What is the optimal community-based Tuberculosis screening algorithm for people who inject drugs in a high burden setting?

Background:
Although people who inject drugs (PWID) are a high risk group for Tuberculosis (TB), current case finding strategies fail to identify most TB cases. There is a need for an optimised community-based algorithm to improve TB detection in such disproportionately affected populations.

Methods:
Using Respondent Driven Sampling, we recruited PWID at community sites in Hai Phong, Vietnam, screening for classic TB symptoms, CRP blood measurement, portable on-site Chest X-Ray with Computer-Aided Detection (CAD4TB) and Xpert MTB/RIF on sputum. Any participant suspected of TB by on-site physicians were referred to the infectious disease hospital for confirmatory testing and external experts validated final diagnoses, which were then used as the TB gold standard. We aimed to identify the screening algorithm with the highest case detection at the lowest cost, among different on-site testing combinations. Ingredients-based costing was used to evaluate the cost per test and cost per case detected for each algorithm.

Results:
Among the 1,080 PWID enrolled, 47 (4.4%; 95CI: 2.8-6.4) were diagnosed with TB disease. Compared to the current symptom-based TB screening strategy in Vietnam (‘Double X’), systematic Chest X-Ray with CAD4TB, Xpert MTB/RIF for those with CAD4TB ≥ 50, and referral to care for those with either CAD4TB ≥ 70 or a positive Xpert test, doubled sensitivity (82.9% vs 43.9%) and yield (3.2% vs 1.7%), while maintaining a reasonable cost per TB case detected (439USD vs 309USD for standard of care).

Conclusions:
We defined an acceptable and moderate cost algorithm that improves efficiency for community-based TB screening among PWID in Vietnam. We make the case that active case finding and systematic screening strategies should not limit testing to those with a positive symptom screen to reflect real TB prevalence.