BACKGROUND: Seasonal influenza causes an estimated 120 000 to 710 000 hospitalizations annually in the United States. Treatment with antiviral medications, such as oseltamivir, can reduce risks of hospitalization among people with influenza-associated illness. The US Centers for Disease Control and Prevention recommends initiating antiviral treatment as soon as possible for outpatients with suspected or confirmed influenza who have severe or progressive illness or are at higher risk of influenza complications. METHODS: We developed a probabilistic model to estimate the impact of antiviral treatment in reducing hospitalizations among US outpatients with influenza. Parameters were informed by seasonal influenza surveillance platforms and stratified by age group and whether individuals had a condition associated with higher risk of influenza complications. We modeled different scenarios for influenza antiviral effectiveness and outpatient testing and prescribing practices, then compared our results with a baseline scenario in which antivirals were not used. RESULTS: Across the modeled scenarios, antiviral treatment resulted in 1215 to 14 184 fewer influenza-associated hospitalizations on average when compared with the baseline scenario (0.2%-2.7% reduction). The greatest effects occurred among adults aged ≥65 years and individuals with conditions associated with higher risk of influenza complications. Modeling 50% improvements in access to care, testing, prescribing, and treatment resulted in greater potential impacts, with over 71 000 (13.3%) influenza-associated hospitalizations averted on average compared to baseline. CONCLUSIONS: Our results support recommendations to prioritize outpatient antiviral treatment among older adults and others at higher risk of influenza complications. Improving access to prompt testing and treatment among outpatients with suspected influenza could reduce hospitalizations substantially.

Critical appraisal of the quality of randomised trials is possible only if their design, conduct, analysis, and results are completely and accurately reported. Without transparent reporting of the methods and results, readers will not be able to fully evaluate the reliability and validity of trial findings. The CONSORT (Consolidated Standards of Reporting Trials) statement aims to improve the quality of reporting and provides a minimum set of items to be included in a report of a randomised trial. CONSORT was first published in 1996 and was updated in 2001 and 2010. CONSORT comprises a checklist of essential items that should be included in reports of randomised trials and a diagram for documenting the flow of participants through a trial. The CONSORT statement has been updated (CONSORT 2025) to reflect recent methodological advancements and feedback from end users, ensuring that it remains fit for purpose. Here, we present the updated CONSORT explanation and elaboration document, which has been extensively revised and describes the rationale and scientific background for each CONSORT 2025 checklist item and provides published examples of good reporting. The objective is to enhance the use, understanding, and dissemination of CONSORT 2025 and provide guidance to authors about how to improve the reporting of their trials and ensure trial reports are complete, and transparent.

IMPORTANCE: Well-designed and properly executed randomized trials are considered the most reliable evidence on the benefits of health care interventions. However, there is overwhelming evidence that the quality of reporting is not optimal. The CONSORT (Consolidated Standards of Reporting Trials) statement was designed to improve the quality of reporting and provides a minimum set of items to be included in a report of a randomized trial. CONSORT was first published in 1996, then updated in 2001 and 2010. Herein, we present the updated CONSORT 2025 statement, which aims to account for recent methodological advancements and feedback from end users. OBSERVATIONS: We conducted a scoping review of the literature and developed a project-specific database of empirical and theoretical evidence related to CONSORT to generate a list of potential changes to the checklist. The list was enriched with recommendations provided by the lead authors of existing CONSORT extensions (harms, outcomes, nonpharmacological treatment), other related reporting guidelines (Template for Intervention Description and Replication [TIDieR]), and recommendations from other sources (eg, personal communications). The list of potential changes to the checklist was assessed in a large, international, online, 3-round Delphi survey involving 317 participants and discussed at a 2-day online expert consensus meeting of 30 invited international experts. We have made substantive changes to the CONSORT checklist. We added 7 new checklist items, revised 3 items, deleted 1 item, and integrated several items from key CONSORT extensions. We also restructured the CONSORT checklist, with a new section on open science. The CONSORT 2025 statement consists of a 30-item checklist of essential items that should be included when reporting the results of a randomized trial and a diagram for documenting the flow of participants through the trial. To facilitate implementation of CONSORT 2025, we have also developed an expanded version of the CONSORT 2025 checklist, with bullet points eliciting critical elements of each item. CONCLUSIONS AND RELEVANCE: Authors, editors, reviewers, and other potential users should use CONSORT 2025 when writing and evaluating manuscripts of randomized trials to ensure that trial reports are clear and transparent.

BACKGROUND: The 2023-2024 influenza season included sustained elevated activity from December 2023-February 2024 and continued activity through May 2024. Influenza A(H1N1), A(H3N2), and B viruses circulated during the season. METHODS: During September 1, 2023-May 31, 2024, a multistate sentinel surveillance network of 24 medical centers in 20 U.S. states enrolled adults aged ≥18 years hospitalized with acute respiratory illness (ARI). Consistent with a test-negative design, cases tested positive for influenza viruses by molecular or antigen test, and controls tested negative for influenza viruses and SARS-CoV-2. Vaccine effectiveness (VE) against influenza-associated hospitalization was calculated as (1 - adjusted odds ratio for vaccination) × 100%. RESULTS: Among 7690 patients, including 1170 influenza cases (33% vaccinated) and 6520 controls, VE was 40% (95% CI: 31%-48%) with varying estimates by age (18-49 years: 53% [34%-67%]; 50-64 years: 47% [31%-60%]; ≥65 years: 31% [16%-43%]). Protection was similar among immunocompetent patients (40% [30%-49%]) and immunocompromised patients (32% [7-50%]). VE was statistically significant against influenza B (67% [35%-84%]) and A(H1N1) (36% [21%-48%]) and crossed the null against A(H3N2) (19% [-8%-39%]). VE was higher for patients 14-60 days from vaccination (54% [40%-65%]) than >120 days (18% [-1%-33%]). CONCLUSIONS: During 2023-2024, influenza vaccination reduced the risk of influenza A(H1N1)- and influenza B-associated hospitalizations among adults; effectiveness was lower in patients vaccinated >120 days prior to illness onset compared with those vaccinated 14-60 days prior.

