Comparison of Social Media Content on Hidradenitis Suppurativa: A Cross-Sectional Study.

BACKGROUND: Research has shown that patients may turn to social media seeking information regarding diagnosis and treatment. This study aimed to analyze and compare content related to hidradenitis suppurativa (HS) using the "hashtag" tool across three of the most popular social media platforms to determine the information that patients are exposed to online. METHODS: We identified hashtags across Instagram, TikTok, and Facebook for "#hidradenitissuppurativa." The top 50 videos returned by the algorithm across each site were selected for analysis. Data extracted for comparison included content creator demographics, number of followers, type of content (educational vs. noneducational), and associated hashtags. RESULTS: Sixty-seven percent of posts were created by females (n = 101/150), 10% by males (n = 16/150), and 22% other (n = 33/150). Distribution was similar across all platforms. User accounts on TikTok have a significantly higher number of followers (median = 38,700, range = 902-17,600,000 followers) compared to Facebook (median = 1,375, range = 58-777,000 followers) and Instagram (median = 2,818, range = 57-9,800 followers). CONCLUSION: This study suggests that there is a disproportionate number of patients creating content to raise awareness on HS on social media compared to patient support groups or medical professionals. We propose that social media is a useful platform that dermatologists and official institutional bodies can utilize as an alternative method of health promotion and patient education. Further research to explore social media trends across a range of dermatological conditions can help guide targeted education campaigns in the future.

Predicting the issuance of COVID-19 stay-at-home orders in Africa: Using machine learning to develop insight for health policy research.

During the COVID-19 pandemic, many countries have issued stay-at-home orders (SAHOs) to reduce viral transmission. Because of their social and economic consequences, SAHOs are a politically risky decision for governments. Researchers typically attribute public health policymaking to five theoretically significant factors: political, scientific, social, economic, and external. However, a narrow focus on extant theory runs the risk of biasing findings and missing novel insights. This research employs machine learning to shift the focus from theory to data to generate hypotheses and insights "born from the data" and unconstrained by current knowledge. Beneficially, this approach can also confirm the extant theory. We apply machine learning in the form of a random forest classifier to a novel and multiple-domain data set of 88 variables to identify the most significant predictors of the issuance of a COVID-19-related SAHO in African countries (n = 54). Our data set includes a wide range of variables from sources such as the World Health Organization that cover the five principal theoretical factors and previously ignored domains. Generated using 1000 simulations, our model identifies a combination of theoretically significant and novel variables as the most important to the issuance of a SAHO and has a predictive accuracy using 10 variables of 78%, which represents a 56% increase in accuracy compared to simply predicting the modal outcome.

Following the Science? Examining the Issuance of Stay-At-Home Orders Related to COVID-19 by U.S. Governors.

Informed by the public health policymaking literature, this study's objective is to identify scientific, political, social, economic, and external factors related to U.S. governors' decisions to issue stay-at-home orders (SAHOs) in response to the first wave of the COVID-19 pandemic. Public health experts advocate for social distancing to slow the spread of infectious diseases, but government mandates to social distance can impose substantial social and economic costs. This study uses event history analysis to investigate the issuance of COVID-19-related gubernatorial SAHOs during a 41-day period in the 50 U.S. states. The findings indicate that scientific, political, and economic factors were associated with the issuance of SAHOs, but that external considerations played the largest role, particularly those related to the timing of other governors' decisions. This study offers evidence about how some U.S. political leaders balance public health concerns against other considerations and, more broadly, how state governments address crisis-level issues.