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To characterize institutional masking policies for healthcare personnel (HCP) and identify factors informing masking decisions in the post-COVID-19 pandemic era. Cross-sectional, survey-based study. Healthcare institutions participating in the Society for Healthcare Epidemiology of America (SHEA) and Association for Professionals in Infection Control and Epidemiology (APIC) Research Networks. One representative per institution, including infection preventionists, hospital epidemiologists, or healthcare administrators, knowledgeable about organizational masking policies. A structured, web-based survey was distributed through the SHEA/APIC Research Networks. Survey domains included institutional characteristics, masking strategies outside of transmission-based precautions, epidemiologic and operational factors influencing masking decisions, and mask types required. Responses were collected anonymously via REDCap over a six-week period and analyzed descriptively. A total of 172 unique healthcare institutions completed the survey (41% response rate, n = 172/425). Most respondents were infection preventionists (65%) or hospital epidemiologists (25%). The most common masking approach was a seasonal or situational risk-based strategy (57%), while 7% of institutions reported no formal masking policy. Among institutions using seasonal or situational masking, decisions were most frequently informed by outbreaks or clusters (37%), public health guidance (33%), and HCP illness/absenteeism (27%). Most institutions reported no fixed epidemiologic thresholds for masking decisions. When masking was required, surgical masks were most commonly used (98%). Masking policies and decision-making criteria vary widely across healthcare institutions, reflecting a lack of standardized operational guidance. These findings underscore the need for consensus-based, data-driven frameworks to support consistent, transparent, and evidence-informed masking policies in healthcare settings.

Antimicrobial resistance (AMR) is a major global health threat, and antimicrobial stewardship programmes (ASPs) are a cornerstone strategy to optimise antimicrobial use. International guidelines emphasise multidisciplinary teams; however, evidence describing how stewardship functions are organised, and quantified across professional groups remains fragmented, particularly outside high-income settings. This review synthesised evidence on stewardship functions, activities, and reported full-time equivalent (FTE) staffing patterns. A systematic review was conducted in accordance with PRISMA guidelines. Medline, Scopus, Embase, CINAHL, CABI, and grey-literature sources were searched to November 2025. Studies reporting stewardship functions, activities, or FTE involvement among infectious disease (ID) physicians, ID clinical pharmacists, infection control nurses (ICNs), clinical microbiologists, and hospital epidemiologists were included. Narrative synthesis mapped functions across professional groups. Where data permitted, exploratory post-hoc quantitative synthesis of reported FTEs by hospital bed capacity was conducted using random-effects models. Study quality and bias were assessed. Fifty studies met the inclusion criteria: 44 described stewardship functions and activities, and seven reported FTE data. Responsibilities were distributed across professional groups: ID physicians and ID clinical pharmacists were mainly involved in prescribing oversight and optimisation, ICNs in monitoring and education, clinical microbiologists in diagnostic stewardship, and hospital epidemiologists in surveillance and reporting. Reported FTE involvement varied widely across hospital sizes and contexts, reflecting heterogeneity in design, role definitions, and reporting practices. Exploratory pooled estimates suggested higher reported ID physicians and ID clinical pharmacists involvement with larger hospitals; however, this pattern was affected by heterogeneity and publication bias and should be interpreted as descriptive, not generalisable. Current evidence does not support definitive staffing benchmarks for antimicrobial stewardship. This review highlights variability in how stewardship functions are organised and resourced. Future research should improve comparability through consistent reporting of stewardship activities, workforce roles, and institutional context.

Inferring who infected whom in an outbreak is essential for characterising transmission dynamics and guiding public health interventions. However, this task is challenging due to limited surveillance data and the complexity of immunological and social interactions. Instead of a single definitive transmission tree, epidemiologists often consider multiple plausible trees forming epidemic forests. Various inference methods and assumptions can yield different epidemic forests, yet no formal test exists to assess whether these differences are statistically significant. We propose such a framework using a chi-square test and permutational multivariate analysis of variance (PERMANOVA). We assessed each method's ability to distinguish simulated epidemic forests generated under different offspring distributions. While both methods achieved perfect specificity for forests with 100+ trees, PERMANOVA consistently outperformed the chi-square test in sensitivity across all epidemic and forest sizes. Implemented in the R package mixtree, we provide the first statistical framework to robustly compare epidemic forests.

