Importance:Since 2020, COVID-19 has dramatically impacted the US population and health care system. Reporting requirements, circulating variants, testing practices, and population immunity from vaccination and previous infections evolved as the COVID-19 pandemic progressed. Evidence-based public health policy and resource allocation decisions require current estimates of disease burden. Objective:To estimate the age group-specific burden of COVID-19-associated illnesses, outpatient visits, hospitalizations, and deaths in the US from October 2022 to September 2024. Design, Setting, and Participants:In this cross-sectional study, hierarchical Bayesian modeling, adjusting for underdetection of SARS-CoV-2 due to testing practices and test sensitivity, was applied to hospitalization data from the population-based COVID-19 Hospitalization Surveillance Network (COVID-NET) database, which includes 89 counties and jurisdictional equivalents in 12 states covering approximately 10% of the US population. Data from 94 363 participants from October 2022 to September 2023 (surveillance period, 2022-2023) and from 72 176 participants from October 2023 to September 2024 (surveillance period, 2023-2024) were included, and probabilistic mathematical multiplier models estimated counts of deaths, outpatient visits, and symptomatic illnesses incorporating literature and study-based multipliers. Data were modeled from April 2024 to September 2025. Exposures:COVID-NET patients with a laboratory-confirmed COVID-19-associated hospitalization, defined as a positive SARS-CoV-2 test result within 14 days before or during hospitalization. Main Outcomes and Measures:Estimated national counts with 95% uncertainty intervals (UIs) of outpatient visits, illnesses, hospitalizations, and deaths by age group. Results:In 2022-2023, there were an estimated 43.6 million (95% UI, 25.3-64.0 million) COVID-19-associated illnesses, 10.0 million (95% UI, 7.0-13.1 million) outpatient visits, 1.1 million (95% UI, 0.9-1.4 million) hospitalizations, and 101 300 (95% UI, 73 600-132 500) deaths. In 2023-2024, there were an estimated 33.0 million (95% UI, 20.2-49.0 million) COVID-19-associated illnesses, 7.7 million (95% UI, 5.5-9.9 million) outpatient visits, 879 100 (95% UI, 738 600-1 039 000) hospitalizations, and 100 800 (95% UI, 64 000-140 400) deaths. In 2023-2024, people 65 years and older comprised 17.7% of the total US population but accounted for 47.9% (95% UI, 27.1-66.9) of COVID-19-associated illnesses, 64.3% (95% UI, 53.1-73.4) of outpatient visits, 67.6% (95% UI, 65.9-69.2) of hospitalizations, and 81.2% (95% UI, 70.2-90.6) of deaths. Conclusions and Relevance:In this cross-sectional study, despite declining from the first to the second surveillance period, the COVID-19 burden continued to have a large impact in the US, particularly among adults 65 years and older, underscoring the ongoing importance of prevention measures.

Respiratory viruses are common causes of upper and lower respiratory tract illness and can also result in hospitalization and death. CDC conducts national surveillance using multiple systems to monitor ongoing and seasonal changes in the activity of selected respiratory viruses. This report summarizes U.S. trends in endemic respiratory virus activity during July 2024-June 2025. For SARS-CoV-2 and respiratory syncytial virus (RSV), national and regional trends; population-based hospitalization rates; vital records death counts; and preliminary estimates of associated illnesses, outpatient visits, hospitalizations, and deaths are described, as well as genetic characterization of circulating SARS-CoV-2 viruses. Some viruses, including SARS-CoV-2, showed bimodal peaks in positive laboratory test results, whereas others, including RSV and influenza viruses, were characterized by a single peak. The highest COVID-19-associated hospitalization rates were reported among adults aged ≥75 years (932.6 per 100,000 persons), infants aged <6 months (285.6), and adults aged 65-74 years (274.4). RSV-associated hospitalization rates were highest among infants aged <12 months (1,116.7 per 100,000; 95% CI = 1,078.4-1,157.9), children aged 12-23 months (770.6; 95% CI = 743.1-800.3), and adults aged ≥75 years (426.9; 95% CI = 366.6-510.8). COVID-19 was associated with an estimated 290,000-450,000 hospitalizations and 34,000-53,000 deaths; RSV was associated with 190,000-350,000 hospitalizations and 10,000-23,000 deaths. All circulating SARS-CoV-2 lineages were Omicron JN.1 descendants. Staying up to date with recommended COVID-19, RSV, and influenza vaccinations remains important to reducing the risk for severe disease caused by these viruses.

