Exploring the views of infection consultants in England on a novel delinked funding model for antimicrobials: the SMASH study
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Bibliographic record
Abstract
Abstract Objectives A novel ‘subscription-type’ funding model was launched in England in July 2022 for ceftazidime/avibactam and cefiderocol. We explored the views of infection consultants on important aspects of the delinked antimicrobial funding model. Methods An online survey was sent to all infection consultants in NHS acute hospitals in England. Results The response rate was 31.2% (235/753). Most consultants agreed the model is a welcome development (69.8%, 164/235), will improve treatment of drug-resistant infections (68.5%, 161/235) and will stimulate research and development of new antimicrobials (57.9%, 136/235). Consultants disagreed that the model would lead to reduced carbapenem use and reported increased use of cefiderocol post-implementation. The presence of an antimicrobial pharmacy team, requirement for preauthorization by infection specialists, antimicrobial stewardship ward rounds and education of infection specialists were considered the most effective antimicrobial stewardship interventions. Under the new model, 42.1% (99/235) of consultants would use these antimicrobials empirically, if risk factors for antimicrobial resistance were present (previous infection, colonization, treatment failure with carbapenems, ward outbreak, recent admission to a high-prevalence setting). Significantly higher insurance and diversity values were given to model antimicrobials compared with established treatments for carbapenem-resistant infections, while meropenem recorded the highest enablement value. Use of both ‘subscription-type’ model drugs for a wide range of infection sites was reported. Respondents prioritized ceftazidime/avibactam for infections by bacteria producing OXA-48 and KPC and cefiderocol for those producing MBLs and infections with Stenotrophomonas maltophilia, Acinetobacter spp. and Burkholderia cepacia. Conclusions The ‘subscription-type’ model was viewed favourably by infection consultants in England.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it