IMPORTANCE: In 2023, the first respiratory syncytial virus (RSV) vaccines were recommended for US adults 60 years or older, but few data are available about which patients were most likely to receive vaccine to inform future RSV vaccine outreach efforts. OBJECTIVE: To assess patient- and community-level characteristics associated with RSV vaccine receipt and patient knowledge and attitudes related to RSV disease and RSV vaccines. DESIGN, SETTING, AND PARTICIPANTS: During the first season of RSV vaccine use from October 1, 2023, to April 30, 2024, adults 60 years or older hospitalized with RSV-negative acute respiratory illness were enrolled in this cross-sectional study from 26 hospitals in 20 US states. Sociodemographic and clinical data were abstracted from health records, and structured interviews were conducted for knowledge and attitudes about RSV disease and RSV vaccines. EXPOSURES: Age, sex, race and ethnicity, pulmonary disease, immunocompromised status, long-term care facility residence, medical insurance, social vulnerability index (SVI), and educational level. MAIN OUTCOMES AND MEASURES: The exposures were identified a priori as possible factors associated with RSV vaccine receipt and were entered into a modified Poisson regression model accounting for state clustering, to assess for association with RSV vaccine receipt. Knowledge and attitudes were summarized with frequencies and proportions. RESULTS: Among 6746 hospitalized adults 60 years or older, median age was 73 (IQR, 66-80) years and 3451 (51.2%) were female. Among the 6599 patients with self-reported race and ethnicity, 699 (10.6%) were Hispanic, 1288 (19.5%) were non-Hispanic Black, 4299 (65.1%) were non-Hispanic White, and 313 (4.7%) were other race or ethnicity. There were 700 RSV-vaccinated (10.4%) and 6046 unvaccinated (89.6%) adults. Among 3219 unvaccinated adults who responded to RSV knowledge questions, 1519 (47.2%) had not heard of RSV or were unsure; 2525 of 3218 (78.5%) were unsure if they were eligible for RSV vaccine or thought they were not. In adjusted analyses, characteristics associated with RSV vaccination were being 75 years or older (adjusted risk ratio [ARR], 1.23; 95% CI, 1.10-1.38, P < .001), being male (ARR, 1.15; 95% CI, 1.01-1.30; P = .04), and having pulmonary disease (ARR, 1.39; 95% CI, 1.16-1.67; P < .001), immunocompromised status (ARR, 1.30; 95% CI, 1.14-1.48; P < .001), low (ARR, 1.47; 95% CI, 1.18-1.83, P < .001) or moderate (ARR, 1.47; 95% CI, 1.21-1.79; P < .001) SVI, and educational level consisting of 4 or more years of college (ARR, 2.91; 95% CI, 2.14-3.96; P < .001), at least some college or technical training (ARR, 1.85; 95% CI, 1.35-2.53; P < .001), or grade 12 education or General Educational Development (ARR, 1.44; 95% CI, 1.03-2.00; P = .03). RSV vaccination was less likely among residents of long-term care facilities, patients with Medicaid coverage, and uninsured patients. CONCLUSIONS AND RELEVANCE: In this cross-sectional study of hospitalized adults, knowledge of RSV disease and RSV vaccine eligibility was low. Older adults and those with certain medical conditions were more likely to have received vaccine, suggesting appropriate prioritization, but sociodemographic differences in vaccine uptake occurred.

This article is an example of "Lessons from the Field." In early 2021, a call to action resulted in an unprecedented surge of volunteers for Utah's public health system. This call to action was in support of the rollout of COVID-19 vaccine across the state and the need to vaccinate the population as quickly and efficiently as possible. In this case study, we describe the events that preceded the surge of volunteers as well as challenges and resolutions to volunteer onboarding. Additionally, we discuss the importance of collaboration between local health departments and the Utah Department of Health and Human Services and describe how the partnership was strengthened by this specific emergency response.

