Participatory COVID-19 Surveillance Tool in Rural Appalachia: Real-Time Disease Monitoring and Regional Response

IntroductionFew US studies have examined the usefulness of participatory surveillance during the coronavirus disease 2019 (COVID-19) pandemic for enhancing local health response efforts, particularly in rural settings. We report on the development and implementation of an internet-based COVID-19 participatory surveillance tool in rural Appalachia.MethodsA regional collaboration among public health partners culminated in the design and implementation of the COVID-19 Self-Checker, a local online symptom tracker. The tool collected data on participant demographic characteristics and health history. County residents were then invited to take part in an automated daily electronic follow-up to monitor symptom progression, assess barriers to care and testing, and collect data on COVID-19 test results and symptom resolution.ResultsNearly 6500 county residents visited and 1755 residents completed the COVID-19 Self-Checker from April 30 through June 9, 2020. Of the 579 residents who reported severe or mild COVID-19 symptoms, COVID-19 symptoms were primarily reported among women (n = 408, 70.5%), adults with preexisting health conditions (n = 246, 70.5%), adults aged 18-44 (n = 301, 52.0%), and users who reported not having a health care provider (n = 131, 22.6%). Initial findings showed underrepresentation of some racial/ethnic and non–English-speaking groups.Practical ImplicationsThis low-cost internet-based platform provided a flexible means to collect participatory surveillance data on local changes in COVID-19 symptoms and adapt to guidance. Data from this tool can be used to monitor the efficacy of public health response measures at the local level in rural Appalachia.     

Clinico-Epidemiological Profile of Breakthrough COVID-19 Infection among Vaccinated Beneficiaries from a COVID-19 Vaccination Centre in Bihar,India

BackgroundWhen the whole world is fighting in an unprecedented pace against COVID-19 pandemic, the breakthrough COVID infections poise to dampen the rapid control of the same. We carried out this project with two objectives; first, to estimate the proportion of breakthrough COVID-19 infection among completely vaccinated individuals and second, to study the clinico-epidemiological profile of breakthrough COVID-19 infections among them.MethodsThis cross-sectional analytical study was conducted among 2703 fully vaccinated individuals from AIIMS, Patna COVID Vaccination Centre (CVC), Bihar, India. The participants were selected randomly using a systematic sampling technique from the list of beneficiaries maintained at the CVC. Telephonic interviews were made to collect the information by trained data collectors.ResultsA total of 274 fully vaccinated beneficiaries [10.1% (95% CI: 9.1%, 11.4%)] were diagnosed with breakthrough COVID-19 infection. The infections were more among males (10.4%) and the individuals aged ≤29 years (12.5%). The beneficiary categories, the healthcare-worker and the frontline-worker, were identified as predictors of the breakthrough COVID infections. Only one in three participants had adopted adequate COVID appropriate behaviour following the full vaccination. The majority of the breakthrough infections occurred during the second wave of COVID-19. The majority of the individuals with breakthrough infections were asymptomatic and no death was reported among them.ConclusionOne in every ten fully vaccinated individuals can get the breakthrough COVID infections. The healthcare-worker and the frontline-worker had independent risk of getting the breakthrough infections. Very few with breakthrough infections were serious and no death was reported among them.     

The Interplay Between Policy and COVID-19 Outbreaks in South Asia: Longitudinal Trend Analysis of Surveillance Data

