Introduction:A workforce with data and technology skills is vital to support digital transformation in public health (PH). Using US-based 2024 Public Health Workforce Interests and Needs Survey (PH WINS) national survey data, this study examined informatics/data skills gaps and training needs of governmental PH workers. Methods:2024 PH WINS included data from 48 state health agencies-central office (SHA-CO), 34 large local health departments (LHD) from Big Cities Health Coalition (BCHC), and 1,144 other LHDs. Analysis focused on non-supervisory Tier 1 respondents, their education and position. PH workforce classifications (N = 77) were grouped into five categories. Training needs defined as discordance between job importance and skill need (high importance/low skill). Gaps were assessed for six data/informatics-related questions using SAS v9.4, along with time and resource needs. Results:Of 56,595 respondents, 72% (N = 40,914) worked in Tier 1 roles. Across three PH settings, half (45-54%) had BS/MS/PhD degrees, but not in public health. Half (50-52%) worked for 0-5 years in current position; one-third had 0-5 years in PH practice. Data/informatics-related roles reported least technical skill gaps across all settings: SHA-CO (8.1%), BCHC (6.9%) and LHD (8.1%). For addressing training needs, nurses self-reported most need for time, and data/informatics roles indicated high resource needs (22% BCHC; 20% LHDs). Analysis showed significant skills gaps (p < 0.05) in SHA-CO for all the six data/informatics topics. Discussion:Results highlight a widespread training need in incumbent PH workforce to address the data-related/technical skills gaps. Multi-pronged training approaches are critical to building a data and informatics-savvy PH workforce.

Introduction and Objective: Diabetes prevalence among young adults in the U.S. is poorly characterized by subtype and within subgroups. We used electronic health records (EHR) to estimate diabetes prevalence by type in 2018 and 2022 among young adults in the U.S. Methods: We used data from five Diabetes in Children, Adolescents, and Young Adults Network centers: four health system centers (HS) (Table 1) and a geographic-based center in Colorado (CO), with a total of more than 30 million young adults under surveillance. We used validated computable phenotypes (CP) to identify adults ages 18 to <45 years with diabetes (type 1 [T1D], type 2 [T2D]). Prevalent cases met the CP within the index year or up to several years prior, depending on center capacity. Results: T1D prevalence in young adults in 2018 was 4.7 (4.6, 4.8) per 1000 among the HS and 4.2 (4.1, 4.3) in CO. T2D prevalence in 2018 was 22.3 (22.2, 22.5) per 1000 for the HS and 9.1 (8.8, 9.3) in CO. T1D was most prevalent among non-Hispanic (NH) White and T2D was most prevalent among NH-Black and Hispanic young adults (Table 1). In most centers, T1D was more prevalent in males versus females and decreased with age; T2D was more prevalent in females and increased with age. Conclusion: This was one of the largest EHR-based studies of diabetes prevalence in young adults in the U.S., providing sufficient sample sizes to generate estimates by subtype and subgroup. Disclosure A.G. Hirsch: None. M. Rosenman: None. J. Divers: None. R. Anthopolos: None. T. Crume: None. D. Dabelea: None. S. Kim: None. A. Rajan: None. N. Laiteerapong: Research Support; Current; Novo Nordisk. B.E. Dixon: None. K. Reynolds: Research Support; Ended; Merck & Co., Inc. M. Mefford: Research Support; Current; Merck & Co., Inc. L. Thorpe: None. B.S. Schwartz: None. Funding The DiCAYA Network (Assessing the Burden of Diabetes by Type in Children, Adolescents, and Young Adults) work was funded by the Centers for Disease Control and Prevention’s National Center for Chronic Disease Prevention and Health Promotion and the National Institute of Diabetes and Digestive and Kidney Diseases(U18DP006521 to Children’s Hospital of Pennsylvania; U18DP006512 to University of Florida; U18DP006509 to Geisinger; U18DP006500 to Indiana University and Purdue University at Indianapolis; U18DP006513 to University of South Carolina; U18DP006506 to Kaiser Foundation Hospitals; U18DP006693 and U18DP006694 to Lurie Children’s Hospital; U18DP006517 to University of Colorado Component-A; U18DP006518 to University of Colorado Component-B; and U18DP006633 to New York University Long Island School of Medicine).

Inpatient stroke mortality remains high at 2-5% in the U.S. This retrospective cohort study evaluated all-cause mortality in ischemic stroke hospitalizations using real-world data from a mature health information exchange (HIE) over a 17-year period (2004-2020). We analyzed 88,397 unique inpatient encounters corresponding to 62,271 adult patients aged 45 and over. Ischemic stroke hospitalizations were identified using diagnostic codes. We modeled all-cause inpatient mortality using multivariable logistic regression, adjusting for socio-demographics, Charlson Comorbidity Index, and other relevant factors. Time was included as year fixed effects. Overall, between 2004 and 2020 all-cause inpatient ischemic stroke mortality was 2.36% in Indiana. Compared to 2004, the odds of inpatient mortality among stroke patients were significantly lower for most years. Rural residence was associated with higher adjusted odds (OR 1.31, 95% CI: 1.17, 1.47) for all-cause inpatient ischemic stroke mortality. Compared to commercial insurance, Medicare insurance (OR 1.80, 95% CI: 1.49, 2.17) was associated with higher adjusted odds of all-cause inpatient ischemic stroke mortality. Our findings suggest that inpatient ischemic stroke mortality declined through 2014 relative to 2004 in Indiana. Real-world evidence from our study may inform care delivery especially for rural residents and patients on Medicare in Indiana.