BACKGROUND: Medicare claims are frequently used to study Clostridioides difficile infection (CDI) epidemiology. However, they lack specimen collection and diagnosis dates to assign location of onset. Algorithms to classify CDI onset location using claims data have been published, but the degree of misclassification is unknown. METHODS: We linked patients with laboratory-confirmed CDI reported to four Emerging Infections Program (EIP) sites from 2016-2021 to Medicare beneficiaries with fee-for-service Part A/B coverage. We calculated sensitivity of ICD-10-CM codes in claims within ±28 days of EIP specimen collection. CDI was categorized as hospital, long-term care facility, or community-onset using three different Medicare claims-based algorithms based on claim type, ICD-10-CM code position, duration of hospitalization, and ICD-10-CM diagnosis code presence-on-admission indicators. We assessed concordance of EIP case classifications, based on chart review and specimen collection date, with claims case classifications using Cohen's kappa statistic. RESULTS: Of 12,671 CDI cases eligible for linkage, 9,032 (71%) were linked to a single, unique Medicare beneficiary. Compared to EIP, sensitivity of CDI ICD-10-CM codes was 81%; codes were more likely to be present for hospitalized patients (93.0%) than those who were not (56.2%). Concordance between EIP and Medicare claims algorithms ranged from 68% to 75%, depending on the algorithm used (κ = 0.56-0.66). CONCLUSION: ICD-10-CM codes in Medicare claims data had high sensitivity compared to laboratory-confirmed CDI reported to EIP. Claims-based epidemiologic classification algorithms had moderate concordance with EIP classification of onset location. Misclassification of CDI onset location using Medicare algorithms may bias findings of claims-based CDI studies.

COVID-19 vaccination averted approximately 68,000 hospitalizations during the 2023-24 respiratory season. In June 2024, CDC and the Advisory Committee on Immunization Practices (ACIP) recommended that all persons aged ≥6 months receive a 2024-2025 COVID-19 vaccine, which targets Omicron JN.1 and JN.1-derived sublineages. Interim effectiveness of 2024-2025 COVID-19 vaccines was estimated against COVID-19-associated emergency department (ED) or urgent care (UC) visits during September 2024-January 2025 among adults aged ≥18 years in one CDC-funded vaccine effectiveness (VE) network, against COVID-19-associated hospitalization in immunocompetent adults aged ≥65 years in two networks, and against COVID-19-associated hospitalization among adults aged ≥65 years with immunocompromising conditions in one network. Among adults aged ≥18 years, VE against COVID-19-associated ED/UC visits was 33% (95% CI = 28%-38%) during the first 7-119 days after vaccination. Among immunocompetent adults aged ≥65 years from two CDC networks, VE estimates against COVID-19-associated hospitalization were 45% (95% CI = 36%-53%) and 46% (95% CI = 26%-60%) during the first 7-119 days after vaccination. Among adults aged ≥65 years with immunocompromising conditions in one network, VE was 40% (95% CI = 21%-54%) during the first 7-119 days after vaccination. These findings demonstrate that vaccination with a 2024-2025 COVID-19 vaccine dose provides additional protection against COVID-19-associated ED/UC encounters and hospitalizations compared with not receiving a 2024-2025 dose and support current CDC and ACIP recommendations that all persons aged ≥6 months receive a 2024-2025 COVID-19 vaccine dose.

Annual influenza vaccination is recommended for all persons aged ≥6 months in the United States. Interim influenza vaccine effectiveness (VE) was calculated among patients with acute respiratory illness-associated outpatient visits and hospitalizations from four VE networks during the 2024-25 influenza season (October 2024-February 2025). Among children and adolescents aged <18 years, VE against any influenza was 32%, 59%, and 60% in the outpatient setting in three networks, and against influenza-associated hospitalization was 63% and 78% in two networks. Among adults aged ≥18 years, VE in the outpatient setting was 36% and 54% in two networks and was 41% and 55% against hospitalization in two networks. Preliminary estimates indicate that receipt of the 2024-2025 influenza vaccine reduced the likelihood of medically attended influenza and influenza-associated hospitalization. CDC recommends annual receipt of an age-appropriate influenza vaccine by all eligible persons aged ≥6 months as long as influenza viruses continue to circulate locally.

Candida species are the predominant cause of fungal infections in patients treated in hospital, contributing substantially to morbidity and mortality. Candidaemia and other forms of invasive candidiasis primarily affect patients who are immunocompromised or critically ill. In contrast, mucocutaneous forms of candidiasis, such as oral thrush and vulvovaginal candidiasis, can occur in otherwise healthy individuals. Although mucocutaneous candidiasis is generally not life-threatening, it can cause considerable discomfort, recurrent infections, and complications, particularly in patients with underlying conditions such as diabetes or in those taking immunosuppressive therapies. The rise of difficult-to-treat Candida infections is driven by new host factors and antifungal resistance. Pathogens, such as Candida auris (Candidozyma auris) and fluconazole-resistant Candida parapsilosis, pose serious global health risks. Recent taxonomic revisions have reclassified several Candida spp, potentially causing confusion in clinical practice. Current management guidelines are limited in scope, with poor coverage of emerging pathogens and new treatment options. In this Review, we provide updated recommendations for managing Candida infections, with detailed evidence summaries available in the appendix.

BACKGROUND: While evidence suggests Medicaid expansion can reduce overdose, some expressed concern expansion fueled the US opioid overdose crisis by increasing access to low-cost prescription opioids diverted for non-prescribed use. Ecologic studies find a protective relationship or no relationship between expansion and area-level opioid prescribing. Little is known about the relationship between expansion and opioid use among people experiencing poverty who inject drugs (PWID), a population at heightened risk of overdose likely to benefit from Medicaid expansion. We examined whether expansion was associated with prescription opioid and benzodiazepine misuse among PWID experiencing poverty and whether associations varied by race/ethnicity and HIV status. METHODS: This serial cross-sectional observational study used generalized difference-in-differences models to analyze data (2012, 2015, 2018) from 19,728 PWID aged 18-64 with income ≤138 % of federal poverty line from 13 states in the CDC's National HIV Behavioral Surveillance. Outcomes included past 12-month non-injection and injection prescription opioid misuse and benzodiazepine misuse. RESULTS: The sample (N = 19,728) was 40 % non-Latinx Black persons and 22 % Latinx persons. Past 12-month non-injection prescription opioid misuse was 33 %, injection prescription opioid misuse was 16 %, and benzodiazepine use was 40 %. Across all models, there was no association between expansion and prescription opioid misuse (confidence intervals included 0) or prescription benzodiazepine misuse (confidence intervals included 0). Associations did not vary by race/ethnicity or HIV status. CONCLUSIONS: We found no association between Medicaid expansion and opioid or benzodiazepine misuse overall, by race/ethnicity, or HIV status among a large, geographically diverse sample of PWID. These findings provide empirical evidence that expansion is not associated with prescription opioid or benzodiazepine misuse in a population likely to benefit from expansion.

