Analysis of Factor Affecting Tuberculosis Cases in West Java Province Using Panel Data Regression Approach

Toha Saifudin; Arlisya Shafwan Aisyah; Irma Ayu Indrasta; Dita Amelia

doi:10.31764/jtam.v8i4.24795

Analysis of Factor Affecting Tuberculosis Cases in West Java Province Using Panel Data Regression Approach

Toha Saifudin, Arlisya Shafwan Aisyah, Irma Ayu Indrasta, Dita Amelia

Abstract

Tuberculosis (TB) is a disease that can cause death with the largest number of sufferers after COVID-19. In Indonesia, the number of TB cases reached 724.309 cases in 2022 with the highest number 184.406 cases in West Java Province. Given this situation, Indonesia must try to achieve the health target from SDGs, namely ending the TB epidemic by 2030. Therefore, this research aims to analyze the factors that have a significant influence on the incidence of TB in Indonesia, especially in West Java Province. The research focuses on four variables: percentage of poverty, number of diabetics, number of HIV/AIDS patients, and population density. To provide a more informative analysis, this research uses a combination of cross-section and time series data from 27 regions between 2020 and 2022. So, the method used according to the type of data is panel data regression including common effect, fixed effect, and random effect models. Based on statistical tests, namely through the chow test, hausman test, and lagrange multiplier test, it was found that the best model was fixed effect with an R-squared value of 90%. The research revealed that all the studied factors significantly influence the incidence of TB cases in West Java. The results of this study are expected to help the West Java government in an effort to reduce the number of TB cases and formulate policies by reducing the percentage of poverty and population density in West Java. By ensuring the availability of health facilities such as establishing health centers in densely populated areas and counseling programs also need to be conducted to underscore the importance of TB control in West Java.

Keywords

Tuberculosis; West Java; Regression; Panel; Fixed Effect Model: SDGs.

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References

Amjani, A., & Cusmarih, C. (2023). Hubungan Lingkungan Rumah Terhadap Kejadian Tuberkulosis Paru di RSUD Kabupaten Bekasi. MAHESA : Malahayati Health Student Journal, 3(10), 3131–3140. https://doi.org/10.33024/mahesa.v3i10.11007

Asemahagn, M. A., Alene, G. D., & Yimer, S. A. (2021). Spatial-temporal clustering of notified pulmonary tuberculosis and its predictors in East Gojjam Zone, Northwest Ethiopia. PLoS ONE, 16(1), 1–19. https://doi.org/10.1371/journal.pone.0245378

Bell, A., Fairbrother, M., & Jones, K. (2019). Fixed and random effects models: making an informed choice. Quality and Quantity, 53(2), 1051–1074. https://doi.org/10.1007/s11135-018-0802-x

Biørn, E. (2016). Econometrics of Panel Data: Methods and Applications. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198753445.001.0001

Brilliant, I. I., & Fakhriyana, D. (2023). Modeling The Number Of Tuberculosis Cases In West Java Using The Negative Binomial Approach. Consilium Sanitatis: Journal of Health Science and Policy, 1(2), 107–119. https://doi.org/10.56855/jhsp.v1i2.282

Dewi Anggraeni Chairunnisa, A., & Fauzan, A. (2023). Implementation of Panel Data Regression in the Analysis of Factors Affecting Poverty Levels in Bengkulu Province in 2017-2020. EKSAKTA: Journal of Sciences and Data Analysis, 4(1), 40–45. https://doi.org/10.20885/eksakta.vol4.iss1.art5

Djalic, I., & Terzic, S. (2021). Violation of the assumption of homoscedasticity and detection of heteroscedasticity. Decision Making: Applications in Management and Engineering, 4(1), 1–18. https://doi.org/10.31181/dmame2104001d

Feng, S., Li, G., Tong, T., & Luo, S. (2020). Testing for heteroskedasticity in two-way fixed effects panel data models. Journal of Applied Statistics, 47(1), 91–116. https://doi.org/10.1080/02664763.2019.1634682

Grigore, G. E., Musetescu, R. C., Nicolae, S., & Vladut, O. (2022). A panel data analysis in estimating the economic growth for oil-producing countries.Evidence from the caspian region. Economic Computation and Economic Cybernetics Studies and Research, 56(4), 225–242. https://doi.org/10.24818/18423264/56.4.22.14

Hsiao, C. (2014). Analysis of panel data: Third edition. Analysis of Panel Data: Third Edition, 1–539. https://doi.org/10.1017/CBO9781139839327

Ismaeel, S. S., Midi, H., & Sani, M. (2021). Robust multicollinearity diagnostic measure for fixed effect panel data model. Malaysian Journal of Fundamental and Applied Sciences, 17(5), 636–646. https://doi.org/10.11113/MJFAS.V17N5.2391

Karavias, Y., Narayan, P. K., & Westerlund, J. (2023). Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19. Journal of Business and Economic Statistics, 41(3), 653–666. https://doi.org/10.1080/07350015.2022.2053690

Kementerian Kesehatan RI. (2020). Pedoman Nasional Pelayanan Kedokteran Tata Laksana Tuberkulosis. https://tbindonesia.or.id/download/9840/?tmstv=1718644405

