Barriers to and strategies to address COVID-19 testing hesitancy: a rapid scoping review.

BACKGROUND: Testing is a foundational component of any COVID-19 management strategy; however, emerging evidence suggests that barriers and hesitancy to COVID-19 testing may affect uptake or participation and often these are multiple and intersecting factors that may vary across population groups. To this end, Health Canada's COVID-19 Testing and Screening Expert Advisory Panel commissioned this rapid review in January 2021 to explore the available evidence in this area. The aim of this rapid review was to identify barriers to COVID-19 testing and strategies used to mitigate these barriers. METHODS: Searches (completed January 8, 2021) were conducted in MEDLINE, Scopus, medRxiv/bioRxiv, Cochrane and online grey literature sources to identify publications that described barriers and strategies related to COVID-19 testing. RESULTS: From 1294 academic and 97 grey literature search results, 31 academic and 31 grey literature sources were included. Data were extracted from the relevant papers. The most cited barriers were cost of testing; low health literacy; low trust in the healthcare system; availability and accessibility of testing sites; and stigma and consequences of testing positive. Strategies to mitigate barriers to COVID-19 testing included: free testing; promoting awareness of importance to testing; presenting various testing options and types of testing centres (i.e., drive-thru, walk-up, home testing); providing transportation to testing centres; and offering support for self-isolation (e.g., salary support or housing). CONCLUSION: Various barriers to COVID-19 testing and strategies for mitigating these barriers were identified. Further research to test the efficacy of these strategies is needed to better support testing for COVID-19 by addressing testing hesitancy as part of the broader COVID-19 public health response.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review.

BACKGROUND: Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots. OBJECTIVE: This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR). METHODS: Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences. RESULTS: After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard. CONCLUSIONS: With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/14019.