# Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

^{2}and adjusted R

^{2}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.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}with a population density of approximately 6008 people/km

^{2}and a population growth rate of 2.16% per year from 2011 to 2021 [25]. Its population growth will reach 1.8 million people by 2030 [26]. As the capital city of the Lampung Province, Bandar Lampung has the highest incidence of TB cases in the province [27]. In 2010, from a pool of 13,533 inhabitants, 1353 were found to be AFB smear-positive [28]. In 2011, the Bandar Lampung had 1314 TB cases, including 1000 smear-positive cases.

#### 2.2. Spatial Data Used in This Study

#### 2.2.1. Socio-Demographic Data

#### 2.2.2. Land Use Data

#### 2.3. Methodology

#### 2.3.1. Scatter Plot and Correlation Analysis

_{xy}is the correlation coefficient, n is the number of data points, $\sum}x,{\displaystyle \sum}y$ is the number of each variable, $\sum}xy$ is the sum of the multiplication of the variables x and y. $\sum}{x}^{2},{\displaystyle \sum}{y}^{2$ is the sum of the squares of x and y.

#### 2.3.2. Ordinary Least Square (OLS)

#### 2.3.3. Geographically Weighted Poisson Regression (GWPR)

#### 2.3.4. Model Assessment

^{2}and adjusted R

^{2}, as shown in Equations (8) and (9), respectively:

^{2}, and SST is the square of the difference between the actual Y value and the average value Y = $\sum}_{i=1}^{n$(yᵢ – ӯ)

^{2}[37].

#### 2.3.5. Investigating Spatial Patterns of Incidence Rate and Main Variables

## 3. Results

#### 3.1. Correlation and OLS of AFB Smear-Positive Pulmonary TB

^{2}and adjusted R

^{2}were 0.83 and 0.73. This indicates that the OLS model had significant properties and was a good fit.

_{1}), distance to the urban center (X

_{2}), industrial area (X

_{3}), green open space (X

_{4}), built area (X

_{5}), five-years average pulmonary TB growth rate (X

_{6}), and slum area (X

_{7}).

#### 3.2. Estimation of Pulmonary TB Cases Based on GWPR Method

#### 3.3. Statistical Analysis of GWPR Model

^{2}and adjusted R

^{2}of 0.96 and 0.94.

^{2}, adjusted R

^{2}, and AICc than OLS. The relatively small residuals in most sub-districts indicate that the overall number of cases estimated by the GWPR model was close to the actual value.

#### 3.4. Spatial Pattern of Pulmonary TB Cases

## 4. Discussion

^{2}and adjusted R

^{2}of 0.96 and 0.94, respectively. Based on previous research, the model demonstrated more accurate results according to the higher R

^{2}produced in several previous studies [18,20,40,51]. Our high values of R

^{2}and adjusted R

^{2}imply that the developed model can better represent the spatial variation of pulmonary TB cases in Bandar Lampung. This can be used to analyze pulmonary TB cases control strategies by simulating the number and variables. Moreover, variables applied in this study can also be utilized as a basis for developing further pulmonary TB case spatial models in other urban areas.

^{2}and adjusted R

^{2}of the OLS model (the adjusted R

^{2}is 0.1 point lower). The difference of 0.1 point was identified due to several less relevant variables, causing the adjusted R

^{2}to decrease. To alleviate these statistical shortcoming, future studies should thoroughly consider several variables that significantly affect increasing pulmonary TB cases at the city scale. To this end, Sun et al. [40] stated that environmental factors, climatic factors, and rainy days have a complex impact on increasing the prevalence of TB. Other studies have revealed that temperature, humidity, and sunlight also affect Mycobacterium tuberculosis growth [52,53,54]. Previous studies also suggested that pollution may increase the risk of pulmonary TB in the urban center of the industrial area [55,56]. Therefore, environmental, climatic, and air quality indicators can be explored to analyze their relation to pulmonary TB cases [57]. In this case, in situ data measurement can be collected in some areas to study its relation to pulmonary TB cases on a local scale [52,53,54]. Some research articles also report the number of other infectious disease cases, income per capita [18,40,51], the number of industrial workers, sanitation quality, HIV prevalence, child mortality, smoking, and diabetes rates, which are additional factors associated with the progression of pulmonary TB [39,58,59,60,61,62]. Therefore, future studies can explore various potential variables to understand the spatial pattern of pulmonary TB cases in urban areas, especially in high incidence rate cities.

