"How about me giving blood for the COVID vaccine and not being able to get vaccinated?" A cognitive interview study on understanding of and agreement with broad consent for future use of data and samples in Colombia and Nicaragua.

Broad consent for future use, wherein researchers ask participants for permission to share participant-level data and samples collected within the study for purposes loosely related to the study objectives, is central to enabling ethical data and sample reuse. Ensuring that participants understand broad consent-related language is key to maintaining trust in the study and public health research. We conducted 52 cognitive interviews to explore cohort research participants' and their parents' understanding of the broad consent-related language in the University of California at Berkeley template informed consent (IC) form for biomedical research. Participants and their parents were recruited from long-standing infectious disease cohort studies in Nicaragua and Colombia and interviewed during the COVID-19 pandemic. We conducted semi-structured interviews to assess participants' agreement with the key concepts in the IC after clarifying them through the cognitive interview. Participants did not understand abstract concepts, including collecting and reusing genetic data. Participants wanted to learn about incidental findings, future users and uses. Trust in the research team and the belief that sharing could lead to new vaccines or treatments were critical to participant support for data and sample sharing. Participants highlighted the importance of data and sample sharing for COVID-19 response and equitable access to vaccines and treatments developed through sharing. Our findings on participants' understanding of broad consent and preferences for data and sample sharing can help inform researchers and ethics review committees working to enable ethical and equitable data and sample sharing.

Epidemics of chikungunya, Zika, and COVID-19 reveal bias in case-based mapping.

Accurate tracing of epidemic spread over space enables effective control measures. We examined three metrics of infection and disease in a pediatric cohort (N≈3,000) over two chikungunya and one Zika epidemic, and in a household cohort (N=1,793) over one COVID-19 epidemic in Managua, Nicaragua. We compared spatial incidence rates (cases/total population), infection risks (infections/total population), and disease risks (cases/infected population). We used generalized additive and mixed-effects models, Kulldorf's spatial scan statistic, and intracluster correlation coefficients. Across different analyses and all epidemics, incidence rates considerably underestimated infection and disease risks, producing large and spatially non-uniform biases distinct from biases due to incomplete case ascertainment. Infection and disease risks exhibited distinct spatial patterns, and incidence clusters inconsistently identified areas of either risk. While incidence rates are commonly used to infer infection and disease risk in a population, we find that this can induce substantial biases and adversely impact policies to control epidemics.