Model diagnosis plots from the 2018-2019 county-level delta model. This figure shows the trace plot, the autocorrelation plot, and the density plot of the MCMC values for the model precision parameter A2.

The supplementary material includes details on the rational of input data grouping, the small area models, and model diagnosis and selection.

Trend of single year direct estimates of PSA Screening rates (%) and prostate cancer incidence rates for men ages 55-69 from 2012 to 2019. The plots include direct estimates from the NHIS (blue solid line) and the BRFSS (black dash line), the age-adjusted prostate cancer incidence rates (per 100 K population(red dash line), and the aggregated modeled estimates from the county-level model to the national level (green solid line, data presented in the mid-point of each data period, i.e., 2014 and 2018.5, respectively). USPSTF made Grade D recommendation in May 2012 and Grade C recommendation in May 2018 for Prostate cancer screening.

AbstractBackground: In 2012, the US Preventive Services Task Force recommended against prostate cancer screening using the PSA test for all age groups. In 2018, the US Preventive Services Task Force’s recommendation shifted from a “D” (not recommended) to a “C” (selectively offering PSA-based screening based on professional judgment and patient preferences) in men ages 55 to 69. Limited reliable county-level prostate cancer screening data are available for cancer surveillance purposes. Methods: Utilizing data from the National Health Interview Survey (NHIS) and Behavioral Risk Factor Surveillance System (BRFSS) collected in 2012 to 2019, state- and county-level small area models were developed for estimating PSA testing. Model diagnosis, internal validation, and external validation examining associations of PSA testing and prostate cancer incidence were conducted. Results: Model-based estimates of PSA testing rates were produced for all US states and 3,142 counties for two data periods: 2012 to 2016 and 2018 to 2019. Geographic variations across counties were demonstrated through maps. Moderate positive correlations between PSA-based screening and prostate cancer incidence were observed, e.g., the state-level weighted Pearson’s correlation coefficients were 0.5025 (P value = 0.0002) and 0.3691 (P value = 0.0077) for 2012 to 2016 and 2018 to 2019, respectively. Conclusions: These modeled estimates showed improved precision and adjusted for the differences between the BRFSS and NHIS. The approach of combining the NHIS and BRFSS utilized strengths of the larger sample size of the BRFSS and generally higher response rates and better household coverage from the NHIS. Impact: The resulting small area estimates offer a valuable resource for the cancer surveillance community, aiding in targeted interventions, decision making, and further research endeavors.

Model diagnosis plots from the 2018-2019 county-level delta model. This figure shows the trace plot, the autocorrelation plot, and the density plot of the MCMC values for the parameter associated with covariate “Population density”.

COVID-19-associated hospitalizations, ICU admissions, and in-hospital deaths averted from 2023 to 2024 COVID19 vaccination from the weeks of October 1, 2023, through April 21, 2024, were estimated via a novel multiplier model that utilized causal inference, conditional probabilities of hospitalization, and correlations between data elements in Monte Carlo simulations. Median COVID-19-associated hospitalizations averted were 68,315 (95 % uncertainty interval [UI] 42,831-97,984), ICU admissions averted were 13,108 (95 % UI 4459-25,042), and in- hospital deaths averted were 5301 (95 % UI 101-14,230). Averted COVID-19-associated burden was highest in adults aged 65 years and older (hospitalizations averted 57,665, 95 % UI 35,442-84,006; ICU admissions averted 10,878, 95 % UI 3104-21,591; in-hospital deaths averted 4779, 95 % UI 0-13,132). Expanding the analytic period to comprise the weeks of September 24, 2023, through August 11, 2024, resulted in 107,197 COVID-19associated hospitalizations averted (95 % UI 80,692-137,643), 18,292 COVID-19-associated ICU admissions averted (95 % UI 10,062-28,436), and 6749 COVID-19-associated in-hospital deaths averted (95 % UI 2077-13,557). Older adults had the highest COVID-19-associated averted burden and potential to reduce burden further through increased vaccine coverage. 2023-2024 COVID-19 vaccinations reduced the burden of COVID19-associated severe disease.