BACKGROUND: Real-world COVID-19 vaccine effectiveness (VE) studies are investigating exposures of increasing complexity accounting for time since vaccination. These studies require methods that adjust for the confounding that arises when morbidities and demographics are associated with vaccination and the risk of outcome events. Methods based on propensity scores (PS) are well-suited to this when the exposure is dichotomous, but present challenges when the exposure is multinomial. OBJECTIVE: This simulation study aimed to investigate alternative methods to adjust for confounding in VE studies that have a test-negative design. METHODS: Adjustment for a disease risk score (DRS) is compared with multivariable logistic regression. Both stratification on the DRS and direct covariate adjustment of the DRS are examined. Multivariable logistic regression with all the covariates and with a limited subset of key covariates is considered. The performance of VE estimators is evaluated across a multinomial vaccination exposure in simulated datasets. RESULTS: Bias in VE estimates from multivariable models ranged from -5.3% to 6.1% across 4 levels of vaccination. Standard errors of VE estimates were unbiased, and 95% coverage probabilities were attained in most scenarios. The lowest coverage in the multivariable scenarios was 93.7% (95% CI 92.2%-95.2%) and occurred in the multivariable model with key covariates, while the highest coverage in the multivariable scenarios was 95.3% (95% CI 94.0%-96.6%) and occurred in the multivariable model with all covariates. Bias in VE estimates from DRS-adjusted models was low, ranging from -2.2% to 4.2%. However, the DRS-adjusted models underestimated the standard errors of VE estimates, with coverage sometimes below the 95% level. The lowest coverage in the DRS scenarios was 87.8% (95% CI 85.8%-89.8%) and occurred in the direct adjustment for the DRS model. The highest coverage in the DRS scenarios was 94.8% (95% CI 93.4%-96.2%) and occurred in the model that stratified on DRS. Although variation in the performance of VE estimates occurred across modeling strategies, variation in performance was also present across exposure groups. CONCLUSIONS: Overall, models using a DRS to adjust for confounding performed adequately but not as well as the multivariable models that adjusted for covariates individually.

Disparities are evident in viral hepatitis morbidity, mortality, and outcomes. Disparities are considered an outcome of social determinants of health (SDoH), as systemic differences in the conditions in which people are born, grow, live, work, and age can lead to differences in health outcomes and access to health care services among population groups.1,2 Disparities in viral hepatitis incidence and mortality are described in surveillance reports 3 and the literature4,5; however, an examination of the influence of SDoH on disparities in viral hepatitis incidence, mortality, and outcomes is missing from the literature. This gap in the literature could be a direct result of limitations in viral hepatitis surveillance data in capturing relevant measures. However, examining data on social, economic, physical, and political environments of people affected by viral hepatitis is important for understanding the incidence and outcomes of the disease, developing interventions, and assessing progress toward achieving health equity. 1 This commentary discusses existing disparities in viral hepatitis, explores how SDoH may contribute to these disparities, and highlights opportunities to examine the influence of SDoH on viral hepatitis outcomes.

Black women are disproportionately affected by HIV. We analyzed data from two Centers for Disease Control and Prevention's HIV surveillance systems to better understand HIV prevention strategies used by Black women at risk for and with HIV to help inform efforts to end HIV. Among sexually active Black women, we analyzed 2019 National HIV Behavioral Surveillance data on women without HIV (n = 4,033) and 2018-2020 Medical Monitoring Project data on women with HIV (n = 967). We reported percentages of HIV prevention strategies and services used and assessed differences between groups using Rao-Scott chi-square tests. Among Black women without HIV, 39% were aware of pre-exposure prophylaxis (PrEP); of these, 7% discussed PrEP with a healthcare provider, and 1% used PrEP in the past 12 months. Approximately 16% used a condom with their last sex partner; 36% reported that their last sex partner did not have HIV. Among Black women with HIV, 58% had condom-protected sex, 56% reported having sex while having sustained viral suppression, 3% had condomless sex with a partner on PrEP, and 24% had sex with a partner with HIV; 12% engaged in sex without using any HIV prevention strategy. HIV prevention strategies and services differed by selected demographic characteristics and social determinants of health. Although many sexually active Black women reported using HIV prevention strategies, there is room for improvement among those at risk for or with HIV. Tailoring prevention efforts based on individual needs and circumstances is essential for ending the HIV epidemic.