## 5. Conclusions

^{2}and adjusted R

^{2}of 0.96 and 0.94, respectively. The GWPR model developed in this study can help to simulate the current status and future direction of pulmonary TB transmission. Through spatial analysis, we discovered that the factors of high pulmonary TB growth rate, large population, and large amounts of built, industrial, and slums areas affect the high-rate pulmonary TB cases in the Kedaton, Panjang, and Sukabumi sub-districts of Bandar Lampung. However, the drivers of each sub-district are spatially varied. The variation in pulmonary TB rate and its influencing factors can lead to different control strategies for each sub-district at the local level. In this case, policymakers should realize that geospatial insight is a critical aspect that needs to be adopted as a part of evidence-based policymaking in epidemiology and outbreak management to achieve community health resilience.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Region | Pulmonary TB Deaths |
---|---|

World | 1,500,000 |

Indonesia | 13,174 |

Lampung | 163 |

Bandar Lampung | 32 |

## References

- World Health Organization. Global Tuberculosis Report 2021 [Homepage on the Internet]; World Health Organization: Geneva, Switzerland, 2021; Available online: https://www.who.int/teams/global-tuberculosis-programme/tb-reports (accessed on 15 August 2022).
- Ahmed, A.; Mekonnen, D.; Shiferaw, A.M.; Belayneh, F.; Yenit, M.K. Incidence and determinants of tuberculosis infection among adult patients with HIV attending HIV care in north-east Ethiopia: A retrospective cohort study. BMJ Open
**2018**, 8, e016961. [Google Scholar] [CrossRef] [PubMed] - Chang, C.Y.; Hong, J.Y.; Yuan, M.K.; Chang, S.J.; Lee, Y.M.; Chang, S.C.; Hsu, L.C.; Chen, S.L. Risk factors in patients with AFB smear-positive sputum who receive inappropriate antituberculous treatment. Drug Des. Devel. Ther.
**2013**, 7, 53–58. [Google Scholar] [CrossRef] [PubMed] - Acid-Fast Bacillus (AFB) Tests: MedlinePlus Medical Test. Available online: https://medlineplus.gov/lab-tests/acid-fast-bacillus-afb-tests/ (accessed on 18 April 2022).
- Tanrikulu, A.C.; Acemoglu, H.; Palanci, Y.; Eren Dagli, C. Tuberculosis in Turkey: High altitude and other socio-economic risk factors. Public Health
**2008**, 122, 613–619. [Google Scholar] [CrossRef] [PubMed] - Li, X.X.; Wang, L.X.; Zhang, H.; Jiang, S.W.; Fang, Q.; Chen, J.X.; Zhou, X.N. Spatial variations of pulmonary tuberculosis prevalence co-impacted by socio-economic and geographic factors in People’s Republic of China, 2010. BMC Public Health
**2014**, 14, 257. [Google Scholar] [CrossRef] - Li, X.X.; Ren, Z.P.; Wang, L.X.; Zhang, H.; Jiang, S.W.; Chen, J.X.; Wang, J.F.; Zhou, X.N. Co-endemicity of pulmonary tuberculosis and intestinal helminth infection in the People’s Republic of China. PLoS Negl. Trop. Dis.
**2016**, 10, 1–23. [Google Scholar] [CrossRef] - Rosli, N.M.; Shah, S.A.; Mahmood, M.I. Geographical Information System (GIS) application in tuberculosis spatial clustering studies: A systematic review. Malays. J. Public Health Med.