Introduction and Objective: Type 2 diabetes (T2D) incidence has been increasing among youth. We estimated T2D incidence among youth (10-17 years) and young adults (18-44 years) using multiple networks of electronic health records (EHRs). Methods: The DiCAYA network includes six health systems/networks (HS) and two geographic centers in the United States. Incident T2D was identified in HS and geographic center EHRs leveraging a common data model and using first-time ICD-10 diagnosis codes in 2018-2023. Rates were calculated by age, sex, race/ethnicity, and site type. Results: Among youth aged 10-17, T2D incidence rates were 29.2 and 39.8 per 100,000 persons per year for geographic centers and HS, respectively (Table). Rates increased with age, were higher among females, and were highest among non-Hispanic Black and Hispanic youth. Among young adults aged 18-44, T2D incidence rates were 195.0 and 326.8 per 100,000 persons per year for geographic centers and HS, respectively. Patterns by age and race/ethnicity were similar to those in youth. Sex-specific patterns differed, with female rates higher in geographic centers but male rates higher in HS. Conclusion: T2D incidence rates exhibited similar patterns by age and race/ethnicity in youth and young adults. While the magnitude of rates varied by type of EHR network, findings are consistent with prior evidence of high T2D incidence in U.S. youth. Disclosure M. Mefford: Research Support; Current; Merck & Co., Inc. A. Rajan: None. T. Crume: None. D. Dabelea: None. A.G. Hirsch: None. H.L. Kirchner: None. M.E. Wandai: None. B.E. Dixon: None. T.S. Hannon: None. L.K. Billings: Advisory Panel; Current; Novo Nordisk, Lilly, Sanofi, Amgen Inc., Bayer AG. H.S. Gordon: None. J. Divers: None. L. Thorpe: None. R. Anthopolos: None. D.C. Lee: None. A.D. Liese: None. C. Rudisill: None. I. Zaganjor: None. M. Pavkov: None. K. Reynolds: Research Support; Ended; Merck & Co., Inc. Funding This work was supported by the Centers for Disease Control and Prevention’s National Center for Chronic Disease Prevention and Health Promotion and Prevention and the National Institute for Diabetes and Digestive and Kidney Diseases (grants U18DP006521 to Children’s Hospital of Pennsylvania; U18DP006512 to University of Florida; U18DP006509 to Geisinger; U18DP006500 to Indiana University and Purdue University at Indianapolis; U18DP006513 to University of South Carolina; U18DP006506 to Kaiser Foundation Hospitals; U18DP006693 and U18DP006694 to Lurie Children’s Hospital; U18DP006517 to University of Colorado Component-A; U18DP006518 to University of Colorado Component-B; and U18DP006510 to New York University Long Island School of Medicine).

Background:The 2024-25 influenza season was the most severe in the United States (US) since 2017- 18, with co-circulation of both influenza A virus subtypes (H1N1 and H3N2). Influenza vaccine effectiveness (VE) has varied by season, setting, and patient characteristics. Methods:Using electronic healthcare encounter data from eight US states, we evaluated influenza vaccine effectiveness (VE) against influenza-associated hospitalizations and emergency department or urgent care (ED/UC) encounters from October 2024-April 2025 among children aged 6 months-17 years and adults aged ≥18 years. Using a test-negative, case-control design, we compared the odds of influenza vaccination between acute respiratory illness (ARI) encounters with a positive (cases) versus negative (controls) test for influenza by molecular assay, adjusting for confounders. Results:Analyses included 108,618 encounters (5,764 hospitalizations and 102,854 ED/UC encounters) among children and 309,483 encounters (76,072 hospitalizations and 233,411 ED/UC encounters) among adults. Among children across care settings, 17.0% (6,097/35,765) of cases versus 29.4% (21,449/72,853) of controls were vaccinated. Among adults, 28.2% (21,832/77,477) of cases versus 44.2% (102,560/232,006) of controls were vaccinated. VE was 51% (95% confidence interval [95% CI]: 41-60%) against influenza-associated hospitalizations and 54% (95% CI: 52-55%) against influenza- associated ED/UC encounters among children. VE was 43% (95% CI: 41-46%) against influenza- associated hospitalizations and 49% (95% CI: 47-50%) against influenza-associated ED/UC encounters among adults. Conclusions:Influenza vaccination provided protection against influenza-associated hospitalizations and ED/UC encounters among children and adults in the US during the severe 2024-25 influenza season. These findings support influenza vaccination as an important tool to reduce influenza-associated disease.

Test negative design studies allow for COVID-19 vaccine effectiveness (VE) estimation while minimizing selection bias from healthcare seeking and testing practices. However, failure to consider correlation between vaccination behaviors may result in biased COVID-19 VE estimates. Using different methods to account for potential correlation between COVID-19, influenza, and respiratory syncytial virus (RSV) vaccination status, we investigated variability in VE estimates of the 2023-2024 COVID-19 vaccine against COVID-19-associated emergency department and urgent care (ED/UC) encounters and hospitalizations during the 2023-2024 respiratory virus season. Data were leveraged from VISION, an electronic health record-based platform. VE estimates ≥7 days post vaccination did not increase by more than 5 percentage points against ED/UC encounters and 3 percentage points against hospitalizations when accounting for influenza and RSV vaccination status or excluding influenza and RSV positive controls. As the magnitude of this influence depends on season-specific factors, ongoing monitoring is warranted.

