On April 24, 2023, the American Samoa Department of Health (ASDoH) declared a public health emergency amid concern about a possible measles outbreak given low 2-dose vaccination coverage at the time. ASDoH had received two positive measles immunoglobulin (Ig) M test results after Flag Day festivities 1 week earlier from vaccinated children. ASDoH performed active case finding, took actions to mitigate transmission, and requested technical assistance from CDC. ASDoH implemented a vaccination campaign to improve suboptimal coverage. Confirmatory molecular testing of specimens from these initial persons under investigation (PUIs) was not possible, but subsequent testing of specimens from additional PUIs by Hawaii State Laboratories Division and CDC ruled out measles. In settings with low measles prevalence, measles antibody testing results have low positive predictive value and can lead to difficulties with interpreting results. Testing for additional pathogens revealed a variety of viruses known to cause common childhood viral exanthems. Both molecular and serologic testing should be performed for all suspected measles cases. To decrease the probability of false-positive IgM results, testing should be reserved for cases that meet the Council of State and Territorial Epidemiologists measles case definition, especially those in persons with no evidence of immunity and with a history of recent international travel. In addition, maintaining high measles vaccination coverage can prevent future outbreaks.

Measles and rubella are vaccine-preventable viral diseases and can be prevented by safe, highly effective vaccination with measles- and rubella-containing vaccines. Given the myriad causes of febrile exanthems, laboratory surveillance for both measles and rubella is important to document the incidence of these diseases and to track the progress and maintenance of elimination in near- and post-elimination settings. Diagnostic challenges can hinder effective surveillance and classification challenges can hinder efforts to demonstrate achievement or maintenance of elimination. In this report, we review diagnostic and classification challenges for measles and rubella in near- and post-elimination settings.

Measles, a highly contagious respiratory virus with the potential to cause severe complications, hospitalization, and death, was declared eliminated from the United States in 2000; however, with ongoing global transmission, infections in the United States still occur. On March 7, 2024, the Chicago Department of Public Health (CDPH) confirmed a case of measles in a male aged 1 year residing in a temporary shelter for migrants in Chicago. Given the congregate nature of the setting, high transmissibility of measles, and low measles vaccination coverage among shelter residents, measles virus had the potential to spread rapidly among approximately 2,100 presumed exposed shelter residents. CDPH immediately instituted outbreak investigation and response activities in collaboration with state and local health departments, health care facilities, city agencies, and shelters. On March 8, CDPH implemented active case-finding and coordinated a mass vaccination campaign at the affected shelter (shelter A), including vaccinating 882 residents and verifying previous vaccination for 784 residents over 3 days. These activities resulted in 93% measles vaccination coverage (defined as receipt of ≥1 recorded measles vaccine dose) by March 11. By May 13, a total of 57 confirmed measles cases associated with residing in or having contact with persons from shelter A had been reported. Most cases (41; 72%) were among persons who did not have documentation of measles vaccination and were considered unvaccinated. In addition, 16 cases of measles occurred among persons who had received ≥1 measles vaccine dose ≥21 days before first known exposure. This outbreak underscores the need to ensure high vaccination coverage among communities residing in congregate settings.

Measles is a highly infectious febrile rash illness and was declared eliminated in the United States in 2000. However, measles importations continue to occur, and U.S. measles elimination status was threatened in 2019 as the result of two prolonged outbreaks among undervaccinated communities in New York and New York City. To assess U.S. measles elimination status after the 2019 outbreaks and to provide context to understand more recent increases in measles cases, CDC analyzed epidemiologic and laboratory surveillance data and the performance of the U.S. measles surveillance system after these outbreaks. During January 1, 2020-March 28, 2024, CDC was notified of 338 confirmed measles cases; 97 (29%) of these cases occurred during the first quarter of 2024, representing a more than seventeenfold increase over the mean number of cases reported during the first quarter of 2020-2023. Among the 338 reported cases, the median patient age was 3 years (range = 0-64 years); 309 (91%) patients were unvaccinated or had unknown vaccination status, and 336 case investigations included information on ≥80% of critical surveillance indicators. During 2020-2023, the longest transmission chain lasted 63 days. As of the end of 2023, because of the absence of sustained measles virus transmission for 12 consecutive months in the presence of a well-performing surveillance system, U.S. measles elimination status was maintained. Risk for widespread U.S. measles transmission remains low because of high population immunity. However, because of the increase in cases during the first quarter of 2024, additional activities are needed to increase U.S. routine measles, mumps, and rubella vaccination coverage, especially among close-knit and undervaccinated communities. These activities include encouraging vaccination before international travel and rapidly investigating suspected measles cases.

