Antibiotic prescribing for care-home residents: a population-based, cross-classified multilevel analysis in Scotland, UK
Bibliographic record
Abstract
BACKGROUND: There is wide variation in antibiotic prescribing across care-homes for older people, with implications for resident outcomes and antimicrobial resistance. OBJECTIVE: To quantify variation in antibiotic prescribing and associations with resident, care-home and general practice characteristics. DESIGN: Population-based analyses using administrative data. SETTING AND SUBJECTS: 148 care-homes in two Scottish regions, with 6633 residents registered with 139 general practices. METHODS: Prescriptions for any antibiotic and for broad-spectrum antibiotics between 1 April 2016 and 31 March 2017 were analysed using cross-classified multilevel negative binomial regression. RESULTS: For any antibiotics, the mean prescription rate was 6.61 (SD 3.06) per 1000 resident bed-days (RBD). In multivariate analysis, prescribing was associated with resident age [incidence rate ratio (IRR) 1.30 [95% confidence interval 1.19 to 1.41] for 90+ versus <80 years old] and comorbidity (1.88 [1.71 to 2.06] for Charlson Comorbidity Index 3+ versus 0), and the care-home's sampling rate for microbiological culture (1.53 [1.28 to 1.84] for >7 versus <3.5 samples per 1000 RBD), with residual unexplained variation between care-homes (median IRR 1.29 [1.23 to 1.36]) and general practices (1.11 [1.05 to 1.18]). For broad-spectrum antibiotics, the mean rate was 0.98 (0.92) per 1000 RBD. Broad-spectrum prescribing was also associated with resident age, sex, comorbidity and sampling rate, with larger residual unexplained variation between care-homes (1.56 [1.36 to 1.77]) and general practices (1.51 [1.31 to 1.72]). CONCLUSION: Variation in prescribing was influenced by resident case-mix, but there is significant unexplained variation between care-homes and between general practices, indicating a need for antibiotic stewardship to target both.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".