Understanding the dynamics of antibody responses following vaccination and SARS-CoV-2 infection is important for informing effective vaccination strategies and other public health interventions. This study investigates SARS-CoV-2 antibody dynamics in a Puerto Rican cohort, analyzing how IgG levels vary by vaccination status and previous infection. We assess waning immunity and the distribution of hybrid immunity with the aim to inform public health strategies and vaccination programs in Puerto Rico and similar settings. We conducted a prospective, longitudinal cohort study to identify SARS-CoV-2 infections and related outcomes in Ponce, Puerto Rico, from June 2020-August 2022. Participants provided self-collected nasal swabs every week and serum every six months for RT-PCR and IgG testing, respectively. IgG reactivity against nucleocapsid (N) antigens, which generally indicate previous infection, and spike (S1) and receptor-binding domain (RBD) antigens, which indicate history of either infection or vaccination, was assessed using the Luminex Corporation xMAP® SARS-CoV-2 Multi-Antigen IgG Assay. Prior infection was defined by positive RT-PCRs, categorized by the predominant circulating SARS-CoV-2 variant at the event time. Demographic information, medical history, and COVID-19 vaccination history were collected through standardized questionnaires. Of 882 participants included in our analysis, 34.0% experienced at least one SARS-CoV-2 infection, with most (78.7%) occurring during the Omicron wave (December 2021 onwards). SARS-CoV-2 antibody prevalence increased over time, reaching 98.4% by the final serum collection, 67.0% attributable to vaccination alone, 1.6% from infection alone, and 31.4% from both. Regardless of prior infection status, RBD and S1 IgG levels gradually declined following two vaccine doses. A third dose boosted these antibody levels and showed a slower decline over time. N-antibody levels peaked during the Omicron surge and waned over time. Vaccination in individuals with prior SARS-CoV-2 infection elicited the highest and most durable antibody responses. N or S1 seropositivity was associated with lower odds of a subsequent positive PCR test during the Omicron period, with N antibodies showing a stronger association. By elucidating the differential decay of RBD and S1 antibodies following vaccination and the complexities of N-antibody response following infection, this study in a Puerto Rican cohort strengthens the foundation for developing targeted interventions and public health strategies.

BACKGROUND: The COVID-19 pandemic underscored the need for rapid and accurate diagnostic tools. In August 2020, the Abbott BinaxNOW COVID-19 Antigen Card test became available as a timely and affordable alternative for SARS-CoV-2 molecular testing, but its performance may vary due to factors including timing and symptomatology. This study evaluates BinaxNOW diagnostic performance in diverse epidemiological contexts. METHODS: Using RT-PCR as reference, we assessed performance of the BinaxNOW COVID-19 test for SARS-CoV-2 detection in anterior nasal swabs from participants of two studies in Puerto Rico from December 2020 to May 2023. Test performance was assessed by days post symptom onset, collection strategy, vaccination status, symptomatology, repeated testing, and RT-PCR cycle threshold (Ct) values. RESULTS: BinaxNOW demonstrated an overall sensitivity of 84.1% and specificity of 98.8%. Sensitivity peaked within 1-6 days after symptom onset (93.2%) and was higher for symptomatic (86.3%) than asymptomatic (67.3%) participants. Sensitivity declined over the course of infection, dropping from 96.3% in the initial test to 48.4% in testing performed 7-14 days later. BinaxNOW showed 99.5% sensitivity in participants with low Ct values (≤ 25) but lower sensitivity (18.2%) for participants with higher Cts (36-40). CONCLUSIONS: BinaxNOW demonstrated high sensitivity and specificity, particularly in early-stage infections and symptomatic participants. In situations where test sensitivity is crucial for clinical decision-making, nucleic acid amplification tests are preferred. These findings highlight the importance of considering clinical and epidemiological context when interpreting test results and emphasize the need for ongoing research to adapt testing strategies to emerging SARS-CoV-2 variants.

