Spatial association of socio-economic status and prevalence of Tuberculosis in Nepal, 2019

Authors

  • Vijay Sharma Khon Kaen University, Thailand
  • Wongsa Laohasiriwong Dean, Faculty of Public Health, Khon Kaen University, Thailand
  • Roshan Kumar Mahato Assistant Professor, Kathmandu University School of Medical Sciences, Kathmandu University, Dhulikhel, Nepal
  • Kittipong Sornlorm Lecturer, Faculty of Public Health, Khon Kaen University, Thailand

DOI:

https://doi.org/10.62992/ijphap.v1i1.15

Keywords:

Spatial association, Prevalence, Socio-economic status, Tuberculosis

Abstract

Background: Tuberculosis (TB) is a communicable disease. Nepal is a developing country which is still fighting against poverty and many communicable diseases including TB. Although many studies have explored the TB and its associated factors, there are very rare studies addressing the spatial association of TB with associated factors. Hence, this study aims to find out the spatial association of socio-economic status and prevalence of tuberculosis in Nepal in the year 2019.

Methods: This cross-sectional study was carried out by utilizing the data set available from National Tuberculosis Control Center Nepal in the year 2019. A Moran’s I and Local Indicators of Spatial Association (LISA) were used to identify the spatial autocorrelation between TB and associated factors in Nepal.

Results: The results indicated the spatial autocorrelation between TB and socio-economic factors in Nepal. The LISA analysis identified the significant positive spatial local autocorrelation of Night Time Light with 8 high-high and 8 low-low districts, Land Surface Temperature Day with 12 high-high and 5 low-low districts, Land Surface Temperature Night with 12 high-high and 5 low-low districts, Population Density with 7 high-high and 8 low-low districts, Urban Area with 7 high-high and 8 low-low districts, Wall with Cement Stone with 11 high-high and 8 low-low districts, Roof with Reinforced Cement Concrete with 10 high-high and 8 low-low districts, like wise  Fuel Liquefied Petroleum Gas with 9 high-high and 8 low-low districts respectively.

Conclusion: There were significant spatial associations between prevalence of tuberculosis and socio-economic status in Nepal in the year 2019, which should be addressed by new policy recommendations to detect the tuberculosis cases and possible associated factors to minimize the burden of TB in significant way.

References

World Health Organization W. Global Tuberculosis Report. 2021, Global tuberculosis report 2021. Geneva: World Health Organization; 2021. Licence: CC BY-NC-SA 3.0 IGO.

Pai M, Behr MA, Dowdy D, Dheda K, Divangahi M, Boehme CC, et al. Tuberculosis. 2016;2(1):1-23,

World Health Organization W. TB A Global Emergency. 1994, https://apps.who.int/iris/bitstream/handle/10665/58749/WHO_TB_94.177.pdf?sequence=1&isAllowed=y

WHO-SEARO W. Tuberculosis control in the South-East Asia region. 2021, https://www.who.int/southeastasia/health-topics/tuberculosis

unicef. Nepal Multidimensional Poverty Index 2021. https://www.unicef.org/nepal/reports/nepal-multidimensional-poverty-index-2021-report

National Tuberculosis Program N. Annual Report 2075/076 (2018-2019), . 2018-2019. Available from: http://nepalntp.gov.np/wp-content/uploads/2020/04/NTP-Annual-Report-2075-76-2018-19.pdf

Steiniger S, Hunter AJJC, environment, systems u. The 2012 free and open source GIS software map–A guide to facilitate research, development, and adoption. 2013;39:136-50,

Anselin L, Lozano N, Koschinsky JJU. Rate transformations and smoothing. 2006;51:61801,

Anselin LJGa. Local indicators of spatial association—LISA. 1995;27(2):93-115,

Anselin LJSAL, University of Illinois, Champagne-Urbana, Illinois. An introduction to spatial autocorrelation analysis with GeoDa. 2003,

Laohasiriwong W, Puttanapong N, Singsalasang AJGH. Prevalence of hypertension in Thailand: Hotspot clustering detected by spatial analysis. 2018;13(1),

Wu J, Dalal KJIjopm. Tuberculosis in Asia and the pacific: the role of socioeconomic status and health system development. 2012;3(1):8,

Olson NA, Davidow AL, Winston CA, Chen MP, Gazmararian JA, Katz DJJBph. A national study of socioeconomic status and tuberculosis rates by country of birth, United States, 1996–2005. 2012;12(1):1-7,

Odone A, Crampin AC, Mwinuka V, Malema S, Mwaungulu JN, Munthali L, et al. Association between socioeconomic position and tuberculosis in a large population-based study in rural Malawi. 2013;8(10):e77740,