The 51st Annual Meeting of the International Clearinghouse for Birth Defects Surveillance and Research (ICBDSR) was held from 2 to 5 November 2025 in Magaliesburg, South Africa. The meeting brought together clinicians, epidemiologists, public health practitioners and researchers from 24 countries to discuss advances in the surveillance, prevention, and management of birth defects. Hosting the meeting in South Africa reflected a strategic effort to strengthen engagement with low- and middle-income countries and to expand African participation in global birth defects surveillance initiatives. Scientific sessions addressed methodological developments in surveillance systems, etiological research, and approaches to improving the interpretation and communication of surveillance data for policy and public health action. Emphasis was placed on strengthening data quality, harmonising case definitions and reporting practices across registries, and building technical capacity in emerging surveillance programmes. Contributions from African researchers highlighted both the challenges and opportunities associated with implementing surveillance in resource-constrained settings. The meeting underscored the importance of international collaboration, inclusive surveillance networks, and effective translation of data into prevention strategies and health system planning. Strengthening global surveillance remains essential for informing prevention efforts and improving outcomes for children and families affected by birth defects.

Political skepticism and legislative threats like the One Big Beautiful Bill Act (OBBBA) currently shake the structures supporting public health. OBBBA seeks to de-professionalize our credentials by reducing federal loan caps from $50,000 to $20,500. It seems the fundamental necessity of our workforce remains undeniable despite these challenges. I address the anxiety of students and early-career professionals by contrasting this hostile political landscape with economic labor realities. I map the educational continuum, noting undergraduate public health degrees surpassed master's degrees in 2020. They now serve a student body that is over 55% people of color. I measure these trends against 2024 U.S. Bureau of Labor Statistics projections for five core disciplines: biostatistics, epidemiology, health policy, environmental health, and health promotion. Data indicate strong growth across the sector while governmental wages stagnate and loan forgiveness faces legislative peril. Roles like data scientists and medical services managers are projected to grow by 34% and 23%, respectively. Wage disparity is stark. Epidemiologists earn a median of $130,390 in scientific research compared with just $76,180 in local government. I argue market demand and the World Health Organization's projected global shortfall of 18 million health workers by 2030 validate our expertise. The narrative of de-professionalization likely poses a severe risk to health equity. Our labor is essential, whether we are investigating disease outbreaks or advocating for environmental justice. We must remain resilient and train to become the professionals our communities require to survive.

Social epidemiologists frequently aim to quantify how social, spatial, or organizational contexts shape individual outcomes, an aim commonly addressed through the use of multilevel models. These models readily estimate the magnitude of group-level differences, but it is more difficult to formalize the uncertainty in the relative ordering of predicted group-level outcomes, which is often considered qualitatively in practice. We propose an entropy-based coefficient, grounded in information theory, which quantifies the stability of predicted group-level rankings. This metric, termed the separation statistic (S), integrates both the magnitude of group-level differences and their statistical uncertainty, providing a principled summary of how well groups are separated in terms of their predicted outcomes. Our motivation is drawn from Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), a widely used approach in social epidemiology for assessing group-level heterogeneity with multilevel models. We show how the separation statistic can be applied to group-level predictions derived from MAIHDA models and is compatible with both Bayesian and frequentist estimation approaches. The metric can be computed globally, across all groups, or locally, within specific subsets of interest. We demonstrate its utility using applied examples from intersectional MAIHDA and provide accompanying code to facilitate its use in future studies. By quantifying the stability of group-level predictions, the separation statistic offers a broadly applicable tool for describing certainty in the relative ordering of outcomes from multilevel models. Importantly, it should be interpreted as a descriptive measure of ranking uncertainty rather than a prescriptive target, with its limitations carefully considered in applied settings.

Structural racism adversely affects the health and well-being of many Black and Hispanic/Latino Americans. This study sought to establish a theoretical foundation for developing a novel multilevel structural racism measure for use within Black and Hispanic/Latino communities in the United States (US). Based on a framework developed by the National Institute on Minority Health and Health Disparities (NIMHD), a content development team (n = 4 social epidemiologists) pre-selected 68 candidate indicators to be included in the ecological level index based on published literature. Subsequently, an expert panel consisting of leaders of community organizations (n = 3), health equity researchers (n = 5), and social stratification researchers (n = 2) participated in a modified three-phase Delphi panel process. This process yielded thirty-eight ecological-level indicators earmarked for inclusion, 30 for elimination, and 76 newly proposed. After revision of the newly proposed indicators, the final list included 71 indicators for structural racism measurement and spanned various domains of the NIMHD framework. This study sets the stage for a practical tool that can help researchers, clinicians, and policymakers identify structural racism's effects and guide efforts toward equity and justice in healthcare and beyond.

