BackgroundDeclines in childhood vaccination in the U.S. have contributed to a resurgence of vaccine-preventable diseases, including a notable increase in pertussis cases. Traditional pertussis surveillance is limited by underdiagnosis and underreporting. Participatory surveillance systems such as Outbreaks Near Me (ONM) provide an additional population-level data stream by capturing self-reported symptoms. Although pertussis signals are difficult to detect due to low incidence and symptom overlap with other infections, ONM collects free-text descriptions that may contain pertussis-specific information. Advances in large language models (LLMs) enable the extraction of relevant signals from unstructured text to potentially improve forecasting.MethodsWe analyzed U.S. pertussis case data from the CDC and ONM reports from 2022 to 2025. ONM reports were filtered for prolonged cough without alternative diagnoses and further refined using a two-step GPT-4-based pipeline that summarized participant reports and excluded cases inconsistent with pertussis to enhance case specificity. Three datasets were created: CDC-only cases, CDC and ONM filtered cases, and CDC and ONM cases post-LLM processing. Aggregated time series were split into a training set (2022-2024) and a test set (2025, first 7 months). We trained multiple forecasting models (ARIMA, XG-Boost, and linear regression) on the 2022-2024 data, first using CDC-only data to establish a baseline. The best-performing model was then applied to the two datasets, incorporating the ONM participatory data. Performance was evaluated using Mean Absolute Error (MAE).ResultsCDC-reported pertussis cases totaled 862 in 2022, 2,512 in 2023, 11,276 in 2024, and 5,937 in the first seven months of 2025. Of 2,741 ONM-suspected cases, 957 remained after LLM refinement. XGBoost yielded the best baseline performance (MAE 26.65). Incorporating ONM data improved performance: MAE decreased to 25.60 with filtered ONM cases and 24.69 with LLM-processed cases.ConclusionsIntegrating LLM-processing of participatory surveillance data with traditional surveillance enhances the accuracy of pertussis outbreak forecasting. This approach introduces a novel way to leverage free-text data, offering a promising pathway to augment traditional public health surveillance systems.

Background: Highly pathogenic avian influenza (HPAI) A(H5N1) clade 2.3.4.4b, a globally predominant strain, was introduced into poultry in the United States in 2022 via spillover from wild birds, and has since been regularly reported, posing ongoing risks to animal and human health. In 2024, the United States reported the first known HPAI A(H5N1) clade 2.3.4.4b infection in dairy cattle, rapidly evolving into a multispecies outbreak among cattle and poultry, with spillover into humans. Publicly available data remained siloed and fragmented, hindering timely response. Innovative multimodal surveillance methods can enhance situational awareness through comprehensive, standardized data collection, integration, and visualization. Objective: This study aimed to describe observations from the application of enhanced surveillance methods that collect, integrate, and visualize multimodal data for real-time tracking of the 2024-2025 HPAI A(H5) outbreak in the United States as an innovative, transparent, repeatable, and scalable approach for open-source public health surveillance. Methods: Global.health conducted real-time, multimodal surveillance of the United States 2024-2025 HPAI A(H5) outbreak using publicly available data for human cases (Centers for Disease Control and Prevention), animal outbreaks (United States Department of Agriculture), wastewater monitoring (WastewaterSCAN), genomic data (public genomic databases), research updates (scholarly communication), and policy changes and response measures (media and government) for the study period from February 1, 2024 through February 28, 2025. This digital data stream was used to create outbreak resources-an epidemiological linelist, event timeline, and interactive map-using a One Health framework to track emerging hotspots. Results: Global.health curated 70 confirmed human HPAI A(H5) cases across 13 states in a linelist, with exposure for nearly all (n = 65, 92.9%) cases associated with commercial agriculture and related operations. We curated 682 timeline entries across 6 distinct categories: human, cattle, response (eg, research, policy changes, and public health guidance), birds, genome, wastewater, and mammals. The map integrated human cases (n=70) and animal outbreaks (commercial cattle: n=977 and commercial poultry: n=325) into a single view. California was identified as the outbreak epicenter with high numbers of human cases (n=38, 54.3%), commercial cattle outbreaks (n=748, 76.6%), and commercial poultry outbreaks (n=66, 20.3%) during the study period. Wastewater surveillance detected the virus in California, with an unknown source at least 81 days before the first confirmed commercial dairy cattle case. Conclusions: Global.health's approach for integrating traditional and nontraditional public health surveillance data within a One Health framework enhanced early situational awareness during the United States 2024-2025 HPAI A(H5) outbreak, creating open access to resources that improve contextual understanding of the scope and evolution of this emerging zoonotic event. Further research should seek to understand the full potential of multimodal data in outbreak surveillance.