Tanrikulu A, Acemoglu H, Palanci Y, Dagli CEJPH. Tuberculosis in Turkey: high altitude and other socio-economic risk factors. 2008;122(6):613-9,

e Silva MdA, Oliveira CDL, Neto RGT, Camargos PAJBPH. Spatial distribution of tuberculosis from 2002 to 2012 in a midsize city in Brazil. 2016;16(1):1-8,

Mahara G, Yang K, Chen S, Wang W, Guo XJMS. Socio-economic predictors and distribution of tuberculosis incidence in Beijing, China: a study using a combination of spatial statistics and GIS technology. 2018;6(2):26,

Chen Y-Y, Chang J-R, Wu C-D, Yeh Y-P, Yang S-J, Hsu C-H, et al. Combining molecular typing and spatial pattern analysis to identify areas of high tuberculosis transmission in a moderate-incidence county in Taiwan. 2017;7(1):1-8,

Tipayamongkholgul M, Podang J, Siri SJJotMAoTCT. Spatial analysis of social determinants for tuberculosis in Thailand. 2013;96:S116-21,

Rasam AA, Shariff NM, Dony J, Misni AJIJoE, Technology. Socioenvironmental factors and tuberculosis: an exploratory spatial analysis in peninsular Malaysia. 2018;7(3.11):187-92,

Dhanaraj B, Papanna MK, Adinarayanan S, Vedachalam C, Sundaram V, Shanmugam S, et al. Prevalence and risk factors for adult pulmonary tuberculosis in a metropolitan city of South India. 2015;10(4):e0124260,

Beiranvand R, Karimi A, Delpisheh A, Sayehmiri K, Soleimani S, Ghalavandi SJIjoph. Correlation assessment of climate and geographic distribution of tuberculosis using geographical information system (GIS). 2016;45(1):86,

Mohidem NA, Osman M, Hashim Z, Muharam FM, Mohd Elias S, Shaharudin RJPo. Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia. 2021;16(6):e0252146,

Zhang Y, Liu M, Wu SS, Jiang H, Zhang J, Wang S, et al. Spatial distribution of tuberculosis and its association with meteorological factors in mainland China. 2019;19(1):1-7,

Huang K, Yang X-J, Hu C-Y, Ding K, Jiang W, Hua X-G, et al. Short-term effect of ambient temperature change on the risk of tuberculosis admissions: Assessments of two exposure metrics. 2020;189:109900,

Wubuli A, Li Y, Xue F, Yao X, Upur H, Wushouer QJPO. Seasonality of active tuberculosis notification from 2005 to 2014 in Xinjiang, China. 2017;12(7):e0180226,

Mollalo A, Mao L, Rashidi P, Glass GEJIjoer, health p. A GIS-based artificial neural network model for spatial distribution of tuberculosis across the continental United States. 2019;16(1):157,

Kuddus MA, McBryde ES, Adegboye OAJSr. Delay effect and burden of weather-related tuberculosis cases in Rajshahi province, Bangladesh, 2007–2012. 2019;9(1):1-13,

Onozuka D, Hagihara AJIjob. The association of extreme temperatures and the incidence of tuberculosis in Japan. 2015;59(8):1107-14,

Amenuvegbe GK, Francis A, Fred BJBrn. Low tuberculosis case detection: a community and health facility based study of contributory factors in the Nkwanta South district of Ghana. 2016;9(1):1-7,

Khan AJ, Khowaja S, Khan FS, Qazi F, Lotia I, Habib A, et al. Engaging the private sector to increase tuberculosis case detection: an impact evaluation study. 2012;12(8):608-16, https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(12)70116-0/fulltext

Lin Y-J, Lin H-C, Yang Y-F, Chen C-Y, Ling M-P, Chen S-C, et al. Association between ambient air pollution and elevated risk of tuberculosis development. 2019;12:3835,

Low C-T, Lai P-C, Tse W-SC, Tsui C-K, Lee H, Hui P-KJSs, et al. Exploring tuberculosis by types of housing development. 2013;87:77-83,

Singh S, Kashyap GC, Puri PJBpm. Potential effect of household environment on prevalence of tuberculosis in India: evidence from the recent round of a cross-sectional survey. 2018;18(1):1-10.

Downloads

Published

01-12-2022

Issue

Section

Original Article

Categories

How to Cite

1.
Spatial association of socio-economic status and prevalence of Tuberculosis in Nepal, 2019. IJPHAP [Internet]. 2022 Dec. 1 [cited 2024 Jun. 20];1(1). Available from: https://ijphap.com/index.php/home/article/view/15