The impact of gene Xpert MTB/RIF in the detection of Tuberculosis and Multidrug-Resistant Tuberculosis and its spatial pattern in Ribeirão Preto-SP

Abstract

Tuberculosis (TB) is one of the oldest infectious diseases in the world and remains a serious public health problem worldwide. MDR-TB is characterized as resistance to at least two of the first-line (most effective) drugs used to treat the disease, which are isoniazid and rifampicin. Overall, more than 480,000 drug-resistance cases were detected and reported in 2015. In 2010, in the Global Stop TB 2011-2015 plan, WHO authorized the use of the GeneXpert MTB/RIF system for the Rapid Molecular Test for TB (TRM-TB). In Brazil, the Xpert MTB/RIF was approved for incorporation into the SUS 2013 and was introduced as a diagnostic technology for TB in Ribeirão Preto in 2014. The objective was to analyze the impact of Gene Xpert MTB/RIF in the detection of Tuberculosis and Multidrug-Resistant Tuberculosis and its spatial distribution pattern of TB, MDR-TB, TB in children, extrapulmonary TB and HIV-TB in Ribeirão Preto-SP. It is a descriptive epidemiological study that will be carried out in Ribeirão Preto-SP. The study population will be composed of TB cases reported in the Tuberculosis Patient Control System (TBWeb) from 2000 to 2017 of the Epidemiological Surveillance of the Ribeirão Preto Municipal Secretariat. Clinical variables will be used to trace the epidemiological profile of the disease in the municipality and also operational service variables. The descriptive analysis of the cases will be performed by means of descriptive statistics of the quantitative parameters through the software Statistica version 12.0. The annual incidence rate of TB, MDR-TB, HIV-TB, TB in children, TB associated with other diseases and extra-pulmonary TB will be calculated considering the method of direct standardization according to sex and age group. In order to classify the time trend and observe the impact of the TRM implementation, after the calculation of the incidence rates, these will be logarithmized to classify the time trend of the event in decreasing, stationary or increasing in the period from 2000 to 2017 using the time series statistics of the StataSE 13 program which considers the self-regression method known as Prais-Winsten. Later, the cases will be geocoded by standardizing and matching the addresses with the StreetBase® digital segment segment map. It will be done the analysis of the density of points, defined as kernel intensity estimator through the software ArcGis 10.2. To test the spatial autocorrelation, the Global and Local Moran Index will be performed through the GeoDa software, and later, for the detection of spatial and temporal-temporal clusters of the cases, the spatial analysis technique will be called the scanning using the SatScan software. (AU)