Final response rates (RR) for the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS). This table summarizes the final household-level and adult RR for NHIS across 2013, 2015, 2018 and 2019, along with the state-level minimum, maximum and median RR for BRFSS in 2012, 2014, 2016 and 2018.

Model diagnosis plots from the 2018-2019 county-level delta model. This figure shows the trace plot, the autocorrelation plot, and the density plot of the MCMC values for the parameter associated with covariate “Dentist rate per 100k population”.

Model diagnosis plots from the 2018-2019 county-level theta model. This figure shows the trace plot, the autocorrelation plot, and the density plot of the MCMC values for the model precision parameter A1.

Model diagnosis plots from the 2018-2019 county-level delta model. This figure shows the trace plot, the autocorrelation plot, and the density plot of the MCMC values for the parameter associated with covariate “% pop speak language other than English at home”.

State and county-level covariates pool. This tables lists all potential covariates and their sources, with indicators for the final covariates selected for each theta-model and delta-model in the state- and county-level models, respectively.

Model diagnosis plots from the 2018-2019 county-level theta model. This figure shows the trace plot, the autocorrelation plot, and the density plot of the MCMC values for the parameter associated with covariate “% of rural areas”.

BACKGROUND:In 2012, the US Preventive Services Task Force recommended against prostate cancer screening using the PSA test for all age groups. In 2018, the US Preventive Services Task Force's recommendation shifted from a "D" (not recommended) to a "C" (selectively offering PSA-based screening based on professional judgment and patient preferences) in men ages 55 to 69. Limited reliable county-level prostate cancer screening data are available for cancer surveillance purposes. METHODS:Utilizing data from the National Health Interview Survey (NHIS) and Behavioral Risk Factor Surveillance System (BRFSS) collected in 2012 to 2019, state- and county-level small area models were developed for estimating PSA testing. Model diagnosis, internal validation, and external validation examining associations of PSA testing and prostate cancer incidence were conducted. RESULTS:Model-based estimates of PSA testing rates were produced for all US states and 3,142 counties for two data periods: 2012 to 2016 and 2018 to 2019. Geographic variations across counties were demonstrated through maps. Moderate positive correlations between PSA-based screening and prostate cancer incidence were observed, e.g., the state-level weighted Pearson's correlation coefficients were 0.5025 (P value = 0.0002) and 0.3691 (P value = 0.0077) for 2012 to 2016 and 2018 to 2019, respectively. CONCLUSIONS:These modeled estimates showed improved precision and adjusted for the differences between the BRFSS and NHIS. The approach of combining the NHIS and BRFSS utilized strengths of the larger sample size of the BRFSS and generally higher response rates and better household coverage from the NHIS. IMPACT:The resulting small area estimates offer a valuable resource for the cancer surveillance community, aiding in targeted interventions, decision making, and further research endeavors.

Empirical percentiles for county-level estimates of Prostate Specific Antigen (PSA) screening rate among men aged 55-69. This table presents summary statistics for county-level NHIS direct estimates, BRFSS direct estimates, and final modeled estimated for the two data periods, 2012-2016 and 2018-2019.

Background: Weekly county-level COVID-19 mortality and emergency department (ED) visits data are critical data sources for understanding COVID-19 trends, but subject to reporting delays, sampling variability, potential instability and concerns due to statistical reliability as well as data suppression due to small numbers and the need to protect personally identifiable information. Such suppression limits meaningful examination of county-level variation in COVID-19 mortality rates and ED visits. Methods: In this study, we use Bayesian inference on latent Gaussian models in the software R-INLA (Integrated Nested Laplace Approximation) to generate reliable weekly estimates of COVID-19 ED visits and mortality rates at the county level in order to examine spatiotemporal variation. Results: The results demonstrate that weekly county-level COVID-19 mortality rates and ED visits can be accurately modeled using the INLA method. Model-based estimates reflect marked geographic variability for the years 2020-2025. Conclusions: Effective public health interventions rely on access to timely and detailed spatiotemporal data. Granular estimates that are subject to less reporting noise, such as those produced via INLA modeling, can be used to guide surveillance, improve response strategies, enhance preparedness, and inform public health policy.

