Inclusion of non-medical interventions in model-based economic evaluations for tuberculosis: A scoping review
Bibliographic record
Abstract
BACKGROUND: The economic evaluation of health interventions is important in priority setting. Several guidance documents exist to support the conduct of economic evaluations, however, there is limited guidance for the evaluation of non-medical interventions. For tuberculosis (TB), where equity-deserving groups are disproportionately impacted, assessing interventions aimed at addressing social risk factors is necessary to effectively reduce TB burden. OBJECTIVE: This scoping review seeks to assess the existing literature on model-based economic evaluations of TB interventions to gauge the extent to which non-medical interventions have been evaluated in low-TB-incidence jurisdictions. As a secondary objective, this review aims to characterize key features of existing economic evaluations of medical and non-medical interventions. METHODS: A literature search was conducted in the grey literature and MEDLINE, Embase, EconLit, and PsychINFO databases to September 6, 2022 following the Arksey and O'Malley framework. Eligible articles were those that used decision-analytic modeling for economic evaluation of TB interventions in low-TB-incidence jurisdictions. RESULTS: This review identified 127 studies that met the inclusion criteria; 11 focused on prevention, 73 on detection, and 43 on treatment of TB. Only three studies (2%) evaluated non-medical interventions, including smoking reduction strategies, improving housing conditions, and providing food vouchers. All three non-medical intervention evaluations incorporated TB transmission and robust uncertainty analysis into the evaluation. The remainder of the studies evaluated direct medical interventions, eight of which were focused on specific implementation components (e.g., video observed therapy) which shared similar methodological challenges as the non-medical interventions. The majority of remaining evaluated medical interventions were focused on comparing various screening programs (e.g., immigrant screening program) and treatment regimens. CONCLUSIONS: This scoping review identified a gap in literature in the evaluation of non-medical TB interventions. However, the identified articles provided useful examples of how economic modeling can be used to explore non-traditional interventions using existing economic evaluation methods.
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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 | 0.112 | 0.372 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".