P-613. Direct Impact of Pediatric Immunization on Reduction in Antibiotic Prescribing in the United States
Bibliographic record
Abstract
Abstract Background Immunization programs, by preventing infectious diseases for which antibiotics are prescribed, have the potential to reduce antibiotic use and development of antimicrobial resistance. However, limited data are available regarding the impact of vaccines on antibiotic prescribing at the population level. This study evaluated the potential impact of pediatric immunization on prescribed antibiotic treatment courses in the United States (US).Table 1.Antibiotic Usage Assumptions by Disease Methods A previously published decision tree model was used to estimate disease cases averted in the total US population by the routine childhood immunization schedule. Antibiotic courses for the 14 vaccine-preventable diseases (VPDs) covered by the US immunization schedule estimated from disease- or syndrome-specific real-world prescribing rates (where available) and clinical practice guidelines (Table 1); importantly, real-world prescribing rates include antibiotics prescribed for viral VPD cases, whether or not secondary bacterial infections are definitively diagnosed. Antibiotic prescription rates were multiplied by disease cases averted to estimate antimicrobial courses averted in a single year for the 2023 total US population.Table 2.Annual Reduction in Antibiotic Treatment Courses for the 2023 US Population Results Routine childhood immunization was estimated to have averted 9.2 million annual antibiotic courses in the 2023 US population (Table 2). The number of antibiotic courses averted were largest for pneumococcus (4.8 million), pertussis (2.2 million), and measles (1.1 million). Over 90% reduction in antibiotic courses was estimated for 8 of the 14 modeled VPDs. Residual annual antibiotic courses due to VPDs in 2023 remained highest for pneumococcus (1.6 million), influenza (0.9 million), and pertussis (0.2 million). Conclusion Routine pediatric immunization in the US results in reduced annual antibiotic prescribing. The estimates provided herein represent only the direct impact on antibiotic prescribing because the model did not account for potential reductions in antibiotic prescribing for fully vaccinated children presenting with nonspecific febrile illnesses, in whom the likelihood of bacterial VPD would be lower. Disclosures Justin Carrico, BS, Merck & Co., Inc.: Advisor/Consultant Sandra E. Talbird, MSPH, Merck & Co., Inc.: Advisor/Consultant Amanda Eiden, PhD, MBA, MPH, Merck & Co., Inc.: Stocks/Bonds (Private Company) Cristina Carias, PhD, Merck & Co., Inc.: Stocks/Bonds (Private Company) Min Huang, PhD, Merck & Co., Inc.: Employee|Merck & Co., Inc.: Stocks/Bonds (Public Company) John C. Lang, PhD, MSc, MSc, BSc, Merck & Co., Inc.: Stocks/Bonds (Private Company)|Merck Canada Inc.: Employee Gary S. Marshall, MD, GSK: Advisor/Consultant|GSK: Grant/Research Support|GSK: Honoraria|Merck & Co., Inc.: Advisor/Consultant|Merck & Co., Inc.: Grant/Research Support|Merck & Co., Inc.: Honoraria|Moderna: Advisor/Consultant|Moderna: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Pfizer: Honoraria|Sanofi: Advisor/Consultant|Sanofi: Grant/Research Support|Sanofi: Honoraria|Seqirus: Advisor/Consultant|Seqirus: Grant/Research Support|Seqirus: Honoraria Goran Bencina, PhD, Merck & Co., Inc.: Stocks/Bonds (Private Company)
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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 | 0.000 | 0.000 |
| 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".