During the COVID-19 pandemic, public health authorities have encouraged the use of face masks to minimize transmission within the community. To assess mask wear during a COVID-19 surge and guide public health response efforts, including public messaging on mask recommendations, we compared observed mask use in the largest city in each of Idaho's 2 most populous counties, both without a current mask mandate. We recorded mask usage by every third person exiting stores of 5 retail chains in Boise and Nampa during November 8-December 5, 2021. Observations were conducted during three time periods (morning, afternoon, and evening) on weekday and weekend days. A multivariable model with city, retail chain, and city-chain interaction was used to assess mask wear differences by city for each chain. Of 3021 observed persons, 22.0% wore masks. In Boise, 31.3% (430/1376) of observed persons wore masks; in Nampa, 14.3% (236/1645) wore masks. Among all persons wearing masks, > 94% wore masks correctly; cloth and surgical masks were most common. By retail chain, observed individuals at Boise locations were 2.3-5.7 times as likely to wear masks than persons at respective Nampa locations. This study provided a rapid, nonconfrontational assessment of public use of mitigation measures in 2 Idaho cities during a COVID-19 surge.

When the U.S. COVID-19 public health emergency declaration expires on May 11, 2023, national reporting of certain categories of COVID-19 public health surveillance data will be transitioned to other data sources or will be discontinued; COVID-19 hospitalization data will be the only data source available at the county level (1). In anticipation of the transition, national COVID-19 surveillance data sources and indicators were evaluated for purposes of ongoing monitoring. The timeliness and correlations among surveillance indicators were analyzed to assess the usefulness of COVID-19-associated hospital admission rates as a primary indicator for monitoring COVID-19 trends, as well as the suitability of other replacement data sources. During April 2022-March 2023, COVID-19 hospital admission rates from the National Healthcare Safety Network (NHSN)(†) lagged 1 day behind case rates and 4 days behind percentages of positive test results and COVID-19 emergency department (ED) visits from the National Syndromic Surveillance Program (NSSP). In the same analysis, National Vital Statistics System (NVSS) trends in the percentage of deaths that were COVID-19-associated, which is tracked by date of death rather than by report date, were observable 13 days earlier than those from aggregate death count data, which will be discontinued (1). During October 2020-March 2023, strong correlations were observed between NVSS and aggregate death data (0.78) and between the percentage of positive SARS-CoV-2 test results from the National Respiratory and Enteric Viruses Surveillance System (NREVSS) and COVID-19 electronic laboratory reporting (CELR) (0.79), which will also be discontinued (1). Weekly COVID-19 Community Levels (CCLs) will be replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrated >99% concordance by county during February 2022-March 2023. COVID-19-associated hospital admission levels are a suitable primary metric for monitoring COVID-19 trends, the percentage of COVID-19 deaths is a timely disease severity indicator, and the percentages of positive SARS-CoV-2 test results from NREVSS and ED visits serve as early indicators for COVID-19 monitoring. Collectively, these surveillance data sources and indicators can support monitoring of the impact of COVID-19 and related prevention and control strategies as ongoing public health priorities.

COVID-19 can lead to severe outcomes in children (1). Vaccination decreases risk for COVID-19 illness, severe disease, and death (2). On December 13, 2020, CDC recommended COVID-19 vaccination for persons aged ≥16 years, with expansion on May 12, 2021, to children and adolescents (children) aged 12-15 years, and on November 2, 2021, to children aged 5-11 years (3). As of March 8, 2023, COVID-19 vaccination coverage among school-aged children remained low nationwide, with 61.7% of children aged 12-17 years and approximately one third (32.7%) of those aged 5-11 years having completed the primary series (3). Intention to receive COVID-19 vaccine and vaccination coverage vary by demographic characteristics, including race and ethnicity and socioeconomic status (4-6). Seattle Public Schools (SPS) implemented a program to increase COVID-19 vaccination coverage during the 2021-22 school year, focusing on children aged 5-11 years during November 2021-June 2022, with an added focus on populations with low vaccine coverage during January 2022-June 2022.(†) The program included strategic messaging, school-located vaccination clinics, and school-led community engagement. Vaccination data from the Washington State Immunization Information System (WAIIS) were analyzed to examine disparities in COVID-19 vaccination by demographic and school characteristics and trends over time. In December 2021, 56.5% of all SPS students, 33.7% of children aged 5-11 years, and 81.3% of children aged 12-18 years had completed a COVID-19 primary vaccination series. By June 2022, overall series completion had increased to 80.3% and was 74.0% and 86.6% among children aged 5-11 years and 12-18 years, respectively. School-led vaccination programs can leverage community partnerships and relationships with families to improve COVID-19 vaccine access and coverage.