**2018**, 18, 70–80. [Google Scholar] - Tadesse, S.; Enqueselassie, F.; Hagos, S. Spatial and space-time clustering of tuberculosis in Gurage Zone, Southern Ethiopia. PLoS ONE
**2018**, 13, e0198353. [Google Scholar] [CrossRef] - Masabarakiza, P.; Adel Hassaan, M. Spatial-temporal analysis of tuberculosis incidence in Burundi using GIS. Cent. Afr. J. Public Health
**2019**, 5, 280. [Google Scholar] [CrossRef] - Auchincloss, A.H.; Gebreab, S.Y.; Mair, C.; Diez Roux, A.V. A review of spatial methods in epidemiology, 2000–2010. Annu. Rev. Public Health
**2012**, 33, 107–122. [Google Scholar] [CrossRef] - Mahara, G.; Yang, K.; Chen, S.; Wang, W.; Guo, X. Socio-economic predictors and distribution of tuberculosis incidence in Beijing, China: A study using a combination of spatial statistics and GIS technology. Med. Sci.
**2018**, 6, 26. [Google Scholar] [CrossRef] - Mollalo, A.; Mao, L.; Rashidi, P.; Glass, G.E. A gis-based artificial neural network model for spatial distribution of tuberculosis across the continental united states. Int. J. Environ. Res. Public Health
**2019**, 16, 157. [Google Scholar] [CrossRef] [Green Version] - Alene, K.A.; Viney, K.; Moore, H.C.; Wagaw, M.; Clements, A.C.A. Spatial patterns of tuberculosis and HIV coinfection in Ethiopia. PLoS ONE
**2019**, 14, e0226127. [Google Scholar] [CrossRef] - Alves, L.S.; Dos Santos, D.T.; Arcoverde, M.A.M.; Berra, T.Z.; Arroyo, L.H.; Ramos, A.C.V.; De Assis, I.S.; De Queiroz, A.A.R.; Alonso, J.B.; Alves, J.D.; et al. Detection of risk clusters for deaths due to tuberculosis specifically in areas of southern Brazil where the disease was supposedly a non-problem. BMC Infect. Dis.
**2019**, 19, 628. [Google Scholar] [CrossRef] - Li, Q.; Liu, M.; Zhang, Y.; Wu, S.; Yang, Y.; Liu, Y.; Amsalu, E.; Tao, L.; Liu, X.; Zhang, F.; et al. The spatio-temporal analysis of the incidence of tuberculosis and the associated factors in mainland China, 2009–2015. Infect. Genet. Evol.
**2019**, 75, 103949. [Google Scholar] [CrossRef] - Abdul Rasam, A.R.; Mohd Shariff, N.; Dony, J.F. Geospatial-Based Model for Diagnosing Potential High-Risk Areas of Tuberculosis Disease in Malaysia. MATEC Web. Conf.
**2019**, 266, 02007. [Google Scholar] [CrossRef] - Wei, W.; Yuan-Yuan, J.; Ci, Y.; Ahan, A.; Ming-Qin, C. Local spatial variations analysis of smear-positive tuberculosis in Xinjiang using Geographically Weighted Regression model. BMC Public Health
**2016**, 16, 1058. [Google Scholar] [CrossRef] - Wang, Q.; Guo, L.; Wang, J.; Zhang, L.; Zhu, W.; Yuan, Y.; Li, J. Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013–2016. Trop. Med. Int. Health
**2019**, 24, 1104–1113. [Google Scholar] [CrossRef] - Dangisso, M.H.; Datiko, D.G.; Lindtjørn, B. Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: A method to identify clustering for targeted interventions. Glob. Health Action
**2020**, 13, 1785737. [Google Scholar] [CrossRef] - World Health Organization. Global Tuberculosis Report 2020; World Health Organization: Geneva, Switzerland, 2020; ISBN 9789240013131. [Google Scholar]
- Rood, E.; Khan, A.H.; Modak, P.K.; Mergenthaler, C.; Van Gurp, M.; Blok, L.; Bakker, M. A spatial analysis framework to monitor and accelerate progress towards SDG 3 to end TB in Bangladesh. ISPRS Int. J. Geo-Inf.