BACKGROUND: People who inject drugs (PWID) are especially vulnerable to harms from opioid use disorder (OUD). Medications for OUD (MOUD) effectively reduce overdose and infectious disease transmission risks. OBJECTIVE: We investigate whether state Medicaid coverage for methadone and buprenorphine is related to past-year MOUD use among PWID using cross-sectional, multilevel analyses with individual-level data on PWID from the Centers for Disease Control and Prevention's 2018 National HIV Behavioral Surveillance. The sample included 8,142 PWID aged 18-64 who reported daily opioid use from 22 U.S. metropolitan areas. Our outcome was any self-reported MOUD use in the past 12 months. Exposures were state Medicaid coverage and prior authorization requirements for methadone and buprenorphine. We interacted these exposures with PWID race/ethnicity, insurance status, and spatial access to treatment and harm reduction resources. RESULTS: Compared with PWID in states without Medicaid methadone coverage, odds of past-year MOUD use were 73% (p<0.05) higher among PWID in states with methadone coverage requiring prior authorization and 80% (p<0.05) higher among PWID in states with coverage without prior authorization. Insured PWID were twice as likely to report MOUD use than uninsured PWID, with no statistically significant differences between Medicaid versus other insurance. Medicaid prior authorization requirements for buprenorphine were not significantly associated with MOUD use. Non-Hispanic Black PWID were significantly less likely to use MOUD than non-Hispanic White and Hispanic PWID. CONCLUSIONS: State Medicaid methadone coverage was strongly associated with higher odds that PWID utilized MOUD, suggesting that expanding methadone insurance coverage could improve MOUD treatment in a vulnerable population.

BACKGROUND: clinical guidelines recommend initiation of antiviral therapy as soon as possible for patients hospitalized with confirmed or suspected influenza. METHODS: A multicenter US observational sentinel surveillance network prospectively enrolled adults (aged ≥18 years) hospitalized with laboratory-confirmed influenza at 24 hospitals during October 1, 2022-July 21, 2023. A multivariable proportional odds model was used to compare peak pulmonary disease severity (no oxygen support, standard supplemental oxygen, high-flow oxygen/non-invasive ventilation, invasive mechanical ventilation, or death) after the day of hospital admission among patients starting oseltamivir treatment on the day of admission (early) versus those who did not (late or not treated), adjusting for baseline (admission day) severity, age, sex, site, and vaccination status. Multivariable logistic regression models were used to evaluate the odds of intensive care unit (ICU) admission, acute kidney replacement therapy or vasopressor use, and in-hospital death. RESULTS: A total of 840 influenza-positive patients were analyzed, including 415 (49%) who started oseltamivir treatment on the day of admission, and 425 (51%) who did not. Compared with late or not treated patients, those treated early had lower peak pulmonary disease severity (proportional aOR: 0.60, 95% CI: 0.49-0.72), and lower odds of intensive care unit admission (aOR: 0.24, 95% CI: 0.13-0.47), acute kidney replacement therapy or vasopressor use (aOR: 0.40, 95% CI: 0.22-0.67), and in-hospital death (aOR: 0.36, 95% CI: 0.18-0.72). CONCLUSION: Among adults hospitalized with influenza, treatment with oseltamivir on day of hospital admission was associated reduced risk of disease progression, including pulmonary and extrapulmonary organ failure and death.

BACKGROUND: In test-negative studies of vaccine effectiveness (VE), including patients with co-circulating, vaccine-preventable, respiratory pathogens in the control group for the pathogen of interest can introduce a downward bias on VE estimates. METHODS: A multicenter sentinel surveillance network in the US prospectively enrolled adults hospitalized with acute respiratory illness from September 1, 2022-March 31, 2023. We evaluated bias in estimates of VE against influenza-associated and COVID-19-associated hospitalization based on: inclusion vs exclusion of patients with a co-circulating virus among VE controls; observance of VE against the co-circulating virus (rather than the virus of interest), unadjusted and adjusted for vaccination against the virus of interest; and observance of influenza or COVID-19 against a sham outcome of respiratory syncytial virus (RSV). RESULTS: Overall VE against influenza-associated hospitalizations was 6 percentage points lower when patients with COVID-19 were included in the control group, and overall VE against COVID-19-associated hospitalizations was 2 percentage points lower when patients with influenza were included in the control group. Analyses of VE against the co-circulating virus and against the sham outcome of RSV showed that downward bias was largely attributable the correlation of vaccination status across pathogens, but also potentially attributable to other sources of residual confounding in VE models. CONCLUSION: Excluding cases of confounding respiratory pathogens from the control group in VE analysis for a pathogen of interest can reduce downward bias. This real-world analysis demonstrates that such exclusion is a helpful bias mitigation strategy, especially for measuring influenza VE, which included a high proportion of COVID-19 cases among controls.