Recent investments in built environment infrastructure to create healthy communities have highlighted the need for equity and environmental justice. Although the benefits of healthy community design (e.g., connecting transportation systems and land use changes) are well established, some reports suggest that these changes may increase property values. These increases can raise the risk of displacement for people with low incomes and/or who are from racial and ethnic minority groups, who would then miss out on benefits from changes in community design. This review scanned the literature for displacement mitigation and prevention measures, with the goal of providing a compilation of available strategies for a wide range of audiences including public health practitioners. A CDC librarian searched the Medline, EbscoHost, Scopus, and ProQuest Central databases, and we identified grey literature using Google and Google Scholar searches. The indexed literature search identified 6 articles, and the grey literature scan added 18 articles. From these 24 total articles, we identified 141 mitigation and prevention strategies for displacement and thematically characterized each by domain using an adapted existing typology. This work provides a well-categorized inventory for practitioners and sets the stage for future evaluation research on the implementation of strategies and practices to reduce displacement.

The B.1.1.529 (Omicron) variant of SARS-CoV-2, the virus that causes COVID-19, was first clinically identified in the United States on December 1, 2021, and spread rapidly. By late December, it became the predominant strain, and by January 15, 2022, it represented 99.5% of sequenced specimens in the United States* (1). The Omicron variant has been shown to be more transmissible and less virulent than previously circulating variants (2,3). To better understand the severity of disease and health care utilization associated with the emergence of the Omicron variant in the United States, CDC examined data from three surveillance systems and a large health care database to assess multiple indicators across three high-COVID-19 transmission periods: December 1, 2020-February 28, 2021 (winter 2020-21); July 15-October 31, 2021 (SARS-CoV-2 B.1.617.2 [Delta] predominance); and December 19, 2021-January 15, 2022 (Omicron predominance). The highest daily 7-day moving average to date of cases (798,976 daily cases during January 9-15, 2022), emergency department (ED) visits (48,238), and admissions (21,586) were reported during the Omicron period, however, the highest daily 7-day moving average of deaths (1,854) was lower than during previous periods. During the Omicron period, a maximum of 20.6% of staffed inpatient beds were in use for COVID-19 patients, 3.4 and 7.2 percentage points higher than during the winter 2020-21 and Delta periods, respectively. However, intensive care unit (ICU) bed use did not increase to the same degree: 30.4% of staffed ICU beds were in use for COVID-19 patients during the Omicron period, 0.5 percentage points lower than during the winter 2020-21 period and 1.2 percentage points higher than during the Delta period. The ratio of peak ED visits to cases (event-to-case ratios) (87 per 1,000 cases), hospital admissions (27 per 1,000 cases), and deaths (nine per 1,000 cases [lagged by 3 weeks]) during the Omicron period were lower than those observed during the winter 2020-21 (92, 68, and 16 respectively) and Delta (167, 78, and 13, respectively) periods. Further, among hospitalized COVID-19 patients from 199 U.S. hospitals, the mean length of stay and percentages who were admitted to an ICU, received invasive mechanical ventilation (IMV), and died while in the hospital were lower during the Omicron period than during previous periods. COVID-19 disease severity appears to be lower during the Omicron period than during previous periods of high transmission, likely related to higher vaccination coverage,(†) which reduces disease severity (4), lower virulence of the Omicron variant (3,5,6), and infection-acquired immunity (3,7). Although disease severity appears lower with the Omicron variant, the high volume of ED visits and hospitalizations can strain local health care systems in the United States, and the average daily number of deaths remains substantial.(§) This underscores the importance of national emergency preparedness, specifically, hospital surge capacity and the ability to adequately staff local health care systems. In addition, being up to date on vaccination and following other recommended prevention strategies are critical to preventing infections, severe illness, or death from COVID-19.

We describe coronavirus disease (COVID-19) among US food manufacturing and agriculture workers and provide updated information on meat and poultry processing workers. Among 742 food and agriculture workplaces in 30 states, 8,978 workers had confirmed COVID-19; 55 workers died. Racial and ethnic minority workers could be disproportionately affected by COVID-19.

