Journal of general internal medicine
October 14, 2026 · via Europe PMC
Racial Differences in Respiratory Tract Infection Diagnosis and Antibiotic Prescriptions.
Martinez KA, Rezaee A, Criswell V, Rothberg MB.
AI summary — for doctors, generated from the abstract only
Lower antibiotic prescribing for non-White patients with respiratory tract infections is partly explained by differences in the diagnosis assigned, particularly sinusitis.
What was studied
Observational cohort of adults at Cleveland Clinic urgent care diagnosed with an RTI from 2016 to 2022 (100,052 visits, 62,972 patients), using mixed effects logistic regression with and without accounting for diagnosis type.
Key findings
- Without accounting for diagnosis, non-White patients were less likely to receive an antibiotic (aOR 0.79; 95% CI 0.76-0.82); after adjustment it was weaker but significant (aOR 0.91; 0.87-0.95).
- Non-White patients were less likely to be diagnosed with sinusitis versus unspecified RTI (aOR 0.61; 0.56-0.66).
- Non-White patients were less likely to be prescribed antibiotics for both sinusitis and unspecified RTI.
Clinical relevance
Suggests that diagnostic labelling, and potentially bias or differences in documentation, contributes to prescribing disparities; the abstract does not say which prescribing is appropriate.
Who should care
Limitations
Observational, single health system; race was dichotomised as White versus non-White (84% vs 16%), and appropriateness of prescribing or clinical severity is not assessed.
Diagnosis differences explain only part of the antibiotic prescribing gap by race, leaving a residual disparity.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Non-White patients are less likely than White patients to be prescribed antibiotics for respiratory tract infections (RTIs). Our objective was to understand whether this could be explained by differences in RTI diagnosis type by race.
Methods
We conducted an observational cohort study of adults receiving urgent care at Cleveland Clinic in Northeast Ohio who were diagnosed with an RTI between 2016 and 2022. We used mixed effects logistic regression of antibiotic prescription by patient race (White versus non-White) in two models: 1) not accounting for RTI diagnosis type, and 2) accounting for diagnosis type. We used mixed effects logistic regression, including physician and individual patient as random effects, to assess differences in the odds of being diagnosed with sinusitis versus unspecified RTI by race. Finally, we assessed odds of being prescribed an antibiotic by race, stratified by diagnosis. Analyses were conducted at the visit level.
Results
Among 100,052 visits with 62,972 unique patients (median visits per patient was 1), 21% percent were for sinusitis and 30% were for unspecified RTI. Eighty-four percent of patients were White and 16% were non-White. In the model not controlling for diagnosis, compared to White patients, non-White patients were significantly less likely to be prescribed an antibiotic (aOR: 0.79; 95%CI:0.76-0.82). After adjusting for diagnosis, the association between non-White race and antibiotic prescription was weaker but still significant (aOR:0.91; 95%CI:0.87-0.95). Compared to White patients, non-White patients were significantly less likely to be diagnosed with sinusitis versus unspecified RTI (aOR:0.61; 95%CI:0.56-0.66). Non-White patients were significantly less likely to be prescribed antibiotics for sinusitis and unspecified RTI than White patients.
Conclusions
Racial differences in diagnosis of sinusitis partially explain lower rates of antibiotic prescription among non-White patients.
DOI
10.1007/s11606-026-10930-5
PMID
42875615
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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