Antibiotic Prescribing for Nonbacterial Acute Upper Respiratory Infections in Elderly Persons
Bibliographic record
Abstract
BACKGROUND: Reducing inappropriate antibiotic prescribing for acute upper respiratory tract infections (AURIs) requires a better understanding of the factors associated with this practice. OBJECTIVE: To determine the prevalence of antibiotic prescribing for nonbacterial AURIs and whether prescribing rates varied by physician characteristics. DESIGN: Retrospective analysis of linked administrative health care data. SETTING: Primary care physician practices in Ontario, Canada (January-December 2012). PATIENTS: Patients aged 66 years or older with nonbacterial AURIs. Patients with cancer or immunosuppressive conditions and residents of long-term care homes were excluded. MEASUREMENTS: Antibiotic prescriptions for physician-diagnosed AURIs. A multivariable logistic regression model with generalized estimating equations was used to examine whether prescribing rates varied by physician characteristics, accounting for clustering of patients among physicians and adjusting for patient-level covariates. RESULTS: The cohort included 8990 primary care physicians and 185 014 patients who presented with a nonbacterial AURI, including the common cold (53.4%), acute bronchitis (31.3%), acute sinusitis (13.6%), or acute laryngitis (1.6%). Forty-six percent of patients received an antibiotic prescription; most prescriptions were for broad-spectrum agents (69.9% [95% CI, 69.6% to 70.2%]). Patients were more likely to receive prescriptions from mid- and late-career physicians than early-career physicians (rate difference, 5.1 percentage points [CI, 3.9 to 6.4 percentage points] and 4.6 percentage points [CI, 3.3 to 5.8 percentage points], respectively), from physicians trained outside of Canada or the United States (3.6 percentage points [CI, 2.5 to 4.6 percentage points]), and from physicians who saw 25 to 44 patients per day or 45 or more patients per day than those who saw fewer than 25 patients per day (3.1 percentage points [CI, 2.1 to 4.0 percentage points] and 4.1 percentage points [CI, 2.7 to 5.5 percentage points], respectively). LIMITATION: Physician rationale for prescribing was unknown. CONCLUSION: In this low-risk elderly cohort, 46% of patients with a nonbacterial AURI were prescribed antibiotics. Patients were more likely to receive prescriptions from mid- or late-career physicians with high patient volumes and from physicians who were trained outside of Canada or the United States. PRIMARY FUNDING SOURCE: Ontario Ministry of Health and Long-term Care, Academic Medical Organization of Southwestern Ontario, Schulich School of Medicine and Dentistry, Western University, and Lawson Health Research Institute.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".