**2019**, 8, 14. [Google Scholar] [CrossRef] - Pemerintah Provinsi Lampung Dinkes. Riskesdas Profil Kesehatan Provinsi Lampung Tahun 2019; Pemerintah Provinsi Lampung Dinkes: Bandar Lampung, Indonesia, 2019; p. 136. [Google Scholar]
- Badan Pusat Statistik Kota Bandar Lampung. Bandar Lampung in Figure 2021 [Homepage on the Internet]; Badan Pusat Statistik Kota Bandar Lampung: Bandar Lampung, Indonesia, 2021; Available online: https://bandarlampungkota.bps.go.id/publication/2021/02/26/89c1b3d0038567aff884ca04/kota-bandar-lampung-dalam-angka-2021.html (accessed on 25 August 2022).
- Badan Pusat Statistik Kota Bandar Lampung. Bandar Lampung in Figure 2022 [Homepage on the Internet]; Badan Pusat Statistik Kota Bandar Lampung: Bandar Lampung, Indonesia, 2022; Available online: https://bandarlampungkota.bps.go.id/publication/2022/02/25/0890a0fd32082cf574db32af/kota-bandar-lampung-dalam-angka-2022.html (accessed on 25 August 2022).
- Profil Perumahan Dan Kawasan Permukiman Kota Bandar Lampung—Perkim.Id. Available online: https://perkim.id/pofil-pkp/profil-kabupaten-kota/profil-perumahan-dan-kawasan-permukiman-kota-bandar-lampung/ (accessed on 20 April 2022).
- Rencana Strategis Dinkes Provinsi Lampung Tahun 2015–2019; Dinas Kesehatan Provinsi Lampung: Bandar Lampung, Indonesia, 2019; Volume 58.
- Lestari, A. Pengaruh Terapi Psikoedukasi Keluarga Terhadap Pengetahuan dan Tingkat Ansietas Keluarga Dalam Merawat Anggota Keluarga yang Mengalami Tuberculosis Paru di Kota Bandar Lampung. J. Ilmiah Kesehatan
**2011**, 1. [Google Scholar] [CrossRef] - Dinas Kesehatan Kota Bandar Lampung. Bandar Lampung Health Profile 2015-2020 [Homepage on the Internet]. Dinas Kesehatan Kota Bandar Lampung: Bandar Lampung, Indonesia. 2020. Available online: https://dinkeskotabalam.com/laporan (accessed on 15 August 2022).
- BAPPEDA|Kota Bandar Lampung. Available online: https://bappeda.bandarlampungkota.go.id/ (accessed on 19 January 2022).
- Gong, P.; Li, X.; Wang, J.; Bai, Y.; Chen, B.; Hu, T.; Liu, X.; Xu, B.; Yang, J.; Zhang, W.; et al. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sens. Environ.
**2020**, 236, 111510. [Google Scholar] [CrossRef] - Kumar, C.; Singh, P.K.; Rai, R.K. Under-five mortality in high focus states in india: A district level geospatial analysis. PLoS ONE
**2012**, 7, e0037515. [Google Scholar] [CrossRef] - Li, C.; Li, F.; Wu, Z.; Cheng, J. Exploring spatially varying and scale-dependent relationships between soil contamination and landscape patterns using geographically weighted regression. Appl. Geogr.
**2017**, 82, 101–114. [Google Scholar] [CrossRef] - Nakaya, T.; Fotheringham, A.S.; Brunsdon, C.; Charlton, M. Geographically weighted poisson regression for disease association mapping. Stat. Med.
**2005**, 24, 2695–2717. [Google Scholar] [CrossRef] - Fotheringham, A.S.; Brunsdon, C.; Charlton, M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships; Wiley: Chichester, UK, 2002. [Google Scholar]
- Soewarno. Hidrologi Aplikasi Metode Statistik untuk Analisa Data, 1st ed.; NOVA: Bandung, Indonesian, 1995. [Google Scholar]