Asbestos exposure remains a persistent occupational hazard in China, yet updated national estimates of asbestos-related diseases (ARDs) after 2019 are scarce. This study quantified long-term trends and demographic patterns of ARDs from 1990 to 2023 using Global Burden of Disease data and joinpoint regression. We analyzed incidence, prevalence, deaths, and disability-adjusted life years (DALYs) for asbestosis and asbestos-attributable cancers (mesothelioma, tracheal/bronchus/lung cancer, laryngeal cancer, and ovarian cancer). Absolute numbers and age-standardized rates were assessed overall and stratified by sex and age. joinpoint regression identified significant temporal inflection points. The absolute burden of ARDs increased continuously from 1990 to 2023. Age-standardized prevalence and incidence rates of asbestosis peaked in 2001, while mortality and DALY rates peaked in 2004. Major turning points for asbestos-attributable cancers occurred around 2010-2011, marking historical peaks followed by declines. A modeled increase in mortality and DALYs was observed from 2020 to 2022 across nearly all ARDs. Males consistently demonstrated higher burdens than females, and older adults (≥65 years) carried the greatest burden, with a secondary mesothelioma peak at 55-59 years in males. Although ARD indicators have declined from historical peaks, a statistically modeled increase was observed in 2020-2022, warranting continued public-health attention. These findings aim to provide evidence for clinicians, epidemiologists, and policymakers to strengthen occupational disease prevention, reinforce labor protection laws, and improve asbestos-control policies in China.

Comprehensive exams are a standard component of doctoral-level training programs in epidemiology. In this manuscript, we describe current practices in the administration of comprehensive examinations across doctoral programs in epidemiology and discuss variation in the content, format, and structure of these exams. We present results from a survey about the comprehensive exam process at thirty-three different epidemiology programs. We also discuss a symposium focused on comprehensive exams that was part of the 2025 Society for Epidemiologic Research annual meeting. The symposium included speakers with specific expertise on designing comprehensive exams as well as faculty from several different epidemiology programs across North America.. The survey found important differences in format (ie, written, oral, general epidemiology, substantive topics), when the exams are administered, and what level of epidemiology knowledge they are targeting. In this manuscript, we also discuss common challenges programs face, including creating and grading the exams. By sharing this information with a wide audience of epidemiologists, we aim to facilitate discussion to improve the comprehensive exam process for doctoral students in our field. This work also fits with abroader goal to initiate discussion about doctoral-level training in epidemiology.

We sought to generate recommendations for the tennis community regarding the provision of mental healthcare and the promotion of mental wellness among elite tennis athletes, including junior tennis athletes. Recommendations were generated using a multimethod approach that included an in-person summit held during the 2022 US Open Tennis Championships in New York, New York, USA, and an asynchronous Delphi consensus process. Participants (n=83) were purposively selected to ensure diverse representation from all seven governing bodies of tennis and the broader tennis ecosystem, including: current and former elite adult and junior players, coaches, sport administrators, parents, physiotherapists, athletic trainers, physicians (sports medicine, psychiatry, neurology), other licensed mental health providers, epidemiologists and other academic subject matter experts. The summit included expert presentations and cross-organisation group discussion, which were used to generate provisional consensus statements. Statements were rated anonymously and refined iteratively based on synthesis of open-ended feedback until predetermined numeric thresholds were reached. The summit also included discussion of implementation challenges and priorities. As a result of this process, 2 foundational principles and 25 actionable recommendations were adopted, covering 5 domains: (1) standards of care, (2) education, (3) media and social media, (4) safeguarding and (5) research. The foundational principles emphasised that mental health must be prioritised by tennis governing bodies and that these bodies should collaborate across the tennis ecosystem. This output is a starting point for coordinated mental health efforts in tennis. More broadly, this pragmatic process is a replicable model for cross-organisational collaboration in other high-performance sports.

