Screening, isolation, and decolonisation strategies in the control of meticillin resistant Staphylococcus aureus in intensive care units: cost effectiveness evaluation
Bibliographic record
Abstract
OBJECTIVE: To assess the cost effectiveness of screening, isolation, and decolonisation strategies in the control of meticillin resistant Staphylococcus aureus (MRSA) in intensive care units. DESIGN: Economic evaluation based on a dynamic transmission model. SETTING: England and Wales. Population Theoretical population of patients on an intensive care unit. MAIN OUTCOME MEASURES: Infections, deaths, costs, quality adjusted life years (QALYs), incremental cost effectiveness ratios for alternative strategies, and net monetary benefits. RESULTS: All decolonisation strategies improved health outcomes and reduced costs. Although universal decolonisation (regardless of MRSA status) was the most cost effective in the short term, strategies using screening to target MRSA carriers may be preferred owing to the reduced risk of selecting for resistance. Among such targeted strategies, universal admission and weekly screening with polymerase chain reaction coupled with decolonisation using nasal mupirocin was the most cost effective. This finding was robust to the size of intensive care units, prevalence of MRSA on admission, proportion of patients classified as high risk, and precise value of willingness to pay for health benefits. All strategies using isolation but not decolonisation improved health outcomes but costs were increased. When the prevalence of MRSA on admission to the intensive care unit was 5% and the willingness to pay per QALY gained was between £20,000 (€23,000; $32,000) and £30,000, the best such strategy was to isolate only those patients at high risk of carrying MRSA (either pre-emptively or after identification by admission and weekly screening for MRSA using chromogenic agar). Universal admission and weekly screening using polymerase chain reaction based detection of MRSA coupled with isolation was unlikely to be cost effective unless prevalence was high (10% of patients colonised with MRSA on admission). CONCLUSIONS: MRSA control strategies that use decolonisation are likely to be cost saving in an intensive care unit setting provided resistance is lacking, and combining universal screening using polymerase chain reaction with decolonisation is likely to represent good value for money if untargeted decolonisation is considered unacceptable. In intensive care units where decolonisation is not implemented, evidence is insufficient to support universal screening for MRSA outside high prevalence settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".