RESUMEN
Tuberculosis (TB) is a disease caused by the bacillus Mycobacterium tuberculosis (MTB). Human immunodeficiency virus (HIV) infection and type 2 diabetes mellitus (T2DM) are among the main risk factors for the development of TB and increase the risk of drug-resistant TB developing (DR-TB). The aim of this study was to estimate the prevalence of DR-TB in patients with HIV or T2DM in Sinaloa, Mexico. This was an observational and cross-sectional study. The analysis was conducted using the clinical data of patients registered on the National Epidemiological Surveillance System for TB (SINAVE/PUI-TB) platform with a presumed diagnosis of TB during 2019 to 2021 in Sinaloa, Mexico. The prevalence of DR-TB was estimated in HIV and T2DM patients, as well as the odds ratios for their sociodemographic variables, using the Chi-square test. There were 2, 4, and 4 TB-HIV cases and 2, 6, and 9 TB-T2DM cases during 2019, 2020, and 2021, respectively, whereas there were 2 and 1 DRTB-HIV and DRTB-T2DM cases, respectively. The results indicated that the WHO guidelines for DR-TB were not properly applied to this high-risk population. Hence, the appropriate application of guidelines for TB and DR-TB detection in these patients needs to be immediately implemented by the State health system.
RESUMEN
Monkeypox (Mpox) is an emerging zoonotic disease with the potential for severe complications. Early identification and diagnosis are essential to prompt treatment, control its spread, and reduce the risk of human-to-human transmission. This study aimed to develop a clinical diagnostic tool and describe the clinical and sociodemographic features of 19 PCR-confirmed Mpox cases during an outbreak in a nonendemic region of northwestern Mexico. The median age of patients was 35 years, and most were male. Mpox-positive patients commonly reported symptoms such as fever, lumbago, and asthenia, in addition to experiencing painful ulcers and a high frequency of HIV infection among people living with HIV (PLWH). Two diagnostic models using logistic regression were devised, with the best model exhibiting a prediction accuracy of 0.92 (95% CI: 0.8-1), a sensitivity of 0.86, and a specificity of 0.93. The high predictive values and accuracy of the top-performing model highlight its potential to significantly improve early Mpox diagnosis and treatment in clinical settings, aiding in the control of future outbreaks.