Antibiotic use in children: Assessing the risk of methicillin resistance using different study designs
Bibliographic record
Abstract
Children have generally received a considerable number of outpatient antibacterial prescriptions, but recent trends in utilization are unknown.Concurrently, resistance to methicillin has been increasingly reported for infections with Staphylococcus aureus in the community, but an association with antibacterials has not been shown for children.In assessing this association, within-subject study designs such as the case-time-control can control exposure time trends in addition to unmeasured and unmeasurable stable confounders.The efficiency of such analyses has not yet been assessed for matched casecontrol data.The objectives of this thesis are to study the patterns of antibacterial use in outpatient children, to assess the association between antibacterial prescriptions and the risk of methicillin-resistant Staphylococcus aureus (MRSA) in children in the community using a matched case-control design, and to establish the statistical efficiency of casecrossover and case-time-control analyses from these data.Using the UK General Practice Research Database, I identified the cohort of all children aged 0-19 years and their antibacterial prescriptions from 1993-2007.I described current use and changes with a Poisson model.Next, I obtained odds ratios for the risk of MRSA diagnoses in children prescribed antibiotics compared to non-users from conditional logistic regression in a matched case-control study nested in this cohort.I then compared the standard error (SE) with those from case-crossover and case-timecontrol analyses.The cohort included 1,751,645 children with 5,835,891 antibiotic prescriptions.After 2000, prescribing rates increased steadily to 568/1000 person-years (95% confidence interval (CI) 559-577) in 2007.This increase was largest in boys and girls aged 1-4 years and similar for most classes of antibacterials.From 1994-2007, 297 children were diagnosed with MRSA in the cohort to which 9,357 controls were matched.The adjusted rate ratio (RR) of MRSA with any prescription was 3.5 (95% CI 2.6 -4.8).The risk generally increased with increasing numbers of prescriptions.It also varied for different antibacterial classes.Results were robust in sensitivity analyses.Of 297 cases, 60 and 28 received antibacterials only during risk and control period, respectively, leading to a case-crossover odds ratio (OR) of 2.1, 95% CI 1.4 -3.4,SE 1.
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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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".