Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression
Tuberculosis (TB) is a highly infectious disease, representing one of the major causes of death worldwide. Sustainable Development Goal 3.3 implies a serious decrease in the incidence of TB cases. Hence, this study applied a spatial analysis approach to investigate patterns of pulmonary TB cases and...
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MDPI AG
2022-08-01
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| Series: | Tropical Medicine and Infectious Disease |
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| Online Access: | https://www.mdpi.com/2414-6366/7/9/212 |
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| author | Helina Helmy Muhammad Totong Kamaluddin Iskhaq Iskandar Suheryanto |
| author_facet | Helina Helmy Muhammad Totong Kamaluddin Iskhaq Iskandar Suheryanto |
| author_sort | Helina Helmy |
| collection | DOAJ |
| description | Tuberculosis (TB) is a highly infectious disease, representing one of the major causes of death worldwide. Sustainable Development Goal 3.3 implies a serious decrease in the incidence of TB cases. Hence, this study applied a spatial analysis approach to investigate patterns of pulmonary TB cases and its drivers in Bandar Lampung (Indonesia). Our study examined seven variables: the growth rate of pulmonary TB, population, distance to the city center, industrial area, green open space, built area, and slum area using geographically weighted Poisson regression (GWPR). The GWPR model demonstrated excellent results with an R<sup>2</sup> and adjusted R<sup>2</sup> of 0.96 and 0.94, respectively. In this case, the growth rate of pulmonary TB and population were statistically significant variables. Spatial pattern analysis of sub-districts revealed that those of Panjang and Kedaton were driven by high pulmonary TB growth rate and population, whereas that of Sukabumi was driven by the accumulation of high levels of industrial area, built area, and slums. For these reasons, we suggest that local policymakers implement a variety of infectious disease prevention and control strategies based on the spatial variation of pulmonary TB rate and its influencing factors in each sub-district. |
| first_indexed | 2024-03-09T22:19:26Z |
| format | Article |
| id | doaj.art-a0ad8795b406453c8d8df67cd43b55d4 |
| institution | Directory Open Access Journal |
| issn | 2414-6366 |
| language | English |
| last_indexed | 2024-03-09T22:19:26Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tropical Medicine and Infectious Disease |
| spelling | doaj.art-a0ad8795b406453c8d8df67cd43b55d42023-11-23T19:17:44ZengMDPI AGTropical Medicine and Infectious Disease2414-63662022-08-017921210.3390/tropicalmed7090212Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson RegressionHelina Helmy0Muhammad Totong Kamaluddin1Iskhaq Iskandar2Suheryanto3Graduate School of Environmental Science, Sriwijaya University, Palembang 30139, IndonesiaDepartment of Pharmacology, Faculty of Medicine, Sriwijaya University, Palembang 30114, IndonesiaDepartment of Physics, Faculty of Mathematics and Natural Sciences, Sriwijaya University, Indralaya 30662, IndonesiaDepartment of Chemistry, Faculty of Mathematics and Natural Sciences, Sriwijaya University, Indralaya 30662, IndonesiaTuberculosis (TB) is a highly infectious disease, representing one of the major causes of death worldwide. Sustainable Development Goal 3.3 implies a serious decrease in the incidence of TB cases. Hence, this study applied a spatial analysis approach to investigate patterns of pulmonary TB cases and its drivers in Bandar Lampung (Indonesia). Our study examined seven variables: the growth rate of pulmonary TB, population, distance to the city center, industrial area, green open space, built area, and slum area using geographically weighted Poisson regression (GWPR). The GWPR model demonstrated excellent results with an R<sup>2</sup> and adjusted R<sup>2</sup> of 0.96 and 0.94, respectively. In this case, the growth rate of pulmonary TB and population were statistically significant variables. Spatial pattern analysis of sub-districts revealed that those of Panjang and Kedaton were driven by high pulmonary TB growth rate and population, whereas that of Sukabumi was driven by the accumulation of high levels of industrial area, built area, and slums. For these reasons, we suggest that local policymakers implement a variety of infectious disease prevention and control strategies based on the spatial variation of pulmonary TB rate and its influencing factors in each sub-district.https://www.mdpi.com/2414-6366/7/9/212infectious diseaseepidemiologyhealthgeographic information systemspatial sciencegeostatistics |
| spellingShingle | Helina Helmy Muhammad Totong Kamaluddin Iskhaq Iskandar Suheryanto Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression Tropical Medicine and Infectious Disease infectious disease epidemiology health geographic information system spatial science geostatistics |
| title | Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression |
| title_full | Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression |
| title_fullStr | Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression |
| title_full_unstemmed | Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression |
| title_short | Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression |
| title_sort | investigating spatial patterns of pulmonary tuberculosis and main related factors in bandar lampung indonesia using geographically weighted poisson regression |
| topic | infectious disease epidemiology health geographic information system spatial science geostatistics |
| url | https://www.mdpi.com/2414-6366/7/9/212 |
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