SPATIAL MODELING OF MATERNAL HEALTH: GEOGRAPHICALLY WEIGHTED POISSON REGRESSION ON MATERNAL MORTALITY FACTORS
Abstract
Data from the 2021 West Java Provincial Health Profile Report, accessed from the official website of the West Java Provincial Health Office, reveals a significant surge in maternal mortality cases, rising from 165 in 2020 to 460 in 2021. In support of efforts to reduce maternal mortality rates, this study investigates the contributing factors to this phenomenon across various districts in West Java Province. The data used is from the year 2021. This study aims to evaluate the effectiveness of Poisson regression, negative binomial regression, and Geographically Weighted Poisson Regression (GWPR) models in capturing the variability of maternal deaths in the study area for that year. A comprehensive analysis revealed that the distribution of maternal mortality fits the Poisson model, displaying significant spatial heterogeneity. Acknowledging this variability, the GWPR approach using an Adaptive Kernel Bisquare weighting was selected due to its capability to produce localized parameter estimates, which more accurately reflect the specific conditions of each location. The analyzed independent variables include the number of community health centers, coverage of antenatal services at the first (K1) and fourth (K4) visits, management of obstetric complications, and coverage of iron supplementation for pregnant women. Of the five variables, only three showed statistically significant effects; therefore, the study proceeded using these three variables. The results indicate that GWPR provides the best explanation for the variability in maternal mortality rates, with an adjusted R² value of 63.17% and a MAPE of 37.70%.
Downloads
References
Dinas kesehatan Jawa Barat, “Profil kesehatan Jawa Barat tahun 2021,” Jawa Barat, 2022.
Direktorat Jenderal Kesehatan Masyarakat Kementerian Kesehatan Direktorat Jenderal Kesehatan Masyarakat Kementerian Kesehatan, Rencana aksi program kesehatan masyarakat tahun 2020-2025. 2020.
Kementerian kesehatan republik Indonesia, “Profil kesehatan Indonesia,” Jakarta, 2022.
M. T. Ruiz-Cantero, M. Guijarro-Garvi, D. R. Bean, J. R. Martínez-Riera, and J. Fernández-Sáez, “Governance commitment to reduce maternal mortality. A political determinant beyond the wealth of the countries,” Health Place, vol. 57, pp. 313–320, May 2019, doi: 10.1016/j.healthplace.2019.05.012.
P. R. Chaniago, D. Devianto, and I. R. HG, “Analisis faktor risiko angka kematian angka kematian ibu dengan pendekatan regresi poisson,” Jurnal Matematika UNAND, vol. VII, no. 2, pp. 126–131, 2018.
S. Martafiyah, C. Supriadi, and T. Saifudin, “Modeling the maternal mortality rate in Indonesia using geographically weighted Poisson Regression approach,” 2023, p. 080011. doi: 10.1063/5.0181066.
M. Syairaji, D. S. Nurdiati, B. S. Wiratama, Z. D. Prüst, K. W. M. Bloemenkamp, and K. J. C. Verschueren, “Trends and causes of maternal mortality in Indonesia: a systematic review,” BMC Pregnancy Childbirth, vol. 24, no. 1, p. 515, Jul. 2024, doi: 10.1186/s12884-024-06687-6.
R. Dewi Puspita Sari, D. Wulan Sumekar Rengganis Wardani, S. Bakri, and H. Busman, “Kurdish Studies Indonesian Maternal Mortality: A Systematic Review of Three-Level Determinants 1992-2024,” no. 2, pp. 2051–4883, 2024, doi: 10.58262/ks.v12i2.154.
L. Paulo Fávero, H. Luiz Corrêa, and M. F. C Haddad, “Count Data Regression Analysis: Concepts, Overdispersion Detection, Zero-inflation Identification, and Applications with R,” vol. 26, 2021, doi: https://doi.org/10.7275/44nn-cj68.
R. E. Caraka and H. Yasin, Geographically weighted regression (GWR) sebuah pendekatan regresi geografis. MOBIUS, 2017.
D. C. Wheeler, “Geographically weighted regression,” in Handbook of regional science, Springer, 2021, pp. 1895–1921.
A. Poliart, F. Kirakoya-Samadoulougou, M. Ouédraogo, P. Collart, D. Dubourg, and S. Samadoulougou, “Using geographically weighted Poisson regression to examine the association between socioeconomic factors and hysterectomy incidence in Wallonia, Belgium,” BMC Womens Health, vol. 21, no. 1, p. 373, Dec. 2021, doi: 10.1186/s12905-021-01514-y.
