Impact of Case Detection and COVID-19-Related Disruptions on Tuberculosis in Vietnam: A Modeling Analysis

Background Vietnam, a high-burden tuberculosis (TB) country, experienced marked declines in TB notifications during the COVID-19 pandemic. We assessed the impact of pandemic-related disruptions on TB case detection and transmission using a dynamic transmission model calibrated to local demographic and epidemiological observations. Methods We developed an age-structured compartmental TB transmission model to estimate COVID-19's impact on TB in Vietnam. Four model assumptions reflecting reductions in detection and/or transmission were calibrated to notification data, with the best-fitting assumption used for future projections and to evaluate the effects of enhanced case detection scenarios. Results COVID-19 significantly disrupted TB services in Vietnam, resulting in an estimated 2000 additional TB episodes (95% credible interval [CrI]: 200-5100) and 1100 TB-related deaths (95% CrI: 100-2700) in 2021. By 2035, the cumulative impact of these disruptions could reach 22 000 additional TB episodes (95% CrI: 2200-63 000) and 5900 deaths (95% CrI: 600-16 600) by 2035. We predicted two hypothetical scenarios of enhancing TB case detection. Under the ambitious scenario, enhancing TB case detection could mitigate these potential impacts by preventing 17.8% of new TB episodes (95% CrI: 13.1%-21.9%) and 34.2% (95% CrI: 31.5%-37.0%) of TB-related deaths by 2035, compared with no enhancement. Conclusions COVID-19-related disruptions have hindered TB detection in Vietnam, likely causing long-term increases in new TB episodes and deaths. However, the uncertainty around these effects is considerable. Sustained investment in diagnostics, system resilience, and patient-centric policies has the potential to achieve benefits that are substantially larger than these pandemic-related setbacks.