Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data.

BACKGROUND: Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health-related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. OBJECTIVE: The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. METHODS: A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. RESULTS: A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings. CONCLUSIONS: Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions.

COVID-19 vaccine website accessibility dashboard.

BACKGROUND: The COVID-19 pandemic has exacerbated historical inequities for people with disabilities including barriers in accessing online information and healthcare appointment websites. These barriers were brought to the foreground during the vaccine rollout and registration process. OBJECTIVE: This cross-sectional study aimed to examine accessibility of U.S. state and territory COVID-19 information and registration centralized websites. METHODS: The Johns Hopkins Disability Health Research Center created a COVID-19 Vaccine Dashboard compiling COVID-19 information and vaccine registration web pages from 56 states and territories in the United States (U.S.) reviewed between March 30 through April 5, 2021 and analyzed accessibility using WAVE Web Accessibility Evaluation Tool (WAVE). WAVE identifies website accessibility barriers, including insufficient contrast, alternative text, unlabeled buttons, total number of errors, and error density. Web pages were ranked and grouped into three groups by number of errors, creating comparisons between states on accessibility barriers for people with disabilities. RESULTS: All 56 U.S states and territories had COVID-19 information web pages and 29 states had centralized state vaccine registration web pages. Total errors, error density, and alert data were utilized to generate accessibility scores for each web page, the median score was 259 (range = 14 to 536 and IQR = 237) for information pages, and 146 (range = 10 to 281 and IQR = 105) for registration pages. CONCLUSIONS: These results highlight barriers people with disabilities may encounter when accessing information and registering for the COVID-19 vaccine, which underscore inequities in the pandemic response for the disability community and elevate the need to prioritize accessibility of public health information.