Trends and forecast of drug-resistant tuberculosis: a global perspective from the GBD study 2021 - Yi Guo; Jiacheng Li; Lihong Lin (Frontiers in Public Health)
BackgroundTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. Drug-resistant tuberculosis (DRTB) includes multidrug-resistant tuberculosis without extensive drug resistance (MDRTB) and extensively drug-resistant tuberculosis (EDRTB). Recently, with the continued rise of DRTB, global TB prevention and control efforts have faced even greater challenges.AimsThis study aimed to quantify the changes in age-standardized incidence rate (ASIR) of two types of DRTB from 1991 to 2021 using the Global Burden of Disease (GBD) database, and to examine the epidemiological differences across various regions and countries and applied the autoregressive integrated moving average (ARIMA) model to predict the epidemiological trends of MDRTB and EDRTB from 2022 to 2030.MethodsData were extracted from the GBD database from 1991 to 2021. Estimated annual percentage changes (EAPC) in DRTB ASIR by regions, were calculated to quantify the temporal trends. ARIMA model was applied to predict ASIR between 2022 and 2030.ResultsFrom 1991 to 2021, the global composition of DRTB shifted, with EDRTB increasing in developed regions and MDRTB remaining dominant in regions like sub-Saharan Africa. The highest ASIRs for MDRTB in 2021 were seen in Somalia, while the highest for EDRTB were in Moldova. Significant regional variations were observed, with East Asia showing a decrease in MDRTB and Oceania experiencing large increases in both MDRTB and EDRTB. Additionally, country-specific trends varied widely, with Slovenia showing the greatest decrease in MDRTB and Papua New Guinea the largest increase in EDRTB.ConclusionThis study highlights the ongoing dominance of MDRTB in low SDI regions and the expected decline of EDRTB in high SDI regions due to improved treatments and diagnostics. Global predictions suggest a reduction in DRTB burden by 2030, with a focus on early diagnosis and treatment optimization.