Recent measles outbreaks in the USA have emerged despite the availability of the highly effective measles–mumps–rubella (MMR) vaccine. Current surveillance systems rely primarily on telephone surveys with provider verification or school-entry data, methods prone to incompleteness and systematic exclusion of vulnerable populations. Here, to address these limitations, we used a validated digital participatory surveillance platform to collect parental reports of ≥1-dose MMR vaccination among children under 5 years of age. Applying Small Area Estimation methods to generate granular, county-level coverage estimates nationwide, we found substantial geographic variation, including areas with MMR coverage <60

BACKGROUND:Rare and undiagnosed genetic disorders affect millions of patients globally, and many patients endure years of inconclusive testing. Conventional genomic interpretation can be insufficiently sensitive and costly and is rarely repeated as knowledge evolves. METHODS:We conducted a retrospective multicohort reanalysis using a large language model (LLM)-assisted workflow that ingests clinician notes, Human Phenotype Ontology (HPO) terms, and a filtered variant table to propose explanation-rich candidate hypotheses for expert adjudication under American College of Medical Genetics and Genomics and Association for Molecular Pathology criteria. A diagnosis was defined a priori as a variant classified as pathogenic or likely pathogenic, confirmed in a Clinical Laboratory Improvement Amendments-certified laboratory, and returned to families. Secondary outputs included "rediscoveries" of externally established diagnoses not yet available locally and hypothesis generation signals. RESULTS:Across four cohorts, new local diagnoses were made in 10 of 100 rare disease neurodevelopmental cases (10.0%, [exact binomial: 95% confidence interval (CI), 4.9 to 17.6]), 4 of 61 neuromuscular cases (6.6%, [CI, 1.8 to 16.0]), 2 of 200 cases of sudden unexpected death in pediatrics (1.0% [CI, 0.1 to 3.6]), and 2 of 15 early psychosis cases (13.3% [CI, 1.7 to 40.5]) for an overall diagnostic yield of 18 of 376 (4.8%, [CI, 2.9 to 7.5]). We identified seven rediscoveries in which pathogenic or likely pathogenic findings had been established externally but were not available in the local research record at the time of review. In one case, the model's synthesis of genotype-quality patterns and phenotype concordance triaged a putative 22q11.2 deletion that was subsequently confirmed by whole-genome sequencing. The workflow also generated testable biological hypotheses, including a candidate association between the sphingosine-1-phosphate receptor 1 gene (S1PR1) and vitiligo. CONCLUSIONS:In retrospective reanalysis, an explanation-first LLM applied to routine HPO terms and variant tables produced clinically relevant gains in diagnostic yield, surfaced overlooked pathogenic findings, and generated biologically grounded hypotheses. These results motivate prospective multicenter evaluation with predefined end points, calibration reporting, and comparator baselines. (Funded by the U.S. National Institute of Child Health and Human Development and others.).

Despite widespread implementation of mask mandates for COVID-19 transmission control, studies examining their effectiveness have yielded mixed results ranging from strong benefits to no effect. These inconsistencies may arise from a variety of methodological and measurement challenges, including the implicit assumption that mandates truly modify masking behavior-the essential mechanism for transmission interruption. Here, we leverage self-reported mask adherence data from >34 000 individuals collected via a digital participatory surveillance platform between June 2, 2020, and January 1, 2021, to examine this assumption. Using an interrupted time series approach, we aggregate masking observations at the county level to analyze the effect of mandates on masking uptake across 555 diverse U.S. counties. We evaluate masking during the 14 days premandate and postmandate issuance, finding a modest 1-3 percentage point overall increases in masking. However, substantial heterogeneity was observed, with larger changes seen in counties initially exhibiting low mask adherence, the U.S. West, and on masking uptake in public compared to private settings. Our findings suggest that conflicting estimates of the effect of mandates on transmission reduction may reflect modification by heterogeneity in the mandates' alteration of masking behavior. Future interventions should tailor mandates to local context and baseline adherence for maximal behavioral change.

