Brittany L. Morgan Bustamante

Fungal diseases are an emerging public health threat, yet their epidemiology remains poorly understood due to limited surveillance. Electronic health record (EHR) datasets offer opportunities for studying fungal diseases, but their national representativeness is unclear. We compared inpatient encounters for aspergillosis and histoplasmosis between Oracle EHR Real World Data (OERWD) and the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP NIS), a nationally representative discharge-derived database. We analyzed inpatient encounters from both databases and applied calibration weights to OERWD to improve alignment with national benchmarks. Relative differences (RDs) in encounter counts were compared using Wald tests. Survey-weighted quasibinomial models estimated prevalence and 95% CIs; temporal trends were evaluated with Mann–Kendall tests. Dataset differences were assessed using interaction terms between a dataset indicator and demographic or geographic subgroups. OERWD had fewer total and disease-specific encounters than HCUP NIS. Weighting improved precision, and prevalence estimates did not differ significantly for most strata (31 of 42), indicating broad agreement in demographic and geographic distributions. Both datasets showed higher aspergillosis prevalence in the Pacific division and higher histoplasmosis prevalence in the East South Central division, with males and older adults consistently exhibiting higher prevalence. These findings indicate that, while HCUP NIS is optimal for nationally representative prevalence estimates, weighting EHR-derived data can reproduce key demographic and geographic prevalence patterns observed in national benchmarks. Benchmarking EHR-derived estimates against national standards represents an important step toward establishing their representativeness and supporting future studies leveraging EHR data to investigate fungal disease epidemiology in greater clinical detail.

Background: Aspergillosis is a fungal infection associated with rising hospitalizations and substantial morbidity and mortality. In the United States, data remain fragmented due to the absence of centralized surveillance. This study aimed to evaluate demographic, geographic, and temporal trends in aspergillosis diagnoses across the United States and evaluate changes in those patterns following the emergence of COVID-19. Methods: We conducted a retrospective cohort study using electronic health record data from 142 US healthcare systems (Oracle Health), including adults aged ≥18 years who received care between 2013 and 2023. The cohort included over 76 million patients and 127 million person-years. Aspergillosis prevalence was calculated using post-stratification weights. Adjusted prevalence ratios (aPRs) were estimated via quasi-Poisson and Bayesian spatiotemporal regression. COVID-19-related shifts were evaluated using estimated marginal means. Results: From 2013 to 2023, aspergillosis prevalence increased by 5% annually, peaking in 2022. Rhode Island had the highest state-level aPR; Utah the lowest. Diagnosis was higher among males (aPR 1.37), older adults (≥65 years vs 18–24 years: aPR 4.95), and urban residents (rural aPR 0.86). Following the emergence of COVID-19, prevalence increased disproportionately among Hispanic or Latino patients and several racial minority groups. A nonsignificant upward trend was also observed among rural residents. Conclusions: This study provides a comprehensive national assessment of aspergillosis diagnosis patterns in the United States, revealing rising prevalence and shifts in affected populations following the emergence of COVID-19. These findings may aid earlier clinical recognition, especially among groups not traditionally considered high-risk, and support efforts to expand diagnostic access and improve fungal disease surveillance.

Background: Where surveillance data are limited, nationally representative electronic health records allow for geographic, temporal, and demographic characterization of the fungal diseases blastomycosis and histoplasmosis. Methods: We identified incident blastomycosis and histoplasmosis cases from 2013 to 2023 within Oracle EHR Real-World Data, which comprises 1.6 billion healthcare encounters nationally. To characterize spatiotemporal incidence trends, we used generalized estimating equations weighted for nonrepresentativeness of electronic health record–reporting facilities. We computed standardized incidence rate ratios (sIRRs), which relay relative differences in standardized incidence rates between regions, race/ethnicity, gender, and age subgroups and the national population. Results: National incidence rates in 2023 were 2.4 (95% confidence interval [CI]: 1.6–3.5) and 1.9 times (95% CI: 1.6–2.2) rates in 2013, for blastomycosis and histoplasmosis, respectively. Blastomycosis incidence rates among Hispanic or Latino and non-Hispanic Black individuals were 60% (sIRR: 1.6 [95% CI: 1.0–2.4]) and 30% (sIRR: 1.3 [95% CI: 1.0–1.6]) higher than the standardized national incidence rate. Histoplasmosis incidence rates were elevated among non-Hispanic White patients (sIRR: 1.05 [95% CI: 1.02–1.08]). Standardized incidence rates of both diseases were higher among older and male patients, were consistently elevated in the Upper Midwest and Ohio Valley regions, and increased greatly in the Northern Rockies and Plains from 2013 to 2023. We estimated high incidence in states (blastomycosis: Illinois, Kentucky, and West Virginia; histoplasmosis: Missouri, Iowa, and Oklahoma) that do not report to surveillance. Conclusions: This analysis revealed increasing incidence rates of blastomycosis and histoplasmosis, with increasing diagnoses outside of historically endemic regions, and notable differences in incidence by race/ethnicity, gender, and age.