- Helland, I.S. On the interpretation and use of R
^{2}in regression analysis. Biometrics**1987**, 43, 61. [Google Scholar] [CrossRef] - Noorcintanami, S.; Widyaningsih, Y.; Abdullah, S. Geographically weighted models for modelling the prevalence of tuberculosis in Java. J. Phys.: Conf. Ser.
**2021**, 1722, 012089. [Google Scholar] - Bui, L.V.; Mor, Z.; Chemtob, D.; Ha, S.T.; Levine, H. Use of geographically weighted poisson regression to examine the effect of distance on tuberculosis incidence: A case study in Nam Dinh, Vietnam. PLoS ONE
**2018**, 13, e0207068. [Google Scholar] [CrossRef] - Sun, W.; Gong, J.; Zhou, J.; Zhao, Y.; Tan, J.; Ibrahim, A.N.; Zhou, Y. A spatial, social and environmental study of tuberculosis in China using statistical and GIS technology. Int. J. Environ. Res. Public Health
**2015**, 12, 1425–1448. [Google Scholar] [CrossRef] - Dos Santos, M.A.P.S.; Albuquerque, M.F.P.M.; Ximenes, R.A.A.; Lucena-Silva, N.L.C.L.; Braga, C.; Campelo, A.R.L.; Dantas, O.M.S.; Montarroyos, U.R.; Souza, W.V.; Kawasaki, A.M.; et al. Risk factors for treatment delay in pulmonary tuberculosis in Recife, Brazil. BMC Public Health
**2005**, 5, 25. [Google Scholar] [CrossRef] - Edelson, P.J.; Phypers, M. TB transmission on public transportation: A review of published studies and recommendations for contact tracing. Travel Med. Infect. Dis.
**2011**, 9, 27–31. [Google Scholar] - Ogbudebe, C.L.; Chukwu, J.N.; Nwafor, C.C.; Meka, A.O.; Ekeke, N.; Madichie, N.O.; Anyim, M.C.; Osakwe, C.; Onyeonoro, U.; Ukwaja, K.N.; et al. Reaching the underserved: Active tuberculosis case finding in urban slums in southeastern Nigeria. Int. J. Mycobacteriol.
**2015**, 4, 18–24. [Google Scholar] [CrossRef] - Bam, K.; Bhatt, L.P.; Thapa, R.; Dossajee, H.K.; Angdembe, M.R. Illness perception of tuberculosis (TB) and health seeking practice among urban slum residents of Bangladesh: A qualitative study. BMC Res. Notes
**2014**, 7, 572. [Google Scholar] [CrossRef] - Banu, S.; Rahman, M.T.; Uddin, M.K.M.; Khatun, R.; Ahmed, T.; Rahman, M.M.; Husain, M.A.; van Leth, F. Epidemiology of tuberculosis in an urban slum of Dhaka City, Bangladesh. PLoS ONE
**2013**, 8, e0077721. [Google Scholar] [CrossRef] [Green Version] - Kerubo, G.; Amukoye, E.; Niemann, S.; Kariuki, S. Drug susceptibility profiles of pulmonary Mycobacterium tuberculosis isolates from patients in informal urban settlements in Nairobi, Kenya. BMC Infect. Dis.
**2016**, 16, 583. [Google Scholar] [CrossRef] - Oppong, J.R.; Mayer, J.; Oren, E. The global health threat of African urban slums: The example of urban tuberculosis. Afr. Geogr. Rev.
