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Record W3118998861 · doi:10.1093/ofid/ofaa439.258

214. Antibiotic Use for Common Infections in British Columbia: A Review of Outpatient Prescribing from 2000 - 2018

2020· review· en· W3118998861 on OpenAlexaffabout
Ariana Saatchi, David M. Patrick, Andrew M. Morris, Michael E. Silverman, Marcus Povitz, Salimah Z. Shariff, Fawziah Marra

Bibliographic record

VenueOpen Forum Infectious Diseases · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of CalgaryWestern UniversityLawson Health Research InstituteUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsMedicineAntimicrobial stewardshipMedical prescriptionPharmacyReimbursementFormularyFamily medicineMedical recordDrug Utilization ReviewAntibioticsStewardship (theology)PopulationAntibiotic resistanceHealth careEnvironmental healthInternal medicineNursing

Abstract

fetched live from OpenAlex

Abstract Background Antimicrobial resistance continues to jeopardize the future of modern medicine; as 92% of all antibiotics are used in the community, it is imperative to parse outpatient prescribing. In British Columbia (BC), efforts to curb the use of these essential medications have included: stewardship campaigns, practitioner guidelines, and vaccine scheduling amendments. This study reviews the trends in antibiotic use over the past two decades to identify new targets for Provincial stewardship and intervention. Methods Antibiotic prescription information was extracted from PharmaNet, a centralized data system that links all pharmacies with prescriptions dispensed in the community setting. The Medical Service Plan records reimbursement claims submitted by physicians for services provided, including diagnostic codes. Antibiotic prescriptions were extracted from PharmaNet and then matched to the billing system using anonymized patient identifiers. Prescription rates were calculated, and trends were examined by major anatomical therapeutic chemical (ATC) classification. Results Our study included 3,564,258 individuals over an 18-year period, with a total of 26,108,576 antibiotic prescriptions issued, for common infections. Overall antibiotic utilization decreased 18% (from 228 to 187 prescriptions per 1000 population) over the course of the study period. This trend was reflected in both Beta-Lactam (-37%) and Macrolide (-50%) antibiotics; two of the most common classes prescribed in the outpatient setting. A significant outlier was the J01X class of Other Antibacterials, which increased by a staggering 218%, by 2018. Further analyses are currently underway to stratify these changes in magnitude by demographic variables to identify specific, new targets for stewardship. Rates of outpatient antibiotic prescriptions, for common infections, per 1000 population, by major ATC class, over time. Conclusion Outpatient antibiotic prescribing has decreased steadily since 2000. These promising results can be ascribed to the various Provincial initiatives to quell the misuse of these medications. However, many of the indications tied to these prescriptions do not warrant the use of antibiotics, and further analyses are necessary to evaluate prescribing quality to fully delineate the state of antibiotic use in BC. Next steps also include comparing BC rates with Ontario, another large province of Canada. Disclosures All Authors: No reported disclosures

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.067
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.019
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.028
GPT teacher head0.293
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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