As the most severe form of tuberculosis (TB), TB meningitis (TBM) is still associated with high mortality even in developed countries. In certain U.S. states more than 50% of the TBM patients eventually die or have neurological complications despite having advanced health care settings. This population-based analysis aimed to determine the risk factors and trends associated with TBM morbidity and mortality using state-wide surveillance data.
De-identified surveillance data of all confirmed TB patients from the state of Texas reported between 01/2010 and 12/2017 to the National TB Surveillance System was analyzed. Spatial distribution of TBM cases was presented by Stata's Geographic Information Systems mapping. Univariate and multiple logistic regression were used to identify risk factors associated with meningitis morbidity and mortality. Non-parametric trend testing was used for the morbidity and mortality trends.
Among 10,103 TB patients reported from Texas between 2010 and 2017, 192 (1.9%) had TBM. Over the 8-year period, TBM proportion fluctuated between 1.5% and 2.7% with peaks in 2011 (2.7%) and 2016 (2.1%) and an overall trend z=-1.32, p=0.19. TBM had higher mortality at diagnosis (8.9%), during treatment (20.3%) and overall (22.9%) than non-TBM (1.9%, 6.8%, and 7.2%, respectively, p<0.001). While the mortality during treatment was unchanged overtime in non-TBM patients (z=0.5, p=0.62), it has consistently increased in TBM patients since 2013 (z=3.09, p=0.002). TBM patients had more than 7 times the odds for overall death in multivariate analysis [OR 7.25 (95% CI 4.64, 11.33), p<0.001]. TBM patients were younger, more likely to present with miliary TB or HIV(+). Age ≥45 years, resident of a long-term care facility, IDU, diabetes, chronic kidney disease, abnormal chest radiograph, positive AFB smear or culture, culture not converted from positive to negative, and HIV(+) were independently associated with a higher mortality.
TBM remains a challenge in Texas with significantly higher mortality. Risk factors determined by multivariate modeling will inform health professionals and lay a foundation for the development of more effective strategies for TBM prevention and management.
D. T. Nguyen,
E. A. Graviss, None