Data on COVID-19 vaccination coverage in pregnancy are sparse, with most literature focused on original monovalent and bivalent vaccines. We assessed coverage of 2023-2024 COVID-19 vaccines among pregnant women across ten health care organizations in the United States participating in the Vaccine Safety Datalink (VSD). Among 167,478 women aged 18-49 years with a pregnancy during September 2023-August 2024, 23,941 (14.3%) received a 2023-2024 COVID-19 vaccine dose prior to or during their pregnancy. We identified higher coverage with increasing age groups (range 4.8% to 21.2% vaccinated) and increasing healthcare utilization during pregnancy (range 9.8% to 17.6% vaccinated). We found differences in coverage by maternal race and ethnicity (range 7.2% to 23.2% vaccinated), and by calendar month of pregnancy start (range 1.3% to 17.6% vaccinated). Uptake of 2023-2024 COVID-19 vaccines was low in this population. The VSD will continue monitoring COVID-19 vaccination coverage in pregnancy in the 2024-2025 season, including coverage by maternal race and ethnicity.

An electronic public health surveillance (e-PHS) embracing One Health and participatory approaches will collect and analyze data at the human-animal-environment interface to enhance real-time information for the prevention and control of public health emergencies (PHE) such as infectious disease outbreaks. Yet full implementation is suboptimal worldwide. Leveraging the capabilities of emerging digital technologies legally and ethically, we described the scope, added benefits, and applicability of a novel cloud-based, artificial intelligence-enabled One Health Integrated Disease Surveillance and Response health information system (AI-OneHIS) data infrastructure for modernizing the existing traditional PHS models. This multifaceted innovation will ensure faster data capture, seamless interoperability of fragmented HIS, and precise decision support, while preserving their structures, functionalities, and capabilities for routine operations and data sovereignty. This should enable the prevention, timely detection, and effective response to PHE for improved health outcomes if implemented with fidelity on a strong governance-collaboration-informatics-analytics framework.

Leaders and supervisors oversee informatics and data modernization projects in public health agencies, some of which involve significant changes to the technical infrastructure. Informatics/data knowledge is vital and studies that examine education, experience, and skills gaps of public health leaders are limited. The 2024 Public Health Workforce Interests and Needs Survey (PHWINS) data on Tier 2 (supervisors, managers) and Tier 3 ( executives) (N=15,681) were analyzed to understand training needs relevant to informatics. One-fourth of respondents had BS/MS/PhD degrees, but those degrees were not in public health. More than half (58%) of respondents across public health settings have <5 years of experience in their current position, but close to one-fourth had >2 decades of experience in public health practice. Technical skill needs for data/informatics jobs was lower than other job roles. Of the 6 questions examined for training needs, the one on quality improvement had the most skill gap. Approximately one-fifth of respondents across all job categories and across various public health settings expressed that time and resources are both needed to address their training needs.

Importance:The 2025-2026 COVID-19 vaccine, targeting JN.1 and JN.1-derived sublineages, became available in the US in September 2025. Objective:To assess the estimated interim effectiveness of 2025-2026 COVID-19 vaccines against medically attended COVID-19 among immunocompetent adults aged 18 years or older in the US. Design, Setting, and Participants:This case-control study used a test-negative design to investigate patient encounters captured in the Virtual SARS-CoV-2, Influenza, and Other Respiratory Viruses Network, an electronic medical record-based network of health care systems (253 emergency departments/urgent cares [ED/UCs] and 179 hospitals in 7 states) from September 3, 2025, to December 31, 2025. Patient encounters with COVID-19-like illness and a molecular or antigen SARS-CoV-2 test 10 days before to 3 days after the encounter date were included. Exposure:2025-2026 COVID-19 vaccination regardless of prior COVID-19 vaccination. Main Outcomes and Measures:The main outcomes were COVID-19-associated ED/UC encounters and COVID-19-associated hospitalizations. Cases were defined as encounters with a positive molecular or antigen SARS-CoV-2 test and controls as encounters with a negative molecular SARS-CoV-2 test. The odds of 2025-2026 COVID-19 vaccination among cases and controls, adjusting for confounders, were compared and used to estimate vaccine effectiveness (VE) as (1 - adjusted odds ratio) × 100%. Results:In 85 725 ED/UC encounters among adults aged 18 years and older (51 841 [60%] aged 18-64 years; 51 775 female [60%]), 206 of 3941 cases (5%) and 9453 of 81 784 controls (12%) received a 2025-2026 COVID-19 vaccination. Estimated VE against COVID-19-associated ED/UC encounters was 50% (95% CI, 42%-57%; median [IQR] time since 2025-2026 COVID-19 vaccine dose receipt, 47 [27-69] days). In 26 073 hospitalizations with a COVID-19-like illness (17 530 [67%] aged ≥65 years; 13 985 female [54%]), 60 of 1022 cases (6%) received a 2025-2026 COVID-19 vaccination compared with 3080 of 25 051 controls (12%). Estimated VE against COVID-19-associated hospitalization was 55% (95% CI, 41%-66%; median [IQR] time since 2025-2026 COVID-19 vaccine dose receipt, 46 [26-68] days). Among patients aged 65 years or older, estimated VE against ED/UC encounters was 48% (95% CI, 37%-56%; median [IQR] time since dose receipt, 48 [27-69] days; 33 884 encounters) and against hospitalization was 53% (95% CI, 37%-65%; median [IQR] time since dose receipt, 46 [26-69] days; 17 530 hospitalizations). Conclusions and Relevance:In this study, receipt of 2025-2026 COVID-19 vaccination was associated with additional protection beyond existing immunity in adults against medically attended COVID-19, including ED/UC encounters and hospitalizations, compared with no receipt of a 2025-2026 vaccine dose. These findings suggest that adults can reduce their likelihood of severe COVID-19-associated outcomes by obtaining a 2025-2026 COVID-19 vaccination.