BACKGROUND: Assessments of COVID-19 vaccine effectiveness are needed to monitor the protection provided by updated vaccines against severe COVID-19. We evaluated the effectiveness of original monovalent and bivalent (ancestral strain and Omicron BA.4/5) COVID-19 vaccination against COVID-19-associated hospitalization and severe in-hospital outcomes. METHODS: During September 8, 2022 to August 31, 2023, adults aged ≥ 18 years hospitalized with COVID-19-like illness were enrolled at 26 hospitals in 20 US states. Using a test-negative case-control design, we estimated vaccine effectiveness (VE) with multivariable logistic regression adjusted for age, sex, race/ethnicity, admission date, and geographic region. RESULTS: Among 7028 patients, 2924 (41.6%) were COVID-19 case patients, and 4104 (58.4%) were control patients. Compared to unvaccinated patients, absolute VE against COVID-19-associated hospitalization was 6% (-7%-17%) for original monovalent doses only (median time since last dose [IQR] = 421 days [304-571]), 52% (39%-61%) for a bivalent dose received 7-89 days earlier, and 13% (-10%-31%) for a bivalent dose received 90-179 days earlier. Absolute VE against COVID-19-associated invasive mechanical ventilation or death was 51% (34%-63%) for original monovalent doses only, 61% (35%-77%) for a bivalent dose received 7-89 days earlier, and 50% (11%-71%) for a bivalent dose received 90-179 days earlier. CONCLUSION: Bivalent vaccination provided protection against COVID-19-associated hospitalization and severe in-hospital outcomes within 3 months of receipt, followed by a decline in protection to a level similar to that remaining from previous original monovalent vaccination by 3-6 months. These results underscore the benefit of remaining up to date with recommended COVID-19 vaccines.

Neisseria meningitidis (Nm) and Neisseria gonorrhoeae (Ng) are human pathogens that sometimes occupy the same anatomical niche. Ng, the causative agent of gonorrhea, infects 87 million individuals annually worldwide and is an urgent threat due to increasing drug resistance. Ng is a pathogen of the urogenital tract and may infect the oropharyngeal or rectal site, often asymptomatically. Conversely, Nm is an opportunistic pathogen. While often a commensal in the oropharyngeal tract, it is also the leading cause of bacterial meningitis with 1.2 million cases globally, causing significant morbidity and mortality. Horizontal gene transfer (HGT) is likely to occur between Ng and Nm due to their shared anatomical niches and genetic similarity, which poses challenges for accurate detection and treatment. Routine surveillance through the Gonococcal Isolate Surveillance Project and Strengthening the U.S. Response to Resistant Gonorrhea detected six concerning urogenital Neisseria isolates with contradicting species identification in Milwaukee (MIL). While all six isolates were positive for Ng using nucleic acid amplification testing (NAAT) and matrix-assisted laser desorption/ionization time of flight identified the isolates as Ng, two biochemical tests, Gonochek-II and API NH, classified them as Nm. To address this discrepancy, we performed whole-genome sequencing (WGS) using Illumina MiSeq on all isolates and employed various bioinformatics tools. Species detection analysis using BMScan, which uses WGS data, identified all isolates as Ng. Furthermore, Kraken revealed over 98% of WGS reads mapped to the Ng genome and <1% to Nm. Recombination analysis identified putative HGT in all MIL isolates within the γ-glutamyl transpeptidase (ggt) gene, a key component in the biochemical tests used to differentiate between Nm and Ng. Further analysis identified Nm as the source of HGT event. Specifically, the active Nm ggt gene replaced the Ng pseudogenes, ggt1 and ggt2. Together, this study demonstrates that closely related Neisseria species sharing a niche underwent HGT, which led to the misidentification of species following biochemical testing. Importantly, NAAT accurately detected Ng. The misidentification highlights the importance of using WGS to continually evaluate diagnostic or bacterial identification tests.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity. | Seasonal influenza (flu) viruses cause outbreaks every winter. People infected with influenza typically develop mild respiratory symptoms. But flu infections can cause serious illness in young children, older adults and people with chronic medical conditions. Infected or vaccinated individuals develop some immunity, but the viruses evolve quickly to evade these defenses in a process called antigenic drift. As the viruses change, they can re-infect previously immune people. Scientists update the flu vaccine yearly to keep up with this antigenic drift. The immune system fights flu infections by recognizing two proteins, known as antigens, on the virus’s surface, called hemagglutinin (HA) and neuraminidase (NA). However, mutations in the genes encoding these proteins can make them unrecognizable, letting the virus slip past the immune system. Scientists would like to know how these changes affect the size, severity and timing of annual influenza outbreaks. Perofsky et al. show that tracking genetic changes in HA and NA may help improve flu season predictions. The experiments compared the severity of 22 flu seasons caused by the A(H3N2) subtype in the United States with how much HA and NA had evolved since the previous year. The A(H3N2) subtype experiences the fastest rates of antigenic drift and causes more cases and deaths than other seasonal flu viruses. Genetic changes in HA and NA were a better predictor of A(H3N2) outbreak severity than the blood tests for protective antibodies that epidemiologists traditionally use to track flu evolution. However, the prevalence of another subtype of influenza A circulating in the population, called A(H1N1), was an even better predictor of how severe A(H3N2) outbreaks would be. Perofsky et al. are the first to show that genetic changes in NA contribute to the severity of flu seasons. Previous studies suggested a link between genetic changes in HA and flu season severity, and flu vaccines include the HA protein to help the body recognize new influenza strains. The results suggest that adding the NA protein to flu vaccines may improve their effectiveness. In the future, flu forecasters may want to analyze genetic changes in both NA and HA to make their outbreak predictions. Tracking how much of the A(H1N1) subtype is circulating may also be useful for predicting the severity of A(H3N2) outbreaks. | eng

