Spatial Analysis of Pulmonary Tuberculosis Incidence in Kupang City in 2019-2021

Aprila Chandra Kristin Nahak; Indriati A. Tedju Hinga; Honey Ivoney Ndoen; Yendris K Samruth

doi:10.14710/jphtcr.v7i1.20682

DOI: https://doi.org/10.14710/jphtcr.v7i1.20682

Spatial Analysis of Pulmonary Tuberculosis Incidence in Kupang City in 2019-2021

Aprila Chandra Kristin Nahak¹ , Indriati A. Tedju Hinga², Honey Ivoney Ndoen², Yendris K Samruth²

¹Public Health Study Program, Faculty of Public Health, Universitas Nusa Cendana, Kupang, Nusa tenggara timur, Indonesia

²Department of Epidemiology and Biostatistics, Faculty of Public Health, Universitas Nusa Cendana, Kupang, Nusa tenggara timur, Indonesia

Received: 30 Oct 2023; Published: 30 Apr 2024.

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

BibTex Citation Data :

@article{JPHTCR20682,
    author = {Aprila Nahak and Indriati Hinga and Honey Ndoen and Yendris Samruth},
    title = {Spatial Analysis of Pulmonary Tuberculosis Incidence in Kupang City in 2019-2021},
    journal = {Journal of Public Health for Tropical and Coastal Region},
  volume = {7},
    number = {1},
    year = {2024},
    keywords = {uberculosis, spatial, autocorrelation, QGIS, GeoDa},
    abstract = {  Abstract    Introduction : Pulmonary tuberculosis has a fairly high number of cases in several regions of Indonesia, including Kupang City. In 2019, tuberculosis cases in Kupang City amounted to 667 cases, in 2020 it increased to 693 cases and in 2021 it decreased to 419 cases. This study aims to analyze the factors that influence the incidence of pulmonary tuberculosis which are studied in a spatial analysis model.   Methods  : This research is a quantitative research with an analytical observational design using an approach to Geographic Information Systems (GIS). This study aims to see the autocorrelation of population density, poor families, and healthy homes with the incidence of pulmonary tuberculosis and to see the pattern of relationships that are formed.The data used in this research is secondary data taken from related agencies. The samples in this study were 11 community health centers in Kupang City, namely Naioni Health Center, Alak Health Center, Manutapen Health Center, Sikumana Health Center, Penfui Health Center, Bakunase Health Center, Oebobo Health Center, Oepoi Health Center, Pasir Panjang Health Center, Kupang City Health Center, and Oesapa Community Health Center. Spatial analysis uses computer programs, namely the QGIS and GeoDa applications. QGIS was used to produce a map of the distribution of pulmonary tuberculosis cases in 11 health centers in Kupang City in 2019-2021 and the GeoDa application was used to see the pattern of TB distribution in Kupang City in 2019-2021, in general or globally through the LISA (Local Indicators Spatial Autocorrelation) tests.   Results : Based on the results of the LISA (Local Indicators Spatial Autocorrelation) bivariate test, it shows that population density, poor families, and healthy housing coverage have no relationship with the incidence of TB in Kupang City (p-value > 0.05) and show a random pattern of case distribution (Morans Index ' I is smaller than E[I] = -0.1000). However, in 2019-2020, the Morans I Index value covering healthy homes was greater than E[I] = -0.1000, which shows a clustered pattern of case distribution.   Conclusion : It was concluded that there was no positive spatial autocorrelation between population density, poor families, and healthy homes with the incidence of pulmonary tuberculosis in Kupang City in 2019-2021 },
   issn = {2597-4378},   pages = {83--95}  doi = {10.14710/jphtcr.v7i1.20682},
    url = {https://ejournal2.undip.ac.id/index.php/jphtr/article/view/20682}
}

Citation Format:

Abstract

Abstract
Introduction: Pulmonary tuberculosis has a fairly high number of cases in several regions of Indonesia, including Kupang City. In 2019, tuberculosis cases in Kupang City amounted to 667 cases, in 2020 it increased to 693 cases and in 2021 it decreased to 419 cases. This study aims to analyze the factors that influence the incidence of pulmonary tuberculosis which are studied in a spatial analysis model.

Methods : This research is a quantitative research with an analytical observational design using an approach to Geographic Information Systems (GIS). This study aims to see the autocorrelation of population density, poor families, and healthy homes with the incidence of pulmonary tuberculosis and to see the pattern of relationships that are formed.The data used in this research is secondary data taken from related agencies. The samples in this study were 11 community health centers in Kupang City, namely Naioni Health Center, Alak Health Center, Manutapen Health Center, Sikumana Health Center, Penfui Health Center, Bakunase Health Center, Oebobo Health Center, Oepoi Health Center, Pasir Panjang Health Center, Kupang City Health Center, and Oesapa Community Health Center. Spatial analysis uses computer programs, namely the QGIS and GeoDa applications. QGIS was used to produce a map of the distribution of pulmonary tuberculosis cases in 11 health centers in Kupang City in 2019-2021 and the GeoDa application was used to see the pattern of TB distribution in Kupang City in 2019-2021, in general or globally through the LISA (Local Indicators Spatial Autocorrelation) tests.

Results: Based on the results of the LISA (Local Indicators Spatial Autocorrelation) bivariate test, it shows that population density, poor families, and healthy housing coverage have no relationship with the incidence of TB in Kupang City (p-value > 0.05) and show a random pattern of case distribution (Morans Index ' I is smaller than E[I] = -0.1000). However, in 2019-2020, the Morans I Index value covering healthy homes was greater than E[I] = -0.1000, which shows a clustered pattern of case distribution.

Conclusion: It was concluded that there was no positive spatial autocorrelation between population density, poor families, and healthy homes with the incidence of pulmonary tuberculosis in Kupang City in 2019-2021

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Keywords: uberculosis, spatial, autocorrelation, QGIS, GeoDa

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