The COVID-19 pandemic accelerated the development of AI-driven tools to improve public health surveillance and outbreak management. While AI programs have shown promise in disease surveillance, they also present issues such as data privacy, prejudice, and human-AI interactions. This sixth session of the of the WHO Pandemic and Epidemic Intelligence Innovation Forum examines the use of Artificial Intelligence (AI) in public health by collecting the experience of key global health organizations, such the Boston Children's Hospital, the Global South AI for Pandemic Epidemic Preparedness Response (AI4PEP) network, Medicines Sans Frontières (MSF), and the University of Sydney. AI's utility in clinical care, particularly in diagnostics, medication discovery, and data processing, has resulted in improvements that may also benefit public health surveillance. However, the use of AI in global health necessitates careful consideration of ethical issues, particularly those involving data use and algorithmic bias. As AI advances, particularly with large language models, public health officials must develop governance frameworks that stress openness, accountability, and fairness. These systems should address worldwide differences in data access and ensure that AI technologies are tailored to specific local needs. Ultimately, AI's ability to improve healthcare efficiency and equity is dependent on multidisciplinary collaboration, community involvement, and inclusive AI designs in ensuring equitable healthcare outcomes to fit the unique demands of global communities.

Objectives. To determine the association between parental characteristics and MMR (measles-mumps- rubella) vaccination status of children in the United States. Methods. We conducted a cross-sectional study from July 2023 to April 2024 using a digital health survey via OutbreaksNearMe, weighted to target national population characteristics. We analyzed the responses of 19 892 parents of children younger than 5 years to examine the association between self-reported parental characteristics (i.e., sociodemographics, politics, COVID-19 vaccination status) and children's MMR vaccination rates using logistic regression. Results. Children of parents who received at least 1 dose of the COVID-19 vaccine had higher MMR vaccination rates (80.8%) than did children of unvaccinated parents (60.9%; odds ratio [OR] = 1.84; 95% confidence interval [CI] = 1.68, 2.00). We observed lower MMR vaccination rates among children of parents who identified as Republican versus Democratic (OR = 0.73; 95% CI = 0.64, 0.82), parents on Medicaid or Medicare versus private insurance (OR = 0.85; 95% CI = 0.76, 0.95), and minority (OR = 0.44) versus White (OR = 0.71) parents. We found higher MMR vaccination rates in the Northeast and Midwest United States. Conclusions. Early data indicate that parental sociodemographic characteristics and COVID-19 vaccine status are associated with children's MMR vaccine uptake, emphasizing the need for further investigations into multipronged public health interventions. (Am J Public Health. 2025;115(3):369-373. https://doi.org/10.2105/AJPH.2024.307912).

