Economic and mental health impacts of multiple adverse events: Hurricane Harvey, other flooding events, and the COVID-19 pandemic.

OBJECTIVES: To assess the economic and mental health impacts of COVID-19 in the presence of previous exposure to flooding events. METHODS: Starting in April 2018, the Texas Flood Registry (TFR) invited residents to complete an online survey regarding their experiences with Hurricane Harvey and subsequent flooding events. Starting in April 2020, participants nationwide were invited to complete a brief online survey on their experiences during the pandemic. This study includes participants in the TFR (N = 20,754) and the COVID-19 Registry (N = 8568) through October 2020 (joint N = 2929). Logistic regression and generalized estimating equations were used to examine the relationship between exposure to flooding events and the economic and mental health impacts of COVID-19. RESULTS: Among COVID-19 registrants, 21% experienced moderate to severe anxiety during the pandemic, and 7% and 12% of households had difficulty paying rent and bills, respectively. Approximately 17% of Black and 15% of Hispanic households had difficulty paying rent, compared to 5% of non-Hispanic white households. The odds of COVID-19 income loss are 1.20 (1.02, 1.40) times higher for those who previously had storm-related home damage compared to those who did not and 3.84 (3.25-4.55) times higher for those who experienced Harvey income loss compared to those who did not. For registrants for whom Harvey was a severe impact event, the odds of having more severe anxiety during the pandemic are 5.14 (4.02, 6.58) times higher than among registrants for whom Harvey was a no meaningful impact event. CONCLUSIONS: Multiple crises can jointly and cumulatively shape health and wellbeing outcomes. This knowledge can help craft emergency preparation and intervention programs.

Symptomatic, Presymptomatic, and Asymptomatic Transmission of SARS-CoV-2 in a University Student Population, August-November 2020.

OBJECTIVES: The impact and risk of SARS-CoV-2 transmission from asymptomatic and presymptomatic hosts remains an open question. This study measured the secondary attack rates (SARs) and relative risk (RR) of SARS-CoV-2 transmission from asymptomatic and presymptomatic index cases as compared with symptomatic index cases. METHODS: We used COVID-19 test results, daily health check reports, and contact tracing data to measure SARs and corresponding RRs among close contacts of index cases in a cohort of 12 960 young adults at the University of Notre Dame in Indiana for 103 days, from August 10 to November 20, 2020. Further analysis included Fisher exact tests to determine the association between symptoms and COVID-19 infection and z tests to determine statistical differences between SARs. RESULTS: Asymptomatic rates of transmission of SARS-CoV-2 were higher (SAR = 0.19; 95% CI, 0.14-0.24) than was estimated in prior studies, producing an RR of 0.75 (95% CI, 0.54-1.07) when compared with symptomatic transmission. In addition, the transmission rate associated with presymptomatic cases (SAR = 0.25; 95% CI, 0.21-0.30) was approximately the same as that for symptomatic cases (SAR = 0.25; 95% CI, 0.19-0.31). Furthermore, different symptoms were associated with different transmission rates. CONCLUSIONS: Asymptomatic and presymptomatic hosts of SARS-CoV-2 are a risk for community spread of COVID-19, especially with new variants emerging. Moreover, typical symptom checks may easily miss people who are asymptomatic or presymptomatic but still infectious. Our study results may be used as a guide to analyze the spread of SARS-CoV-2 variants and help inform appropriate public health measures as they relate to asymptomatic and presymptomatic cases.

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

Data-driven testing program improves detection of COVID-19 cases and reduces community transmission.

COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability. In this study, we analyze a data-driven COVID-19 testing program implemented at a mid-sized university, which utilized two simple, diverse, and easily interpretable machine learning models to predict which students were at elevated risk and should be tested. The program produced a positivity rate of 0.53% (95% CI 0.34-0.77%) from 20,862 tests, with 1.49% (95% CI 1.15-1.89%) of students testing positive within five days of the initial test-a significant increase from the general surveillance baseline, which produced a positivity rate of 0.37% (95% CI 0.28-0.47%) with 0.67% (95% CI 0.55-0.81%) testing positive within five days. Close contacts who were predicted by the data-driven models were tested much more quickly on average (0.94 days from reported exposure; 95% CI 0.78-1.11) than those who were manually contact traced (1.92 days; 95% CI 1.81-2.02). We further discuss how other universities, business, and organizations could adopt similar strategies to help quickly identify positive cases and reduce community transmission.

