ABSTRACT
We use a concepts and categories research perspective to explore how prior conceptual knowledge influences thinking about a novel disease, namely COVID-19. We collected measures of how similar people thought COVID-19 was to several existing concepts that may have served as other possible comparison points for the pandemic. We also collected participants' self-reported engagement in pandemic-related behaviors. We found that thinking the COVID-19 pandemic was similar to other serious disease outbreaks predicted greater social distancing and mask-wearing, whereas likening COVID-19 to the seasonal flu predicted engaging in significantly fewer of these behaviors. Thinking of COVID-19 as similar to zombie apocalypse scenarios or moments of major societal upheaval predicted stocking-up behaviors, but not disease mitigation behaviors. These early category comparisons influenced behaviors over a six-month span of longitudinal data collection. Our findings suggest that early conceptual comparisons track with emergent disease categories over time and influence the behaviors people engage in related to the disease. Our research illustrates how early concept formation influences behaviors over time, and suggests ways for public health experts to communicate with the public about emergent diseases.
Subject(s)
COVID-19 , Pandemics , Disease Outbreaks , Health Behavior , Humans , SARS-CoV-2ABSTRACT
BACKGROUND: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. OBJECTIVE: The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). METHODS: We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. RESULTS: In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. CONCLUSIONS: In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.