Article Sidebar

Submitted
Jan 23, 2023
Accepted
Feb 20, 2023
Published
Jun 30, 2023
Download
pdf
Statistic
Read Counter : 170 Download : 145

Downloads

Download data is not yet available.

Main Article Content

Abstract

Pneumonia is an acute respiratory infection that attacks the lungs and can cause inflammation of the air sacs due to the alveoli is filled with pus and fluid. This research aims at identifying factors influencing pneumonia and mapping its incidence rate for toddlers in the Purwakarta Regency. Many factors influence pneumonia, but due to the limitation of data or information, some factors cannot be included in the model and are called omitted variables. The incidence rate of toddler pneumonia in sub-districts of Purwakarta Regency is assumed to be related to one another or have a spatial dependency. Therefore, modeling pneumonia with the Fixed Effect Spatial Model can accommodate spatial aspects. The results show that MR2 measles immunization, low birth weight, exclusive breastfeeding, and clean and healthy living habits significantly affect the incidence rate of toddler pneumonia. Based on the mapping results, Wanayasa sub-district has a high incidence rate of toddler pneumonia, while some sub-districts such as Campaka, Pondoksalam, and Darangdan have low incidence rates.

Keywords

Pneumonia, Omitted Variables, Spatial Dependencies, Fixed Effect Spatial Models

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
1.
Nisrina N, Handoko B, Andriyana Y. The Analysis of Factors Influencing Incidence Rates of Toddler Pneumonia in Purwakarta Districts Using Panel Data Spatial Regression. EKSAKTA [Internet]. 2023Jun.30 [cited 2024Nov.21];24(02):122-3. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/396

