Implementation of the Texas Community-Engaged Statewide Consortium for the Prevention of COVID-19.

The Community Engagement Alliance (CEAL) Against COVID-19 Disparities aims to conduct community-engaged research and outreach. This paper describes the Texas CEAL Consortium's activities in the first year and evaluates progress. The Texas CEAL Consortium comprised seven projects. To evaluate the Texas CEAL Consortium's progress, we used components of the RE-AIM Framework. Evaluation included estimating the number of people reached for data collection and education activities (reach), individual project goals and progress (effectiveness), partnerships established and partner engagement (adoption), and outreach and education activities (implementation). During the one-year period, focus groups were conducted with 172 people and surveys with 2107 people across Texas. Partners represented various types of organizations, including 11 non-profit organizations, 4 academic institutions, 3 civic groups, 3 government agencies, 2 grassroots organizations, 2 faith-based organizations, 1 clinic, and 4 that were of other types. The main facets of implementation consisted of education activities and the development of trainings. Key recommendations for future consortiums relate to funding and research logistics and the value of strong community partnerships. The lessons learned in this first year of rapid deployment inform ongoing work by the Texas CEAL Consortium and future community-engaged projects.

Work-related and personal predictors of COVID-19 transmission: evidence from the UK and USA.

OBJECTIVE: To develop evidence of work-related and personal predictors of COVID-19 transmission. SETTING AND RESPONDENTS: Data are drawn from a population survey of individuals in the USA and UK conducted in June 2020. BACKGROUND METHODS: Regression models are estimated for 1467 individuals in which reported evidence of infection depends on work-related factors as well as a variety of personal controls. RESULTS: The following themes emerge from the analysis. First, a range of work-related factors are significant sources of variation in COVID-19 infection as indicated by self-reports of medical diagnosis or symptoms. This includes evidence about workplace types, consultation about safety and union membership. The partial effect of transport-related employment in regression models makes the chance of infection over three times more likely while in univariate analyses, transport-related work increases the risk of infection by over 40 times in the USA. Second, there is evidence that some home-related factors are significant predictors of infection, most notably the sharing of accommodation or a kitchen. Third, there is some evidence that behavioural factors and personal traits (including risk preference, extraversion and height) are also important. CONCLUSIONS: The paper concludes that predictors of transmission relate to work, transport, home and personal factors. Transport-related work settings are by far the greatest source of risk and so should be a focus of prevention policies. In addition, surveys of the sort developed in this paper are an important source of information on transmission pathways within the community.