Systematic differences in TB treatment outcomes across Brazil by patient- and area-related factors: an analysis of national disease registry data

Background Many individuals initiating tuberculosis (TB) treatment do not successfully complete the regimen. Understanding variation in treatment outcomes could reveal opportunities to improve the effectiveness of TB treatment services. Methods We extracted data on treatment outcomes and patient covariates from Brazil's National Disease Notification Information System, for new TB patients diagnosed during 2015-2018. We analysed whether or not patients experienced an unsuccessful treatment outcome (any death on treatment, loss to follow-up or treatment failure). We constructed a statistical model (logistic regression with regularised two-way interactions) to predict treatment outcomes as a function of socio-demographic factors, co-prevalent health conditions, health behaviours, membership of vulnerable populations and form of TB disease. We used this model to decompose state- and municipality-level variation in treatment outcomes into differences attributable to patient-level and area-level factors. Results Treatment outcomes data for 259 449 individuals were used for the analysis. Across Brazilian states, variation in unsuccessful treatment due to patient-level factors was substantially less than variation due to area-level factors, with the difference between best and worst performing states (lowest and highest fraction with unsuccessful treatment, respectively) equal to 7.1 and 13.3 percentage points for patient-level and area-level factors. Similar results were estimated at the municipality level, with 9.3 percentage points separating best and worst performing municipalities according to patient-level factors, and 20.5 percentage points separating best and worst performing municipalities to area-level factors. Results were similar when we analysed loss to follow-up as an outcome. Conclusions Our analysis revealed substantial variation in TB treatment outcomes across states and municipalities, with only a minority attributable to patient-level factors. Area-level variation likely reflects consequences of differences in health system organisation or socio-environmental factors not reflected in patient-level data. Further research on these factors is needed to identify effective approaches to TB care, reduce geographic disparities and improve treatment outcome.