A new score for improving cardiovascular risk prediction and prevention.

BACKGROUND AND AIMS: The ultrasonographic detection of subclinical atherosclerosis (scATS) at carotid and femoral vascular sites using the atherosclerosis burden score (ABS) improves the risk stratification for atherosclerotic cardiovascular disease beyond traditional cardiovascular (CV) risk factors. However, its predictive value should be further enhanced. We hypothesize that combining the ABS and the Framingham risk score (FHRS) to create a new score called the FHRABS will improve CV risk prediction and prevention. We aim to investigate if incorporating the ABS into the FHRS improved CV risk prediction in a primary prevention setting. METHODS AND RESULTS: 1024 patients were included in this prospective observational cohort study. Carotid and femoral plaques were ultra-sonographic detected. Major incident cardiovascular events (MACEs) were collected. The receiver operating characteristic curve (ROC-AUC) and Youden's index (Ysi) were used to compare the incremental contributions of each marker to predict MACEs. After a median follow-up of 6.0 ± 3.3 years, 60 primary MACEs (5.8%) occurred. The ROC-AUC for MACEs prediction was significantly higher for the FHRABS (0.74, p < 0.024) and for the ABS (0.71, p < 0.013) compared to the FHRS alone (0.71, p < 0.46). Ysi or the FHRABS (42%, p < 0.001) and ABS (37%, p < 0.001) than for the FHRS (31%). Cox proportional-hazard models showed that the CV predictive performance of FHRS was significantly enhanced by the ABS (10.8 vs. 5.5, p < 0.001) and FHRABS (HR 23.30 vs. 5.50, p < 0.001). CONCLUSIONS: FHRABS is a useful score for improving CV risk stratification and detecting patients at high risk of future MACEs. FHRABS offers a simple-to-use, and radiation-free score with which to detect scATS in order to promote personalized CV prevention.

The deployment of social media by political authorities and health experts to enhance public information during the COVID-19 pandemic.

Social media have increasingly been used by political bodies and experts to disseminate health information to the public. However, we still know little about how the communication of these actors on social media is received by other users and how it reflects trends in public trust. We examined social media dynamics in the communication of information by major actors (n = 188) involved in COVID-19 online discussions in Switzerland. These actors are scientists (experts), policymakers (government officials, cantonal executives, and other parties), and representatives of mass media. We found little correlation between Twitter features (other users' engagement and negativity in other users' replies) and the level of public trust found in representative opinion surveys. We used topic modelling in combination with correspondence analysis, and including additional variables for actor types and the period of the public debate further enabled us to detect salient episodes related to the pandemic on social media. In particular, we found that differing roles were played by the (health) experts and political authorities in terms of both topics and influence on the specific timing of the pandemic. The results of this study provide helpful conclusions for communication among political authorities, health experts, and the public.

A systematic literature review of how and whether social media data can complement traditional survey data to study public opinion.

In this article, we review existing research on the complementarity of social media data and survey data for the study of public opinion. We start by situating our review in the extensive literature (N = 187) about the uses, challenges, and frameworks related to the use of social media for studying public opinion. Based on 187 relevant articles (141 empirical and 46 theoretical) - we identify within the 141 empircal ones six main research approaches concerning the complementarity of both data sources. Results show that the biggest share of the research has focused on how social media can be used to confirm survey findings, especially for election predictions. The main contribution of our review is to detail and classify other growing complementarity approaches, such as comparing both data sources on a given phenomenon, using survey measures as a proxy in social media research, enriching surveys with SMD, recruiting individuals on social media to conduct a second survey phase, and generating new insight on "old" or "under-investigated" topics or theories using SMD. We discuss the advantages and disadvantages associated with each of these approaches in relation to four main research purposes, namely the improvement of validity, sustainability, reliability, and interpretability. We conclude by discussing some limitations of our study and highlighting future paths for research.