Spatiotemporal patterns of drug-resistant TB

Geographic information enhances understanding of the spatial and temporal spread of TB and aids in documenting MDR TB outbreaks.

Spatiotemporal analysis alone does not reveal the mechanisms behind increased MDR TB transmission.

The low cost of georeferencing tools and free software like Google Earth makes this approach appealing for resource-limited settings.

Integrating spatial data into standard TB collection tools can improve early, targeted interventions, leading to timely treatment and reduced MDR TB transmission.[1]

 

Improved prediction of multidrug-resistant (MDR) TB can lead to earlier diagnosis, better treatment outcomes, and reduced transmission risk.

Univariable analysis identified age at diagnosis, history of TB treatment, and sputum smear–negative disease as significant risk factors for MDR TB.

Multivariable analysis confirmed age at diagnosis, history of TB treatment, sputum smear-negative disease, and HIV-positive status as independent predictors of MDR TB.

Including information about the location of diagnosis or patient residence enhanced the prediction of MDR TB among those tested.

Implementing less strict criteria for ordering drug susceptibility testing (DST) at health centers with a high risk of MDR TB might be a practical strategy when resources are constrained.[2]

References:

1. Lin, H., Shin, S., Blaya, J.A., Zhang, Z., Cegielski, P., Contreras, C., Asencios, L., Bonilla, C., Bayona, J., Paciorek, C.J. and Cohen, T., 2011. Assessing spatiotemporal patterns of multidrug-resistant and drug-sensitive tuberculosis in a South American setting. Epidemiology & Infection, 139(11), pp.1784-1793.

2. Lin HH, Shin SS, Contreras C, Asencios L, Paciorek CJ, Cohen T. Use of spatial information to predict multidrug resistance in tuberculosis patients, Peru. Emerg Infect Dis. 2012 May;18(5):811-3. doi: 10.3201/eid1805.111467.