Importance Aspergillosis is an under-recognized fungal infection associated with rising hospitalizations, concerns about antifungal resistance, and substantial morbidity and mortality in the U.S. Yet, national data on aspergillosis remain fragmented due to absence of centralized surveillance. Objective To examine demographic and geographic variation and temporal trends in aspergillosis prevalence in the U.S. from 2013 to 2023; to assess how the COVID-19 pandemic influenced prevalence across population subgroups. Design Retrospective cohort study using a large, national electronic health record (EHR) database. Setting 142 healthcare systems in the U.S. using Oracle Health EHR systems, encompassing disparate care settings. Participants Adults aged 18 years and older who received care between January 1, 2013, and December 31, 2023. The cohort included over 76 million patients, and over 127 million person-years, with 20,764 aspergillosis diagnoses. Exposures Calendar year, patient characteristics (age, sex, race, ethnicity, residence type), and state of residence. COVID-19 was treated as a time-varying exposure. Main Outcomes and Measures Annual and overall prevalence of aspergillosis per 100,000 person-years; adjusted prevalence ratios (aPRs) by demographic group and state, estimated using quasi-Poisson and Bayesian spatiotemporal regression. COVID-19-related shifts in prevalence were assessed using estimated marginal means. Results Between 2013 and 2023, aspergillosis prevalence increased 5% annually, peaking in 2022. Rhode Island had the highest aPR across all years; Utah the lowest. Prevalence was higher in males (aPR: 1.37), older adults (aPR for ≥65 vs. 18 to 34: 4.95), and urban residents (rural aPR: 0.86). Post-COVID-19, prevalence increased disproportionately among Hispanic or Latino patients and several racial minority groups. Suggestive trends of increasing prevalence were also observed among rural residents. Conclusions and Relevance This study provides the most comprehensive national assessment to date of aspergillosis patterns in the U.S., revealing evidence of rising prevalence and emerging disparities shaped by the COVID-19 pandemic. Clinicians should consider regional and demographic risk patterns to support earlier diagnosis, guide antifungal treatment, and improve outcomes—especially among older adults and populations disproportionately affected by COVID-19. Clinicians practicing in rural or underserved areas should be particularly mindful of shifting risk profiles and advocate for improved diagnostic resources to reduce delays in care. Question How has aspergillosis prevalence changed in the U.S. over time, and how did the COVID-19 pandemic influence prevalence among demographic and geographic subgroups? Findings In a nationally representative EHR cohort of over 76 million adults, aspergillosis prevalence increased by 5% annually from 2013 to 2023. After the emergence of COVID-19, prevalence increased disproportionately among Hispanic or Latino patients and racial minority groups. Meaning Findings suggest increasing aspergillosis prevalence in the U.S., with indications of widening disparities. While subject to data limitations, these trends underscore the importance of considering regional and demographic factors in clinical decision-making, particularly for populations disproportionately affected by COVID-19.

Electronic health records (EHRs) are increasingly used in public health research. However, biases may exist when using EHRs due to whether someone is captured in the data. Assessing the impact of bias in generating disparities identified with EHR data is difficult because information about health care–seeking behaviors is not included in the record. We developed an agent-based model (ABM) to simulate the health care–seeking behavior for community-acquired methicillin-resistant Staphylococcus aureus infection in a subregion of California. The ABM assumed no difference in prevalence across the study area. We modeled the health care–seeking process to see if geographic differences in prevalence would emerge from the ABM when only looking at those who sought treatment, matching empirical data. The ABM reproduced prevalence in observed data for 9 of the 21 geographies. Simulated differences in prevalence across geographies did not reach the magnitude in observed data, and spatial patterns had low to moderate agreement. Our results suggest that geographic disparities in the methicillin-resistant Staphylococcus aureus prevalence previously identified in California EHR data may be due to determinants beyond bias and health care–seeking behaviors. Future studies could adapt this model for other health outcomes by adjusting the health care–seeking behavior parameters and modifying the disease progression process. This article is part of a Special Collection on Cross-National Gerontology.

