Moving from intervention management to disease management for Clostridioides difficile infection: an economic evaluation exploring the impact of a systems approach to health technology assessment
Bibliographic record
Abstract
OBJECTIVE: Our objective was to test the impact of assessing the cost-effectiveness of a hypothetical new preventative intervention in combination with a new treatment strategy and to compare this to the conventional approach which typically assesses a new preventative intervention independently from changes to the treatment strategy. METHODS: We used traditional cost-effectiveness methodology and Clostridiodes difficile infection as a case study to identify the optimal implementation strategy for a new vaccine assuming static downstream treatment interventions (conventional approach) vs. in combination with a new (more effective, more costly) treatment. Using a de novo decision-analytic Markov model, we compared the cost-effectiveness of 4 hypothetical vaccine implementation strategies (assuming a static downstream treatment strategy), to the cost-effectiveness of the same 4 hypothetical vaccine implementation strategies, in combination with 6 new downstream treatment strategies. Our optimization targets included maximizing clinical outcomes, minimizing costs, and minimizing the incremental cost-effectiveness ratio. RESULTS: Assuming static downstream treatment strategies (conventional approach), the optimal vaccine strategy was to vaccinate individuals with elective hospital admissions ($41,446/quality adjusted life year [QALY]). When considered in combination with new downstream treatment strategies (disease management approach), the optimal vaccine implementation strategy was to vaccinate long-term care residents and to treat all infections with the new treatment strategy ($18,356/QALY). CONCLUSIONS: Interactions between the implementation of Clostridiodes difficile vaccine and treatment strategies may not be captured with conventional analyses. Considering the dynamics between prevention and treatment interventions can be accomplished with current analytical tools, and doing so could have implications on optimal population-level disease management.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".