Highly pathogenic avian influenza (HPAI) outbreaks have primarily affected wild and domesticated bird populations, with occasional human spillover. In 2024, the United States (US) reported the first known HPAI H5N1 infection in dairy cattle, which rapidly evolved into a multispecies outbreak among cattle and poultry with spillover into humans. Publicly available data remained siloed and fragmented across agencies, which has implications for timely response. Innovative multimodal surveillance methods present an opportunity to enhance early situational awareness through comprehensive, standardized data collection, integration, and visualization. This study aimed to describe observations from the application of enhanced surveillance methods that collect, integrate, and visualize multimodal data for real-time tracking of the 2024-2025 HPAI outbreak in the US as an innovative, transparent, repeatable, and scalable approach for open source public health surveillance for zoonotic or other emerging or re-emerging pathogens. The Global.health consortium conducted real-time, multimodal data collection on the US HPAI outbreak between February 1, 2024 and February 28, 2025 using publicly available data for human cases, animal outbreaks, wastewater surveillance, genomic data, research updates, policy actions, and response measures. This digital data stream of traditional and non-traditional sources was used to create outbreak resources—a line-list, event timeline, and interactive map—using a One Health framework to track emerging hotspots Seventy human HPAI cases were confirmed across 13 US states, with exposure for nearly all (92.9%) cases associated with commercial agriculture and related operations. Only one human case of HPAI had ever been documented in the US prior to 2024, underscoring a sharp rise in incidence. We curated 682 Timeline entries across six distinct categories: human, cattle, response, birds, genome, wastewater, and mammals. California was identified as the outbreak epicenter with leading numbers in human cases (n= 38, 54.3%), cattle (n=748, 76.6%), and poultry infections (n=66, 20.3%) during the study period. Wastewater surveillance provided an early warning sign, identifying viral presence in California at least 81 days before the first dairy cattle case. The integration of traditional and non-traditional public health surveillance data into a single view within a One Health framework improved contextual understanding and enhanced situational awareness during the 2024-2025 HPAI outbreak in the US. Wastewater detections identified early viral presence, marking a critical window for intervention, policy action, and response to curb spread. Access to an open source multimodal data platform - like that put forward by Global.health - in real-time can assist researchers, public health officials, and decision makers in understanding the origins, scope, and evolution of emerging zoonotic diseases that fragmented, more traditional surveillance systems may be unable to readily provide. Further research should be conducted to understand the full potential of multimodal data in real-time outbreak surveillance.

In 2025, the Parasitology Subgroup of the International Society of Blood Transfusion (ISBT) Transfusion-Transmitted Infectious Diseases (TTID) Working Party (WP) transitioned into the Emerging Pathogens and Parasitology (EPP) Subgroup (referred to here as the EPP). This followed recognition that the parasitology subgroup's relevance was limited in scope given the small number of transfusion-transmissible parasites that still lacked effective mitigation. The EPP was proposed to address themes that are not adequately covered by existent subgroups of the TTID WP. In addition to maintaining a focus on transfusion-transmissible parasitic infections, a major objective of the EPP is horizon scanning for emerging pathogens. Horizon scanning refers to a systematic and proactive approach of information gathering and evaluation to identify early-and often subtle-signals of possible threats, which in this case pertain to blood safety. The EPP will characterize those risks to guide decision making and preparedness, pertaining to the safety and sufficiency of the blood supply. We describe the scope, structure and functioning of the EPP, within the broader TTID WP. We include examples of projects that may be pursued and outputs from horizon scanning a contemporary emerging pathogen. This collectively highlights the strategic relevance and objectives of the EPP.

This Viewpoint discusses updating the existing recommendation for an additional early MMR dose to infants aged 6 to 11 months traveling to any region with increased probability of measles exposure.

The COVID-19 pandemic has significantly disrupted influenza forecasting, making it challenging for hospitals to anticipate the severity of upcoming flu seasons relative to typical annual respiratory virus patterns. Even for influenza, health facilities often lack precise information on potential influenza surges, which hinders hospital management’s ability to anticipate necessary changes with respect to hospital staffing and resource allocation for an influx of patients. This study addresses this critical gap by developing an enhanced predictive model for pediatric influenza hospitalizations in Massachusetts. By integrating data from the Health and Human Services (HHS) Protect Public Data Hub, Centers for Disease Control and Prevention (CDC) FluSurv-NET, U.S. Department of Transportation (DOT) mobility data, and regional hospitalization rates, we demonstrate how recent improvements in data analytics and population tracking can amplify disease forecasts, and more precisely anticipate hospital burden relative to historical patient intake and hospital utilization trends.