SARS-CoV-2 Variant Tracking and Mitigation During In-Person Learning at a Midwestern University in the 2020-2021 School Year.

Importance: The COVID-19 pandemic led many higher education institutions to close campuses during the 2020-2021 academic year. As campuses prepared for a return to in-person education, many institutions were mandating vaccines for students and considering the same for faculty and staff. Objective: To determine the association between vaccination coverage and the levels and spread of SARS-CoV-2, even in the presence of highly-transmissible variants and congregate living, at a midsized university in the US. Design, Setting, and Participants: This case series was conducted at a midsized Midwestern university during the spring 2021 semester. The university developed a saliva-based surveillance program capable of high-throughput SARS-CoV-2 polymerase chain reaction testing and genomic sequencing with the capacity to deliver results in less than 24 hours. On April 7, 2021, the university announced a vaccine requirement for all students for the fall 2021 semester and announced the same requirement for faculty and staff on May 20, 2021. The university hosted an onsite mass vaccination clinic using the 2-dose Pfizer-BioNTech vaccine during April 8 to 15 and April 29 to May 6, 2021. Data were analyzed for 14 894 individuals from the university population who were tested for COVID-19 on campus from January 6 to May 20, 2021. Main Outcomes and Measures: Positive SARS-CoV-2 diagnosis was confirmed by quantitative reverse transcription-polymerase chain reaction of saliva specimens, and variant identity was assessed by quantitative reverse transcription-polymerase chain reaction and next-generation sequencing of viral genomes. Results: Between January 6 and May 20, 2021, the university conducted 196 185 COVID-19 tests for 14 894 individuals and identified 1603 positive cases. Within those positive cases, 950 individuals (59.3%) were male, 644 (40.2%) were female, 1426 (89.0%) were students, and 1265 (78.9%) were aged 17 to 22 years. Among the 1603 positive cases, 687 were identified via polymerase chain reaction of saliva specimens. The Alpha (B.1.1.7) variant constituted 218 of the 446 total positives sequenced (48.9%). By May 20, 2021, 10 068 of 11 091 students (90.8%), 814 of 883 faculty (92.2%), and 2081 of 2890 staff (72.0%) were vaccinated. The 7-day rolling average of positive cases peaked at 37 cases on February 17 but declined to zero by May 14, 2021. The 7-day moving average of positive cases was inversely associated with cumulative vaccination coverage, with a statistically significant Pearson correlation coefficient of -0.57 (95% CI, -0.68 to -0.44). Conclusions and Relevance: This case series study elucidated the association of a robust vaccination program with a statistically significant decrease in positive COVID-19 cases among the study population even in the presence of highly transmissible variants and congregate living.

Response to a COVID-19 Outbreak on a University Campus - Indiana, August 2020.

Institutions of higher education adopted different approaches for the fall semester 2020 in response to the coronavirus disease 2019 (COVID-19) pandemic. Approximately 45% of colleges and universities implemented online instruction, more than one fourth (27%) provided in-person instruction, and 21% used a hybrid model (1). Although CDC has published COVID-19 guidance for institutions of higher education (2-4), little has been published regarding the response to COVID-19 outbreaks on college and university campuses (5). In August 2020, an Indiana university with approximately 12,000 students (including 8,000 undergraduate students, 85% of whom lived on campus) implemented various public health measures to reduce transmission of SARS-CoV-2, the virus that causes COVID-19. Despite these measures, the university experienced an outbreak involving 371 cases during the first few weeks of the fall semester. The majority of cases occurred among undergraduate students living off campus, and several large off-campus gatherings were identified as common sources of exposure. Rather than sending students home, the university switched from in-person to online instruction for undergraduate students and instituted a series of campus restrictions for 2 weeks, during which testing, contact tracing, and isolation and quarantine programs were substantially enhanced, along with educational efforts highlighting the need for strict adherence to the mitigation measures. After 2 weeks, the university implemented a phased return to in-person instruction (with 85% of classes offered in person) and resumption of student life activities. This report describes the outbreak and the data-driven, targeted interventions and rapid escalation of testing, tracing, and isolation measures that enabled the medium-sized university to resume in-person instruction and campus activities. These strategies might prove useful to other colleges and universities responding to campus outbreaks.