e-ISSN 2598-3849       print ISSN 2527-8878

Vol 4, No 2 (2019)

Analysis Of Risk for Class Shifting And Determinants of BPJS Kesehatan Membership Using Generalized Ordered Logit-Unconstrained Partial Proportional Odds Model

Siskarossa Ika Oktora, Ika Yuni Wulansari, Geri Yesa Ermawan

Abstract


Abstract The main source of funding of BPJS Kesehatan comes from the different premium class in which the participant registered. The medical benefits among classes are equivalent, except inpatient facilities. But when the improvement health degree is not linear with the incurred costs, problem would arise. This study aims to analyze class shifting and determinants of BPJS Kesehatan mem- bership. Around 1.53 percent of participants access higher classes, while 5.62 percent access lower classes. Class III participants with inpatient status severity level 2 and 3, reaching 41% and 43%, respectively. In addition, 60% of non-PBI participants are Class II premium participants; most of them are male, productive age, and workers. This research using Generalized Ordered Log- it-Unconstrained Partial Proportional Odds Model concludes that participants who are married tend to choose higher premium class. Whereas productive age participants and a worker is in the lower premium class. The recommendation is the evaluation of membership based on class premium contributions considering potential participants (productive age and workers) who tend should be conducted in a lower class. Although mutual assistance is the principle of National Health Insurance, specific mechanisms should be established to examine the relation of age and health status to each participant regarding the difference in the registered class, besides their economic factors. Abstrak Pendanaan utama BPJS Kesehatan adalah iuran peserta yang besarnya tergantung dari kelas premi yang didaftarkan peser- ta. Manfaat medis setiap kelas adalah setara kecuali fasilitas ruang inap. Di sisi lain, hal ini dapat menimbulkan permasalahan ketika derajat kesehatan tidak linier dengan biaya yang seharusnya dikeluarkan. Penelitian ini bertujuan untuk melihat per- bedaan antara kelas premi saat peserta mengakses pelayanan kesehatan dengan kelas premi yang didaftarkan. Ditemukan 1,53 persen peserta mengakses kelas lebih tinggi dibanding kelas yang terdaftar, dan 5,62 persen peserta yang mengakses kelas lebih rendah dibanding kelas yang terdaftar. Berdasarkan tingkat keparahan saat menjalani rawat inap, diketahui bah- wa peserta kelas III dengan status rawat inap tingkat keparahan 3 (berat) dan 2 (sedang) masing-masing mencapai 41% dan 43%. Selain itu hampir 60 persen peserta yang membayar iuran sesuai dengan ketentuan yang ditetapkan (non PBI) adalah peserta iuran premi Kelas II yang sebagian besar merupakan peserta laki-laki, berusia produktif, dan berstatus sebagai pekerja. Hasil analisis dengan metode Generalized Ordered Logit-Unconstrained Partial Proportional Odds Model disimpulkan bahwa peserta berstatus kawin cenderung berada pada kelas premi yang lebih tinggi. Sedangkan peserta usia produktif serta peserta dengan status pekerja cenderung berada pada kelas premi yang lebih rendah. Rekomendasi yang diberikan adalah evaluasi kepesertaan berdasarkan iuran premi kelas dapat dilakukan kembali mengingat peserta potensial (usia produktif dan berstatus sebagai pekerja) cenderung berada pada kelas yang lebih rendah. Selain itu meskipun asas gotong royong menjadi prinsip pelaksanaan Program JKN, namun sebaiknya dapat dibuat mekanisme tertentu agar dapat dicermati terkait dengan faktor usia dan derajad kesehatan peserta terhadap perbedaan kelas premi peserta yang didaftarkan tanpa mengabaikan kemampuan ekonomi yang bersangkutan.

Keywords


BPJS premium class; class shifting; Generalized Ordered Logit-Unconstrained Partial Proportional Odds Model

References


(1) Agresti, A. 2000. Categorical data analysis (2nd ed).

(2) New York: John Wiley & Sons.

(3) Agresti, A. 2010. Analysis of ordinal categorical data

(4) (2nd ed). New York: John Wiley & Sons.

(5) Andersen RM. 1968. Behavioral model of families’ use of health services. Research Series No 25, IL: Center for Health Administration Studies, University of Chicago.

(6) Anderson, M., Dobkin, C., Gross, T. 2012. The effect of health insurance coverage on the use of medical services. American Economic Journal: Economic Policy, American Economic Asso- ciation. 4(1): 1-27.

(7) Banerjee S. 2015. Utilization patterns and out-of- pocket expenses for different health care services among american retirees. EBRI Issue Brief. 411:1–20.

(8) Banerjee, S. 2016. Differences in Out-of-Pocket Health Care Expenses of Older Single and Cou- ple Households. EBRI Notes. 37(1).

(9) Billings, J., Zeitel, L., Lukomnik, J., Carey, T. S., Blank, A. E., Newman, L. 1993. Impact of so- cioeconomic status on hospital use in New York City. Health affairs. 12(1): 162-173.

(10) BPJS. 2019. Available on https://bpjs kesehatan. go.id/bpjs/pages/detail/2013/4. [19 Oktober

(11) 2019].

