management, and technology in obstetrics to reduce hospital-based maternal mortality in Senegal and Mali (QUARITE): A cluster-randomized trial. Lancet 382 (9887): 146–157.

    Google Scholar 

  • Eggleston, K., and A. Bir. 2009. Measuring selection incentives in managed care: Evidence from the Massachusetts state employee insurance program. The Journal of Risk and Insurance 76 (1): 159–175.

    Google Scholar 

  • Eling, M., R. Jia, and Y. Yao. 2017. Between-group adverse selection: Evidence from group critical illness insurance. The Journal of Risk and Insurance 84 (2): 771–809.

    Google Scholar 

  • Ellis, R.P. 2008. Risk adjustment in health care markets: Concepts and applications. In Financing health care: New ideas for a changing society, ed. M. Lu. Edmonton and Alberta: Wiley.

    Google Scholar 

  • Ettner, S.L., R.G. Frank, T.G. McGuire, J.P. Newhouse, and E.H. Notman. 1998. Risk adjustment of mental health and substance abuse payments. Inquiry 35 (2): 223–239.

    Google Scholar 

  • Frimpong, J.A., S. Helleringer, J.K. Awoonor-Williams, T. Aguilar, J.F. Phillips, and F. Yeji. 2014. The complex association of health insurance and maternal health services in the context of a premium exemption for pregnant women: A case study in northern Ghana. Health Policy and Planning 29 (8): 1043–1053.

    Google Scholar 

  • Geruso, M., and T.G. McGuire. 2016. Tradeoffs in the design of health plan payment systems: Fit, power and balance. Journal of Health Economics 47 (1): 1–19.

    Google Scholar 

  • Glazer, J., and T.G. McGuire. 2000. Optimal risk adjustment in markets with adverse selection: An application to managed care. American Economic Review 90 (4): 1055–1071.

    Google Scholar 

  • Gruber, J., and M. Owings. 1996. Physician financial incentives and cesarean section delivery. RAND Journal of Economics 27 (1): 99–123.

    Google Scholar 

  • Jones, A.M. 2000. Chapter six: Health econometrics. In Handbook of health economics, eds. A.J. Cuyler, and J.P Newhouse, Vol. 1, Part A, 265-344, Elsevier.

  • Jones, A.M., J. Lomas, and N. Rice. 2014. Applying beta-type size distributions to healthcare cost regressions. Journal of Applied Econometrics 29 (4): 649–670.

    Google Scholar 

  • Jones, A.M., J. Lomas, P.T. Moore, and N. Rice. 2016. A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: An application to healthcare costs. Journal of the Royal Statistical Society Series A 179 (4): 951–974.

    Google Scholar 

  • Kautter, J., G.C. Pope, M. Ingber, S. Freeman, L. Patterson, M. Cohen, and P. Keenan. 2014. The HHS-HCC risk adjustment model for individual and small group markets under the Affordable Care Act. Medicare & Medicaid Research Review 4 (3): 16.

    Google Scholar 

  • Leibowitz, A., J.L. Buchanan, and J. Mann. 1992. A randomized trial to evaluate the effectiveness of a Medicaid HMO. Journal of Health Economics 11 (3): 235–257.

    Google Scholar 

  • Liu, X., H. Yan, and D. Wang. 2010. The evaluation of ‘safe motherhood’ program on maternal care utilization in rural western China: A difference in difference approach. BMC Public Health 10 (1): 1–6.

    Google Scholar 

  • Maciejewski, M.L., C.F. Liu, and S.D. Fihn. 2009. Performance of comorbidity, risk adjustment, and functional status measures in expenditure prediction for patients with diabetes. Diabetes Care 32 (1): 75–80.

    Google Scholar 

  • Manning, W.G., and J. Mullahy. 2001. Estimating log models: To transform or not to transform? Journal of Health Economics 20 (4): 461–494.

    Google Scholar 

  • Manning, W.G., C.N. Morris, J.P. Newhouse, L.L. Orr, N. Duan, E.B. Keeler, L. Leibowitz, K.H. Marquis, M.S. Marquis, and C.E. Phelps. 1981. A two-part model of the demand for medical care: Preliminary results from the health insurance study’. In Health, Economics, and Health Economics, Proceedings of the World Congress on Health Economics, Leiden, The Netherlands, September 1980, eds. J. van der Gaag, and M. Perlman, 103-123, North Holland.

  • McQuestion, M.J., and A. Velasquez. 2006. Evaluating program effects on institutional delivery in Peru. Health Policy 77 (2): 221–232.

    Google Scholar 

  • McWilliams, J.M., J. Hsu, and J.P. Newhouse. 2012. New risk-adjustment system was associated with reduced favorable selection in Medicare Advantage. Health Affairs 31 (12): 2630–2640.

    Google Scholar 

  • Mihaylova, B., A. Briggs, A. O’Hagan, and S.G. Thompson. 2011. Review of statistical methods for analysing healthcare resources and costs. Health Economics 20 (8): 897–916.