**2015**, 34, 182–195. [Google Scholar] [CrossRef] - Hargreaves, J.R.; Boccia, D.; Evans, C.A.; Adato, M.; Petticrew, M.; Porter, J.D.H. The social determinants of tuberculosis: From evidence to action. Am. J. Public Health
**2011**, 101, 654–662. [Google Scholar] [CrossRef] - Duarte, R.; Lönnroth, K.; Carvalho, C.; Lima, F.; Carvalho, A.C.C.; Muñoz-Torrico, M.; Centis, R. Tuberculosis, social determinants and co-morbidities (including HIV). Pulmonology
**2018**, 24, 115–119. [Google Scholar] - Rachow, A.; Ivanova, O.; Wallis, R.; Charalambous, S.; Jani, I.; Bhatt, N.; Kampmann, B.; Sutherland, J.; Ntinginya, N.E.; Evans, D.; et al. TB sequel: Incidence, pathogenesis and risk factors of long-term medical and social sequelae of pulmonary TB—A study protocol. BMC Pulm. Med.
**2019**, 19, 4. [Google Scholar] [CrossRef] - Goschin, Z.; Druica, E. Regional factors hindering tuberculosis spread in Romania: Evidence from a semiparimetric GWR model. J. Soc. Sci. Econ.
**2017**, 6, 15–29. [Google Scholar] - Krishnan, R.; Thiruvengadam, K.; Jayabal, L.; Selvaraju, S.; Watson, B.; Malaisamy, M.; Nagarajan, K.; Tripathy, S.P.; Chinnaiyan, P.; Chandrasekaran, P. An influence of dew point temperature on the occurrence of Mycobacterium tuberculosis disease in Chennai, India. Sci. Rep.
**2022**, 12, 6147. [Google Scholar] [CrossRef] - Xu, M.; Li, Y.; Liu, B.; Chen, R.; Sheng, L.; Yan, S.; Chen, H.; Hou, J.; Yuan, L.; Ke, L.; et al. Temperature and humidity associated with increases in tuberculosis notifications: A time-series study in Hong Kong. Epidemiol. Infect.
**2020**, 149, e8. [Google Scholar] [CrossRef] - Fernandes, F.M.d.C.; Martins, E.d.S.; Pedrosa, D.M.A.S.; Evangelista, M.d.S.N. Relationship between climatic factors and air quality with tuberculosis in the Federal District, Brazil, 2003–2012. Braz. J. Infect. Dis.
**2017**, 21, 369–375. [Google Scholar] [CrossRef] - Lin, Y.J.; Lin, H.C.; Yang, Y.F.; Chen, C.Y.; Ling, M.P.; Chen, S.C.; Chen, W.Y.; You, S.H.; Lu, T.H.; Liao, C.M. Association between ambient air pollution and elevated risk of tuberculosis development. Infect. Drug Resist.
**2019**, 12, 3835–3847. [Google Scholar] [CrossRef] [PubMed] - Lai, T.C.; Chiang, C.Y.; Wu, C.F.; Yang, S.L.; Liu, D.P.; Chan, C.C.; Lin, H.H. Ambient air pollution and risk of tuberculosis: A cohort study. Occup. Environ. Med.
**2016**, 73, 56–61. [Google Scholar] [CrossRef] [PubMed] - Huang, S.; Xiang, H.; Yang, W.; Zhu, Z.; Tian, L.; Deng, S.; Zhang, T.; Lu, Y.; Liu, F.; Li, X.; et al. Short-term effect of air pollution on tuberculosis based on kriged data: A time-series analysis. Int. J. Environ. Res. Public Health
**2020**, 17, 1522. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Dye, C.; Lönnroth, K.; Jaramillo, E.; Williams, B.G.; Raviglione, M. Trends in tuberculosis incidence and their determinants in 134 countries. Bull. World Health Organ.
