Correcting drug-resistance prevalence in tuberculosis using a predictive model of diagnostic access
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Background
Drug resistance (DR) poses a major challenge to tuberculosis (TB) elimination. Incomplete access to diagnostic tests can lead to under detection and biased DR-TB prevalence indicators. This study aimed to estimate the prevalence of DR-TB, identified through any available diagnostic method, while correcting for disparities in access to rapid molecular testing ( Xpert ® MTB/RIF ), used as a proxy for overall diagnostic capacity.
Methods
We conducted a cross-sectional study of new TB cases in individuals aged over 18 (n=406,331), using 2015-2020 data from the Brazilian Information System on Notifiable Diseases (SINAN). We assumed that access to Xpert ® MTB/RIF was an indicator of better diagnostic conditions for detecting drug resistance in general, because patients who underwent this test had a higher prevalence of all forms of resistance (not just to rifampicin). We developed a multilevel mixed-effects logistic regression model, incorporating individual- and municipality-level variables, to predict access to Xpert ® MTB/RIF testing in a random sample of 81,027 observations. The model was validated using out-of-sample observations across geographic areas. We then used inverse probability weighting based on predicted access to calculate corrected estimates of DR-TB prevalence.
Results
The model showed good performance (AUC=80·93%, 95%CI: 80·71%-81·33%). The prevalence of DR-TB reported by the surveillance system was 1·54% (95%CI: 1·50%-1·57%). Among patients tested with Xpert ® MTB/RIF , DR-TB prevalence was 3·86% (95%CI: 3·75%-3·96%), and the corrected weighted prevalence was 5·95% (95%CI: 5·54%-6·38%).
Conclusions
The prevalence of DR-TB may be substantially underestimated due to uneven access to testing. Our approach highlights the importance of correcting for diagnostic access to improve surveillance indicators.
