The case for using mixed methods for designing, implementing, and disseminating evidence-based interventions for public health practice

Can evidence drive health equity in the COVID-19 pandemic and beyond?

Using scoping review methods, we systematically searched multiple online databases for publications in the first year of the pandemic that proposed pragmatic population or health system-level solutions to health inequities. We found 77 publications with proposed solutions to pandemic-related health inequities. Most were commentaries, letters, or editorials from the USA, offering untested solutions, and no robust evidence on effectiveness. Some of the proposed solutions could unintentionally exacerbate health inequities. We call on health policymakers to co-create, co-design, and co-produce equity-focussed, evidence-based interventions with communities, focussing on those most at risk to protect the population as a whole. Epidemiologists collaborating with people from other relevant disciplines may provide methodological expertise for these processes. As epidemiologists, we must interrogate our own methods to avoid propagating any unscientific biases we may hold. Epidemiology must be used to address, and never exacerbate, health inequities-in the pandemic and beyond.

Social mood during the Covid-19 vaccination process in Spain. A sentiment analysis of tweets and social network leaders

In accordance with the cognitive orientation contemplated in the resolution of complex problems posed in public decision-making using decision support systems and social networks, this work studies the possibility of identifying the state of mind of society through the state of mind of network leaders. Using sentiment and emotion analysis as research techniques and Twitter as a representative social network, the study corpus considers tweets and retweets in Spanish about COVID-19 in the period from February 27, 2020 to December 31, 2021. As cognitive orientation claims, the proposed techniques will allow us to extract the arguments that support the different positions and decisions from the analysis of the tweets issued exclusively by social leaders. In the case study considered, the COVID-19 vaccination process in Spain, the reduction in the number of tweets' authors (more than 8,000) to the network leaders (just 8) was greater than 99 %; and the subsequent reduction in the number of associated tweets was greater than 88 % from the 18,193 tweets in society to the 2,145 tweets of the eight social leaders. The impressive degree of information compression achieved may be useful to establish new directions of social mood analysis applied to healthcare and business management.

Bringing Micro to the Macro: How Citizen Science Data Enrich Geospatial Visualizations to Advance Health Equity

Social and spatial contexts affect health, and understanding nuances of context is key to informing successful interventions for health equity. Layering mixed methods and mixed scale data sources to visualize patterns of health outcomes facilitates analysis of both broad trends and person-level experiences across time and space. We used micro-scale citizen scientist-collected data from four Bay Area communities along with aggregate epidemiologic and population-level data sets to illustrate barriers to, and facilitators of, physical activity in low-income aging adults. These data integrations highlight the synergistic value added by combining data sources, and what might be missed by relying on either a micro- or macro-level data source alone. Mixed methods and granularity data integration can generate a deeper understanding of environmental context, which in turn can inform more relevant and attainable community, advocacy, and policy improvements.