Spatial patterns of multidrug resistant tuberculosis and relationships to socio-economic, demographic and household factors in northwest Ethiopia

Abstract

BackgroundUnderstanding the geographical distribution of multidrug-resistant tuberculosis (MDR-TB) in high TB burden countries such as Ethiopia is crucial for effective control of TB epidemics in these countries, and thus globally. We present the first spatial analysis of multidrug resistant tuberculosis, and its relationship to socio-economic, demographic and household factors in northwest Ethiopia.MethodsAn ecological study was conducted using data on patients diagnosed with MDR-TB at the University of Gondar Hospital MDR-TB treatment centre, for the period 2010 to 2015. District level population data were extracted from the Ethiopia National and Regional Census Report. Spatial autocorrelation was explored using Moran’s I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate Poisson regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian Markov chain Monte Carlo (MCMC) simulation approach with Gibbs sampling, in WinBUGS.ResultsA total of 264 MDR-TB patients were included in the analysis. The overall crude incidence rate of MDR-TB for the six-year period was 3.0 cases per 100,000 population. The highest incidence rate was observed in Metema (21 cases per 100,000 population) and Humera (18 cases per 100,000 population) districts; whereas nine districts had zero cases. Spatial clustering of MDR-TB was observed in districts located in the Ethiopia-Sudan and Ethiopia-Eritrea border regions, where large numbers of seasonal migrants live. Spatial clustering of MDR-TB was positively associated with urbanization (RR: 1.02; 95%CI: 1.01, 1.04) and the percentage of men (RR: 1.58; 95% CI: 1.26, 1.99) in the districts; after accounting for these factors there was no residual spatial clustering.ConclusionSpatial clustering of MDR-TB, fully explained by demographic factors (urbanization and percent male), was detected in the border regions of northwest Ethiopia, in locations where seasonal migrants live and work. Cross-border initiatives including options for mobile TB treatment and follow up are important for the effective control of MDR-TB in the region.

The association between internal migration and pulmonary tuberculosis in China, 2005–2015: a spatial analysis

“…Population density was also positively correlated with SS + PTB. Similar results have been reported in other studies [46][47][48]. Due to poor financial conditions, migrants are more likely to be living in crowded places that favor the spread of Mycobacterium tuberculosis; this explains the association between population density and SS + PTB.…”

Spatial clustering and socio-demographic determinants of HIV infection in Ethiopia, 2015–2017

“…The LISA analysis indicated that HIV was geographically clustered in the north-west districts of Amhara Region. A high (Alemayehu et al, 2017;Alene et al, 2017;Deribew, 2009). Indeed the districts identified in this study as being important spatiotemporal HIV hotspots are known for being highly agricultural areas as well as transport corridors for truck drivers, with large numbers of other HIV MARPs such as day labourers during the harvesting season, commercial sex workers, and truckers.…”

Spatiotemporal analysis of drug-resistant TB patients registered in selected districts of Karnataka, South India: a cross-sectional study

“…Biological (malnutrition, HIV infection, and age distribution) and social determinants (poverty and behavioral risk factors) of TB are aggregated in geographical areas and eventually can lead to aggregation of TB patients in such areas. The previous studies on geospatial patterns of MDR-TB in Peru [4], Moldova [5], and Georgia [6] have shown MDR-TB patients to be aggregated within geographical areas. If the DR-TB is characterized by areas of concentrated risk rather than spatially uniform risk, intensitfied control activities at areas with concentrated risk may be efficient than adopting blanket approach for control of the disease.…”