CDC recommends annual influenza vaccination for all persons aged ≥ 6 months. We estimated 2024–2025 seasonal influenza vaccine effectiveness (VE) against influenza–associated hospitalizations among adults.Figure 1.2024–2025 seasonal influenza vaccine effectiveness against influenza–associated hospitalizations among adults aged ≥ 18 years — VISION Network, October 2024–March 2025Abbreviations: CI = Confidence interval; ICU = Intensive care unit; IQR = Interquartile range; VE = vaccine effectiveness.a) Patients were considered vaccinated if they received ≥1 2024–2025 influenza vaccine dose ≥14 days before the index date, defined as the earlier date of the most recent influenza test and the hospital admission date.b) VE was estimated using multivariable logistic regression models comparing the odds of receipt of ≥1 2024–2025 influenza vaccine dose versus no dose among cases and controls. Models were adjusted for age, sex, race and ethnicity, calendar day, and healthcare system. Age and calendar day were treated as natural cubic splines with 4 degrees of freedom.c) Influenza A and B coinfections were excluded from influenza A and B case counts and from VE estimates against influenza A and B.d) Patients were considered immunocompromised if they had ≥1 ICD-10 discharge diagnosis code for any of the following conditions: hematologic malignancy, solid malignancy, bone marrow transplant, solid organ transplant, rheumatologic/inflammatory disorder, other intrinsic immunodeficiency condition, or HIV/AIDS.e) To estimate VE against ICU admission, cases were restricted to encounters with ICU admission and no in-hospital death. Data from the VISION Network were used to estimate influenza VE using a test-negative, case-control design. The analysis included hospitalizations among adults aged ≥ 18 years with ≥ 1 acute respiratory illness (ARI)–associated ICD-10 discharge diagnosis code from October 1, 2024–March 7, 2025 in six US healthcare systems. Cases were ARI hospitalizations with a positive molecular influenza test within 10 days before to 72 hours after the admission date. Controls were ARI hospitalizations with a negative molecular influenza test during the same interval. VE was estimated using multivariable logistic regression comparing the odds of receipt of ≥ 1 2024–2025 influenza vaccine dose versus no dose among cases and controls. VE models were adjusted for age, sex, race and ethnicity, calendar day, and healthcare system. A total of 31,338 ARI hospitalizations met inclusion criteria, including 4,969 cases and 26,369 controls (Figure). Overall VE against influenza–associated hospitalizations was 46% (95% CI=43–50%) with a median time since vaccination of 79 days (IQR=50–107). When stratified by time since vaccination, VE was 46% (95% CI=38–53%) at 14–59 days, 41% (95% CI=36–45%) at 60–119 days, and 9% (95% CI=-1 to 19%) at ≥ 120 days. VE was 46% (95% CI=42–50%) against influenza A and 65% (95% CI=41–80%) against influenza B. Among immunocompetent and immunocompromised adults, VE was 49% (95% CI=45–53%) and 33% (95% CI=23–42%), respectively. VE against influenza–associated intensive care unit (ICU) admission was 49% (95% CI=38–58%) and against in-hospital death was 48% (95% CI=31–62%). 2024–2025 seasonal influenza vaccines provided protection against influenza–associated hospitalizations among adults with evidence of decreased VE ≥ 120 days after vaccination. VE point estimates were higher against influenza B than against influenza A and among immunocompetent versus immunocompromised adults. VE against influenza–associated ICU admission and in-hospital death were similar to that against hospitalization. Zachary Weber, PhD, MS, Centers for Disease Control and Prevention, Contract #200-2019-F-06819: Grant/Research Support Duck-Hye Yang, PhD, Centers for Disease Control and Prevention, Contract #200-2019-F-06819: Grant/Research Support Stephanie Irving, MHS, Westat: Grant/Research Support Sara Y. Tartof, PhD, MPH, Centers for Disease Control and Prevention: Grant/Research Support Nicola P. Klein, MD, PhD, AstraZeneca: Grant/Research Support|Centers for Disease Control and Prevention: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|Janssen: Grant/Research Support|Merck: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi Pasteur: Grant/Research Support|Seqirus: Grant/Research Support Shaun J. Grannis, MD, MS, Centers for Disease Control and Prevention: Grant/Research Support|National Institutes of Health NCATS: Grant/Research Support|National Institutes of Health NIMH: Grant/Research Support Toan Ong, PhD, Centers for Disease Control and Prevention via Westat: Grant/Research Support|Patent Title: Systems and Methods For Record Linkage: Patent Number: PCT/US2018/047961|PCORI: Travel Support|Regenstrief Institute: Advisor/Consultant|Regenstrief Institute: Travel Support Sarah W. Ball, MPH, ScD, Centers for Disease Control and Prevention, Contract #200-2019-F-06819: Grant/Research Support|Centers for Disease Control and Prevention, Contract #75D30121D12779: Grant/Research Support|Novavax: Grant/Research Support Malini B. DeSilva, MD, MPH, Centers for Disease Control and Prevention Vaccine Safety Datalink: Grant/Research Support|Westat: Grant/Research Support Padma Kppolu, MPH, Westat: Grant/Research Support S. Bianca Salas, MPH, Centers for Disease Control and Prevention: Grant/Research Support|Pfizer: Grant/Research Support Lina S. Sy, MPH, AstraZeneca: Grant/Research Support|Dynavax: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|Moderna: Grant/Research Support Bruno Lewin, MD, Centers for Disease Control and Prevention: Grant/Research Support|National Institutes of Health: Grant/Research Support Richard Contreras, MS, Centers for Disease Control and Prevention: Grant/Research Support Ousseny Zerbo, PhD, Centers for Disease Control and Prevention: Grant/Research Support|Moderna: Grant/Research Support|National Institutes of Health: Grant/Research Support|Pfizer: Grant/Research Support John R. Hansen, MPH, Centers for Disease Control and Prevention: Grant/Research Support Lawrence Block, MPH, MPA, Centers for Disease Control and Prevention: Grant/Research Support Karen B. Jacobson, MD, MPH, Centers for Disease Control and Prevention: Grant/Research Support|National Institutes of Health: Grant/Research Support|Pfizer: Grant/Research Support William F. Fadel, PhD, Centers for Disease Control and Prevention: Grant/Research Support Catia Chavez, MPH, Westat: Grant/Research Support Adam Yates, PhD, Beehive Study: Grant/Research Support|Centers for Disease Control and Prevention, Contract #200-2019-F-06819: Grant/Research Support Lindsey Kirshner, MPH, Centers for Disease Control and Prevention, Contract #200-2019-F-06819: Grant/Research Support Charlene E. McEvoy, MD, MPH, Astra Zeneca: Grant/Research Support|Centers for Disease Control and Prevention: Grant/Research Support|Department of Defense: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|National Institutes of Health: Grant/Research Support|PCORI: Grant/Research Support Karthik Natarajan, PhD, Centers for Disease Control and Prevention: Grant/Research Support

