Multidrug Resistance of Escherichia coli From Outpatient Uncomplicated Urinary Tract Infections in a Large United States Integrated Healthcare Organization.

Background: Urinary tract infections (UTIs) cause significant disease and economic burden. Uncomplicated UTIs (uUTIs) occur in otherwise healthy individuals without underlying structural abnormalities, with uropathogenic Escherichia coli (UPEC) accounting for 80% of cases. With recent transitions in healthcare toward virtual visits, data on multidrug resistance (MDR) (resistant to ≥3 antibiotic classes) by care setting are needed to inform empiric treatment decision making. Methods: We evaluated UPEC resistance over time by care setting (in-person vs virtual), in adults who received outpatient care for uUTI at Kaiser Permanente Southern California between January 2016 and December 2021. Results: We included 174 185 individuals who had ≥1 UPEC uUTI (233 974 isolates) (92% female, 46% Hispanic, mean age 52 years [standard deviation 20]). Overall, prevalence of UPEC MDR decreased during the study period (13% to 12%) both in virtual and in-person settings (P for trend <.001). Resistance to penicillins overall (29%), coresistance to penicillins and trimethoprim-sulfamethoxazole (TMP-SMX) (12%), and MDR involving the 2 plus ≥1 antibiotic class were common (10%). Resistance to 1, 2, 3, and 4 antibiotic classes was found in 19%, 18%, 8%, and 4% of isolates, respectively; 1% were resistant to ≥5 antibiotic classes, and 50% were resistant to none. Similar resistance patterns were observed over time and by care setting. Conclusions: We observed a slight decrease in both class-specific antimicrobial resistance and MDR of UPEC overall, most commonly involving penicillins and TMP-SMX. Resistance patterns were consistent over time and similar in both in-person and virtual settings. Virtual healthcare may expand access to UTI care.

Validation of Autism Spectrum Disorder Diagnoses in Large Healthcare Systems with Electronic Medical Records.

To identify factors associated with valid Autism Spectrum Disorder (ASD) diagnoses from electronic sources in large healthcare systems. We examined 1,272 charts from ASD diagnosed youth <18 years old. Expert reviewers classified diagnoses as confirmed, probable, possible, ruled out, or not enough information. A total of 845 were classified with 81% as a confirmed, probable, or possible ASD diagnosis. The predictors of valid ASD diagnoses were >2 diagnoses in the medical record (OR 2.94; 95% CI 2.03-4.25; p < 0.001) and being male (OR 1.51; 95% CI 1.05-2.17; p = 0.03). In large integrated healthcare settings, at least two diagnoses can be used to identify ASD patients for population-based research.