On November 5, 2022, Columbus Public Health, Ohio and the Ohio Department of Health were notified of two children aged 2 years who were admitted to a central Ohio hospital with rash, fever, cough, and congestion, suggestive of measles. Both children were undergoing medical evaluation and treatment for other etiologies before measles was considered in the differential diagnosis. Neither child had received measles, mumps, and rubella (MMR) vaccine, and neither had known contact with a person with measles. Each patient subsequently received a positive measles real-time reverse transcription–polymerase chain reaction (RT-PCR) test result. Neither child had traveled internationally, but during June 12–October 8, 2022, four internationally imported measles cases had been confirmed among unvaccinated Franklin County, Ohio residents who had traveled to areas in East Africa where measles outbreaks were ongoing (1). Investigation of the U.S.-acquired measles cases identified additional measles cases, and local and state health departments confirmed a community outbreak on November 9, 2022. During this community measles outbreak in central Ohio, 85 locally acquired measles cases were confirmed with rash onsets during October 22–December 24, 2022; however, no definitive link to the previous international importations was established. The outbreak was declared over on February 4, 2023, 42 days (two measles incubation periods) after the last reported case.

BACKGROUND: On Aug 29, 2021, Operation Allies Welcome (OAW) was established to support the resettlement of more than 80 000 Afghan evacuees in the USA. After identification of measles among evacuees, incoming evacuee flights were temporarily paused, and mass measles vaccination of evacuees aged 6 months or older was introduced domestically and overseas, with a 21-day quarantine period after vaccination. We aimed to evaluate patterns of measles virus transmission during this outbreak and the impact of control measures. METHODS: We conducted a measles outbreak investigation among Afghan evacuees who were resettled in the USA as part of OAW. Patients with measles were defined as individuals with an acute febrile rash illness between Aug 29, 2021, and Nov 26, 2021, and either laboratory confirmation of infection or epidemiological link to a patient with measles with laboratory confirmation. We analysed the demographics and clinical characteristics of patients with measles and used epidemiological information and whole-genome sequencing to track transmission pathways. A transmission model was used to evaluate the effects of vaccination and other interventions. FINDINGS: 47 people with measles (attack rate: 0·65 per 1000 evacuees) were reported in six US locations housing evacuees in four states. The median age of patients was 1 year (range 0-26); 33 (70%) were younger than 5 years. The age distribution shifted during the outbreak towards infants younger than 12 months. 20 (43%) patients with wild-type measles virus had rash onset after vaccination. No fatalities or community spread were identified, nor further importations after flight resumption. In a non-intervention scenario, transmission models estimated that a median of 5506 cases (IQR 10-5626) could have occurred. Infection clusters based on epidemiological criteria could be delineated into smaller clusters using phylogenetic analyses; however, sequences with few substitution count differences did not always indicate single lines of transmission. INTERPRETATION: Implementation of control measures limited measles transmission during OAW. Our findings highlight the importance of integration between epidemiological and genetic information in discerning between individual lines of transmission in an elimination setting. FUNDING: US Centers for Disease Control and Prevention.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work. Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below: AIpert-pwllnod: Natural Sciences and Engineering Research Council of Canada; Caltech-CS156: Gary Clinard Innovation Fund; CEID-Walk: University of Georgia; CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook; COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health; Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information & Data Science Pilot Project; Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation; CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation; DDS-NBDS: NSF III-1812699; epiforecasts-ensemble1: Wellcome Trust (210758/Z/18/Z) FDANIHASU: supported by the Intramural Research Program of the NIH/NIDDK; GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowment, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines, CDC MInD-Healthcare U01CK000531-Supplement; IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096); Imperial-ensemble1: SB acknowledges funding from the Wellcome Trust (219415); Institute of Business Forecasting: IBF; IowaStateLW-STEM: NSF DMS-1916204, Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics; IUPUI CIS: NSF; JHU_CSSE-DECOM: JHU CSSE: National Science Foundation (NSF) RAPID Real-time Forecasting of COVID-19 risk in the USA. 2021-2022. Award ID: 2108526. National Science Foundation (NSF) RAPID Development of an interactive web-based dashboard to track COVID-19 in real-time. 2020. Award ID: 2028604; JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers for Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant); JHU_UNC_GAS-StatMechP ol: NIH NIGMS: R01GM140564; JHUAPL-Bucky: US Dept of Health and Human Services; KITmetricslab-select_ensemble: Daniel Wolffram gratefully acknowledges support by the Klaus Tschira Foundation; LANL-GrowthRate: LANL LDRD 20200700ER; MIT-Cassandra: MIT Quest for Intelligence; MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01; CA NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE); NotreDame-FRED: NSF RAPID DEB 2027718; NotreDame-mobility: NSF RAPID DEB 2027718; PSI-DRAFT: NSF RAPID Grant # 2031536; QJHong-Encounter: NSF DMR-2001411 and DMR-1835939; SDSC_ISG-TrendModel: The development of the dashboard was partly funded by the Fondation Privee des Hopitaux Universitaires de Geneve; UA-EpiCovDA: NSF RAPID Grant # 2028401; UChicagoCHATTOPADHYAY-UnIT: Defense Advanced Research Projects Agency (DARPA) #HR00111890043/P00004 (I. Chattopadhyay, University of Chicago); UCSB-ACTS: NSF RAPID IIS 2029626; UCSD_NEU-DeepGLEAM: Google Faculty Award, W31P4Q-21-C-0014; UMass-MechBayes: NIGMS #R35GM119582, NSF #1749854, NIGMS #R35GM119582; UMich-RidgeTfReg: This project is funded by the University of Michigan Physics Department and the University of Michigan Office of Research; UVA-Ensemble: National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and Virginia Dept of Health Grant VDH-21-501-0141; Wadnwani_AI-BayesOpt: This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID). The work described in this article was implemented under the TRACETB Project, managed by WIAI under the terms of Cooperative Agreement Number 72038620CA00006. The contents of this manuscript are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government; WalmartLabsML-LogForecasting: Team acknowledges Walmart to support this study Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced are available online at https://github.com/reichlab/covid19-forecast-hub https://github.com/reichlab/covid19-forecast-hub

