Introduction
Urinary tract infection (UTI) is one of the most common infectious diseases encountered in the outpatient setting. Reference Wagenlehner, Bjerklund Johansen, Cai, Koves, Kranz, Pilatz and Tandogdu1 Despite this burden, national measurement efforts focus primarily on device-related infections in hospitalized patients, requiring labor-intensive chart reviews and the application of complex definitions. Reference Advani, Cawcutt, Klompas, Marschall, Meddings and Patel2 Consequently, contemporary data on the prevalence of outpatient UTIs remain scarce.
Prior US studies have reported outpatient UTI burden through an economic lens. One study reported that complicated UTIs (cUTIs) accounted for 59% of the 30-day Medicare spending among adult beneficiaries. Reference Lodise, Nowak and Rodriguez3 To fill current gaps in surveillance of infectious diseases, the US Department of Health and Human Services recommended leveraging clinician-assigned diagnosis during ambulatory care visits. Reference Baxter, Rubin, Steinberg, Carroll, Shapiro and Yang4 Publicly available national databases like National Ambulatory Medical Care Survey (NAMCS) have been used to measure several outpatient conditions (eg sexually transmitted infections). 5,Reference Unigwe, Yang, Song, Lo-Ciganic, Hincapie-Castillo, Cook and Park6
The Centers for Disease Control and Prevention (CDC) identified outpatient UTI prevalence as a critical measurement gap in 2023. 7 It is imperative to characterize the outpatient burden of UTIs to assess the need for and impact of stewardship interventions. This study aimed to (1) assess outpatient UTI burden, (2) evaluate the feasibility of using nationwide databases for UTI surveillance, and (3) suggest steps for improving future reporting.
Methods
Study Design: We conducted a cross-sectional analysis of data from NAMCS between 2016 to 2019. NAMCS, a nationally representative survey of U.S. physician office visits, is conducted by the National Center for Health Statistics of the CDC. 5 Data from 2017 and 2020 onwards were not available at the time of this study. Institutional review board approval was not required as this was a secondary analysis of publicly available data.
Case Definitions: UTI diagnosis required documentation of one of the following International Classification of Diseases (ICD), Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis: acute cystitis (N30.0); other chronic cystitis (N30.2); other cystitis (N30.8); cystitis unspecified (N30.9); UTI, site not specified (N39.0); acute pyelonephritis (N10); non-obstructive reflux-associated chronic pyelonephritis (N11.0); chronic obstructive pyelonephritis (N11.1); pyonephrosis (N13.6). cUTI definitions were based on regulatory and professional society guidance at the time of the study (Supplement 1). Reference Lodise, Nowak and Rodriguez3 UTIs that did not meet the cUTI definition were categorized as uncomplicated UTIs (uUTIs).
Analysis: We included UTI encounters in patients aged ≥15 years, calculated annualized prevalence rates, and stratified by uUTI and cUTI. Visit weights were applied to estimate national UTI prevalence. Specifically, we used the validated multiplicity estimator method Reference Burt and Hing8 to extrapolate visit-level data to patient-level estimates. This method reduces the contributions of patients with multiple encounters in a given year by multiplying visit weights by the inverse of the multiplicity factor, yielding a patient weight (Supplement 2). Patient weights were then applied to estimate the total number of patients with UTI, uUTI, and cUTI for each year. Prevalence was calculated per 100,000 people using U.S. Census Bureau data as the denominator. Statistical analyses were performed using SAS Enterprise Guide, Version 7 (SAS Institute, Cary, NC).
Results
During the 3-year study period, NAMCS recorded 31,368 unweighted encounters, corresponding to 926,865,040 annualized weighted encounters. Of these, 372 were unweighted UTI encounters, equating to 9,799,054 (95% CI: 8,683,188–10,914,919) annualized weighted UTI encounters. (Supplement 3). The highest number of weighted UTI encounters was 12,208,220 (95% CI: 9,866,810–14,549,631) in 2019, and lowest in 2018 with 7,461,052 (95% CI: 6,457,715–8,464,389) encounters.
Prevalence of UTIs: Across the 3-year study period, the annualized prevalence of UTIs was 1,511 (95% CI: 1,234–1,787) per 100,000 persons. uUTI and cUTI prevalence rates were 851 (95% CI: 685–1,017) and 659 (95% CI: 495–824) per 100,000 persons respectively. The highest UTI prevalence was observed in patients aged 25–44 years (4,978 [95% CI: 4,640–5,316] per 100,000), followed by those aged ≥75 years (1,870 [95% CI: 1,281–2,460] per 100,000). UTIs were more prevalent in female patients (1,803 [95% CI: 1,486–2,120] per 100,000) than in males (966 [95% CI: 637–1,296] per 100,000). Among females, uUTI and cUTI prevalence rates were 1,473 (95% CI: 1,185–1,761) and 330 (95% CI: 231–428) per 100,000, respectively. All male UTIs were classified as cUTIs per existing guidelines, with most unweighted female cUTIs defined as such due to urologic abnormalities (92.5%).