Past global healthcare crises have exposed vulnerabilities in healthcare systems, including inefficiencies in hospital operations, delayed response times, and overburdened infrastructure. Traditional hospital systems built for routine care were sometimes not resilient or adaptable in the face of such crises, resulting in global failures. This narrative review examines how Lean Six Sigma (LSS) principles can be incorporated into conventional hospital operations to enhance pandemic preparedness by building the resilience of hospital infrastructure, streamlining processes during time-sensitive situations, and improving waste reduction, all while being adaptable and sustainable. This narrative review synthesizes literature using Coronavirus Disease 2019 (COVID-19) as a benchmark to evaluate hospital response strategies, failures, and factors contributing to failure, including the critical assessment of ethical disruptions, operational weaknesses, and healthcare business models. LSS principle applications, i.e. DMAIC, Value Stream Mapping, SIPOC, FMEA, and Control Charts, were explored for facilitating efficient care, crisis response, and policy integration. Various case studies were used to support the comparative analysis and insights. The literature indicates that adoption of LSS tools in the most vulnerable aspects of healthcare, including patient triage, supply chain optimization, and controlling and reducing mortality, has been associated with measurable improvements. Most importantly, integrating data-driven LSS resulted in enhanced surge responsiveness and ethical compliance within national healthcare frameworks and policies. However, despite its efficacy, there are institutional barriers like capital constraints, resistance to change, data inconsistencies, and a lack of legislative frameworks that impede widespread LSS adoption. LSS offers an adaptable and scalable methodology to re-engineer conventional hospital operations and pandemic preparedness. The emphasis on 'kaizen' (continuous improvement), data-informed decision making, and focus on precision aligns with the needs of healthcare systems as revealed by recent crises. To unlock the potential for preparedness, healthcare systems and legislation must focus on institutionalizing LSS across public and private sectors through strategic investment, education, and cross-sector collaborations. This review provides a comprehensive framework for policymakers, governments, epidemiologists, doctors, and hospital business managers for building resilient, efficient, and pandemic-ready hospitals.

Cancer epidemiologists increasingly contribute evidence evaluating the causal effect of policies on cancer outcomes. The validity of such evidence relies on attention to conceptual, analytic, and data aspects of causal inference research. To highlight these aspects and contribute policy-relevant evidence, we evaluated Virginia's Human Papillomavirus (HPV) vaccination mandate on cervical cancer incidence. HPV vaccination mandates were intended to increase vaccine uptake and reduce cervical cancer incidence, yet causal evidence on cancer incidence remains scarce. Virginia implemented an HPV vaccine mandate in 2008, providing a natural experiment within a causal inference framework. After motivating why traditional methods may be invalid, we estimated the effect of Virginia's HPV vaccine mandate on cervical cancer incidence among females aged 20-24 with a synthetic control triple differences design. Our post-period begins in 2016, when the first mandate-exposed cohort reached ages 20-24. State-level cancer registry data (2003-2019) were used to compare Virginia's incidence trends to a weighted combination of control states, while adjusting for within-state trends across age groups exposed and not exposed to the mandate. Cervical cancer incidence among 20-24-year-old females declined over time in both Virginia and comparison states. Ignoring within-state differential age-group trends, the synthetic control difference-in-differences design showed no evidence of any effect. The synthetic control triple difference design estimated that Virginia's mandate was associated with 1.6 additional cervical cancer cases per 100,000 population (CI = 0.4, 2.7). Alternative specifications, including adjustments for screening and multiple sensitivity analyses, produced consistent results in direction and inference. Compared to states with no mandate, Virginia's HPV vaccine school-entry mandate was not associated with reduced cervical cancer incidence. Our results should be interpreted as evidence about the implementation and effectiveness of Virginia's mandate policy, not as evidence against the established effectiveness of HPV vaccination as a cancer prevention tool.

Dr. Anthony Harris is Professor of Epidemiology and Public Health and Internal Medicine, and Head of the Division Genomic Epidemiology and Clinical Outcomes of Health Care Outcomes Research. He is an infectious disease physician and epidemiologist whose research interests include emerging pathogens, antimicrobial-resistant bacteria, hospital epidemiology/infection control, epidemiologic methods in infectious diseases, and medical informatics. He has published over 360 papers. He has current or has had funding from the NIH, CDC, VA, and AHRQ to study antibiotic resistance and hospital epidemiology. He is extremely proud of his mentoring track record.

'Ri-Medi' is a pilot project implementation of a medication review and deprescribing (MRDP) service in the primary care setting, involving two health districts of the Local Health Authority Roma 1 (Rome, Italy). General practitioners (GPs) are supported in this activity by a multidisciplinary team consisting of epidemiologists, pharmacists, statisticians, pharmacologists, geriatricians, internists, and healthcare managers. Together with GPs, the team chooses specific therapeutic focuses for which updated evidence-based information and indications on the target population are provided. The focus is on the elderly population in hyper-polypharmacy (10+ drugs). For the pilot phase, the therapeutic focus was on potentially inappropriate statin use in primary prevention in patients aged 80+ years. For each GP, the number of patients at risk was estimated. In addition, a survey among a sample of voluntary GPs was launched to investigate their interest in participating in this activity, to collect potential critical issues and impressions, and to identify areas of interest for future MRDP campaigns of interest.

