How Endpoint Definitions Shape Epidemiologic Inference on Tuberculosis Treatment Outcomes: A Secondary Analysis from...

Tuberculosis treatment outcomes are often dichotomized into heterogeneous composites that may obscure outcome-specific associations. We compared baseline characteristics across mutually exclusive out-comes-loss to follow-up (LTFU), death, and not evaluated/transferred versus documented cure-and examined the composite endpoints secondarily in a prospective cohort of adults with smear-positive pulmonary tuberculosis treated in Lima, Peru, in 2010-2011.

The complete-case cohort included 1233 participants. The primary analysis used ridge-penalized multinomial logistic regression (lambda = 0.16); a mean bias-reduced multinomial model assessed sparse-data sensitivity.

Overall, 1016 participants were cured, 127 experienced LTFU, 30 died, and 60 were not evaluated/transferred. Drug use was associated primarily with LTFU (aOR 4.15; 95% CI 2.55-6.75), whereas HIV-positive status showed the largest, although imprecisely estimated, association with death (aOR 32.12; 95% CI 8.95-115.33; six deaths among 22 HIV-positive participants). The bias-reduced estimate was similar (aOR 35.56; 95% CI 9.80-129.08). Multidrug-resistant tuberculosis was associated with several non-cure categories; its association with the combined not evaluated/transferred category was largely driven by participants still in treatment at database closure. Composite endpoints changed the magnitude and interpretation of several associations and did not eliminate sparse outcome-exposure cells.

Endpoint definitions therefore materially shaped epidemiologic inference. Dis-aggregated outcomes should be reported alongside composite indicators, and findings from this historical cohort should not be interpreted as contemporary risk estimates for Lima.