Impact of infection control interventions on rates of Staphylococcus aureus bacteraemia in National Health Service acute hospitals, East Midlands, UK, using interrupted time-series analysis
Bibliographic record
Abstract
BACKGROUND: Reducing healthcare-associated infection (HCAI) is a UK national priority. Multiple national and regional interventions aimed at reduction have been implemented in National Health Service acute hospitals, but assessment of their effectiveness is methodologically challenging. AIM: To assess the effectiveness of national and regional interventions undertaken between 2004 and 2008 on rates of meticillin-resistant Staphylococcus aureus (MRSA) and meticillin-sensitive Staphylococcus aureus (MSSA) bacteraemia within acute hospitals in the East Midlands, using interrupted time-series analysis. METHODS: We used segmented regression to compare rates of MRSA and MSSA bacteraemia in the pre-intervention, implementation, and post-intervention phases for combined intervention packages in eight acute hospitals. FINDINGS: Most of the change in MSSA and MRSA rates occurred during the implementation phase. During this phase, there were significant downward trends in MRSA rates for seven of eight acute hospital groups; in four, this was a steeper quarter-on-quarter decline compared with the pre-intervention phase, and, in one, an upward trend in the pre-intervention phase was reversed. Regarding MSSA, there was a significant positive effect in four hospital groups: one upward trend during the pre-intervention phase was reversed, two upward trends plateaued, and in one hospital group an indeterminate trend decreased significantly. However, there were significant increasing trends in quarterly MSSA rates in four hospital groups during the implementation or post-intervention periods. CONCLUSION: The impact of interventions varied by hospital group but the overall results suggest that national and regional campaigns had a beneficial impact on MRSA and MSSA bacteraemia within the East Midlands.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".