References

  1. B. Dadonaite and M. Roser. (2019). Pneumonia. Our World in Data. https://ourworldindata.org/pneumonia
  2. S. N. Grief and J. K. Loza. (2028). Guidelines for the Evaluation and Treatment of Pneumonia, Prim Care, vol. 45, no. 3, pp. 485–503.
  3. W. S. Lim. (2022).Pneumonia—Overview. Encyclopedia of Respiratory Medicine, Elsevier, pp. 185–197.
  4. N. Rezaei and A. J. Rodriguez-Morales. (2022). Pneumonia. IntechOpen.
  5. L. Huang et al. (2011). HIV-associated Pneumocystis pneumonia. Proc Am Thorac Soc, vol. 8, no. 3, pp. 294–300.
  6. I. Govender, O. M. Maphasha, S. Rangiah, and C. Steyn. (2019). An overview of Pneumocystis jirovecii pneumonia for the African generalist practitioner. Afr Health Sci, vol. 19, no. 4, pp. 3200–3207.
  7. G. Nevez, P. M. Hauser, and S. Le Gal. (2020). Pneumocystis jirovecii. Trends in Microbiology, vol. 28, no. 12, pp. 1034–1035.
  8. Dinas Kesehatan Kabupaten Purwakarta. (2019). Profil Kesehatan Kabupaten Purwakarta 2019. Dinas Kesehatan Kabupaten Purwakarta, Purwakarta.
  9. Dinas Kesehatan Kabupaten Purwakarta. (2020). Profil Kesehatan Kabupaten Purwakarta 2020. Dinas Kesehatan Kabupaten Purwakarta, Purwakarta.
  10. Dinas Kesehatan Kabupaten Purwakarta. (2021). Profil Kesehatan Kabupaten Purwakarta 2021. Dinas Kesehatan Kabupaten Purwakarta, Purwakarta.
  11. Dinas Kesehatan Provinsi Jawa Barat. (2020).Profil Kesehatan Jawa Barat Tahun 2020. Dinas Kesehatan Provinsi Jawa barat. www.diskes.jabarprov.go.id
  12. J. P. Elhorst. (2014). Spatial Econometrics From Cross-Sectional Data to Spatial Panels. New York: Springer.
  13. H. A. Klaiber, J. K. Abbott, and V. K. Smith. (2017). Some like it (Less) hot: Extracting trade-off measures for physically coupled amenities. Journal of the Association of Environmental and Resource Economists, vol. 4, no. 4, pp. 1053–1079.
  14. M. Leiva, F. Vasquez-Lavín, and R. D. Ponce Oliva. (2020). Do immigrants increase crime? Spatial analysis in a middle-income country. World Development, vol. 126.
  15. A.-T. Renner. (2020). Inefficiencies in a healthcare system with a regulatory split of power: a spatial panel data analysis of avoidable hospitalisations in Austria. European Journal of Health Economics, vol. 21, no. 1, pp. 85–104.
  16. E. Biørn. (2017). Econometrics of Panel Data. Oxford: Oxford University Press.
  17. H. Kelejian and G. Piras. (2017). Spatial Econometrics. Elsevier Science.
  18. H. Kelejian and G. Piras. (2022). A simple test for stability of a spatial model. Spatial Economic Analysis, vol. 17, no. 2, pp. 245–261.
  19. C. Jiang, D. L. Vecchia, E. Ronchetti, and O. Scaillet. (2021). Saddlepoint Approximations for Spatial Panel Data Models. Journal of the American Statistical Association.
  20. L. Li and Z. Yang. (2021). Spatial dynamic panel data models with correlated random effects. Journal of Econometrics, vol. 221, no. 2, pp. 424–454.
  21. U. K. Müller and M. W. Watson. (2022). Spatial Correlation Robust Inference in Linear Regression and Panel Models. Journal of Business and Economic Statistics.
  22. S. Leorato and M. Mezzetti. (2021). A Bayesian Factor Model for Spatial Panel Data with a Separable Covariance Approach. Bayesian Analysis, vol. 16, no. 2. pp. 489–519.
  23. H. Zareifard and M. Jafari Khaledi. (2021). A heterogeneous Bayesian regression model for skewed spatial data. Spatial Statistics, vol. 46.
  24. H. Zareifard, M. Jafari Khaledi, and O. Dahdouh. (2019). Multivariate spatial modelling through a convolution-based skewed process. Stochastic Environmental Research and Risk Assessment, vol. 33, no. 3, pp. 657–671.
  25. Sudartianto, Firman, Y. Suparman, and I. Ginanjar. (2021). A Fixed Effect Panel Spatial Error Model in Identifying Factors of Poverty in West Java Province. Journal of Physics: Conference Series,vol. 1776, no. 1.
  26. M. T. Majeed and M. Mazhar. (2021). An empirical analysis of output volatility and environmental degradation: A spatial panel data approach. Environmental and Sustainability Indicators, vol. 10.
  27. Y. Liu, X. Chen, and Y. Zhang. (2022). Analysis of Business Environment and Medical Insurance Coverage Rates in the Destination of China’s Migrant Population: Based on Geographically and Temporally Weighted Regression Model for Panel Data. Mathematical Problems in Engineering, vol. 2022.
  28. N. Paramita, M. Masjkur, and Indahwati. (2021). Spatial Regression Model with Optimum Spatial Weighting Matrix on GRDP Data of Sulawesi Island. Journal of Physics: Conference Series, vol. 1863, no. 1.
  29. S. Viriyathorn, M. Phaiyarom, P. Rueangsom, and R. Suphanchaimat. (2022). Spatial panel data analysis on the relationship between provincial economic status and enrolment in the social security scheme amongst migrant workers in Thailand, 2015–2018. International Journal of Environmental Research and Public Health, vol. 19, no. 1.
  30. X. Dai, Z. Yan, M. Tian, and M. Tang. (2020). Quantile regression for general spatial panel data models with fixed effects. Journal of Applied Statistics, vol. 47, no. 1, pp. 45–60.
  31. Y. Zhang, J. Jiang, and Y. Feng. (2021).Penalized quantile regression for spatial panel data with fixed effects. Communications in Statistics - Theory and Methods.
  32. L. Li and Z. Yang. (2020). Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity. Regional Science and Urban Economics, vol. 8.
  33. M. S. Merk and P. Otto. (2020). Estimation of the spatial weighting matrix for regular lattice data—An adaptive lasso approach with cross-sectional resampling Environmetrics, vol. 33, no. 1.
  34. P. Otto and R. Steinert. (2022). Estimation of the Spatial Weighting Matrix for Spatiotemporal Data under the Presence of Structural Breaks. Journal of Computational and Graphical Statistics.