Media bias exposure and the incidence of COVID-19 in the USA

Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing

INTRODUCTION

The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as 'statistical alerts') generated is expected.

OBJECTIVES

The objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept.

METHODS

The VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+.

RESULTS

Overall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs.

CONCLUSION

Our approach reduced the need for manual exclusion of disproportionality signals related to listed AEFIs and may lead to better optimization of time and resources in signal management.

"It Bothered Me": The Mental Burden of COVID-19 Media Reports on Community-Dwelling Elderly People

Background and Objectives: Elderly people may have difficulties understanding the quality and quantity of information about the COVID-19 epidemic, which can put an additional mental strain on their health and well-being. The purpose of this study was to explore the processing of COVID-19 information among older people. Materials and Methods: A qualitative study was carried out in summer 2021. The sampling was based on the snowball method. This approach allowed us to communicate with the next potential participants relatively freely and without reservations. Two female researchers (both MD, PhD) conducted the interviews. All interviews were held in Serbian. The data were analyzed using qualitative content analysis. Results: The interviews were conducted with 13 participants (average age 71 years). The analysis of qualitative content suggested that four topics could be identified: (1) sources of information, (2) information interest and need, (3) reporting of information and (4) suggestions for better reporting. The participants were troubled by the excess of information, repetitive information about death tolls, unqualified people in media discussing the pandemic and inconsistent reporting. These features caused the participants to feel the psychological burden in processing all the pieces of information. Conclusions: The elderly people in Serbia followed mainstream media to get information about COVID-19; however, they perceived a variety of problems with reporting, which made the understanding of the information difficult and psychologically burdensome. These findings should be taken into consideration when delivering health-related information to elderly people.

What did the pandemic teach us about effective health communication? Unpacking the COVID-19 infodemic

BACKGROUND

The spread of unvetted scientific information about COVID-19 presents a significant challenge to public health, adding to the urgency for increased understanding of COVID-19 information-seeking preferences that will allow for the delivery of evidence-based health communication. This study examined factors associated with COVID-19 information-seeking behavior.

METHODS

An online survey was conducted with US adults (N = 1800) to identify key interpersonal (e.g., friends, health care providers) and mediated (e.g., TV, social media) sources of COVID-19 information. Logistic regression models were fitted to explore correlates of information-seeking.

RESULTS

Study findings show that the first sought and most trusted sources of COVID-19 information had different relationships with sociodemographic characteristics, perceived discrimination, and self-efficacy. Older adults had greater odds of seeking information from print materials (e.g., newspapers and magazines) and TV first. Participants with less educational attainment and greater self-efficacy preferred interpersonal sources first, with notably less preference for mass media compared to health care providers. Those with more experiences with discrimination were more likely to seek information from friends, relatives, and co-workers. Additionally, greater self-efficacy was related to increased trust in interpersonal sources.

CONCLUSION

Study results have implications for tailoring health communication strategies to reach specific subgroups, including those more vulnerable to severe illness from COVID-19. A set of recommendations are provided to assist in campaign development.