The Global Expansion of LTBI Screening and Treatment Programs: Exploring Gaps in the Supporting Economic Evidence
Bibliographic record
Abstract
The global burden of latent TB infection (LTBI) and the progression of LTBI to active TB disease are important drivers of ongoing TB incidence. Addressing LTBI through screening and TB preventive treatment (TPT) is critical in order to end the TB epidemic by 2035. Given the limited resources available to health ministries around the world in the fight against TB, we must consider economic evidence for LTBI screening and treatment strategies to ensure that limited resources are used to achieve the biggest health impact. In this narrative review, we explore key economic evidence around LTBI screening and TPT strategies in different populations to summarize our current understanding and highlight gaps in existing knowledge. When considering economic evidence supporting LTBI screening or evaluating different testing approaches, a disproportionate number of economic studies have been conducted in high-income countries (HICs), despite the vast majority of TB burden being borne in low- and middle-income countries (LMICs). Recent years have seen a temporal shift, with increasing data from low- and middle-income countries (LMICs), particularly with regard to targeting high-risk groups for TB prevention. While LTBI screening and prevention programs can come with extensive costs, targeting LTBI screening among high-risk populations, such as people living with HIV (PLHIV), children, household contacts (HHC) and immigrants from high-TB-burden countries, has been shown to consistently improve the cost effectiveness of screening programs. Further, the cost effectiveness of different LTBI screening algorithms and diagnostic approaches varies widely across settings, leading to different national TB screening policies. Novel shortened regimens for TPT have also consistently been shown to be cost effective across a range of settings. These economic evaluations highlight key implementation considerations such as the critical nature of ensuring high rates of adherence and completion, despite the costs associated with adherence programs not being routinely assessed and included. Digital and other adherence support approaches are now being assessed for their utility and cost effectiveness in conjunction with novel shortened TPT regimens, but more economic evidence is needed to understand the potential cost savings, particularly in settings where directly observed preventive therapy (DOPT) is routinely conducted. Despite the growth of the economic evidence base for LTBI screening and TPT recently, there are still significant gaps in the economic evidence around the scale-up and implementation of expanded LTBI screening and treatment programs, particularly among traditionally hard-to-reach populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".