BACKGROUND: The 2022 mpox outbreak disproportionately affected gay, bisexual, and other men who have sex with men (GBMSM). Mpox cases continue to be reported nationally. Vaccination is a tool to prevent the spread of and serious disease from mpox. To understand mpox vaccine uptake and hesitancy, a virtual focus group with unvaccinated GBMSM was conducted. METHODS: In November 2022, a 60-minute, virtual focus group was conducted within an artificial intelligence (AI) platform that engages participants in chat-based conversation. The AI system uses machine learning and natural language processing to analyze and provide results immediately to the moderator. Descriptive frequencies, cross-tabulations and qualitative themes were analyzed. RESULTS: Fifty-one GBMSM ages 18-55 participated, of whom 12 had attempted to get the mpox vaccine. The top barriers in accessing the vaccine included challenges in scheduling appointments (4/12), available vaccine locations (3/12), and transportation (2/12). Nine participants reported not wanting the vaccine and 22 were undecided; Of these, 15 (4/9 and 11/22, respectively) said they did not think they needed the vaccine due to low perceived risk or monogamy.. Among the undecided, after receiving health messaging about mpox, 12/22 said the messaging made them reconsider getting the vaccine. CONCLUSION: During an outbreak, many unvaccinated GBMSM who may be at increased risk for mpox either wanted the vaccine or, with appropriate health messaging, may be open to getting the vaccine. Messaging about mpox vaccine efficacy, potential side effects, and how to access the vaccine may improve vaccine uptake especially as cases continue to occur.

BACKGROUND: Assessing variant-specific COVID-19 vaccine effectiveness (VE) and severity can inform public health risk assessments and decisions about vaccine composition. BA.2.86 and its descendants, including JN.1 (referred to collectively as "JN lineages"), emerged in late 2023 and exhibited substantial divergence from co-circulating XBB lineages. METHODS: We analyzed patients hospitalized with COVID-19-like illness at 26 hospitals in 20 U.S. states admitted October 18, 2023-March 9, 2024. Using a test-negative, case-control design, we estimated effectiveness of an updated 2023-2024 (Monovalent XBB.1.5) COVID-19 vaccine dose against sequence-confirmed XBB and JN lineage hospitalization using logistic regression. Odds of severe outcomes, including intensive care unit (ICU) admission and invasive mechanical ventilation (IMV) or death, were compared for JN versus XBB lineage hospitalizations using logistic regression. RESULTS: 585 case-patients with XBB lineages, 397 case-patients with JN lineages, and 4,580 control-patients were included. VE in the first 7-89 days after receipt of an updated dose was 54.2% (95% CI = 36.1%-67.1%) against XBB lineage hospitalization and 32.7% (95% CI = 1.9%-53.8%) against JN lineage hospitalization. Odds of ICU admission (adjusted odds ratio [aOR] 0.80; 95% CI = 0.46-1.38) and IMV or death (aOR 0.69; 95% CI = 0.34-1.40) were not significantly different among JN compared to XBB lineage hospitalizations. CONCLUSIONS: Updated 2023-2024 COVID-19 vaccination provided protection against both XBB and JN lineage hospitalization, but protection against the latter may be attenuated by immune escape. Clinical severity of JN lineage hospitalizations was not higher relative to XBB.

BACKGROUND: The 2022-2023 United States influenza season had unusually early influenza activity with high hospitalization rates. Vaccine-matched A(H3N2) viruses predominated, with lower levels of A(H1N1)pdm09 activity also observed. METHODS: Using the test-negative design, we evaluated influenza vaccine effectiveness (VE) during the 2022-2023 season against influenza A-associated emergency department/urgent care (ED/UC) visits and hospitalizations from October 2022 to March 2023 among adults (aged ≥18 years) with acute respiratory illness (ARI). VE was estimated by comparing odds of seasonal influenza vaccination among case-patients (influenza A test positive by molecular assay) and controls (influenza test negative), applying inverse-propensity-to-be-vaccinated weights. RESULTS: The analysis included 85 389 ED/UC ARI encounters (17.0% influenza A positive; 37.8% vaccinated overall) and 19 751 hospitalizations (9.5% influenza A positive; 52.8% vaccinated overall). VE against influenza A-associated ED/UC encounters was 44% (95% confidence interval [CI], 40%-47%) overall and 45% and 41% among adults aged 18-64 and ≥65 years, respectively. VE against influenza A-associated hospitalizations was 35% (95% CI, 27%-43%) overall and 23% and 41% among adults aged 18-64 and ≥65 years, respectively. CONCLUSIONS: VE was moderate during the 2022-2023 influenza season, a season characterized with increased burden of influenza and co-circulation with other respiratory viruses. Vaccination is likely to substantially reduce morbidity, mortality, and strain on healthcare resources.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2(nd) most accurate model measured by WIS in 2021-22 and the 5(th) most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Wildlife translocation and cross-species transmission can impede control and elimination of emerging zoonotic diseases. Tracking the geographic origin of both host and virus (i.e., translocation versus local infection) may help determine the most effective response when high-risk cases of emerging pathogens are identified in wildlife. In May 2022, a coyote (Canis latrans) infected with the raccoon (Procyon lotor) rabies virus variant (RRV) was collected in Lewis County, West Virginia, USA, an area free from RRV. We applied host population genomics and RRV phylogenetic analyses to determine the most likely geographic origin of the rabid coyote. Coyote genomic analyses included animals from multiple eastern states bordering West Virginia, with the probable origin of the rabid coyote being the county of collection. The RRV phylogenetic analyses included cases detected from West Virginia and neighboring states, with most similar RRV sequences collected in a county 80 km to the northeast, within the oral rabies vaccination zone. The combined results suggest that the coyote was infected in an RRV management area and carried the RRV to Lewis County, a pattern consistent with coyote local movement ecology. Distant cross-species transmission and subsequent host movement presents a low risk for onward transmission in raccoon populations. This information helped with emergency response decision-making, thereby saving time and resources.

