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Record W3096096326 · doi:10.1017/ice.2020.534

Significant Regional Differences in Antibiotic Use Across 576 US Hospitals and 11,701,326 Million Admissions, 2016–2017

2020· article· en· W3096096326 on OpenAlexaff
Katherine E Goodman, Sara E. Cosgrove, Lisa Pineles, Laurence S. Magder, Deverick J. Anderson, Elizabeth Dodds Ashley, Ronald E. Polk, Hude Quan, William E. Trick, Keith F. Woeltje, Surbhi Leekha, Anthony Harris

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineAntibioticsRetrospective cohort studyEmergency medicineAntibiotic resistanceCohortPediatricsInternal medicine

Abstract

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Background: Reducing inappropriate antibiotic use is critical for fighting antibiotic resistance. Quantifying the amount and diversity of antibiotic use in US hospitals is foundational to these efforts but hampered by limited national surveillance. The current study aims to address this knowledge gap by examining adult inpatient antibiotic usage, including regional, facility, and case-mix differences, across 576 hospitals and nearly 12 million encounters in 2016–2017. Methods: We conducted a retrospective cohort study of patients aged ≥18 years discharged from hospitals in the Premier Healthcare Database, a repository of nearly 1 of every 4 annual US hospitalizations, between January 1, 2016, and December 31, 2017. Detailed hospital- and patient-level data were extracted for each admission. Facilities were classified geographically by census division. Using daily antibiotic charge data, we mapped antibiotics to 18 mutually exclusive classes and to categories based upon spectrum of activity. Patient-level data were transformed into hospital case-mix variables (eg, hospital mean patient age), and relationships between antibiotic days of therapy (DOTs), and these and other facility-level variables were evaluated in negative binomial regression models. Results: The study included 11,701,326 adult admissions, totaling 64,064,632 patient days across 576 US hospitals. Overall, antibiotics were used in 65% of all hospitalizations, at a rate of 870 DOTs per 1,000 patient days. The most commonly used classes per patient days were β-lactam/β-lactamase inhibitor combinations (206 DOTs), third- and fourth-generation cephalosporins (128 DOTs), and glycopeptides (113 DOTs) (Fig. 1). By spectrum of activity, antipseudomonal agents (245 DOTs) were the most common. Crude usage rates varied by geographic region (Fig. 2). In multivariable analyses, teaching hospitals, and/or larger bed sizes were independently associated with lower use across a range of antibiotic classes (adjusted IRR ranges, 0.90–0.94 and 0.96–0.98, respectively). Significant regional differences also persisted. Compared to the South Atlantic region (chosen as the reference category because it had the largest representation in the cohort), rates of total antibiotic use were 6%, 15%, and 18% lower on average in the Pacific, New England, and the Middle Atlantic regions, respectively. By class, carbapenems reflected the most geographic variability. Conclusions: In a large, diverse cohort of US hospitals, adult inpatients received antibiotics at a rate similar to, but higher than, previously published estimates. In adjusted models, lower antibiotic use was frequently associated with facilities likely to have robust antibiotic stewardship programs—those with teaching status and larger bed size. Further research to understand other reasons for regional differences in antibiotic use such as different rates of resistance is needed. Funding: This work was supported by Funding: from the Agency for Healthcare Research and Quality (AHRQ) (R01-HS026205 to A.D.H.). Disclosures: None

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.282
Teacher spread0.246 · 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
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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