Applying a Health Access Framework to Understand and Address Food Insecurity.

The prevalence of food insecurity (FI) in United States households has fluctuated between 10% and 15% for the past two decades, well above the Healthy People 2030 goal. FI is associated with increased use of healthcare services and the prevalence of multiple health conditions. Our current efforts to address FI may be limited by measures that lack granularity, timeliness, and consideration of larger food access barriers (e.g., availability of food providers and lack of knowledge regarding where to obtain food). If the Healthy People 2030 goal of reducing FI to 6% is to be met, we need better and faster methods for monitoring and tracking FI in order to produce timely interventions. In this paper, we review key contributors of FI from an access barrier perspective, investigate the limitations of current FI measures, and explore how data from one nonprofit organization may enhance our understanding of FI and facilitate access to resources at the local level. We also propose a conceptual framework illustrating how nonprofit organizations may play an important role in understanding and addressing FI and its intertwined social needs, such as housing and healthcare problems.

Hospital Discharge During COVID-19: The Role of Social Resources.

CLINICAL TRIAL REGISTRATION: This study is a part of a parent study: Social Needs and Resources in the Evaluation and Enhancement of Discharge Support. The trial registry is clinicaltrials.gov, number NCT04248738. Initial release was 1/27/2020 and the first participant was enrolled on 2/4/2020. Link to the information on the registry: https://clinicaltrials.gov/ct2/show/NCT04248738.

Exploring experiences of COVID-19-positive individuals from social media posts.

AIMS: This study aimed to explore the experience of individuals who claimed to be COVID-19 positive via their Twitter feeds. BACKGROUND: Public social media data are valuable to understanding people's experiences of public health phenomena. To improve care to those with COVID-19, this study explored themes from Twitter feeds, generated by individuals who self-identified as COVID-19 positive. DESIGN: This study utilized a descriptive design for text analysis for social media data. METHODS: This study analysed social media text retrieved by tweets of individuals in the United States who self-reported being COVID-19 positive and posted on Twitter in English between April 2, 2020, and April 24, 2020. In extracting embedded topics from tweets, we applied topic modelling approach based on latent Dirichlet allocation and visualized the results via LDAvis, a related web-based interactive visualization tool. RESULTS: Three themes were mined from 721 eligible tweets: (i) recognizing the seriousness of the condition in COVID-19 pandemic; (ii) having symptoms of being COVID-19 positive; and (iii) sharing the journey of being COVID-19 positive. CONCLUSION: Leveraging the knowledge and context of study themes, we present experiences that may better reflect patient needs while experiencing COVID-19. The findings offer more descriptive support for public health nursing and other translational public health efforts during a global pandemic.