Background: Effective surveillance of seasonal influenza is crucial to understanding disease burden and impact. Traditional surveillance accounts for those who interact with the health care system, including those who are testing for diseases like influenza. However, care seeking and testing are not as common with influenza and can lead to bias. Better understanding who is being captured by current surveillance methods can help further knowledge around influenza and identify areas of improvement in surveillance, disease mitigation, and intervention efforts. Objective: This study aimed to examine who is testing for influenza amongst a United States representative survey population, across three seasons influenza seasons spanning 2021 to 2024. Methods: Outbreaks near me (ONM) is a participatory surveillance system that, in partnership with SurveyMonkey, conducted a web-based, weekly cross-sectional survey. ONM Survey data from three influenza seasons was used in this study: 2021-2022, 2022-2023, and 2023-2024. Tested for influenza was defined as a "yes" response to "In the past 30 days, have you been tested for influenza (flu)?" Descriptive proportions applying survey weights reflecting US census targets were produced to understand which demographic groups were testing for influenza. A weighted multivariate logistic regression was conducted for influenza testing by income, adjusting for other demographics and COVID-19 testing. Descriptive proportions and multivariate regressions were conducted by influenza season. Results: In total, 940,172 responses were collected, with similar amounts in 2021-2022 (n=335,964) and 2022-2023 (n=334,584), and slightly less in 2023-2024 (n=269,624). Generally, low levels of influenza testing were reported in each season at 4.2%, 9.1%, and 8.9%, respectively. Weighted proportions of those who tested for influenza only and no other diseases (like COVID-19) were even lower (0.4%, 971/335,964; 1.5%, 4,382/334,584; and 2.0%, 4579/269,624; respectively). Broadly, those who had lower income tested for influenza at progressively higher proportions. A similar trend was observed season to season with education level as well. Across the 3 observed influenza seasons, lower household annual income (under US $15,000) was associated with higher odds of testing for influenza (2021-2022: adjusted odds ratio [AOR] 1.41, 95% CI 1.34-1.48; 2022-2023: AOR 1.42, 95% CI 1.35-1.49; 2023-2024: AOR 1.25, 95% CI 1.18-1.34), while those with higher incomes (over US $150,000) were less likely to have been tested for influenza (2021-2022: AOR 0.64, 95% CI 0.55-0.86; 2022-2023: AOR 0.82, 95% CI 0.73-0.91; 2023-2024: AOR 0.66, 95% CI 0.56-0.76). Conclusions: Within this study population, individuals who fall within lower-income brackets tested for influenza more than their higher-income counterparts. In all 3 seasons spanning 2021-2024, lower income was associated with higher proportions of influenza testing and an increased likelihood of having tested for influenza in the past 30 days. These trends suggest that populations that may experience more barriers to care are not only accessing influenza testing but doing so differently than groups that historically access care.

BackgroundDoctor review websites have become increasingly popular as a source of information for patients looking to select a primary care provider. Zocdoc is one such platform that allows patients to not only rate and review their experiences with doctors but also directly schedule appointments. This study examines how several physician characteristics including gender, age, race, languages spoken in a physician’s office, education, and facial attractiveness impact the average numerical rating of primary care doctors on Zocdoc. ObjectiveThe aim of this study was to investigate the association between physician characteristics and patient satisfaction ratings on Zocdoc. MethodsA data set of 1455 primary care doctor profiles across 30 cities was scraped from Zocdoc. The profiles contained information on the physician’s gender, education, and languages spoken in their office. Age, facial attractiveness, and race were imputed from profile pictures using commercial facial analysis software. Each doctor profile listed an average overall satisfaction rating, bedside manner rating, and wait time rating from verified patients. Descriptive statistics, the Wilcoxon rank sum test, and multivariate logistic regression were used to analyze the data. ResultsThe average overall rating on Zocdoc was highly positive, with older age, lower facial attractiveness, foreign degrees, allopathic degrees, and speaking more languages negatively associated with the average rating. However, the effect sizes of these factors were relatively small. For example, graduates of Latin American medical schools had a mean overall rating of 4.63 compared to a 4.77 rating for US graduates (P<.001), a difference roughly equivalent to a 2.8% decrease in appointments. On multivariate analysis, being Asian and having a doctor of osteopathic medicine degree were positively associated with higher overall ratings, while attending a South Asian medical school and speaking more European and Middle Eastern languages in the office were negatively associated with higher overall ratings. ConclusionsOverall, the findings suggest that age, facial attractiveness, education, and multilingualism do have some impact on web-based doctor reviews, but the numerical effect is small. Notably, bias may play out in many forms. For example, a physician's appearance or accent may impact a patient's trust, confidence, or satisfaction with their physician, which could in turn influence their take-up of preventative services and lead to either better or worse health outcomes. The study highlights the need for further research in how physician characteristics influence patient ratings of care.