**2009**, 87, 683–691. [Google Scholar] [CrossRef] [PubMed] - Khaliq, A.; Khan, I.H.; Akhtar, M.W.; Chaudhry, M.N. Environmental risk factors andsocial determinants of pulmonary tuberculosis in Pakistan. Epidemiol. Open Access
**2015**, 5, 201. [Google Scholar] [CrossRef] - Gurung, L.M.; Bhatt, L.D.; Karmacharya, I.; Yadav, D.K. Dietary practice and nutritional status of tuberculosis patients in Pokhara: A cross sectional study. Front. Nutr.
**2018**, 5, 63. [Google Scholar] [CrossRef] - Heunis, J.C.; Kigozi, N.G.; Chikobvu, P.; Botha, S.; Van Rensburg, H.D. Risk factors for mortality in TB patients: A 10-year electronic record review in a South African province. BMC Public Health
**2017**, 17, 1–7. [Google Scholar] [CrossRef] - Shimeles, E.; Enquselassie, F.; Aseffa, A.; Tilahun, M.; Mekonen, A.; Wondimagegn, G.; Hailu, T. Risk factors for tuberculosis: A case–control study in Addis Ababa, Ethiopia. PLoS ONE
**2019**, 14, e0212235. [Google Scholar] [CrossRef] - Ren, H.; Lu, W.; Li, X.; Shen, H. Specific urban units identified in tuberculosis epidemic using a geographical detector in Guangzhou, China. Infect. Dis. Poverty
**2022**, 11, 44. [Google Scholar] [CrossRef] - Asemahagn, M.A.; Alene, G.D.; Yimer, S.A. Spatial-temporal clustering of notified pulmonary tuberculosis and its predictors in East Gojjam Zone, Northwest Ethiopia. PLoS ONE
**2021**, 16, e0245378. [Google Scholar] [CrossRef] - Liao, W.B.; Ju, K.; Gao, Y.M.; Pan, J. The association between internal migration and pulmonary tuberculosis in China, 2005-2015: A spatial analysis. Infect. Dis. Poverty
**2020**, 9, 1–12. [Google Scholar] [CrossRef] [PubMed] - Gwitira, I.; Karumazondo, N.; Shekede, M.D.; Sandy, C.; Siziba, N.; Chirenda, J. Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018. PLoS ONE
**2021**, 16, e0249523. [Google Scholar] [CrossRef] [PubMed] - Im, C.; Kim, Y. Spatial pattern of tuberculosis (TB) and related socio-environmental factors in South Korea, 2008–2016. PLoS ONE
**2021**, 16, e0255727. [Google Scholar] [CrossRef] - Wang, W.; Guo, W.; Cai, J.; Guo, W.; Liu, R.; Liu, X.; Ma, N.; Zhang, X.; Zhang, S. Epidemiological characteristics of tuberculosis and effects of meteorological factors and air pollutants on tuberculosis in Shijiazhuang, China: A distribution lag non-linear analysis. Environ. Res.
**2021**, 195, 110310. [Google Scholar] [CrossRef] [PubMed] - Shaweno, D.; Karmakar, M.; Alene, K.A.; Ragonnet, R.; Clements, A.C.; Trauer, J.M.; Denholm, J.T.; McBryde, E.S. Methods used in the spatial analysis of tuberculosis epidemiology: A systematic review. BMC Med.
**2018**, 16, 193. [Google Scholar] [CrossRef] - Kementerian Kesehatan, R.I. Indonesia Health Profile 2020 [Homepage on the Internet]; Kementerian Kesehatan RI: Jakarta, Indonesia, 2020; Available online: https://pusdatin.kemkes.go.id/resources/download/pusdatin/profil-kesehatan-indonesia/Profil-Kesehatan-Indonesia-Tahun-2020.pdf (accessed on 15 August 2022).
- Dinas Kesehatan Provinsi Lampung. Lampung Health Profile 2020 [Homepage on the Internet]; Dinas Kesehatan Provinsi Lampung: Bandar Lampung, Indonesia, 2020; Available online: https://dinkes.lampungprov.go.id/wpfd_file/profil-kesehatan-provinsi-lampung-tahun-2020/ (accessed on 15 August 2022).

