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.

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.