Community-acquired Staphylococcus aureus (CA-Sa) skin and soft tissue infections (SSTIs) are historically associated with densely populated urban areas experiencing high poverty rates, intravenous drug use, and homelessness. However, the epidemiology of CA-Sa SSTIs in the United States has been poorly understood since the plateau of the Community-acquired Methicillin-resistant Staphylococcus aureus epidemic in 2010. This study examines the spatial variation of CA-Sa SSTIs in a large, geographically heterogeneous population and identifies neighborhood characteristics associated with increased infection risk. Using a unique neighborhood boundary, California Medical Service Study Areas, a hotspot analysis, and estimates of neighborhood infection risk ratios were conducted for all CA-Sa SSTIs presented in non-Federal California emergency departments between 2016 and 2019. A Bayesian Poisson regression model evaluated the association between neighborhood-level infection risk and population structure, neighborhood poverty rates, and being a healthcare shortage area. Emergency departments in more rural and mountainous parts of California experienced a higher burden of CA-Sa SSTIs between 2016 and 2019. Neighborhoods with high infection rates were more likely to have a high percentage of adults living below the federal poverty level and be a designated healthcare shortage area. Measures of population structure were not associated with infection risk in California neighborhoods. Our results highlight a potential change in the epidemiology of CA-Sa SSTIs in California emergency departments. Future studies should investigate the CA-Sa burden in other geographies to identify whether this shift in epidemiology holds across other states and populations. Further, a more thorough evaluation of potential mechanisms for the clustering of infections seen across California neighborhoods is needed.

Weaned dairy heifers are a relatively understudied production group. Bovine respiratory disease (BRD) is the most common cause of antimicrobial drug (AMD) use, morbidity, and mortality in this production group. The study of antimicrobial resistance (AMR) is complicated because many variables that may affect AMR are related. This study generates hypotheses regarding the farm- and animal-level variables (e.g., vaccination, lane cleaning, and AMD use practices) that may be associated with AMR in respiratory isolates from weaned dairy heifers. A cross-sectional study was performed using survey data and respiratory isolates (Pasteurella multocida, Mannheimia haemolytica, and Histophilus somni) collected from 341 weaned dairy heifers on six farms in California. Logistic regression and Bayesian network analyses were used to evaluate the associations between farm- and animal-level variables with minimum inhibitory concentration (MIC) classification of respiratory isolates against 11 AMDs. Farm-level variables associated with MIC classification of respiratory isolates included the number of source farms of a calf-rearing facility, whether the farm practiced onsite milking, the use of lagoon water for flush lane cleaning, and respiratory and pinkeye vaccination practices. Animal-level variables associated with a MIC classification included whether the calf was BRD-score-positive and time since the last phenicol treatment.

Objective: To evaluate the impact of implementing clinical decision support (CDS) tools for outpatient antibiotic prescribing in the emergency department (ED) and clinic settings. Design: We performed a before-and-after, quasi-experimental study that employed an interrupted time-series analysis. Setting: The study institution was a quaternary, academic referral center in Northern California. Participants: We included prescriptions for patients in the ED and 21 primary-care clinics within the same health system. Intervention: We implemented a CDS tool for azithromycin on March 1, 2020, and a CDS tool for fluoroquinolones (FQs; ie, ciprofloxacin, levofloxacin, and moxifloxacin) on November 1, 2020. The CDS added friction to inappropriate ordering workflows while adding health information technology (HIT) features to easily perform recommended actions. The primary outcome was the number of monthly prescriptions for each antibiotic type, by implementation period (before vs after). Results: Immediately after azithromycin-CDS implementation, monthly rates of azithromycin prescribing decreased significantly in both the ED (−24%; 95% CI, −37% to −10%; P < .001) and outpatient clinics (−47%; 95% CI, −56% to −37%; P < .001). In the first month after FQ-CDS implementation in the clinics, there was no significant drop in ciprofloxacin prescriptions; however, there was a significant decrease in ciprofloxacin prescriptions over time (−5% per month; 95% CI, −6% to −3%; P < .001), suggesting a delayed effect of the CDS. Conclusion: Implementing CDS tools was associated with an immediate decrease in azithromycin prescriptions, in both the ED and clinics. CDS may serve as a valuable adjunct to existing antimicrobial stewardship programs.

Culturally-adapted evidence-based programs (EBPs) are needed to promote healthy behaviors among Native teens and young adults. Little is known about the facilitators and barriers of implementing and sustaining EBPs in Native communities. This paper aims to identify those factors described by educators who implemented the Native Students Together Against Negative Decisions (STAND) curriculum. Methods. We conducted qualitative, semi-structured interviews with 44 Native STAND educators from 48 sites throughout the United States. We used a modified grounded theory approach to explore barriers, facilitators, and sustainability factors related to implementing Native STAND. Results. We learned that disruptions to staffing, coordination, and organizational factors were the most common barriers. Factors that improved implementation success included: tailoring the program to local needs/constraints, having a supportive Project Manager, improved fidelity due to check-in calls, and participation in summer training. Factors that improved sustainability included: access to needed infrastructure, administrative support, community support, and student interest. Discussion. The delivery of Native STAND was further improved by person-to-person communication and resource sharing across sites. Sustaining EBPs in AI/AN settings requires culturally-tailored technical assistance, sufficient implementation funds for materials and staffing, and a community of peer educators to inspire forward progress. Conclusion. EBPs that reflect the needs and experiences of American Indian and Alaska Native (AI/AN) youth are necessary to address systemic inequities in adolescent health outcomes. The Native STAND Dissemination and Implementation study is among the first to assess facilitators and barriers to program delivery in diverse AI/AN settings.