Geographic predictors of primary multidrug-resistant tuberculosis cases in an endemic area of Lima, Peru

L. Shah, H. W. Choi, L. Berrang-Ford, G. Henostroza, F. Krapp, C. Zamudio, S. J. Heymann, J. S. Kaufman, A. Ciampi, C. Seas, E. Gotuzzo, T. F. Brewer

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14 Scopus citations


SETTING: Peru reports among the highest multidrugresistant tuberculosis (MDR-TB) rates in the Americas, with a growing proportion in previously untreated tuberculosis (TB) cases. The identification of clusters of primary MDR-TB compared with drug-susceptible TB (DS-TB) could help prioritize interventions.

OBJECTIVE: To examine the clustering of primary MDR-TB case residences and their proximity to highrisk locations in San Juan de Lurigancho District, Lima, Peru.

DESIGN: Enrolled primary MDR-TB and primary DSTB cases were interviewed and their primary residence was recorded using handheld Global Positioning System devices. Kuldorff's spatial scan statistic was used for cluster detection (SaTScanTM, v. 9.1.1). Identified clusters were visualized in Quantum Geographic Information Systems software (v1.8.0). The following cluster centers were tested: a health centre with the highest TB and MDR-TB rates (Clinic X), a hospital and two prisons. Using regression analyses, we examined predictors of primary MDR-TB cases.

RESULTS: A statistically significant cluster of primary MDR-TB cases was identified within a 2.29 km radius around Clinic X. Proximity to Clinic X remained a significant predictor of primary MDR-TB in adjusted regression analyses.

CONCLUSION: We identified a hotspot of primary MDR-TB cases around Clinic X in a TB-endemic area. Causes of this clustering require investigation; targeted interventions for this high-risk area should be considered.

Original languageEnglish
Pages (from-to)1307-1314
Number of pages8
JournalInternational Journal of Tuberculosis and Lung Disease
Issue number11
StatePublished - 1 Nov 2014
Externally publishedYes


  • Clusters
  • High risk
  • Hotspot
  • Spatial
  • Urban


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