Adaptation and Utilization of a Postmarket Evaluation Model for Digital Contact Tracing Mobile Health Tools in the United States: Observational Cross-sectional Study.

BACKGROUND: Case investigation and contact tracing are core public health activities used to interrupt disease transmission. These activities are traditionally conducted manually. During periods of high COVID-19 incidence, US health departments were unable to scale up case management staff to deliver effective and timely contact-tracing services. In response, digital contact tracing (DCT) apps for mobile phones were introduced to automate these activities. DCT apps detect when other DCT users are close enough to transmit COVID-19 and enable alerts to notify users of potential disease exposure. These apps were deployed quickly during the pandemic without an opportunity to conduct experiments to determine effectiveness. However, it is unclear whether these apps can effectively supplement understaffed manual contact tracers. OBJECTIVE: The aims of this study were to (1) evaluate the effectiveness of COVID-19 DCT apps deployed in the United States during the COVID-19 pandemic and (2) determine if there is sufficient DCT adoption and interest in adoption to meet a minimum population use rate to be effective (56%). To assess uptake, interest and safe use covariates were derived from evaluating DCTs using the American Psychological Association App Evaluation Model (AEM) framework. METHODS: We analyzed data from a nationally representative survey of US adults about their COVID-19-related behaviors and experiences. Survey respondents were divided into three segments: those who adopted a DCT app, those who are interested but did not adopt, and those not interested. Descriptive statistics were used to characterize factors of the three groups. Multivariable logistic regression models were used to analyze the characteristics of segments adopting and interested in DCT apps against AEM framework covariates. RESULTS: An insufficient percentage of the population adopted or was interested in DCTs to achieve our minimum national target effectiveness rate (56%). A total of 17.4% (n=490) of the study population reported adopting a DCT app, 24.7% (n=697) reported interest, and 58.0% (n=1637) were not interested. Younger, high-income, and uninsured individuals were more likely to adopt a DCT app. In contrast, people in fair to poor health were interested in DCT apps but did not adopt them. App adoption was positively associated with visiting friends and family outside the home (odds ratio [OR] 1.63, 95% CI 1.28-2.09), not wearing masks (OR 0.52, 95% CI 0.38-0.71), and adopters thinking they have or had COVID-19 (OR 1.60, 95% CI 1.21-2.12). CONCLUSIONS: Overall, a small percentage of the population adopted DCT apps. These apps may not be effective in protecting adopters' friends and family from their maskless contacts outside the home given low adoption rates. The public health community should account for safe use behavioral factors in future public health contact-tracing app design. The AEM framework was useful in developing a study design to evaluate DCT effectiveness and safety.

Survival analysis of factors affecting the timing of COVID-19 non-pharmaceutical interventions by U.S. universities.

BACKGROUND: During March of 2020 the Centers for Disease Control and Prevention (CDC) announced non-pharmaceutical intervention (NPI) guidance as the primary mitigation strategy against growing COVID-19 community spread due to the absence of a vaccine or effective treatment at that time. CDC guidance states that NPIs are most effective when instituted in an early, targeted, and layered fashion. NPIs are effective in slowing spread, and measures should be custom-tailored to each population. This study examines factors associated with implementation and timing of NPI interventions across large public and private U.S. universities at the onset of the COVID-19 pandemic. METHODS: NPI decisions of interest include when U.S. universities canceled international travel, shifted to online learning, moved faculty/staff to remote work, limited campus housing, and closed campus for all non-essential personnel. Cox proportional hazard analyses of retrospective data were conducted to assess the time to NPI events. Hazard ratios were calculated for university governance, campus setting, religious affiliation, health infrastructure, faculty diversity, and student demographics. The methods control for variance inflation factors, COVID case prevalence, and time varying covariates of spring break and states' state of emergency (SOE) orders. This study captures NPI decisions at 575 U.S. universities during spring of 2020 which affected the movement of seven million students and two million employees. RESULTS: Universities located in districts represented by Democratic party congressional members reported earlier NPI implementation than Republican (Cox proportional hazard ratio (HR) range 0.61-0.80). University religious affiliation was not associated with the timing any of the NPI decisions. Universities with more diverse faculty showed an association with earlier NPI implementation (HR range 0.65-0.76). The existence of university-affiliated health infrastructure was not associated with NPI timing. CONCLUSION: University NPI implementation was largely driven by local COVID-19 epidemiology, culture and political concerns. The timing of university NPI decisions varied by regional politics, faculty demographics, university governance, campus setting, and foreign student prevalence adjusting for COVID-19 state case prevalence and spring break timing. Religious affiliation and presence of university health infrastructure were not associated with timing.

COVID-19 observations and accompanying dataset of non-pharmaceutical interventions across U.S. universities, March 2020.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) publishes COVID-19 non-pharmaceutical intervention (NPI) guidance for specific institutional audiences to limit community spread. Audiences include: business, clinical, public health, education, community, and state/local government. The swift, severe, and global nature of COVID-19 offers an opportunity to systematically obtain a national view of how larger institutions of higher education adopted NPI guidance at the onset of the pandemic. METHOD: An original database of COVID-19-related university NPI policy changes was compiled. Survey team members manually combed university websites and official statements capturing implementation decisions and dates for five NPI variables from 575 U.S. universities, across 50 states and the District of Columbia, during March of 2020. The universities included in this study were selected from the Department of Education Integrated Postsecondary Education Data System (IPEDS), which provides a set of university explanatory variables. Using IPEDS as the basis for the organizational data allows consistent mapping to event-time and institutional characteristic variables including public health announcements, geospatial, census, and political affiliation. RESULTS: The dataset enables event-time analysis and offers a variety of variables to support institutional level study and identification of underlying biases like educational attainment. A descriptive analysis of the dataset reveals that there was substantial heterogeneity in the decisions that were made and the timing of these decisions as they temporally related to key state, national, and global emergency announcements. The WHO pandemic declaration coincided with the largest number of university decisions to implement NPIs. CONCLUSION: This study provides descriptive observations and produced an original dataset that will be useful for future research focused on drivers and trends of COVID-19 NPIs for U.S. Universities. This preliminary analysis suggests COVID-19 university decisions appeared to be made largely at the university level, leading to major variations in the nature and timing of the responses both between and within states, which requires further study.