BackgroundCOVID-19 transmission rates in South Asia initially were under control when governments implemented health policies aimed at controlling the pandemic such as quarantines, travel bans, and border, business, and school closures. Governments have since relaxed public health restrictions, which resulted in significant outbreaks, shifting the global epicenter of COVID-19 to India. Ongoing systematic public health surveillance of the COVID-19 pandemic is needed to inform disease prevention policy to re-establish control over the pandemic within South Asia.ObjectiveThis study aimed to inform public health leaders about the state of the COVID-19 pandemic, how South Asia displays differences within and among countries and other global regions, and where immediate action is needed to control the outbreaks.MethodsWe extracted COVID-19 data spanning 62 days from public health registries and calculated traditional and enhanced surveillance metrics. We use an empirical difference equation to measure the daily number of cases in South Asia as a function of the prior number of cases, the level of testing, and weekly shifts in variables with a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano–Bond estimator in R.ResultsTraditional surveillance metrics indicate that South Asian countries have an alarming outbreak, with India leading the region with 310,310 new daily cases in accordance with the 7-day moving average. Enhanced surveillance indicates that while Pakistan and Bangladesh still have a high daily number of new COVID-19 cases (n=4819 and n=3878, respectively), their speed of new infections declined from April 12-25, 2021, from 2.28 to 2.18 and 3.15 to 2.35 daily new infections per 100,000 population, respectively, which suggests that their outbreaks are decreasing and that these countries are headed in the right direction. In contrast, India’s speed of new infections per 100,000 population increased by 52% during the same period from 14.79 to 22.49 new cases per day per 100,000 population, which constitutes an increased outbreak.ConclusionsRelaxation of public health restrictions and the spread of novel variants fueled the second wave of the COVID-19 pandemic in South Asia. Public health surveillance indicates that shifts in policy and the spread of new variants correlate with a drastic expansion in the pandemic, requiring immediate action to mitigate the spread of COVID-19. Surveillance is needed to inform leaders whether policies help control the pandemic.     

Changes in the pattern and disease burden of acute respiratory viral infections before and during the COVID-19 pandemic

ObjectivesWe conducted a comparative analysis of the differences in the incidence of 8 acute respiratory viruses and the changes in their patterns before and during the coronavirus disease 2019 (COVID-19) pandemic.MethodsThree sentinel surveillance systems of the Korea Disease Control and Prevention Agency and data from the Health Insurance Review and Assessment Service were analyzed. The average numbers of reported cases and the related hospital admissions and outpatient data were compared between April 2018–2019 and 2020–2021. Changes in the disease burden and medical expenditures between these 2 time periods were evaluated.ResultsDuring the COVID-19 pandemic, the number of reported cases of all acute respiratory viral infections, except for human bocavirus, decreased significantly. Data from the Health Insurance Review and Assessment Service also showed decreases in the actual amount of medical service usage and a marked reduction in medical expenditures.Conclusion Non-pharmacological interventions in response to COVID-19 showed preventive effects on the transmission of other respiratory viruses, as well as COVID-19. Although COVID-19 had a tremendous impact on society as a whole, with high social costs, there were also positive effects, such as a reduction in the incidence of acute respiratory viral infections.     

Characteristics,Comorbidities, and Data Gaps for Coronavirus Disease Deaths,Tennessee, USA

As of March 2021, coronavirus disease (COVID-19) had led to >500,000 deaths in the United States, and the state of Tennessee had the fifth highest number of cases per capita. We reviewed the Tennessee Department of Health COVID-19 surveillance and chart-abstraction data during March 15‒August 15, 2020. Patients who died from COVID-19 were more likely to be older, male, and Black and to have underlying conditions (hereafter comorbidities) than case-patients who survived. We found 30.4% of surviving case-patients and 20.3% of deceased patients had no comorbidity information recorded. Chart-abstraction captured a higher proportion of deceased case-patients with >1 comorbidity (96.3%) compared with standard surveillance deaths (79.0%). Chart-abstraction detected higher rates of each comorbidity except for diabetes, which had similar rates among standard surveillance and chart-abstraction. Investing in public health data collection infrastructure will be beneficial for the COVID-19 pandemic and future disease outbreaks.     

