The Economic Impact of Clostridium difficile Infection: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: With Clostridium difficile infection (CDI) on the rise, knowledge of the current economic burden of CDI can inform decisions on interventions related to CDI. We systematically reviewed CDI cost-of-illness (COI) studies. METHODS: We performed literature searches in six databases: MEDLINE, Embase, the Health Technology Assessment Database, the National Health Service Economic Evaluation Database, the Cost-Effectiveness Analysis Registry, and EconLit. We also searched gray literature and conducted reference list searches. Two reviewers screened articles independently. One reviewer abstracted data and assessed quality using a modified guideline for economic evaluations. The second reviewer validated the abstraction and assessment. RESULTS: We identified 45 COI studies between 1988 and June 2014. Most (84%) of the studies were from the United States, calculating costs of hospital stays (87%), and focusing on direct costs (100%). Attributable mean CDI costs ranged from $8,911 to $30,049 for hospitalized patients. Few studies stated resource quantification methods (0%), an epidemiological approach (0%), or a justified study perspective (16%) in their cost analyses. In addition, few studies conducted sensitivity analyses (7%). CONCLUSIONS: Forty-five COI studies quantified and confirmed the economic impact of CDI. Costing methods across studies were heterogeneous. Future studies should follow standard COI methodology, expand study perspectives (e.g., patient), and explore populations least studied (e.g., community-acquired CDI).
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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".