OBJECTIVES:Data modernization (DM) seeks to transform data and information systems in public health (PH) to support core activities. This study characterizes incumbent PH workers who perform informatics and data-centric roles that are critical to DM activities. MATERIALS AND METHODS:Responses from the 2024 Public Health Workforce Interests and Needs Survey (PH WINS) on the US governmental PH workforce were analyzed. Out of 77 PH job classifications, 9 were mapped to informatics/data-centric roles. Demographics, work characteristics, and contribution to informatics and other PH program areas of these roles were examined. RESULTS:Based on weighted responses, informatics and data-centric roles comprise approximately 8% (N = 4786) of the PH workforce. This included PH informatics specialists (<1%), epidemiologists (3.9%), data analytics/related roles (1.7%), and information technology/computer science (IT/CS) workers (2.1%). DISCUSSION:Informatics roles are performed by PH informatics specialists as well as other workers, like epidemiologists, and PH informatics specialists support a broad range of PH activities including surveillance. CONCLUSION:Achieving the goals of DM and the national Public Health Data Strategy will require developing and supporting a variety of informatics and data-centric roles in PH agencies.

PURPOSE:A critical function of public health is to monitor diseases that impede quality of life and burden affected communities. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network aims to advance disease monitoring for diabetes using multi-site electronic health record (EHR) data. METHODS:This work involved validating and refining case definitions for accurate identification of type 1 and type 2 diabetes cases to estimate incidence and prevalence of diabetes in children, adolescents, and young adults through age 44 years. RESULTS:In this essay, we describe the challenges experienced by the Network and lessons learned. Challenges included accessing EHR data, harmonizing EHR data from heterogeneous health systems to a common data model, and developing methods to account for bias introduced by the non-representativeness of health care utilization data. Lessons learned included approaches for data quality assessment, bias correction, and scalability. CONCLUSIONS:As the US continues to evolve its public health data systems and its approach to chronic disease monitoring, the DiCAYA Network offers guidance on factors for success as well as pitfalls to avoid.

