A Descriptive Analysis of Outpatient Antimicrobial Use for Urinary Tract Infections in Virginia
Bibliographic record
Abstract
Background: Data regarding outpatient antibiotic prescribing for urinary tract infections (UTIs) are limited, and they have never been formally summarized in Virginia. Objective: We describe outpatient antibiotic prescribing trends for UTIs based on gender, age, geographic region, insurance payer and International Classification of Disease, Tenth Revision (ICD-10) codes in Virginia. Methods: We used the Virginia All-Payer Claims Database (APCD), administered by Virginia Health Information (VHI), which holds data for Medicare, Medicaid, and private insurance. The study cohort included Virginia residents who had a primary diagnosis of UTI, had an antibiotic claim 0–3 days after the date of the diagnosis and who were seen in an outpatient facility in Virginia between January 1, 2016, and December 31, 2016. A diagnosis of UTI was categorized as cystitis, urethritis or pyelonephritis and was defined using the following ICD-10 codes: N30.0, N30.00, N30.01, N30.9, N30.90, N30.91, N39.0, N34.1, N34.2, and N10. The following antibiotics were prescribed: aminoglycosides, sulfamethoxazole/trimethoprim (TMP-SMX), cephalosporins, fluoroquinolones, macrolides, penicillins, tetracyclines, or nitrofurantoin. Patients were categorized based on gender, age, location, insurance payer and UTI type. We used χ2 and Cochran-Mantel-Haenszel testing. Analyses were performed in SAS version 9.4 software (SAS Institute, Cary, NC). Results: In total, 15,580 patients were included in this study. Prescriptions for antibiotics by drug class differed significantly by gender (P < .0001), age (P < .0001), geographic region (P < .0001), insurance payer (P < .0001), and UTI type (P < .0001). Cephalosporins were prescribed more often to women (32.48%, 4,173 of 12,846) than to men (26.26%, 718 of 2,734), and fluoroquinolones were prescribed more often to men (53.88%, 1,473 of 2,734) than to women (47.91%, 6,155 of 12,846). Although cephalosporins were prescribed most frequently (42.58%, 557 of 1,308) in northern Virginia, fluoroquinolones were prescribed the most in eastern Virginia (50.76%, 1677 of 3,304). Patients with commercial health insurance, Medicaid, and Medicare were prescribed fluoroquinolones (39.31%, 1,149 of 2,923), cephalosporins (56.33%, 1,326 of 2,354), and fluoroquinolones (57.36%, 5,910 of 10,303) most frequently, respectively. Conclusions: Antibiotic prescribing trends for UTIs varied by gender, age, geographic region, payer status and UTI type in the state of Virginia. These data will inform future statewide antimicrobial stewardship efforts. Funding: None Disclosures: Michelle Doll reports a research grant from Molnlycke Healthcare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".