**Figure 3.**Spatial distribution of AFB smear-positive pulmonary tuberculosis (TB) cases in Bandar Lampung in 2020.

**Figure 4.**Estimated and real AFB smear-positive pulmonary tuberculosis (TB) by sub-districts in Bandar Lampung.

**Figure 7.**Spatial variations of pulmonary TB cases, pulmonary TB growth rate, population, built area, industrial area, and slums.

No. | Data | Data Class | Timespan | Reference |
---|---|---|---|---|

1 | Number of Pulmonary Tuberculosis Cases | Socio-demographic | 2020 | [29] |

2 | Pulmonary Tuberculosis Growth Rate | Socio-demographic | 2015–2020 | [29] |

3 | Population | Socio-demographic | 2020 | [24] |

4 | Distance to the Urban Center | Land Use | 2020 | [24] |

5 | Industrial Area | Land Use | 2020 | [30] |

6 | Green Open Space Area | Land Use | 2020 | [30] |

7 | Slums Area | Land Use | 2020 | [30] |

8 | Built Area (GAIA) | Land Use | 1985–2018 | [31] |

Variable | Coefficient | StdError | t-Statistics | Probability | Robust_SE | Robust_t | Robust_Pr | VIF |
---|---|---|---|---|---|---|---|---|

Intercept | −7.420 | 25.487 | −0.291 | 0.078 | 19.944 | −0.372 | 0.716 | - |

Population | 0.002 | 0.001 | 3.320 | 0.006 * | 0.001 | 5.773 | 0.000 * | 2.631 |

Distance to the Urban Center | −3.416 | 1.975 | −1.730 | 0.109 | 1.336 | −2.556 | 0.025 * | 1.828 |

Industrial Area | 0.167 | 3.763 | 0.044 | 0.965 | 2.568 | 0.065 | 0.949 | 3.678 |

Green Open Space | −40.034 | 56.106 | −0.714 | 0.489 | 37.453 | −1.069 | 0.306 | 1.931 |

Built Area | −8.995 | 6.864 | −1.311 | 0.215 | 5.318 | −1.691 | 0.117 | 2.591 |

5 Years Average Pulmonary TB Growth Rate | 5.615 | 1.157 | 4.581 | 0.000 * | 1.195 | 4.697 | 0.001 * | 1.352 |

Slums | 0.249 | 0.143 | 1.735 | 0.108 | 0.078 | 3.190 | 0.008 * | 2.633 |

Diagnostics of OLS | ||||||||

Number of Observations | 20 | Akaike’s Information Criterion (AICc) | 205.284 | |||||

Multiple R-Squared | 0.83 | Adjusted R-Squared | 0.73 | |||||

Joint F-Statistics | 8.288 | Prob (>F), (7,12) degrees of freedom | 0.001 * | |||||

Joint Wald Statistics | 177.349 | Prob (>chi-squared), (7) degrees of freedom | 0.000 * | |||||

Koenker (BP) Statistics | 9.603 | Prob (>chi-squared), (7) degrees of freedom | 0.212 * | |||||

Jarque–Bera Statistics | 0.896 | Prob (>chi-squared), (2) degrees of freedom | 0.639 * |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Helmy, H.; Kamaluddin, M.T.; Iskandar, I.; Suheryanto.
Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression. *Trop. Med. Infect. Dis.* **2022**, *7*, 212.
https://doi.org/10.3390/tropicalmed7090212

**AMA Style**

Helmy H, Kamaluddin MT, Iskandar I, 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*. 2022; 7(9):212.
https://doi.org/10.3390/tropicalmed7090212

**Chicago/Turabian Style**

Helmy, Helina, Muhammad Totong Kamaluddin, Iskhaq Iskandar, and Suheryanto.
2022. "Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression" *Tropical Medicine and Infectious Disease* 7, no. 9: 212.
https://doi.org/10.3390/tropicalmed7090212