Determinants, risk factors and spatial analysis of multi-drug resistant pulmonary tuberculosis in Jodhpur, India

<a href="">Immagine di vectorjuice</a> su Freepik
Submitted: July 22, 2021
Accepted: December 20, 2021
Published: January 18, 2022
Abstract Views: 2086
PDF: 1185
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


This study was planned to estimate the proportion of confirmed multi-drug resistance pulmonary tuberculosis (TB) cases out of the presumptive cases referred to DTC (District Tuberculosis Center) Jodhpur for diagnosis; to identify clinical and socio-demographic risk factors associated with the multidrug-resistant pulmonary TB and to assess the spatial distribution to find out clustering and pattern in the distribution of pulmonary TB with the help of Geographic Information System (GIS). In the Jodhpur district, 150 confirmed pulmonary multi-drug resistant tuberculosis (MDR-TB) cases, diagnosed by probe-based molecular drug susceptibility testing method and categorized as MDR in DTC's register (District Tuberculosis Center), were taken. Simultaneously, 300 control of confirmed non-MDR or drug-sensitive pulmonary TB patients were taken. Statistical analysis was done with logistic regression. In addition, for spatial analysis, secondary data from 2013-17 was analyzed using Global Moran's I and Getis and Ordi (Gi*) statistics. In 2012-18, a total of 12563 CBNAAT (Cartridge-based nucleic acid amplification test) were performed. 2898 (23%) showed M. TB positive but rifampicin sensitive, and 590 (4.7%) showed rifampicin resistant. Independent risk factors for MDR TB were ≤60 years age (AOR 3.0, CI 1.3-7.1); male gender (AOR 3.4, CI 1.8-6.7); overcrowding (AOR 1.6, CI 1.0-2.7); using chulha (smoke appliance) for cooking (AOR 2.5, CI 1.2-4.9), past TB treatment (AOR 5.7, CI 2.9-11.3) and past contact with MDR patient (AOR 10.7, CI 3.7-31.2). All four urban TUs (Tuberculosis Units) had the highest proportion of drug-resistant pulmonary TB. There was no statistically significant clustering, and the pattern of cases was primarily random. Most of the hotspots generated were present near the administrative boundaries of TUs, and the new ones mostly appeared in the area near the previous hotspots. A random pattern seen in cluster analysis supports the universal drug testing policy of India. Hotspot analysis helps cross administrative border initiatives with targeted active case finding and proper follow-up.



PlumX Metrics


Download data is not yet available.


World Health Organisation. Global Health TB Report 2019. Available from:
World Health Organization. Implementing the end TB strategy: the essentials. 2015. Available from:
World Health Organization. Priorities in operational research to improve tuberculosis care and control. Available from:
Balaji V, Daley P, Anand AA, et al. Risk factors for MDR and XDR-TB in a tertiary referral hospital in India. PLoS One 2010;5:e9527.
Vadwai V, Shetty A, Soman R, Rodrigues C. Determination of risk factors for isoniazid monoresistance and multidrug-resistant tuberculosis in treatment failure patients. Scand J Infect Dis 2012;44:48–50.
Johnson J, Kagal A, Bharadwaj R. Factors associated with drug resistance in pulmonary tuberculosis. Indian J Chest Dis Allied Sci 2003;45:105–9.
Sharma SK, Turaga KK, Balamurugan A, et al. Clinical and genetic risk factors for the development of multi-drug resistant tuberculosis in non-HIV infected patients at a tertiary care center in India: a case-control study. Infect Genet Evol 2003;3:183–8.
Nair SA, Raizada N, Singh Sachdeva K, et al. Factors associated with tuberculosis and rifampicin-resistant tuberculosis amongst symptomatic patients in India: A retrospective analysis. PLoS One 2016;11:e0150054.
Atre SR, D′Souza DB, Vira TS, et al. Risk factors associated with MDR-TB at the onset of therapy among new cases registered with the RNTCP in Mumbai, India. Indian J Public Health 2011;55:14.
Central TB Division, Ministry of Health and Family Welfare, Government of India. Guidelines for programmatic management of drug resistant TB in India 2021. Available from:
Subhash HS, Ashwin I, Mukundan U, et al. Drug Resistant tuberculosis in diabetes mellitus: A retrospective study from South India. Trop Doct 2003;33:154–6.
ArcGIS [Internet]. How spatial autocorrelation (Global Moran’s I) works. Available from:
ArcGIS [Internet]. Hot Spot Analysis (Getis-Ord Gi*). Available from:
Porwal C, Kaushik A, Makkar N, et al. Incidence and risk factors for extensively drug-resistant tuberculosis in Delhi Region. PLoS One 2013;8:e55299.
Stosic M, Vukovic D, Babic D, et al. Risk factors for multidrug-resistant tuberculosis among tuberculosis patients in Serbia: a case-control study. BMC Public Health 2018;18:1114.
Zhang C, Wang Y, Shi G, et al. Determinants of multidrug-resistant tuberculosis in Henan province in China: a case control study. BMC Public Health 2016;16:42.
Hirpa S, Medhin G, Girma B, et al. Determinants of multidrug-resistant tuberculosis in patients who underwent first-line treatment in Addis Ababa: a case control study. BMC Public Health 2013;13:782.
O’Dwyer LA, Burton DL. Potential meets reality: GIS and public health research in Australia. Aust N Z J Public Health 1998;22:819–23.
Mishra VK, Retherford RD, Smith KR. Cooking with biomass fuels increases the risk of tuberculosis. Natl Fam Health Surv Bull 1999;(13):1–4.
Tiwari N, Adhikari CMS, Tewari A, Kandpal V. Investigation of geo-spatial hotspots for the occurrence of tuberculosis in Almora district, India, using GIS and spatial scan statistic. Int J Health Geogr 2006;5:33.
Higgs BW, Mohtashemi M, Grinsdale J, Kawamura LM. Early detection of tuberculosis outbreaks among the San Francisco homeless: Trade-offs between spatial resolution and temporal scale. PLoS One 2007;2:e1284.
Álvarez-Hernández G, Fara-Valencia F, Reyes-Castro PA, Rascón-Pacheco RA. An analysis of spatial and socio-economic determinants of tuberculosis in Hermosillo, Mexico, 2000-2006. Int J Tuberc Lung Dis 2010;14:708-13.
Houlihan CF, Mutevedzi PC, Lessells RJ, et al. The tuberculosis challenge in a rural South African HIV programme. BMC Infect Dis 2010;10:23.
Li L, Xi Y, Ren F. Spatio-temporal distribution characteristics and trajectory similarity analysis of tuberculosis in Beijing, China. Int J Environ Res Public Health 2016;13:291.
Ng I-C, Wen T-H, Wang J-Y, Fang C-T. Spatial dependency of tuberculosis incidence in Taiwan. PPLoS One 2012;7:e50740.
Shaweno D, Karmakar M, Alene KA, et al. Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review. BMC Med 2018;16:193.
Central TB Division India. Guideline for PMDT in India 2017. Available from:

Supporting Agencies

No funding from any source

How to Cite

Ladha, Nikhilesh, Pankaj Bhardwaj, Nishant Kumar Chauhan, Kikkeri Hanumantha Setty Naveen, Vijaya Lakshmi Nag, and Dandabathula Giribabu. 2022. “Determinants, Risk Factors and Spatial Analysis of Multi-Drug Resistant Pulmonary Tuberculosis in Jodhpur, India”. Monaldi Archives for Chest Disease 92 (4).