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information& Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44

BACKGROUND: Congenital cytomegalovirus (cCMV) is not a nationally notifiable condition, and little is known about how U.S. health departments (HDs) currently conduct cCMV surveillance. METHODS: We surveyed U.S. HDs that conduct cCMV surveillance or screening activities identified through a web-based assessment. Meetings were held with each HD to enhance our understanding of survey responses. RESULTS: Ten states are systematically collecting cCMV case data to track cCMV cases during early infancy and to provide resources and services to families. Cases are ascertained using cCMV diagnostic codes, reported diagnosis, or laboratory results. Data elements collected for each case include demographics (all 10 states), clinical signs (8 states), laboratory data (4 states), treatment (4 states), and long-term outcomes (1 state). Annual number of cases reported by HDs ranged from 3 to 47 cases/year in seven states, which was much lower than the expected number of cCMV cases. All 10 HDs have the ability to analyze data collected and four disseminate findings. Major challenges of surveillance reported by HDs were lack of standardized case definitions, personnel constraints, and limited funding. CONCLUSIONS: A comprehensive account of cCMV disease burden is severely limited by low case ascertainment and paucity of data on long-term outcomes. A standardized public health case definition for cCMV would improve consistency in measuring disease prevalence across jurisdictions and over time. Surveillance for cCMV has the potential to increase disease awareness and inform strategies to prevent cCMV-associated disabilities.

On August 29, 2021, the United States government oversaw the emergent establishment of Operation Allies Welcome (OAW), led by the U.S. Department of Homeland Security (DHS) and implemented by the U.S. Department of Defense (DoD) and U.S. Department of State (DoS), to safely resettle U.S. citizens and Afghan nationals from Afghanistan to the United States. Evacuees were temporarily housed at several overseas locations in Europe and Asia* before being transported via military and charter flights through two U.S. international airports, and onward to eight U.S. military bases,(†) with hotel A used for isolation and quarantine of persons with or exposed to certain infectious diseases.(§) On August 30, CDC issued an Epi-X notice encouraging public health officials to maintain vigilance for measles among Afghan evacuees because of an ongoing measles outbreak in Afghanistan (25,988 clinical cases reported nationwide during January-November 2021) (1) and low routine measles vaccination coverage (66% and 43% for the first and second doses, respectively, in 2020) (2).