CONTEXT: In 2021, the Centers for Disease Control and Prevention (CDC) launched CORE, an agency-wide strategy to embed health equity as a foundational component across all areas of the agency's work. The CDC established a definition of health equity science (HES) and principles to guide the development, implementation, dissemination, and use of the HES framework to move beyond documenting inequities to investigating root causes and promoting actionable approaches to eliminate health inequities. The HES framework may be used by state and local health departments to advance health equity efforts in their jurisdictions. OBJECTIVE: Identify implementation considerations and opportunities for providing technical assistance and support to state and local public health departments in advancing HES. DESIGN: A series of implementation consultations and multi-jurisdictional facilitated discussions were held with state and local health departments and community partners in 5 states to gather feedback on the current efforts, opportunities, and support needs to advance HES at the state and local levels. The information shared during these activities was analyzed using inductive and deductive methods, validated with partners, and summarized into themes and HES implementation considerations. RESULTS: Five themes emerged regarding current efforts, opportunities, and support needed to implement HES at state and local health departments. These themes included the following criteria: (1) enhancing the existing health equity evidence base; (2) addressing interdisciplinary public health practice and data needs; (3) recognizing the value of qualitative data; (4) evaluating health equity programs and policies; and (5) including impacted communities in the full life cycle of health equity efforts. Within these themes, we identified HES implementation considerations, which may be leveraged to inform future efforts to advance HES at the state and local levels. CONCLUSION: Health equity efforts at state and local health departments may be strengthened by leveraging the HES framework and implementation considerations.

BACKGROUND: Detection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown. METHODS: We conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods. RESULTS: Real-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92). CONCLUSIONS: Automated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).

BACKGROUND: Hospital-onset bacteraemia and fungaemia (HOB) is being explored as a surveillance and quality metric. The objectives of the current study were to determine sources and preventability of HOB in hospitalised patients in the USA and to identify factors associated with perceived preventability. METHODS: We conducted a cross-sectional study of HOB events at 10 academic and three community hospitals using structured chart review. HOB was defined as a blood culture on or after hospital day 4 with growth of one or more bacterial or fungal organisms. HOB events were stratified by commensal and non-commensal organisms. Medical resident physicians, infectious disease fellows or infection preventionists reviewed charts to determine HOB source, and infectious disease physicians with training in infection prevention/hospital epidemiology rated preventability from 1 to 6 (1=definitely preventable to 6=definitely not preventable) using a structured guide. Ratings of 1-3 were collectively considered 'potentially preventable' and 4-6 'potentially not preventable'. RESULTS: Among 1789 HOB events with non-commensal organisms, gastrointestinal (including neutropenic translocation) (35%) and endovascular (32%) were the most common sources. Overall, 636/1789 (36%) non-commensal and 238/320 (74%) commensal HOB events were rated potentially preventable. In logistic regression analysis among non-commensal HOB events, events attributed to intravascular catheter-related infection, indwelling urinary catheter-related infection and surgical site infection had higher odds of being rated preventable while events with neutropenia, immunosuppression, gastrointestinal sources, polymicrobial cultures and previous positive blood culture in the same admission had lower odds of being rated preventable, compared with events without those attributes. Of 636 potentially preventable non-commensal HOB events, 47% were endovascular in origin, followed by gastrointestinal, respiratory and urinary sources; approximately 40% of those events would not be captured through existing healthcare-associated infection surveillance. DISCUSSION: Factors identified as associated with higher or lower preventability should be used to guide inclusion, exclusion and risk adjustment for an HOB-related quality metric.

BACKGROUND AIMS: The National Health and Nutrition Examination Survey (NHANES) underestimates the true prevalence of hepatitis C virus (HCV) infection. By accounting for populations inadequately represented in NHANES, we created two models to estimate the national hepatitis C prevalence among US adults during 2017-2020. APPROACH RESULTS: The first approach (NHANES+) replicated previous methodology by supplementing hepatitis C prevalence estimates among the US noninstitutionalized civilian population with a literature review and meta-analysis of hepatitis C prevalence among populations not included in the NHANES sampling frame. In the second approach (persons who inject drugs [PWID] adjustment), we developed a model to account for underrepresentation of PWID in NHANES by incorporating the estimated number of adult PWID in the United States and applying PWID-specific hepatitis C prevalence estimates. Using the NHANES+ model, we estimated HCV RNA prevalence of 1.0% (95% confidence interval [CI]: 0.5%-1.4%) among US adults in 2017-2020, corresponding to 2,463,700 (95% CI: 1,321,700-3,629,400) current HCV infections. Using the PWID adjustment model, we estimated HCV RNA prevalence of 1.6% (95% CI: 0.9%-2.2%), corresponding to 4,043,200 (95% CI: 2,401,800-5,607,100) current HCV infections. CONCLUSIONS: Despite years of an effective cure, estimated prevalence of hepatitis C in 2017-2020 remains unchanged from 2013-2016 when using comparable methodology. When accounting for increased injection drug use, estimated prevalence of hepatitis C is substantially higher than previously reported. National action is urgently needed to expand testing, increase access to treatment, and improve surveillance, especially among medically underserved populations, to support hepatitis C elimination goals.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.