OBJECTIVES:State-level abortion bans in the United States have created a complex legal landscape that forces many prospective patients to travel long distances to access abortion care. The financial strain and logistical difficulties associated with travelling out of state for abortion care may present an insurmountable barrier to some individuals, especially to those with limited resources. Tracking the impact of these abortion bans on travel and housing is crucial for understanding abortion access and economic changes following the Dobbs U.S. Supreme Court decision.STUDY DESIGN:This study used occupancy data from an average of 2,349,635 (standard deviation = 111,578) U.S. Airbnb listings each month from October 1st, 2020, through April 30th, 2023, to measure the impact of abortion bans on travel for abortion care and the resulting economic effects on regional economies.METHODS:The study used a synthetic difference-in-differences design to compare monthly-level occupancy rate data from 1-bedroom entire-place Airbnb rentals within a 30-min driving distance of abortion clinics in states with and without abortion bans.RESULTS:The study found a 1.4 percentage point decrease in occupancy rates of Airbnbs around abortion clinics in states where abortion bans were in effect, demonstrating reductions in Airbnb use in states with bans. In the 6-month period post Dobbs, this decrease translates to 16,548 fewer renters and a $1.87 million loss in revenue for 1-bedroom entire-place Airbnbs within a 30-min catchment area of abortion facilities in states with abortion restrictions.CONCLUSION:This novel use of Airbnb data provides a unique perspective on measuring demand for abortion and healthcare services and demonstrates the value of this data stream as a tool for understanding economic impacts of health policies.

In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the “Google Health Studies” mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency.

Abstract Background Participatory surveillance of self-reported symptoms and vaccination status can be used to supplement traditional public health surveillance and provide insights into vaccine effectiveness and changes in the symptoms produced by an infectious disease. The University of Maryland COVID Trends and Impact Survey provides an example of participatory surveillance that leveraged Facebook’s active user base to provide self-reported symptom and vaccination data in near real-time. Methods Here, we develop a methodology for identifying changes in vaccine effectiveness and COVID-19 symptomatology using the University of Maryland COVID Trends and Impact Survey data from three middle-income countries (Guatemala, Mexico, and South Africa). We implement conditional logistic regression to develop estimates of vaccine effectiveness conditioned on the prevalence of various definitions of self-reported COVID-like illness in lieu of confirmed diagnostic test results. Results We highlight a reduction in vaccine effectiveness during Omicron-dominated waves of infections when compared to periods dominated by the Delta variant (median change across COVID-like illness definitions: −0.40, IQR[−0.45, −0.35]. Further, we identify a shift in COVID-19 symptomatology towards upper respiratory type symptoms (i.e., cough and sore throat) during Omicron periods of infections. Stratifying COVID-like illness by the National Institutes of Health’s (NIH) description of mild and severe COVID-19 symptoms reveals a similar level of vaccine protection across different levels of COVID-19 severity during the Omicron period. Conclusions Participatory surveillance data alongside methodologies described in this study are particularly useful for resource-constrained settings where diagnostic testing results may be delayed or limited.

This study quantifies the change in travel times for military service personnel to abortion facilities following the US Supreme Court Dobbs decision and estimates the cost of an abortion-related travel reimbursement policy.

This cross-sectional study examines US trends in adult obesity prevalence from 2013 to 2023.

