Pembentukkan Tabel Morbiditas Penyakit Kronis Berdasarkan Angka Prevalensi
DOI:
https://doi.org/10.12962/limits.v22i1.3377Keywords:
Chronic disease, Morbidity table, Gompertz-Makeham, Prevalence rateAbstract
A morbidity table is an important mathematical tool in health sciences and actuarial studies, providing an overview of disease rates in a population at a given time. This table offers information on the number of disease cases by age group, type of disease, or geographical region. The values from morbidity tables are used to compare disease rates between populations, evaluate the effectiveness of health programs, and predict future healthcare service needs.
In the world of health insurance, morbidity tables play a crucial role in premium pricing calculations. By using specific morbidity tables, insurance providers can set premium prices that align with the conditions of the population being covered, preventing company losses due to premiums being set too low, while also ensuring that people do not feel burdened by excessively high premiums.
Despite their importance, creating morbidity tables requires extensive research and large amounts of data. In Indonesia, accurately constructing morbidity tables is challenging due to its vast geographical diversity. Therefore, this study will use the prevalence rate of chronic diseases as an alternative. Prevalence rate is a statistical measure that describes the proportion of individuals in a population suffering from a specific disease at a given time. This approach simplifies data collection, especially since the government regularly releases prevalence data for certain diseases.
References
A. Listiani, K. Alim, A. S. Anggraeni, and A. R. Effendie, “A dynamic model of Indonesian National Health Insurance participation types,” J Phys Conf Ser, vol. 1341, no. 6, p. 062028, Oct. 2019, doi: 10.1088/1742-6596/1341/6/062028.
A. Listiani, K. Alim, A. S. Anggraeni, and A. R. Effendie, “Multidimensional credibility premium: Application to JKN (Jaminan Kesehatan Nasional),” 2019, p. 030003. doi: 10.1063/1.5139123.
A. S. Anggraeni, A. Listiani, K. Alim, and A. R. Effendie, “Morbidity-Mortality Table Construction for Eleven Chronical Diseases (ECD) Using Constant Force Assumption,” J Phys Conf Ser, vol. 1341, no. 6, p. 062030, Oct. 2019, doi: 10.1088/1742-6596/1341/6/062030.
F. Sabila, T. P. Ningrum, W. Andika, and F. P. Gurning, “Studi Literatur: Analisis Efektivitas Pemanfaatan Program Jaminan Kesehatan Nasional (JKN) Pada Fasilitas Kesehatan Tingkat Pertama Di Indonesia,” Indonesian Journal of Health Science, vol. 4, no. 4, pp. 378–397, Jun. 2024, doi: 10.54957/ijhs.v4i4.939.
E. Espinoza, “Penentuan Premi Bulanan Asuransi Kesehatan Berjangka Perawatan Rumah Sakit Untuk Perorangan,” Jurnal Matematika UNAND, vol. 5, no. 4, p. 30, Nov. 2016, doi: 10.25077/jmu.5.4.30-35.2016.
J. M. Hoem, “Markov Chain Models in Life Insurance,” Blätter der DGFVM, vol. 9, no. 2, pp. 91–107, 1969, doi: 10.1007/BF02810082.
E. Pitacco, “Actuarial models for pricing disability benefits: Towards a unifying approach,” Insur Math Econ, vol. 16, no. 1, pp. 39–62, 1995, doi: 10.1016/0167-6687(94)00030-I.
L. Newman et al., “Global Estimates of the Prevalence and Incidence of Four Curable Sexually Transmitted Infections in 2012 Based on Systematic Review and Global Reporting,” PLoS One, vol. 10, no. 12, p. e0143304, Dec. 2015, doi: 10.1371/journal.pone.0143304.
S. W.-C. CHAN, “Coping With Chronic Health Conditions,” Journal of Nursing Research, vol. 32, no. 1, p. e308, Jan. 2024, doi: 10.1097/jnr.0000000000000600.
N. N. N. S. Hendra Perdana, “MODEL MULTI STATUS DALAM PENENTUAN ASURANSI KESEHATAN PENDERITA PENYAKIT JANTUNG,” Bimaster:Buletin Ilmiah Matematika, Statistika dan Terapannya, vol. 8, no. 3, Jul. 2019, doi: 10.26418/bbimst.v8i3.33647.
Moch. T. Hakiki and H. Umam, “Distribusi Power Gompertz-Makeham: Sifat-Sifat Statistika dan Aplikasinya,” Journal of Mathematics Education and Science, vol. 6, no. 2, pp. 107–117, Oct. 2023, doi: 10.32665/james.v6i2.1910.
K. Alim, A. Listiani, A. S. Anggraeni, and A. R. Effendie, “Critical illness insurance pricing with stochastic interest rates model,” J Phys Conf Ser, vol. 1341, no. 6, p. 062026, Oct. 2019, doi: 10.1088/1742-6596/1341/6/062026.
A. Irina and A. Sergejs, “Application of ordinary least square method in nonlinear models,” International Statistical Institute, 2007, [Online]. Available: https://iase-web.org/documents/papers/isi56/CPM81_Arhipova.pdf