Characteristics of UTI encounters: Among 9,799,054 annualized weighted UTI encounters, 4,133,236 (42.2%) were classified as cUTIs (Table 1). Over 55% of all weighted UTI encounters were in adults aged ≥65 years, 71.0% were in female patients, and 75.8% in White patients (Table 2). Other characteristics, including race, ethnicity, insurance coverage, morbidities, laboratory testing, and underlying diagnosis, are shown in Table 2. Most importantly, the diagnosis of “UTI, site not specified” was the most common infection-related diagnosis at physician office visits (in all patients and female patients). The greatest proportion of weighted UTI encounters was due to a visit for a new problem with less than three months onset (40.0%), followed by a routine visit for a chronic problem (24.6%), and a visit for a flare-up of a chronic problem (20.4%).
Table 1. Annual stratified encounters and prevalence of urinary tract infections (UTI)
CI, Confidence Interval; cUTI, complicated urinary tract infection; UTI, urinary tract infection; uUTI, uncomplicated urinary tract infection.
* Total number of encounters was calculated using sample weights, with each patient encounter weight accounting for selection probabilities, physician non-response, and other adjustments to reflect the universe of office-based patient visits in the US.
† The estimated numbers of prevalent UTI, uUTI, and cUTI cases per 100,000 people were calculated by dividing the number of patients with UTI, uUTI, and cUTI for each year by the total US population ≥15 years of age in the same year and multiplying by 100,000.
‡ Calculated as an average annualized estimate by dividing sampling weights by three (the total number of years of data).
Table 2. Demographics and clinical characteristics of stratified UTI encounters
CHIP, Children’s Health Insurance Program; CT, computed tomography; cUTI, complicated urinary tract infection; UTI, urinary tract infection; uUTI, uncomplicated urinary tract infection.
* Calculated as an average annualized estimate by dividing sampling weights by 3 (the total number of years of data).
† Characteristics that were observed in fewer than 30 unweighted encounters (denoted by “-”) did not meet the National Center for Health Statistics statistical reliability criteria (ie, estimates are not reliable) and therefore not presented.
‡ Categories are not mutually exclusive and therefore may not sum to 100%.
Discussion
Our study confirms that UTIs are still the most common infection-related diagnosis in the U.S. ambulatory setting, accounting for approximately 10 million annualized weighted visits over three years. These numbers represent one of the most contemporary estimates of UTIs in office-based physician practices, demonstrating the feasibility of using publicly available databases for outpatient UTI surveillance.
Additionally, we saw differences in UTI burden across sexes, age groups, and race, indicating the need for targeted prevention and treatment strategies in specific populations. Over 55% of weighted UTI encounters occurred in older adults, who are at risk of antibiotic adverse events and an increased healthcare burden. Understanding these patterns is crucial for tailoring antimicrobial stewardship efforts to different demographic needs. Furthermore, the frequent use of the “UTI, site not specified” diagnosis code suggests potential misclassification due to challenges in distinguishing uncomplicated from complicated UTIs.
Our study has some limitations. While the multiplicity method reduces the contributions of patients who make multiple visits to a single provider within a 12-month period, patients who visit multiple outpatient providers throughout the year may still be counted more than once, potentially leading to an overestimation of the true prevalence of UTI. Estimates are limited to office-based physician practices and community health centers but could be expanded by using other databases, such as the Nationwide Inpatient Sample (NIS) and Nationwide Emergency Department Sample (NEDS). NAMCS diagnoses were not assigned using standardized case definitions and may need to be adapted as UTI definitions evolve. As a cross-sectional limited dataset, NAMCS lacks longitudinal patient-level data, culture results, and complete prescription data. Reference Goodson and Shahbazi9
Despite these limitations, nationwide survey data offer advantages for surveillance of outpatient infections like UTIs, where diagnostic tests or prescriptions may not always be required. Monitoring the burden and trends of these infections is crucial for stewardship efforts, specifically tracking trends for overdiagnosis or misclassification. To enhance data quality, linking national databases with electronic health records could develop automated reporting systems, replacing labor-intensive surveillance. 10
In conclusion, this study presents a contemporary picture of the national burden of UTIs, uUTIs and cUTIs. The recently released NHCS interactive dashboard (https://www.cdc.gov/nchs/dhcs/prelim-hc-visits/index.htm) and public-use data files provide new opportunities for analyzing ambulatory UTI trends. Automated diagnosis information, especially when linked to electronic health record databases, represents a crucial tool for tracking outpatient infections, reviewing prescribing practices, and informing stewardship interventions. 10
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/ash.2025.10045
Data availability statement
Data used in this study are publicly available via the U.S. Centers for Disease Control and Prevention National Center for Health Statistics.
Acknowledgments
Prior version of this manuscript was drafted by Liam Crouch, BSc, of Ashfield MedComms, an Inizio company, and funded by GSK.
Financial support
This study was funded by GSK (study 219501).
Competing interests
SDA did not receive any funding to conduct this study. SDA served as a consultant for GSK in the past and was employed by GSK/ViiV Healthcare after the completion of this study (and holds financial equities in GSK). RC, MSD, RD, MP, WYC, and DL received funding from GSK to conduct the study as past or present employees of Analysis Group, Inc. MEL and JJE are employed by GSK and hold financial equities in GSK.
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