What Were the Information Voids? A Qualitative Analysis of Questions Asked by Dear Pandemic Readers between August 2020-August 2021.

In the current infodemic, how individuals receive information (channel), who it is coming from (source), and how it is framed can have an important effect on COVID-19 related mitigation behaviors. In light of these challenges presented by the infodemic, Dear Pandemic (DP) was created to directly address persistent questions related to COVID-19 and other health topics in the online environment. This is a qualitative analysis of 3806 questions that were submitted by DP readers to a question box on the Dear Pandemic website between August 30, 2020 and August 29, 2021. Analyses resulted in four themes: the need for clarification of other sources; lack of trust in information; recognition of possible misinformation; and questions on personal decision-making. Each theme reflects an unmet informational need of Dear Pandemic readers, which may be reflective of the broader informational gaps in our science communication efforts.This study highlights the role of an ad hoc risk communication platform in the current environment and uses questions submitted to the Dear Pandemic question box to identify informational needs of DP readers over the course of the COVID-19 pandemic. These findings may help clarify how organizations addressing health misinformation in the digital space can contribute to timely, responsive science communication and improve future communication efforts.

Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign.

BACKGROUND: The COVID-19 pandemic was accompanied by an "infodemic"-an overwhelming excess of accurate, inaccurate, and uncertain information. The social media-based science communication campaign Dear Pandemic was established to address the COVID-19 infodemic, in part by soliciting submissions from readers to an online question box. Our study characterized the information needs of Dear Pandemic's readers by identifying themes and longitudinal trends among question box submissions. METHODS: We conducted a retrospective analysis of questions submitted from August 24, 2020, to August 24, 2021. We used Latent Dirichlet Allocation topic modeling to identify 25 topics among the submissions, then used thematic analysis to interpret the topics based on their top words and submissions. We used t-Distributed Stochastic Neighbor Embedding to visualize the relationship between topics, and we used generalized additive models to describe trends in topic prevalence over time. RESULTS: We analyzed 3839 submissions, 90% from United States-based readers. We classified the 25 topics into 6 overarching themes: 'Scientific and Medical Basis of COVID-19,' 'COVID-19 Vaccine,' 'COVID-19 Mitigation Strategies,' 'Society and Institutions,' 'Family and Personal Relationships,' and 'Navigating the COVID-19 Infodemic.' Trends in topics about viral variants, vaccination, COVID-19 mitigation strategies, and children aligned with the news cycle and reflected the anticipation of future events. Over time, vaccine-related submissions became increasingly related to those surrounding social interaction. CONCLUSIONS: Question box submissions represented distinct themes that varied in prominence over time. Dear Pandemic's readers sought information that would not only clarify novel scientific concepts, but would also be timely and practical to their personal lives. Our question box format and topic modeling approach offers science communicators a robust methodology for tracking, understanding, and responding to the information needs of online audiences.

Predicting high-cost pediatric patients: derivation and validation of a population-based model.

BACKGROUND: Health care administrators often lack feasible methods to prospectively identify new pediatric patients with high health care needs, precluding the ability to proactively target appropriate population health management programs to these children. OBJECTIVE: To develop and validate a predictive model identifying high-cost pediatric patients using parent-reported health (PRH) measures that can be easily collected in clinical and administrative settings. DESIGN: Retrospective cohort study using 2-year panel data from the 2001 to 2011 rounds of the Medical Expenditure Panel Survey. SUBJECTS: A total of 24,163 children aged 5-17 with family incomes below 400% of the federal poverty line were included in this study. MEASURES: Predictive performance, including the c-statistic, sensitivity, specificity, and predictive values, of multivariate logistic regression models predicting top-decile health care expenditures over a 1-year period. RESULTS: Seven independent domains of PRH measures were tested for predictive capacity relative to basic sociodemographic information: the Children with Special Health Care Needs (CSHCN) Screener; subjectively rated health status; prior year health care utilization; behavioral problems; asthma diagnosis; access to health care; and parental health status and access to care. The CSHCN screener and prior year utilization domains exhibited the highest incremental predictive gains over the baseline model. A model including sociodemographic characteristics, the CSHCN screener, and prior year utilization had a c-statistic of 0.73 (95% confidence interval, 0.70-0.74), surpassing the commonly used threshold to establish sufficient predictive capacity (c-statistic>0.70). CONCLUSIONS: The proposed prediction tool, comprising a simple series of PRH measures, accurately stratifies pediatric populations by their risk of incurring high health care costs.