Kementerian Kesehatan RI. (2023). Laporan Program Penanggulangan Tuberkulosis Tahun 2022 Kementerian Kesehatan Republik Indonesia Tahun 2023. https://tbindonesia.or.id/wp-content/uploads/2023/09/Laporan-Tahunan-Program-TBC-2022.pdf

Liao, N., & He, Y. (2018). Exploring the effects of influencing factors on energy efficiency in industrial sector using cluster analysis and panel regression model. Energy, 158, 782–795. https://doi.org/10.1016/j.energy.2018.06.049

Maison, D. P. (2022). Tuberculosis pathophysiology and anti-VEGF intervention. In Journal of Clinical Tuberculosis and Other Mycobacterial Diseases (Vol. 27). Elsevier Ltd. https://doi.org/10.1016/j.jctube.2022.100300

Marcoulides, K. M., & Raykov, T. (2019). Evaluation of Variance Inflation Factors in Regression Models Using Latent Variable Modeling Methods. Educational and Psychological Measurement, 79(5), 874–882. https://doi.org/10.1177/0013164418817803

Muhith, A., Susanto, I. H., Rahmalia, D., Adzkiya, D., & Herlambang, T. (2022). The Analysis of Demand and Supply of Blood in Hospital in Surabaya City Using Panel Data Regression. Nonlinear Dynamics and Systems Theory, 22(5), 550–560.

Piratla, S., & Singh, B. K. (2023). Airport passenger movement, revenue and expenditure at non-major airports using panel data regression. Social Sciences and Humanities Open, 8(1), 1–18. https://doi.org/10.1016/j.ssaho.2023.100658

Pratiwi, K. D. (2022). E-Commerce and Economic Growth in Indonesia: Analysis of Panel Data Regression. Gema Publica, 7(1), 171–186. https://doi.org/10.14710/gp.7.1.2022.171-186

Prunier, J. G., Colyn, M., Legendre, X., Nimon, K. F., & Flamand, M. C. (2015). Multicollinearity in spatial genetics: Separating the wheat from the chaff using commonality analyses. Molecular Ecology, 24(2), 263–283. https://doi.org/10.1111/mec.13029

Purwanto, E. (2020). Analysis of The Effect of Healthy and Healthy Life and Health (PHBS) Behavior of Tuberculosis Events. Journal of Public Health Science Research, 1(1), 5. https://doi.org/10.30587/jphsr.v1i1.1179

Puspita, S. N., Maryani, S., & Purwantho, H. (2021). Analisis Faktor-Faktor Yang Mempengaruhi Penyerapan Tenaga Kerja Di Provinsi Jawa Tengah. Jurnal Ilmiah Matematika Dan Pendidikan Matematika, 13(2), 141. https://doi.org/10.20884/1.jmp.2021.13.2.4546

Rahmawati, N., Karno, F., & Hermanto, E. M. P. (2024). Analisis Penyakit Tuberkulosis (TBC) pada Provinsi Jawa Timur Tahun 2021 Menggunakan Geographically Weighted Regression (GWR). Indonesian Journal of Applied Statistics, 6(2), 116. https://doi.org/10.13057/ijas.v6i2.78593

Ratnasari, V., Audha, S. H., & Dani, A. T. R. (2023a). Statistical modeling to analyze factors affecting the middle-income trap in Indonesia using panel data regression. MethodsX, 11. https://doi.org/10.1016/j.mex.2023.102379

Ratnasari, V., Audha, S. H., & Dani, A. T. R. (2023b). Statistical modeling to analyze factors affecting the middle-income trap in Indonesia using panel data regression. MethodsX, 11(July), 102379. https://doi.org/10.1016/j.mex.2023.102379

Shrestha, N. (2020). Detecting Multicollinearity in Regression Analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39–42. https://doi.org/10.12691/ajams-8-2-1

Suherni, N., & Maduratna. (2013). Analisis Pengelompokan Kecamatan di Kota Surabaya Berdasarkan Faktor Penyebab Terjadinya Penyakit Tuberkulosis. Jurnal Sains Dan Seni ITS, 2(1), 2337–3520.

Utari, D. T., Yuliana, T., & Hendradewa, A. P. (2020). A Panel Data Analysis of Rice Production in Ngawi Regency, East Java. 474, 212–217. https://doi.org/10.2991/assehr.k.201010.031

Uyanık, G. K., & Güler, N. (2013). A Study on Multiple Linear Regression Analysis. Procedia - Social and Behavioral Sciences, 106, 234–240. https://doi.org/10.1016/j.sbspro.2013.12.027

Wardhana, F. A., & Indawati, R. (2021). Panel Data Regression Analysis For Factors Affecting Infant Mortality Rate In East Java 2013-2017. Indonesian Journal of Public Health, 16(3), 437–448. https://doi.org/10.20473/ijph.v16i3.2021.437-448

Wong, M. K. ai, Yadav, R. P., Nishikiori, N., & Eang, M. T. an. (2013). The association between household poverty rates and tuberculosis case notification rates in Cambodia, 2010. Western Pacific Surveillance and Response Journal : WPSAR, 4(1), 25–33. https://doi.org/10.5365/WPSAR.2013.4.1.002

DOI: https://doi.org/10.31764/jtam.v8i4.24795