(12) Cameron, A. C., Trivedi, P. K., Milne, F., Piggott, J. 1988. A microeconometric model of the demand for health care and health insurance in Austra- lia. The Review of economic studies. 55(1), 85-106.

(13) Cole, S. R., Cande V. A. 2001. Regression models for unconstrained, partially or fully constrained continuation odds ratios. International Journal of Epidemiology. 30:1379-82.

(14) Deb, P. 2001. A discrete random effects probit model with application to the demand for preventive care. Health Economics. 10(5): 371-383.

(15) Economou, A., Nikolaou, A., & Theodossiou, I. 2007. Socioeconomic Status and Health Care Utilization: A Study of the Effects of Low In- come, Unemployment and Hours of Work on the Demand for Health Care in the E.U. (Univer- sity of Aberdeen Business School Working Paper Series; Vol. 2007, No. 15). Centre for European Labour Market Research.

(16) Fukawa, T., 2002. Public health insurance in Japan.

(17) Washington, DC: World Bank.

(18) Fullerton, A. S., & Xu, J. 2018. Constrained and un- constrained partial adjacent category logit mod- els for ordinal response variables. Sociological Methods & Research, 47(2): 169-206.

(19) Gravelle, H., Sutton, M., Morris, S., Windmeijer, F., Leyland, A., Dibben, C., Muirhead, M. 2003. Modelling supply and demand influences on the use of health care: implications for deriving a needs‐based capitation formula. Health econom- ics. 12(12): 985-1004.

(20) Hauser, R. M., Andrew, M. 2006. Another look at the stratification of educational transitions: The logistic response model with partial proportionali- ty constraints. Sociological Methodology. 36:1- 26.

(21) Harmon, C. and Nolan, B. 2001. Health insurance and health services utilisation in Ireland. Health

(22) Economics 10(2): 135–145.

(23) Kleinbaum, D. G., Klein, M. 2010. Logistic Regres- sion A Self-Learning Text (3rd ed). Springer: New York

(24) Kong, J. S. 2010. The Effects of Marital Status & Gender on Health Care Insurance Coverage in the United States. Honors Projects. Paper 111.

(25) Lall, R., M. J. Campbell, S. J. Walters, K. Morgan, and MRC CFAS Co-operative Institute of Pub- lic Health. 2002. A review of ordinal regression models applied on health-related quality of life assessments. Statistical Methods in Medical Re- search. 11: 49–67.

(26) Lestari, F. H., Djamaludin, M. D. 2017. Perception and motivation of national health insurance pro- gram participation in Bogor. Journal of Consum- er Sciences. 2(1): 39-50.

(27) Macassa, G., Hiswåls, A. S., Ahmadi, N., Alfredsson, J., Soares, J., Stankunas, M. 2014. Employment status and health care utilization in a context of economic recession: Results of a population based survey in East Central Sweden. Science Journal of Public Health. 2(6): 610-616.

(28) Ministry of Health. 2011. Buletin Jendela Data dan Informasi Kesehatan. Jakarta: Kementerian Kes- ehatan RI.

(29) Nolan, B. 2006. The interaction of public and pri- vate health insurance: Ireland as a case study. The Geneva Papers on Risk and Insurance-Issues and Practice. 31(4): 633-649

(30) Pandey, K. R., Yang, F., Cagney, K. A., Smieliaus- kas, F., Meltzer, D. O., Ruhnke, G. W. 2019. The impact of marital status on health care uti- lization among Medicare beneficiaries. Medi- cine. 98(12), e14871.

(31) Peterson, B., Harrell, Jr, F. E.1990. Partial propor- tional odds models for ordinal response variables. Applied Statistics. 39: 205–217.

(32) Pohlmeier, W., Ulrich, V. 1995. An econometric model of the two-part decisionmaking process in the demand for health care. Journal of Human Resources. 30(2): 339-361.

(33) Riset Kesehatan Dasar. 2013. Riset Kesehatan Dasar. Jakarta: Badan Penelitian dan Pengembangan Kesehatan.

(34) Schofield, D. 1996. The impact of employment and hours of work on health status and health service use. National Centre for Social and Econom- ic Modelling, Discussion Paper No. 11, Uni- versity of Canberra, Australia.

(35) Schofield, D. 2000. Public hospital expenditure: how is it divided between lower, middle, and upper income groups? Australian Economic Review. 33(4): 303-316.

(36) Van der Heyden, J. H. A., Demarest, S., Tafforeau, J., Van Oyen, H. 2003. Socio-economic differ- ences in the utilisation of health services in Belgium. Health policy. 65(2): 153-165.

(37) Verbrugge, L. M. 1979. Marital status and health. Journal of Marriage and the Family. pp: 267-285.

(38) Williams, R. 2006. Generalized ordered logit/par- tial proportional odds models for ordinal de- pendent variables. Stata Journal. 6(1): 58-82

(39) Windmeijer, F. A., Santos Silva, J. M. 1997. Endog- eneity in count data models: an application to demand for health care. Journal of applied econometrics. 12(3): 281-294.

(40) Winkelmann, R. 2004. Health care reform and the number of doctor visits—an econometric anal- ysis. Journal of Applied Econometrics. 19(4): 455-472.


Full Text: PDF

DOI: 10.7454/eki.v4i2.3390

Refbacks

  • There are currently no refbacks.