Serological response after SARS-CoV2 vaccination in healthcare workers: a multicenter study

Introduction:Characterizing immunological response following COVID-19 vaccination is an important public health issue. The objectives of the present analysis were to investigate the proportion, level and the determinants of humoral response from 21 days to three months after the first dose in vaccinated healthcare workers (HCWs).Methods:We abstracted data on level of anti-SARS-CoV-2 Spike antibodies (IgG) and sociodemographic characteristics of 17,257 HCWs from public hospitals and public health authorities from three centers in Northern Italy who underwent COVID-19 vaccination (average 70.6 days after first dose). We fitted center-specific multivariate regression models and combined them using random-effects meta-analyses.Results:A humoral response was elicited in 99.3% of vaccinated HCW. Female sex, young age, and previous COVID-19 infection were predictors of post-vaccination antibody level, and a positive association was also detected with pre-vaccination serology level and with time between pre- and post-vaccination testing, while a decline of antibody level was suggested with time since vaccination.Conclusions:These results stress the importance of analyzing retrospective data collected via occupational health surveillance of HCWs during the COVID-19 epidemic and following vaccination. They need to be confirmed in larger series based on prospectively collected data.     

Development of a COVID-19 vaccine effectiveness and safety assessment system in Japan: The VENUS study

BackgroundThere are currently no COVID-19 vaccine assessment systems in Japan that allow for the active surveillance of both vaccinated and unvaccinated persons. Herein, we describe the development of Japan’s first COVID-19 vaccine effectiveness and safety assessment system with active surveillance capabilities.MethodsThe Vaccine Effectiveness, Networking, and Universal Safety (VENUS) Study was developed as a multi-source database that links four data types at the individual resident level: Basic Resident Register (base population information), Vaccination Record System (vaccination-related information), Health Center Real-time Information-sharing System on COVID-19 (HER-SYS; information on COVID-19 occurrence), and health care claims data (information on diagnoses, hospitalizations, diagnostic tests, and treatments). These data were obtained from four municipalities. Individual residents were linked across the data types using five matching algorithms based on names, birth dates, and sex; the data were anonymized after linkage. To ascertain the viability of the VENUS Study’s database for COVID-19 vaccine assessments, we examined the trends in COVID-19 vaccinations, COVID-19 cases, and polymerase chain reaction (PCR) test numbers. We also evaluated the linkage rates across the data types.ResultsOur multi-source database was able to monitor COVID-19 vaccinations, COVID-19 cases, and PCR test numbers throughout the pandemic. Using the five algorithms, the data linkage rates between the COVID-19 occurrence information in the HER-SYS and the Basic Resident Register ranged from 85·4% to 91·7%.ConclusionIf used judiciously with an understanding of each data source’s characteristics, the VENUS Study can provide a viable data platform that facilitates active surveillance and comparative analyses for population-based research on COVID-19 vaccine effectiveness and safety in Japan.     

Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model: Modeling Study

BackgroundIn the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important.ObjectiveWe aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19.MethodsWe estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data.ResultsWe estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states.ConclusionsOur novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data.     

Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data,Online News Articles,and Search Queries

BackgroundThe SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.ObjectiveThe main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources.MethodsWe first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis.ResultsOur proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t₁₉₈=–8.722, P<.001; model 2, t₁₉₈=–5.000, P<.001, model 3, t₁₉₈=–1.882, P=.06; model 4, t₁₉₈=–4.644, P<.001; model 5, t₁₉₈=–4.488, P<.001). In Hubei province, our proposed model''s forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t₁₉₈=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks.ConclusionsOur approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies'' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.     

New Methodologies for Conducting Maternal,Infant, and Child Nutrition Research in the Era of COVID-19

The severe acute respiratory syndrome coronavirus disease 2019 (COVID-19) outbreak rapidly became a worldwide pandemic in early 2020. In Australia, government-mandated restrictions on non-essential face-to-face contact in the healthcare setting have been crucial for limiting opportunities for COVID-19 transmission, but they have severely limited, and even halted, many research activities. Our institute’s research practices in the vulnerable populations of pregnant women and young infants needed to adapt in order to continue without exposing participants, or staff, to an increased risk of exposure to COVID-19. Here, we discuss our pre-and-post COVID-19 methods for conducting research regarding nutrition during pregnancy, infancy, and early childhood. We discuss modifications to study methods implemented to avoid face-to-face contact when identifying and recruiting potential participants, gaining informed consent, conducting appointments, and collecting outcome data, and the implications of these changes. The COVID-19 pandemic has required numerous changes to the conduct of research activities, but many of those modifications will be useful in post-COVID-19 research settings.     

