Trends (2014–2023), forecasting (2024–2026), and risk factors of drug-resistant tuberculosis in Iran: a negative binomial time-series generalized linear model

Abstract

Drug-resistant tuberculosis (DR-TB) remains a major challenge for achieving the End TB Strategy. This study aimed to examine temporal trends of DR-TB in Iran, identify demographic and clinical risk factors, and forecast future incidence to support public health planning. A time series study of national surveillance data was conducted using national tuberculosis surveillance data from 2014 to 2023, including 62,899 patients. Temporal trends of drug-resistant TB were first described. Multivariable logistic regression was then applied to identify demographic and clinical risk factors. Finally, future case counts was forecast using a Negative Binomial Time Series Generalized Linear Model (NB-TS-GLM), a method suitable for rare-event count data with temporal dependence. Overall, 389 patients (0.62%) had DR-TB, with incidence rising from 3.41 to 8.39 per 1,000 TB cases by 2023. Retreatment (AOR = 9.81, p < 0.001), smear positivity (AOR = 4.59, p < 0.001), and time (AOR = 1.06, p = 0.004) were significant predictors. The NB-TS-GLM forecasted 121 DR-TB cases by 2026. DR-TB incidence in Iran shows a stable but slightly increasing trend. Retreatment patients, smear-positive, and those with TB contact history are at the highest risk. Predictive modeling provides reliable estimates for resource allocation and highlights the need for early detection and targeted interventions.