BackgroundPatients with Tuberculosis (TB) are a vulnerable group for acquiring HIV infection. Therefore, countries with a concentrated HIV epidemic and high prevalence of TB should provide adequate information about HIV prevention to TB patients.MethodsWe conducted a cross-sectional study to evaluate the level of knowledge on HIV prevention and transmission among newly diagnosed TB patients in Lima, Peru. The survey evaluated knowledge about HIV infection and prevention and was administered before HIV counseling and blood sampling for HIV testing were performed.ResultsA total of 171 TB patients were enrolled; mean age was 31.1 years, 101 (59%) were male. The overall mean level of knowledge of HIV was 59%; but the specific mean level of knowledge on HIV transmission and prevention was only 33.3% and 41.5%, respectively. Age and level of education correlated with overall level of knowledge in the multivariate model (P-value: 0.02 and <0.001 respectively).ConclusionsThe study shows inadequate levels of knowledge about HIV transmission and prevention among newly-diagnosed TB patients in this setting, and underscores the need for implementing educational interventions in this population.

Abstract. Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multidrug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDR-TB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e. the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird (spatial resolution = 0.61 m) data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centres, using a 10 m 2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health centre were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS ® module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases, using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centres and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components which revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

The effectiveness of the World Health Organization's (WHO) treatment category II regimen for tuberculosis in 124 patients was compared to that of 1147 patients receiving treatment category I in Lima, Peru following WHO's guidelines. Drug susceptibility test was available for 85% of patients. Prevalence of multi drug resistance and streptomycin resistance were 5.1% and 20.7%, respectively. Overall cure rate for regimen II was lower than that of regimen I: 67.8% (95% CI: 58.9-75.6.) vs 77.8% (95% CI: 75.3-80.2), p=0.014. Multi-drug resistance exerted a profound effect on cure rates in both regimens. Our results support the phasing-out of treatment category II regimen in Peru.

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