The Use of Cremation Data for Timely Mortality Surveillance During the COVID-19 Pandemic in Ontario,Canada: Validation Study

BackgroundEarly estimates of excess mortality are crucial for understanding the impact of COVID-19. However, there is a lag of several months in the reporting of vital statistics mortality data for many jurisdictions, including across Canada. In Ontario, a Canadian province, certification by a coroner is required before cremation can occur, creating real-time mortality data that encompasses the majority of deaths within the province.ObjectiveThis study aimed to validate the use of cremation data as a timely surveillance tool for all-cause mortality during a public health emergency in a jurisdiction with delays in vital statistics data. Specifically, this study aimed to validate this surveillance tool by determining the stability, timeliness, and robustness of its real-time estimation of all-cause mortality.MethodsCremation records from January 2020 until April 2021 were compared to the historical records from 2017 to 2019, grouped according to week, age, sex, and whether COVID-19 was the cause of death. Cremation data were compared to Ontario’s provisional vital statistics mortality data released by Statistics Canada. The 2020 and 2021 records were then compared to previous years (2017-2019) to determine whether there was excess mortality within various age groups and whether deaths attributed to COVID-19 accounted for the entirety of the excess mortality.ResultsBetween 2017 and 2019, cremations were performed for 67.4% (95% CI 67.3%-67.5%) of deaths. The proportion of cremated deaths remained stable throughout 2020, even within age and sex categories. Cremation records are 99% complete within 3 weeks of the date of death, which precedes the compilation of vital statistics data by several months. Consequently, during the first wave (from April to June 2020), cremation records detected a 16.9% increase (95% CI 14.6%-19.3%) in all-cause mortality, a finding that was confirmed several months later with cremation data.ConclusionsThe percentage of Ontarians cremated and the completion of cremation data several months before vital statistics did not change meaningfully during the COVID-19 pandemic period, establishing that the pandemic did not significantly alter cremation practices. Cremation data can be used to accurately estimate all-cause mortality in near real-time, particularly when real-time mortality estimates are needed to inform policy decisions for public health measures. The accuracy of this excess mortality estimation was confirmed by comparing it with official vital statistics data. These findings demonstrate the utility of cremation data as a complementary data source for timely mortality information during public health emergencies.     

Impact of COVID-19 immunisation on COVID-19 incidence,hospitalisations, and deaths by age group in Germany from December 2020 to October 2021

BackgroundUtilising national surveillance data, we investigated the impact of the COVID-19 immunisation campaign on COVID-19 morbidity and mortality between December/2020 and October/2021 in Germany.MethodsWe compared patterns in immunisation coverage, incidence, hospitalisations, and deaths among 12–17, 18–59, and 60+ year-olds and examined these patterns within the context of anti-pandemic measures.ResultsCOVID-19 incidence increased in all age groups following the end of lockdown restrictions in March/2021, but as Germany experienced successive peaks in incidence, age groups with higher immunisation coverage experienced successively smaller peaks. Notwithstanding corresponding increases during periods of higher incidence, among those aged 60+ years, COVID-19 related hospitalisations and deaths declined considerably as immunisation coverage increased, despite circulation of virus variants known to cause more severe illness.ConclusionAlthough ecological in nature, this study allows us to demonstrate clear patterns of decline in COVID-19 morbidity and mortality in Germany during the course of the immunisation campaign.     