H. Helmy, M. T. Kamaluddin, I. Iskandar, and Suheryanto, “Investigating spatial patterns of pulmonary tuberculosis and main related factors in Bandar Lampung, Indonesia using geographically weighted poisson regression,” Trop Med Infect Dis, vol. 7, no. 9, p. 212, Aug. 2022, doi: 10.3390/tropicalmed7090212.
S. Martafiyah, C. Supriadi, and T. Saifudin, “Modeling the maternal mortality rate in Indonesia using geographically weighted Poisson Regression approach,” 2023, p. 080011. doi: 10.1063/5.0181066.
D. S. Nur Fajarini, Y. S. Dewi, and M. Fatekurohman, “Comparison of geographically weighted artificial neural network and geographically weighted generalized poisson regression on crime cases in East Java Indonesia,” SAR Journal - Science and Research, pp. 19–23, Mar. 2024, doi: 10.18421/SAR71-03.
T. Saifudin, Nur Rahmah Miftakhul Jannah, Risky Wahyuningsih, and Gaos Tipki Alpandi, “Geographically weighted poisson regression for modeling the number of maternal deaths in Papua province,” Jurnal Aplikasi Statistika & Komputasi Statistik, vol. 16, no. 1, pp. 32–42, Jun. 2024, doi: 10.34123/jurnalasks.v16i1.598.
R. A. J. Taylor, “Spatial pattern,” in Taylor’s Power Law, Elsevier, 2019, pp. 13–25. doi: 10.1016/B978-0-12-810987-8.00002-1.
A. Agresti, An Introduction to Categorical Data Analysis: Third Edition. Wiley, 2019. [Online]. Available: http://www.wiley.com/go/wsps
P. Schober and T. R. Vetter, “Count data in medical research: poisson regression and negative binomial regression,” Anesth Analg, vol. 132, no. 5, pp. 1378–1379, May 2021, doi: 10.1213/ANE.0000000000005398.
R. Muntafiah, R. Wasono, and M. Y. Darsyah, “Pemodelan regresi binomial negatif untuk mengatasi overdispersion pada regresi poisson,” Statistika, vol. 2, no. 1, pp. 40–45, May 2014.
X. Zhang, M. Kano, M. Tani, J. Mori, and K. Harada, “Defect Data Modeling and Analysis for Improving Product Quality and Productivity in Steel Industry,” 2018, pp. 2233–2238. doi: 10.1016/B978-0-444-64241-7.50367-0.
Y. Li and K. Zhu, “Spatial dependence and heterogeneity in the location processes of new high‐tech firms in Nanjing, China,” Papers in Regional Science, vol. 96, no. 3, pp. 519–536, Aug. 2017, doi: 10.1111/pirs.12202.
P. Purhadi and H. Yasin, “Mixed geographically weighted regression model (case study: The percentage of poor households in Mojokerto 2008),” European Journal of Scientific Research, vol. 69, no. 2, pp. 188–196, 2012, [Online]. Available: http://www.europeanjournalofscientificresearch.com
K. Kopczewska, Applied Spatial Statistics and Econometrics. Routledge, 2020. doi: 10.4324/9781003033219.
R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability & statistics for engineers & scientists, Ninth Edition. London: Pearson Education, 2016.
A. Djuraidah, Monograph penerapan dan pengembangan regresi spasial dengan studi kasus pada kesehatan, sosial, dan ekonomi. Bogor: IPB Press, 2020.
A. Djuraidah and R. Anisa, Analisis statistika untuk data geospasial: dengan ilustrasi penerapan menggunakan R. Bogor: IPB Press, 2023.
E. H. Briliant, M. Hasan, and S. Kurniawan, Perbandingan regresi linier berganda dan regresi buckley-james pada analisis survival data tersensor kanan, vol. 1, no. 1. 2019.
F. Mudia Sari, H. Frananda Nasution, and P. Robinson Sihombing, “Pemodelan jumlah kematian bayi dengan geographically weighted negative binomial regression,” Lombok Journal of Science (LJS), vol. 3, no. 2, 2021.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput Sci, vol. 7, p. e623, 2021.
Copyright (c) 2025 Alfa Yuliana, Achmad Fauzan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