Cancer is a leading cause of mortality worldwide. The incidence and outcomes of cancer remain unequally distributed on global and local scales, driven in part by unequal carcinogen exposures, healthcare access, socioeconomic factors, and screening compliance. Cancer surveillance systems are critical for informing resource allocation and policies for cancer prevention, screening, diagnosis, and treatment but are limited in many countries. Where they do exist, surveillance systems suffer from long latencies between disease progression, diagnosis, and data reporting as well as reporting bias due to screening disparities and evolving policies. Furthermore, integrated surveillance is lacking for cancer-related targets, including those associated with carcinogen exposure as well as those indicating cancer prevalence or risk factors. Improved geospatial surveillance of cancer-related targets could inform evidence-based screening and prevention efforts and predict future burden for an increasing global health threat. In this article, we─a team of environmental engineers and microbiologists, oncologists, cancer epidemiologists, and bioethicists─assert that wastewater surveillance of cancer-related targets holds promise for integration into existing cancer epidemiology infrastructure to enable earlier and more equitable public health decision-making. First, we highlight opportunities for wastewater surveillance to complement existing cancer surveillance. We next identify candidate targets that indicate cancer risk─including oncoviruses, bacterial carcinogens, oncogenes, and chemical carcinogens─and detail prior evidence of their detection in wastewater and potential use cases. Finally, we present research needs and ethical considerations for this technological expansion of the practice.

Plague, caused by Yersinia pestis, remains a highly fatal zoonosis with endemic foci on three continents. Rapid and accurate diagnosis is essential for patient survival and outbreak containment. Nucleic acid amplification tests (NAATs) have evolved from conventional PCR to real-time PCR, isothermal methods (LAMP, RPA), droplet digital PCR, and CRISPR-based platforms. This 5000-word review provides an in-depth technical comparison of these technologies, their analytical performance (limit of detection [LoD], sensitivity, specificity), clinical validation in human specimens, environmental and vector applications, and point-of-care (POC) readiness. We critically analyze sample preparation challenges, genetic target selection, and multiplexing strategies. Four comprehensive tables summarize target genes, real-time PCR assays, isothermal methods, and point-of-care platforms. Future directions include AI-integrated devices, wearable sensors, and lyophilized CRISPR tests. This review serves as a definitive guide for clinical microbiologists, field epidemiologists, and diagnostic developers.

The COVID-19 pandemic demonstrated the potential role of digital health tools in enhancing pandemic preparedness and response. These tools became essential, supporting not only health care delivery but also decision-making, communication, case identification, contact tracing, surveillance, vaccination rollout, and intervention evaluation. The interest in applying digital health tools to pandemic preparedness and response motivated conversations about digital epidemiology-a field of study that aims to provide insight into health and disease determinants by leveraging diverse digital data sources. In a globalized world, effective preparedness and response to pandemics require coordinated global action. This study investigates experts' opinions on strategies for improving global health security through the effective use of digital epidemiology, considering the current landscape of digital determinants of health. Epidemiologists, public health specialists, data scientists, and professionals with expertise in various components of digital health were recruited through convenience and snowball sampling methods. Their opinions were elicited using an electronic questionnaire developed by the authors in Research Electronic Data Capture (REDCap; Vanderbilt University). To ensure a global perspective, participants were recruited from Africa, North America, Oceania, and Europe. Thematic analysis and the strengths, weaknesses, opportunities, and threats (SWOT) analysis framework were used to analyze participants' responses. Most participants were familiar with the concept of digital epidemiology and expressed positive sentiments about its potential in strengthening global health security. Privacy and security, along with ethical and legal considerations, were ranked by most experts as high priority areas that decision-makers and implementers must consider to ensure sustainable integration of digital epidemiology tools in future pandemic preparedness and response. A SWOT analysis of participants' views on the promise of digital epidemiology revealed fewer strengths and more weaknesses compared to other components of the analysis framework. This study highlights the growing recognition of digital epidemiology as a critical tool for enhancing global health security, particularly using nontraditional data sources and emerging technologies, including artificial intelligence. The study affirms the need for a globally coordinated approach to governance, regulation, and investment in digital health infrastructure to ensure the responsible and effective application of digital innovations in epidemiological practice.