Importance:Antigenically drifted influenza A(H3N2) J.2.4.1 (subclade K) viruses predominated during the 2025-2026 Northern Hemisphere influenza season. Objective:To describe influenza activity and burden, characterize subclade K, evaluate susceptibility to influenza antivirals and postinfluenza vaccination antibodies, and estimate vaccine effectiveness. Design, Setting, and Participants:This surveillance study used multiple data sources, including (1) national surveillance of influenza-positive respiratory specimens collected by approximately 300 clinical laboratories and 100 public health laboratories from October 1, 2025, through March 14, 2026, a subset of which were further characterized; (2) serologic data of people who received 2025-2026 influenza vaccines; (3) influenza admissions data from the Influenza Hospitalization Surveillance Network (ie, 10% of US population) and the associated estimates of US burden; and (4) test-negative, case-control vaccine effectiveness estimates from the Virtual SARS-CoV-2, Influenza, and Other Respiratory Viruses Network. Exposures:Influenza infection, hospitalization, and vaccination. Main Outcomes and Measures:Outcomes included influenza virus type, subtype, and clade; antiviral susceptibility; immunogenicity; influenza-associated outpatient and emergency department visits, hospitalizations, and mortality; estimated influenza illnesses, hospitalizations, and death; and estimated vaccine effectiveness. Results:As of March 14, 2026, of the 55 318 influenza-positive respiratory specimens tested by public health laboratories, most (50 291 specimens [90.9%]) were influenza A, of which 40 779 (81.1%) were subtyped and 35 801 (87.8%) were A(H3N2). Of the 1754 characterized A(H3N2) viruses, most (1626 specimens [92.7%]) were subclade K. Postinfluenza vaccination neutralizing geometric mean antibody titers against subclade K were reduced 1.62 (95% CI, 1.29-2.02)-fold compared with the vaccine virus. All 1729 tested A(H3N2) viruses were sensitive to antivirals. Of the 27 881 recorded influenza hospitalizations, 15 426 (54.7%) were among female patients, and 15 051 (54.0%) were among patients aged 65 years or older. The estimated cumulative influenza-associated hospitalization rate was 80.0 per 100 000 which would correlate with estimates of between 28 000 000 to 49 000 000 illnesses, 360 000 to 740 000 hospitalizations, and 22 000 to 74 000 deaths in the US during the 2025-2026 season. Adjusted interim vaccine effectiveness estimates against influenza-associated emergency department or urgent care encounters and hospitalizations were 35% (95% CI, 33%-38%) and 27% (95% CI, 21%-34%), respectively. Conclusions and Relevance:This surveillance study found that while antigenically drifted viruses predominated and caused substantial morbidity and mortality, influenza vaccines were associated with a reduced risk of influenza among those who were vaccinated, and recommended antivirals remained effective.

OBJECTIVE:We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years). MATERIALS AND METHODS:In our retrospective case study from the DiCAYA Network, SARS-CoV-2 was identified using labs and diagnoses from June 1, 2020 to December 31, 2021. Patients were followed through December 31, 2022 for new diabetes diagnoses. Sites examined incident diabetes by COVID-19 status using Cox proportional hazards models. Results were pooled in meta-analyses. A bias analysis examined potential impact of COVID-19 misclassification scenarios on results, guided by hypotheses that sensitivity would be <50% and would be higher among those who developed diabetes. RESULTS:Prevalence of documented COVID-19 was low overall and variable across sites (children: 4.4%-7.7%, young adults: 6.2%-22.7%). Individuals with documented COVID-19 were at higher risk of incident diabetes compared to those with no documented infection, but results were heterogeneous across sites. Findings were highly sensitive to COVID-19 misclassification assumptions. Observed results could be biased away from the null under several differential misclassification scenarios. DISCUSSION:Although EHR-based documentation of COVID-19 was associated with incident diabetes, COVID-19 phenotypes likely had low sensitivity, with considerable variation across sites. Misclassification assumptions strongly impacted interpretation of results. CONCLUSION:Given the potential for low phenotype sensitivity and misclassification, caution is warranted when interpreting analyses of COVID-19 and incident diabetes using clinical or administrative databases.

OBJECTIVE:To assess adverse pregnancy and birth outcomes after bivalent prefusion F subunit-based respiratory syncytial virus vaccine (RSVpreF) vaccination during the first season of availability. METHODS:This was a target trial emulation study including eight health systems across eight states (California, Oregon, Washington, Colorado, Maryland, Virginia, Minnesota, and Wisconsin) and Washington, DC, in the Vaccine Safety Datalink (VSD). We included pregnant patients aged 16-49 years who had enrolled at a VSD site between September 22, 2023, and February 29, 2024. Exposure was defined as receipt of RSVpreF vaccination between 32 and less than 37 weeks of gestation. Outcomes included preterm birth (PTB), stillbirth, small-for-gestational-age (SGA) birth weight, and hypertensive disorders of pregnancy (HDP) assessed with electronic health record data. Stillbirth cases were confirmed through chart review. Pregnant patients exposed to RSVpreF vaccines were matched 1:1 to unexposed pregnant patients at the gestational week of vaccination by propensity to be vaccinated and VSD site. Unexposed pregnant patients were assigned an index date equivalent to the gestational day of vaccination for their vaccinated match. If the unvaccinated match was subsequently vaccinated, the pair was censored. We report adverse event risks and adjusted risk ratios (aRRs) with corresponding 95% CIs adjusted for nulliparity using a log binomial model with robust variance. RESULTS:We identified 13,966 pregnant patients who received the RSVpreF vaccine. A higher percentage of nulliparous patients were in the vaccinated group (46.4%) compared with the unvaccinated group (38.7%). Comparing RSVpreF vaccinated pregnant patients and their unvaccinated matches, rates of PTB (4.0% vs 4.5%, respectively; aRR 0.90, 95% CI, 0.80-1.00), stillbirth (0.79/1,000 and 0.72/1,000; aRR 0.99, 95% CI, 0.41-2.36), and SGA birth weight (6.8% and 6.5%; aRR 1.02, 95% CI, 0.92-1.12) did not significantly differ. The rate of any HDP among RSVpreF vaccinated patients was 17.3% vs 15.0% among their unvaccinated matches (aRR 1.13, 95% CI, 1.07-1.19). CONCLUSION:Initial prenatal RSVpreF safety surveillance shows a largely favorable safety profile. Although we identified a small but statistically significant increased risk for HDP after RSVpreF vaccination, there was no increased risk for PTB, SGA birth weight, or stillbirth.