BACKGROUND AND OBJECTIVES Geographic accessibility predicts pediatric preventive care utilization, including vaccine uptake. However, spatial inequities in the pediatric coronavirus disease 2019 (COVID-19) vaccination rollout remain underexplored. We assessed the spatial accessibility of vaccination sites and analyzed predictors of vaccine uptake. METHODS In this cross-sectional study of pediatric COVID-19 vaccinations from the US Vaccine Tracking System as of July 29, 2022, we described spatial accessibility by geocoding vaccination sites, measuring travel times from each Census tract population center to the nearest site, and weighting tracts by their population demographics to obtain nationally representative estimates. We used quasi-Poisson regressions to calculate incidence rate ratios, comparing vaccine uptake between counties with highest and lowest quartile Social Vulnerability Index scores: socioeconomic status (SES), household composition and disability (HCD), minority status and language (MSL), and housing type and transportation. RESULTS We analyzed 15 233 956 doses administered across 27 526 sites. Rural, uninsured, white, and Native American populations experienced longer travel times to the nearest site than urban, insured, Hispanic, Black, and Asian American populations. Overall Social Vulnerability Index, SES, and HCD were associated with decreased vaccine uptake among children aged 6 months to 4 years (overall: incidence rate ratio 0.70 [95% confidence interval 0.60–0.81]; SES: 0.66 [0.58–0.75]; HCD: 0.38 [0.33–0.44]) and 5 years to 11 years (overall: 0.85 [0.77–0.95]; SES: 0.71 [0.65–0.78]; HCD: 0.67 [0.61–0.74]), whereas social vulnerability by MSL was associated with increased uptake (6 months–4 years: 5.16 [3.59–7.42]; 5 years–11 years: 1.73 [1.44–2.08]). CONCLUSIONS Pediatric COVID-19 vaccine uptake and accessibility differed by race, rurality, and social vulnerability. National supply data, spatial accessibility measurement, and place-based vulnerability indices can be applied throughout public health resource allocation, surveillance, and research.

Abstract Background The COVID-19 pandemic, and pandemic-related interventions, have had markedly disparate effects on marginalized populations. We examined whether the inequitable distribution of other COVID-19 resources was similarly reflected within the pediatric vaccination rollout. Methods We analyzed from a comprehensive national database of U.S. pediatric vaccination distribution sites and administered doses (VaccineFinder) as of 7/29/2022. We ascertained accessibility by geocoding sites, measuring one-way travel times from every Census tract population center to the nearest site, and weighting tracts by population demographics (rurality, age, race, ethnicity) to obtain nationwide estimates. We used population-weighted quasipoisson regressions adjusted for state fixed effects to compare vaccination uptake between the most and least socially vulnerable quartiles of counties by Social Vulnerability Index (SVI) domains (socioeconomic status, SES; household composition & disability, HCD; minority status & language, MSL; housing type & transportation, HTT). Results We identified 15,233,956 total vaccine doses administered across 27,526 sites. Rural, non-Hispanic, White, and Native populations had longer one-way travel times to the nearest pediatric vaccination site than urban, Hispanic, Black, and Asian populations. Greater social vulnerability by overall SVI, SES, and HCD was associated with decreased vaccine uptake among children in the 6mo-4y (Overall: IRR 0.70 [95%CI 0.60-0.81], SES: 0.66 [0.58-0.75], HCD: 0.38 [0.33-0.44]) and 5y-11y (Overall: 0.85 [0.77-0.95], SES: 0.71 [0.65-0.78], HCD: 0.67 [0.61-0.74]) groups, whereas greater social vulnerability by MSL was associated with increased uptake in both age groups (6mo-4y: 5.16 [3.59-7.42], 5y-11y: 1.73 [1.44-2.08]). Figure 1. Geospatial Visualization of One-Way Travel Time to the Nearest COVID-19 Vaccination Site by Age Figure 2. One-Way Travel Time to the Nearest 6mo-4y COVID-19 Vaccination Site by Race, Ethnicity, Rurality Figure 3. One-Way Travel Time to the Nearest 5y-11y COVID-19 Vaccination Site by Race, Ethnicity, Rurality Conclusion We identified meaningful spatial patterns in the accessibility and uptake of pediatric COVID-19 vaccinations, including decreased vaccine uptake in areas of high SES and HCD vulnerability and greater uptake in areas of high MSL vulnerability. Our modeling and surveillance approaches are generalizable and can be applied to other emerging pathogen response and scarce resource allocation efforts. Disclosures Dena M. Bravata, MD, MS, Castlight Health: Advisor/Consultant Kathleen A. McManus, MD, MSCR, Gilead Sciences, Inc.: Stocks/Bonds