Predisposing,Enabling, and Reinforcing Factors of COVID-19 Prevention Behavior in Indonesia: A Mixed-methods Study

Objectives:To prevent the spread of coronavirus disease 2019 (COVID-19), behaviors such as mask-wearing, social distancing, decreasing mobility, and avoiding crowds have been suggested, especially in high-risk countries such as Indonesia. Unfortunately, the level of compliance with those practices has been low. This study was conducted to determine the predisposing, enabling, and reinforcing factors of COVID-19 prevention behavior in Indonesia.Methods:This cross-sectional study used a mixed-methods approach. The participants were 264 adults from 21 provinces in Indonesia recruited through convenience sampling. Data were collected using a Google Form and in-depth interviews. Statistical analysis included univariate, bivariate, and multivariate logistic regression. Furthermore, qualitative data analysis was done through content analysis and qualitative data management using Atlas.ti software.Results:Overall, 44.32% of respondents were non-compliant with recommended COVID-19 prevention behaviors. In multivariate logistic regression analysis, low-to-medium education level, poor attitude, insufficient involvement of leaders, and insufficient regulation were also associated with decreased community compliance. Based on in-depth interviews with informants, the negligence of the Indonesian government in the initial stages of the COVID-19 pandemic may have contributed to the unpreparedness of the community to face the pandemic, as people were not aware of the importance of preventive practices.Conclusions:Education level is not the only factor influencing community compliance with recommended COVID-19 prevention behaviors. Changing attitudes through health promotion to increase public awareness and encouraging voluntary community participation through active risk communication are necessary. Regulations and role leaders are also required to improve COVID-19 prevention behavior.     

Knowledge mobilization tool to promote,protect, and support breastfeeding during COVID-19

SettingThis knowledge mobilization project was conceptualized to increase awareness among breastfeeding mothers and the general public on safe infant feeding practices during the COVID-19 pandemic by addressing myths and misconceptions associated with breastfeeding practices, guiding breastfeeding mothers to make informed decisions around child feeding practices, and offering meaningful guidance in simple language through a short online animated video.InterventionThis project was undertaken in four phases. During phase 1, an informal discussion was held with the breastfeeding mothers, service providers, and community partner in identifying issues surrounding lactation counselling facilities during the COVID-19 pandemic. During phase 2, recommendations from 23 organizations with regard to breastfeeding during COVID-19 were reviewed and analyzed. During phase 3, using evidence from reliable sources, a 5-minute animated e-resource on breastfeeding during COVID-19 was conceptualized and developed. During phase 4, the e-resource was disseminated to the breastfeeding mothers, general public, post-secondary institutions, and organizations providing services to breastfeeding mothers in Canada.OutcomesThis evidence-based e-resource facilitated addressing misconceptions around breastfeeding during COVID-19 and raising public awareness on safe infant feeding practices during this pandemic. Overall, the video was described as an informative, user-friendly, useful, and easily accessible resource by breastfeeding mothers who were in self-isolation with little access to healthcare services during the pandemic.ImplicationsThis project highlighted the importance of patient engagement and collaboration with the community partner in protecting breastfeeding during the COVID-19 pandemic. It further illustrated how informational e-resources can protect breastfeeding in situations where breastfeeding mothers’ access to healthcare services is compromised.     

Estimating and Characterizing COVID-19 Deaths,Puerto Rico,March–July 2020

ObjectivesUsing the Council of State and Territorial Epidemiologists (CSTE) classification guidelines, we characterized coronavirus disease 2019 (COVID-19)–associated confirmed and probable deaths in Puerto Rico during March–July 2020. We also estimated the total number of possible deaths due to COVID-19 in Puerto Rico during the same period.MethodsWe described data on COVID-19–associated mortality, in which the lower bound was the sum of confirmed and probable COVID-19 deaths and the upper bound was excess mortality, estimated as the difference between observed deaths and average expected deaths. We obtained data from the Puerto Rico Department of Health COVID-19 Mortality Surveillance System, the Centers for Disease Control and Prevention’s National Electronic Disease Surveillance System Base System, and the National Center for Health Statistics.ResultsDuring March–July 2020, 225 COVID-19–associated deaths were identified in Puerto Rico (119 confirmed deaths and 106 probable deaths). The median age of decedents was 73 (interquartile range, 59-83); 60 (26.7%) deaths occurred in the Metropolitana region, and 140 (62.2%) deaths occurred among men. Of the 225 decedents, 180 (83.6%) had been hospitalized and 93 (41.3%) had required mechanical ventilation. Influenza and pneumonia (48.0%), sepsis (28.9%), and respiratory failure (27.1%) were the most common conditions contributing to COVID-19 deaths based on death certificates. Based on excess mortality calculations, as many as 638 COVID-19–associated deaths could have occurred during the study period, up to 413 more COVID-19–associated deaths than originally reported.ConclusionsIncluding probable deaths per the CSTE guidelines and monitoring all-cause excess mortality can lead to a better estimation of COVID-19–associated deaths and serve as a model to enhance mortality surveillance in other US jurisdictions.     