On June 26, 2024, the CDC updated respiratory syncytial virus (RSV) vaccine recommendations to a single dose of RSV vaccine for all adults aged ≥ 75 years and adults aged 60-74 years with increased risk of severe RSV disease. Using electronic health record (EHR) data from the VISION platform, we described characteristics of patients testing negative for RSV who did and did not receive an RSV vaccine and assessed factors associated with RSV vaccine receipt.Figure 1:Characteristics associated with receipt of respiratory syncytial virus (RSV) vaccination among test-negative patients with an emergency department (ED) encounter for RSV-like illness (RLI) during the 2024-2025 season in the VISION network, N=23,403 patientsFigure 2:Characteristics associated with receipt of respiratory syncytial virus (RSV) vaccine among test-negative patients with an inpatient encounter for RSV-like illness (RLI) during the 2024-2025 season in the VISION network, N=25,020 patients Patients with ≥ 1 emergency department (ED) or inpatient encounter at any of 6 participating health systems in 8 states with RSV-like illness (RLI) during October 1, 2024-March 31, 2025 were included. Vaccination status was ascertained from EHR, state and city immunization information systems, and medical claims. Patients who tested positive for SARS-CoV-2 or influenza viruses at the same RLI encounter were excluded. Patient age, sex, race and ethnicity, Medicaid status, number of underlying medical conditions, month of medical encounter, and documented receipt of COVID-19 or influenza vaccines were evaluated as covariates when assessing the odds of vaccination. The best fitting multivariable logistic regression models using Bayesian Information Criterion were chosen. Among 48423 included patients, 2113 (4.4%) had documented RSV vaccine receipt. The odds of RSV vaccination differed by site and increased with calendar time and age. Compared to patients aged 60-64 years, those aged ≥ 75 years were more likely to have received an RSV vaccine (ED: aOR: 3.6, 95%CI: 2.7-4.8, Figure 1; inpatient: aOR: 2.3, 95%CI: 1.7-3.0, Figure 2). Receipt of both influenza and COVID-19 vaccine within the same season had the strongest association with RSV vaccination in both the ED (aOR: 14.88, 95%CI: 11.87-18.89, Figure 1) and hospital setting (aOR: 20.04, 95%CI: 16.20-25.03, Figure 2). Receipt of other respiratory viral vaccines was the strongest indicator of RSV vaccination in the 2024-2025 RSV season in patients testing negative for RSV among all demographic and clinical characteristics considered. RSV vaccination was lower among those aged 60-64 years than older patients. These findings inform future methods to estimate vaccine effectiveness and inform policy implementation. Gabriela Vazquez-Benitez, PhD, MSc, AbbVie: research funding not related to this study|Sanofi: Grant funding for other research not related to this study Stephanie Irving, MHS, Westat: Grant/Research Support Nicola P. Klein, MD, PhD, AstraZeneca: Grant/Research Support|Centers for Disease Control and Prevention: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|Janssen: Grant/Research Support|Merck: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi Pasteur: Grant/Research Support|Seqirus: Grant/Research Support Shaun J. Grannis, MD, MS, Centers for Disease Control and Prevention: Grant/Research Support|National Institutes of Health NCATS: Grant/Research Support|National Institutes of Health NIMH: Grant/Research Support Toan Ong, PhD, Centers for Disease Control and Prevention via Westat: Grant/Research Support|Patent Title: Systems and Methods For Record Linkage: Patent Number: PCT/US2018/047961|PCORI: Travel Support|Regenstrief Institute: Advisor/Consultant|Regenstrief Institute: Travel Support Sarah W. Ball, MPH, ScD, Centers for Disease Control and Prevention, Contract #200-2019-F-06819: Grant/Research Support|Centers for Disease Control and Prevention, Contract #75D30121D12779: Grant/Research Support|Novavax: Grant/Research Support Jingran Cao, MS, Sanofi Pasteur: Grant/Research Support Charlene E. McEvoy, MD, MPH, Astra Zeneca: Grant/Research Support|Centers for Disease Control and Prevention: Grant/Research Support|Department of Defense: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|National Institutes of Health: Grant/Research Support|PCORI: Grant/Research Support Ousseny Zerbo, PhD, Centers for Disease Control and Prevention: Grant/Research Support|Moderna: Grant/Research Support|National Institutes of Health: Grant/Research Support|Pfizer: Grant/Research Support John R. Hansen, MPH, Centers for Disease Control and Prevention: Grant/Research Support Lawrence Block, MPH, MPA, Centers for Disease Control and Prevention: Grant/Research Support Karen B. Jacobson, MD, MPH, Centers for Disease Control and Prevention: Grant/Research Support|National Institutes of Health: Grant/Research Support|Pfizer: Grant/Research Support William F. Fadel, PhD, Centers for Disease Control and Prevention: Grant/Research Support Catia Chavez, MPH, Westat: Grant/Research Support Karthik Natarajan, PhD, Centers for Disease Control and Prevention: Grant/Research Support Ryan E. Wiegand, PhD, Merck & Co., Inc.: Stocks/Bonds (Public Company)|Sanofi S.A.: Stocks/Bonds (Public Company)