INTRODUCTION: Active commuting to work is one way people can be physically active and can be influenced by state-level initiatives. The American Community Survey (ACS) is a potential data source to evaluate changes in active commuting at the state level, but state-level changes have not been well documented. We examined state-level changes in estimates of walking, biking, and taking transit to work (combined and separately) among employed persons between 2006 and 2017. METHODS: Data were from the ACS, a nationally representative annual household survey from the US Census Bureau. We estimated state-level prevalence of walking, bicycling, or taking transit to work (separately and in combination) in 2006 and 2017 and tested differences by year. RESULTS: The prevalence of active commuting to work varied widely among states (2017 range: 1.7% in Alabama and Mississippi to 35.0% in New York). Changes from 2006 to 2017 also varied, with 8 states exhibiting a significant increase (Massachusetts [2.7 percentage points], New York [2.2], Hawaii [1.6], Illinois [1.3], Washington [1.3], New Jersey [1.2], Virginia [0.9], and Michigan [0.4]), and 12 exhibiting a significant decrease (South Dakota [-1.9], Idaho [-1.3], New Hampshire [-1.3], Wisconsin [-1.1], Maryland [-1.0], Nevada [-0.9], Ohio [-0.8], Mississippi [-0.6], Texas [-0.6], Florida [-0.5], Georgia [-0.4], and Indiana [-0.4]). The contributions of walking, bicycling, and taking transit also varied by state. CONCLUSION: Active commuting remains relatively rare across states. States pursuing initiatives to support active transportation may consider using ACS to monitor and evaluate changes in active commuting.

Meat and poultry processing facilities face distinctive challenges in the control of infectious diseases, including coronavirus disease 2019 (COVID-19) (1). COVID-19 outbreaks among meat and poultry processing facility workers can rapidly affect large numbers of persons. Assessment of COVID-19 cases among workers in 115 meat and poultry processing facilities through April 27, 2020, documented 4,913 cases and 20 deaths reported by 19 states (1). This report provides updated aggregate data from states regarding the number of meat and poultry processing facilities affected by COVID-19, the number and demographic characteristics of affected workers, and the number of COVID-19-associated deaths among workers, as well as descriptions of interventions and prevention efforts at these facilities. Aggregate data on confirmed COVID-19 cases and deaths among workers identified and reported through May 31, 2020, were obtained from 239 affected facilities (those with a laboratory-confirmed COVID-19 case in one or more workers) in 23 states.* COVID-19 was confirmed in 16,233 workers, including 86 COVID-19-related deaths. Among 14 states reporting the total number of workers in affected meat and poultry processing facilities (112,616), COVID-19 was diagnosed in 9.1% of workers. Among 9,919 (61%) cases in 21 states with reported race/ethnicity, 87% occurred among racial and ethnic minority workers. Commonly reported interventions and prevention efforts at facilities included implementing worker temperature or symptom screening and COVID-19 education, mandating face coverings, adding hand hygiene stations, and adding physical barriers between workers. Targeted workplace interventions and prevention efforts that are appropriately tailored to the groups most affected by COVID-19 are critical to reducing both COVID-19-associated occupational risk and health disparities among vulnerable populations. Implementation of these interventions and prevention efforts(dagger) across meat and poultry processing facilities nationally could help protect workers in this critical infrastructure industry.

In a follow-up study of mortality among North American synthetic rubber industry workers, cumulative exposure to 1,3-butadiene was positively associated with leukemia. Problems with historical exposure estimation, however, may have distorted the association. To evaluate the impact of potential inaccuracies in exposure estimation, we conducted uncertainty analyses of the relation between cumulative exposure to butadiene and leukemia. We created the 1,000 sets of butadiene estimates using job-exposure matrices consisting of exposure values that corresponded to randomly selected percentiles of the approximate probability distribution of plant-, work area/job group-, and year specific butadiene ppm. We then analyzed the relation between cumulative exposure to butadiene and leukemia for each of the 1,000 sets of butadiene estimates. In the uncertainty analysis, the point estimate of the RR for the first non zero exposure category (>0-<37.5 ppm-years) was most likely to be about 1.5. The rate ratio for the second exposure category (37.5-<184.7 ppm-years) was most likely to range from 1.5 to 1.8. The RR for category 3 of exposure (184.7-<425.0 ppm-years) was most likely between 2.1 and 3.0. The RR for the highest exposure category (425.0+ ppm-years) was likely to be between 2.9 and 3.7. This range off RR point estimates can best be interpreted as a probability distribution that describes our uncertainty in RR point estimates due to uncertainty in exposure estimation. After considering the complete probability distributions of butadiene exposure estimates, the exposure-response association of butadiene and leukemia was maintained. This exercise was a unique example of how uncertainty analyses can be used to investigate and support an observed measure of effect when occupational exposure estimates are employed in the absence of direct exposure measurements.