BACKGROUND: Cadmium (Cd) and lead (Pb) are widespread environmental contaminants that are known nephrotoxins. However, their nephrotoxic effects at low-environmental exposure levels are debated. OBJECTIVE: We examined the association of blood Pb (B-Pb), blood Cd (B-Cd), urinary Pb (U-Pb) and urinary Cd (U-Cd) with estimated glomerular filtration rate (eGFR) and urinary albumin (ALB). METHODS: We used multivariate linear regression to analyze the association between B-Pb, B-Cd, U-Pb, and U-Cd with eGFR and ALB in adult participants (≥20 years of age) in NHANES 2007-2012. The dataset was limited to NHANES individuals with both blood and urinary metal measurements. RESULTS: We found a statistically significant inverse association between eGFR and B-Cd and statistically significant positive associations between eGFR and both U-Cd and U-Pb, as well as statistically significant associations between ALB and the 3rd and 4th quartiles of U-Cd. CONCLUSIONS: The inverse association between eGFR and B-Cd, in conjunction with positive associations between eGFR and ALB with U-Cd, suggest that U-Cd measurement at low levels of exposure may result from changes in renal excretion of Cd due to kidney function and protein excretion. However, renal effects such as hyperfiltration from Cd-mediated kidney damage or creatinine-specific Cd effects cannot be excluded with this cross-sectional design.

On February 2, 2015, the Rhode Island Department of Health was notified of a case of meningococcal disease in a male undergraduate student at Providence College. Three days later, a second case was reported in a male undergraduate with no contact with the first student, indicating an attack rate of 44 cases per 100,000 students, nearly 500 times higher than the national incidence of 0.15 cases per 100,000 among persons aged 17-22 years (Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, CDC, unpublished data, 2013). Both cases were caused by a rare outbreak strain of Neisseria meningitidis serogroup B (ST-9069); neither case was fatal. In response to the outbreak, potential contacts received antibiotic chemoprophylaxis, and a mass vaccination campaign with a recently licensed serogroup B meningococcal (MenB) vaccine was implemented. In collaboration with CDC, the first phase of a meningococcal carriage evaluation was undertaken.

BACKGROUND: Unvaccinated health care personnel are at increased risk for transmitting vaccine-preventable diseases to their patients. The Advisory Committee on Immunization Practices (ACIP) recommends that health care personnel, including students, receive measles, mumps, rubella, hepatitis B, varicella, influenza, and pertussis vaccines. Prematriculation vaccination requirements of health professional schools represent an early opportunity to ensure that health care personnel receive recommended vaccines. OBJECTIVE: To examine prematriculation vaccination requirements and related policies at selected health professional schools in the United States and compare requirements with current ACIP recommendations. DESIGN: Cross-sectional study using an Internet-based survey. SETTING: Medical and baccalaureate nursing schools in the United States and its territories. PARTICIPANTS: Deans of accredited medical schools granting MD (n = 130) and DO (n = 26) degrees and of baccalaureate nursing programs (n = 603). MEASUREMENTS: Proportion of MD-granting and DO-granting schools and baccalaureate nursing programs that require that entering students receive vaccines recommended by the ACIP for health care personnel. RESULTS: 563 schools (75%) responded. More than 90% of all school types required measles, mumps, rubella, and hepatitis B vaccines for entering students; varicella vaccination also was commonly required. Tetanus, diphtheria, and acellular pertussis vaccination was required by 66%, 70%, and 75% of nursing, MD-granting, and DO-granting schools, respectively. Nursing and DO-granting schools (31% and 45%, respectively) were less likely than MD-granting schools (78%) to offer students influenza vaccines free of charge. LIMITATIONS: Estimates were conservative, because schools that reported that they did not require proof of immunity for a given vaccine were considered not to require that vaccine. Estimates also were restricted to schools that train physicians and nurses. CONCLUSION: The majority of schools now require most ACIP-recommended vaccines for students. Medical and nursing schools should adopt policies on student vaccination and serologic testing that conform to ACIP recommendations and should encourage annual influenza vaccination by offering influenza vaccination to students at no cost. PRIMARY FUNDING SOURCE: None.