Background:The 2024-2025 influenza season was the most severe in the United States (US) since 2017-2018, with co-circulation of both influenza A virus subtypes (H1N1 and H3N2). Influenza vaccine effectiveness (VE) has varied by season, setting, and patient characteristics. Methods:Using electronic healthcare encounter data from 7 VISION sites in 8 US states, we evaluated influenza VE against influenza-associated hospitalizations and emergency department or urgent care (ED/UC) encounters from October 2024 to April 2025 among children aged 6 months-17 years and adults aged ≥18 years. Using a test-negative, case-control design, we compared the odds of influenza vaccination between acute respiratory illness encounters with a positive (cases) versus negative (controls) test for influenza by molecular assay, adjusting for confounders. Results:Analyses included 108 618 encounters (5764 hospitalizations and 102 854 ED/UC encounters) among children and 309 483 encounters (76 072 hospitalizations and 233 411 ED/UC encounters) among adults. Among children across care settings, 17.0% (6097/35 765) of cases versus 29.4% (21 449/72 853) of controls were vaccinated. Among adults, 28.2% (21 832/77 477) of cases versus 44.2% (102 560/232 006) of controls were vaccinated. VE was 51% (95% confidence interval [95% CI]: 41%-60%) against influenza-associated hospitalizations and 54% (95% CI: 52%-55%) against influenza-associated ED/UC encounters among children. VE was 43% (95% CI: 41%-46%) against influenza-associated hospitalizations and 49% (95% CI: 47%-50%) against influenza-associated ED/UC encounters among adults. Conclusions:Influenza vaccination provided protection against influenza-associated hospitalizations and ED/UC encounters among children and adults in the US during the severe 2024-2025 influenza season. These findings support influenza vaccination as an important tool to reduce influenza.

OBJECTIVE:In 2023, 2 products, a long-acting monoclonal antibody (nirsevimab) and maternal respiratory syncytial virus (RSV) vaccination, were recommended in the United States to prevent severe RSV disease among infants in their first RSV season. Low uptake during the 2023 to 2024 RSV season limited effectiveness estimates. We estimated nirsevimab and maternal RSV vaccine effectiveness against RSV-associated emergency department (ED) encounters and hospitalization among US infants during the 2024 to 2025 RSV season, when uptake was higher. METHODS:We used electronic health record data from 5 health care systems in test-negative analyses of ED encounters and hospitalizations with RSV-like illness during October 1, 2024 to March 31, 2025, among infants in their first RSV season. Nirsevimab and maternal RSV vaccine effectiveness were estimated by comparing RSV-positive with RSV-negative encounters with respect to immunization status in separate logistic regression models adjusted for age, race and ethnicity, sex, calendar day, and geographic region. RESULTS:Among 3531 ED encounters and 470 hospitalizations included in the nirsevimab analyses, nirsevimab effectiveness against RSV-associated ED encounters and hospitalization was 62% (95% CI: 54-68) and 77% (95% CI: 61-87), respectively. Among 810 ED encounters and 187 hospitalizations included in the maternal RSV vaccine analyses, vaccine effectiveness against RSV-associated ED encounters and hospitalization was 49% (95% CI: 26-65) and 82% (95% CI: 54-93), respectively. CONCLUSION:Nirsevimab and maternal RSV vaccination were effective against RSV-associated ED encounters and hospitalization among infants in their first RSV season. As uptake increases and additional products are recommended, monitoring infant RSV immunization effectiveness remains important.

OBJECTIVE:Survival for individuals with sickle cell disease (SCD) is increasing, and along with it the potential for comorbid conditions that may be caused by cumulative tissue damage. This paper examines the prevalence of comorbidities among individuals with SCD in Indiana and assesses how these vary by age group. METHODS:Data from the Indiana Sickle Cell Data Collection program (2015-2021) were used to identify confirmed and probable SCD cases. Comorbidities-asthma, avascular necrosis, stroke, retinopathy, moyamoya disease, and sickle nephropathy-were identified using ICD-9/10 codes. RESULTS:Among the 1689 individuals, the 0-17 age group was the largest (42.9%). Asthma was the most common comorbidity (23.7%), highest in the 18-39 age group. Avascular necrosis, stroke, and retinopathy increased with age, while moyamoya was most common in youth. Logistic regression showed increasing age was associated with the risk of any comorbidity risk. DISCUSSION:This study confirmed a high comorbidity burden among individuals with SCD, and that the risk of having any of the examined comorbidities increased with age. Findings align with national data, though differences in asthma and stroke prevalence highlight the impact of data sources and population characteristics. CONCLUSION:These results support the need for age-specific, multidisciplinary care strategies and provide valuable insights for clinicians, policymakers, and public health officials to improve outcomes and allocate resources effectively for the SCD population.