Public health practitioners rely on timely surveillance data for planning and decision-making; however, surveillance data are often subject to delays. Epidemic trend categories, based on time-varying effective reproductive number (Rt) estimates that use nowcasting methods, can mitigate reporting lags in surveillance data and detect changes in community transmission before reporting is completed. CDC analyzed the performance of epidemic trend categories for COVID-19 during summer 2024 in the United States and at the state level in New Mexico. COVID-19 epidemic trend categories were estimated and released in real time based on preliminary data, then retrospectively compared with final emergency department (ED) visit data to determine their ability to detect or confirm real-time changes in subsequent ED visits. Across the United States and in New Mexico, epidemic trend categories were an early indicator of increases in COVID-19 community transmission, signifying increases in COVID-19 community transmission in May, and a confirmatory indicator that decreasing COVID-19 ED visits reflected actual decreases in COVID-19 community transmission in September, rather than incomplete reporting. Public health decision-makers can use epidemic trend categories, in combination with other surveillance indicators, to understand whether COVID-19 community transmission and subsequent ED visits are increasing, decreasing, or not changing; this information can guide communications decisions.

Mortality surveillance systems can have limitations, including reporting delays, incomplete reporting, missing data, and insufficient detail on important risk or sociodemographic factors that can impact the accuracy of estimates of current trends, disease severity, and related disparities across subpopulations. The Centers for Disease Control and Prevention used multiple data systems during the COVID-19 emergency response-line-level case‒death surveillance, aggregate death surveillance, and the National Vital Statistics System-to collectively provide more comprehensive and timely information on COVID-19‒associated mortality necessary for informed decisions. This article will review in detail the line-level, aggregate, and National Vital Statistics System surveillance systems and the purpose and use of each. This retrospective review of the hybrid surveillance systems strategy may serve as an example for adaptive informational approaches needed over the course of future public health emergencies. (Am J Public Health. 2024;114(10):1071-1080. https://doi.org/10.2105/AJPH.2024.307743).

On January 31, 2020, the U.S. Department of Health and Human Services (HHS) declared, under Section 319 of the Public Health Service Act, a U.S. public health emergency because of the emergence of a novel virus, SARS-CoV-2.* After 13 renewals, the public health emergency will expire on May 11, 2023. Authorizations to collect certain public health data will expire on that date as well. Monitoring the impact of COVID-19 and the effectiveness of prevention and control strategies remains a public health priority, and a number of surveillance indicators have been identified to facilitate ongoing monitoring. After expiration of the public health emergency, COVID-19-associated hospital admission levels will be the primary indicator of COVID-19 trends to help guide community and personal decisions related to risk and prevention behaviors; the percentage of COVID-19-associated deaths among all reported deaths, based on provisional death certificate data, will be the primary indicator used to monitor COVID-19 mortality. Emergency department (ED) visits with a COVID-19 diagnosis and the percentage of positive SARS-CoV-2 test results, derived from an established sentinel network, will help detect early changes in trends. National genomic surveillance will continue to be used to estimate SARS-CoV-2 variant proportions; wastewater surveillance and traveler-based genomic surveillance will also continue to be used to monitor SARS-CoV-2 variants. Disease severity and hospitalization-related outcomes are monitored via sentinel surveillance and large health care databases. Monitoring of COVID-19 vaccination coverage, vaccine effectiveness (VE), and vaccine safety will also continue. Integrated strategies for surveillance of COVID-19 and other respiratory viruses can further guide prevention efforts. COVID-19-associated hospitalizations and deaths are largely preventable through receipt of updated vaccines and timely administration of therapeutics (1-4).