    Google Scholar 

  • Mullahy, J. 1998. Much ado about two: Reconsidering retransformation and the two-part model in health econometrics. Journal of Health Economics 17: 247–281.

    Google Scholar 

  • Okusanya, B.O., A.A. Roberts, O.J. Akinsola, and B.A. Oye-Adeniran. 2015. Birth plans and health insurance enrolment of pregnant women: A cross-sectional survey at two secondary health facilities in Lagos, Nigeria. The Journal of Maternal-Fetal & Neonatal Medicine 29 (16): 2602–2606.

    Google Scholar 

  • Pope, G.C., J. Kautter, R.P. Ellis, A.S. Ash, J.Z. Ayanian, L.I. Iezzoni, M.J. Ingber, J.M. Levy, and J. Robst. 2004. Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financing Review 25 (4): 119–141.

    Google Scholar 

  • Radermacher, R., S. Srivastava, M. Walsham, C. Sao, and F. Paolucci. 2016. Enhancing the inclusion of vulnerable and high-risk groups in demand-side health financing schemes in Cambodia: A concept for a risk-adjusted subsidy approach. The Geneva Papers on Risk and Insurance—Issues and Practice 41 (2): 244–258.

    Google Scholar 

  • Schokkaert, E., and C. Van de Voorde. 2003. Belgium: Risk adjustment and financial responsibility in a centralized system. Health Policy 65 (1): 5–19.

    Google Scholar 

  • Shen, Y., and R.P. Ellis. 2002. Cost-minimizing risk adjustment. Journal of Health Economics 21 (3): 515–530.

    Google Scholar 

  • Smith, K.V., and S. Sulzbach. 2008. Community-based health insurance and access to maternal health services: Evidence from three West African countries. Social Science and Medicine 66 (12): 2460–2473.

    Google Scholar 

  • Tsai, C.L., S. Clark, A.F. Sullivan, and C.A. Camargo Jr. 2009. Development and validation of a risk-adjustment tool in acute asthma. Health Services Research 44 (5p1): 1701–1717.

    Google Scholar 

  • Van de Ven, W.P.M.M., and R.P. Ellis. 2000. Risk adjustment in competitive health plan markets. In Handbook of health economics, ed. A.J. Culyer and J.P. Newhouse. North-Holland: Elsevier.

    Google Scholar 

  • Wang, H., L. Zhang, W. Yip, and W. Hsiao. 2006. Adverse selection in a voluntary rural mutual health care health insurance scheme in China. Social Science and Medicine 63 (5): 1236–1245.

    Google Scholar 

  • WHO, UNICEF, UNFPA, The World Bank and the United Nations Population Division. 2014. Trends in maternal mortality: 1990 to 2013.

  • Wipf, J., and D. Garand. 2008. Performance indicators for microinsurance: A handbook for microinsurance practitioners, ADA, BRS and CGAP working group on microinsurance

  • Yao, Y. 2013. Development and sustainability of emerging health insurance markets: Evidence from microinsurance in Pakistan. The Geneva Papers on Risk and Insurance—Issues and Practice 38 (1): 160–180.

    Google Scholar 

  • Yao, Y., Y. Chen, and J. Shi. 2017a. Payment reform of medical insurance payment: Commentary of international and domestic research progress and China’s practice. China Health Economics 4: 36–39.

    Google Scholar 

  • Yao, Y., J.T. Schmit, and J.R. Sydnor. 2017b. The role of pregnancy in micro health insurance: Evidence of adverse selection from Pakistan. Journal of Risk and Insurance 84 (4): 1073–1102.

    Google Scholar 

  • Zhang, L., and H. Wang. 2008. Dynamic process of adverse selection: Evidence from a subsidized community-based health insurance in rural China. Social Science and Medicine 67 (7): 1173–1182.

    Google Scholar 

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Acknowledgements

We are grateful to the anonymous referee, and also to Wei Zheng, Nannan Zhang, Ruo Jia, Richard Butler and the participants in the APRIA and EGRIE 2017 Conference for their helpful comments. This research was supported by the National Natural Science Foundation of China (NSFC) (71503014), the research seed fund of the School of Economics at Peking University, and Insurance Society of China (ISCKT2017-N-1-4). We are grateful to research assistant Yunlong Wang for his excellent work. All errors are our own.

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Correspondence to Julie Shi.

Appendix

Appendix

See Tables 6, 7, 8, 9, and 10.

Table 6 Summary of average bill in PKR (Pakistani rupees) for maternity-related claims
Table 7 Summary statistics of maternity-related claim amount by diagnosis type
Table 8 Comparison of regression results of OLS and weighted OLS models
Table 9 Comparison of model fit between Tobit model and Monte Carlo simulation
Table 10 Simulation results for loss ratio by Ln OLS model, TPM, and weighted OLS model

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Yao, Y., Schmit, J. & Shi, J. Promoting sustainability for micro health insurance: a risk-adjusted subsidy approach for maternal healthcare service. Geneva Pap Risk Insur Issues Pract 44, 382–409 (2019). https://doi.org/10.1057/s41288-018-00115-5

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