Association of comorbidity profiles with antibiotic selection for urinary tract infections in women: a real-world cross-sectional study from a primary care setting in Zanjan, Iran

Urinary tract infections (UTIs) are a major reason for antibiotic prescribing in women worldwide. In low-resource settings, little is known about how patient comorbidity profiles influence antibiotic prescribing in primary care. We conducted a retrospective cross-sectional study of 510 adult women with clinician-documented uncomplicated UTIs (January–June 2024) at a primary care clinic in Zanjan, Iran. Antibiotics were grouped as cefalexin, other first-line agents (nitrofurantoin, trimethoprim-sulfamethoxazole), amoxicillin, or other antibiotics. Multinomial logistic regression was the primary analysis. Model 1 included individual comorbidities; Model 2 included a composite variable (≥ 2 metabolic comorbidities: diabetes, hypertension, dyslipidemia). Mean age was 48.5 (± 14.9) years; 72.2% had at least one comorbidity. Cefalexin was the most prescribed antibiotic (54.3%). In multinomial analysis (with results transformed to present odds of receiving cefalexin relative to each comparator), diabetes was significantly associated with cefalexin prescribing compared to other first-line agents (aOR 3.1, 95% CI 2.0–4.8, p < 0.001), amoxicillin (aOR 2.9, 95% CI 1.3–6.7, p = 0.010), and other antibiotics (aOR 2.8, 95% CI 1.6–5.0, p < 0.001). Age ≥ 60 years was also significantly associated with cefalexin prescribing compared to other first-line agents (aOR 2.1, 95% CI 1.5–3.1, p < 0.001), but not with other antibiotics (aOR 1.7, 95% CI 0.9–3.2, p = 0.085). The composite variable of ≥ 2 metabolic comorbidities was similarly associated (aOR 2.7, 95% CI 1.8–4.2, p < 0.001). Diabetes and metabolic comorbidity clusters are descriptively associated with cefalexin prescribing in this Iranian primary care setting. These findings are hypothesis-generating and should not be interpreted causally. Prospective studies with comprehensive clinical and microbiological data are needed before any practice recommendations can be made.