COVID-19 Vaccine Perceptions,Intentions, and Uptake Among Young Adults in the United States: Prospective College-Based Cohort Study

BackgroundUptake of the COVID-19 vaccine among US young adults, particularly those that belong to racial and ethnic minorities, remains low compared to their older peers. Understanding vaccine perceptions and their influence on vaccination uptake among this population remains crucial to achieving population herd immunity.ObjectiveWe sought to study perceptions of COVID-19 vaccines as well as intended and actual vaccine uptake among one population of college students, faculty, and staff.MethodsAs part of a larger study aimed at investigating the dynamics of COVID-19 transmission, serology, and perception on a college campus, participants were asked about their views on the COVID-19 vaccine in February 2021. Vaccination status was assessed by self-report in April 2021. Logistic regression was used to calculate prevalence ratios with marginal standardization.ResultsWe found that non-White participants were 25% less likely to report COVID-19 vaccination compared to White participants. Among those who were unvaccinated, Black and other non-White participants were significantly more likely to indicate they were unwilling to receive a COVID-19 vaccine compared to White participants. The most common reason for unwillingness to receive the vaccine was belief that the vaccine approval process was rushed.ConclusionsThere are racial differences in perceptions of the COVID-19 vaccine among young adults, and these differences might differentially impact vaccine uptake among young racial and ethnic minorities. Efforts to increase vaccine uptake among college populations might require campaigns specifically tailored to these minority groups.     

Developing a Flexible National Wastewater Surveillance System for COVID-19 and Beyond

Background: Wastewater testing offers a cost-effective strategy for measuring population disease prevalence and health behaviors. For COVID-19, wastewater surveillance addresses testing gaps and provides an early warning for outbreaks. As U.S. federal agencies build a National Wastewater Surveillance System around the pandemic, thinking through ways to develop flexible frameworks for wastewater sampling, testing, and reporting can avoid unnecessary system overhauls for future infectious disease, chronic disease, and drug epidemics.Objectives: We discuss ways to transform a historically academic exercise into a tool for epidemic response. We generalize lessons learned by a global network of wastewater researchers around validation and implementation for COVID-19 and opioids while also drawing on our experience with wastewater-based epidemiology in the United States.Discussion: Sustainable wastewater surveillance requires coordination between health and safety officials, utilities, labs, and researchers. Adapting sampling frequency, type, and location to threat level, community vulnerability, biomarker properties, and decisions that wastewater data will inform can increase the practical value of the data. Marketplace instabilities, coupled with a fragmented testing landscape due to specialization, may require officials to engage multiple labs to test for known and unknown threats. Government funding can stabilize the market, balancing commercial pressures with public good, and incentivize data sharing. When reporting results, standardizing metrics and contextualizing wastewater data with health resource data can provide insights into a community’s vulnerability and identify strategies to prevent health care systems from being overwhelmed. If wastewater data will inform policy decisions for an entire community, comparing characteristics of the wastewater treatment plant’s service population to those of the larger community can help determine whether the wastewater data are generalizable. Ethical protocols may be needed to protect privacy and avoid stigmatization. With data-driven approaches to sample collection, analysis, and interpretation, officials can use wastewater surveillance for adaptive resource allocation, pandemic management, and program evaluation. https://doi.org/10.1289/EHP8572