BACKGROUND: Human exposure to complex, changing, and variably correlated mixtures of environmental chemicals has presented analytical challenges to epidemiologists and human health researchers. There has been a wide variety of recent advances in statistical methods for analyzing mixtures data, with most methods having open-source software for implementation. However, there is no one-size-fits-all method for analyzing mixture data given the considerable heterogeneity in scientific focus and study design. For example, some methods focus on predicting the overall health effect of a mixture and others seek to disentangle main effects and pairwise interactions. Some methods are only appropriate for cross-sectional designs, while other methods can accommodate longitudinally measured exposures or outcomes. OBJECTIVES: This article focuses on simplifying the task of identifying which methods are most appropriate to a particular study design, data type, and scientific focus. METHODS: We present an organized workflow for statistical analysis considerations in environmental mixtures data and two example applications implementing the workflow. This systematic strategy builds on epidemiological and statistical principles, considering specific nuances for the mixtures' context. We also present an accompanying methods repository to increase awareness of and inform application of existing methods and new methods as they are developed. DISCUSSION: We note several methods may be equally appropriate for a specific context. This article does not present a comparison or contrast of methods or recommend one method over another. Rather, the presented workflow can be used to identify a set of methods that are appropriate for a given application. Accordingly, this effort will inform application, educate researchers (e.g., new researchers or trainees), and identify research gaps in statistical methods for environmental mixtures that warrant further development.

Candida auris is an emerging yeast that is frequently resistant to antifungal drugs. C. auris can cause invasive infections associated with high mortality and can colonize patients asymptomatically, which facilitates transmission in health care settings. Since it was first reported in the United States in 2016, C. auris has been identified in multiple states, with increasing numbers of cases reported annually. Monitoring national trends in cases identified through clinical testing and screening for colonization is critical to guide infection prevention and control efforts. 2022-2024. State and jurisdictional health departments voluntarily report clinical and screening C. auris cases to CDC using standardized case definitions of the Council of State and Territorial Epidemiologists. Clinical cases are defined as detection of C. auris from specimens collected for diagnostic purposes; screening cases are defined as detection from colonization screening swabs. Cases were reported to CDC through the Research Electronic Data Capture (REDCap) or Data Collation and Integration for Public Health Event Response (DCIPHER) platforms. Data included patient age and sex, case type, specimen type (for clinical cases), health care facility type, Antimicrobial Resistance Laboratory Network geographic region, and specimen collection date. Analyses were descriptive and limited to cases with specimens collected during 2022-2024. During 2022-2024, a total of 13,507 clinical C. auris cases were reported to CDC, increasing from 2,882 in 2022 to 4,428 in 2023 and 6,197 in 2024, with smaller annual percentage increases over time (53.7% from 2022 to 2023 and 39.9% from 2023 to 2024). Most clinical cases occurred among adults aged ≥45 years (87.8%) and among males (61.0%). The most common specimen types among all clinical cases were urine (31.5%) and blood (30.2%); by year, the proportion of blood as the specimen type was 34.4% in 2022, 30.2% in 2023, and 25.6% in 2024. Most clinical cases were identified through specimens collected in acute care hospitals (76.6%) and long-term acute care hospitals (17.8%).During the same period, a total of 27,853 screening cases were reported to CDC, increasing from 6,226 in 2022 to 9,195 in 2023 and 12,432 in 2024. Screening cases most frequently occurred among adults aged ≥45 years (90.0%) and males (57.9%). Among cases with known facility type, the proportion of specimens collected in acute care hospitals increased from 24.7% in 2022 to 50.7% in 2024, whereas the proportion of specimens collected in long-term acute care hospitals decreased from 56.1% to 35.7% during the same period. The number of clinical and screening C. auris cases reported to CDC increased during 2022-2024, indicating ongoing transmission in U.S. health care settings. Although annual percentage increases in clinical cases declined over time, absolute case counts reported to CDC continued to rise. The increasing proportion of screening cases with specimens collected in acute care hospitals might reflect increased use of screening in acute care hospitals, including screening at admission. Because of increases in the number of reported C. auris cases, sustained infection prevention and control efforts in health care facilities, including adherence to transmission-based precautions, environmental disinfection with agents effective against C. auris, and communication of C. auris status during patient transfers remain essential to preventing clinical infections and colonization. Because this pathogen is frequently resistant to antifungal drugs, continued investment in laboratory capacity and surveillance, including antifungal susceptibility testing and screening of patients at high risk for C. auris infection, can support timely detection and guide prevention strategies. Ongoing public health coordination at federal, state, and local levels is critical to limit further spread and to address emerging antifungal drug resistance.