Abstract
Background From Ebola, Zika, to the latest COVID-19 pandemic, outbreaks of highly infectious diseases continue to reveal severe consequences of social and health inequalities. People from low socioeconomic and educational backgrounds as well as low health literacy tend to be affected by the uncertainty, complexity, volatility, and progressiveness of public health crises and emergencies. A key lesson that governments have taken from the ongoing coronavirus pandemic is the importance of developing and disseminating highly accessible, actionable, inclusive, coherent public health advice, which represent a critical tool to help people with diverse cultural, educational backgrounds and varying abilities to effectively implement health policies at the grassroots level. Objective We aimed to translate the best practices of accessible, inclusive public health advice (purposefully designed for people with low socioeconomic and educational background, health literacy levels, limited English proficiency, and cognitive/functional impairments) on COVID-19 from health authorities in English-speaking multicultural countries (USA, Australia, and UK) to adaptive tools for the evaluation of the accessibility of public health advice in other languages. Methods We developed an optimised Bayesian classifier to produce probabilistic prediction of the accessibility of official health advice among vulnerable people including migrants and foreigners living in China. We developed an adaptive statistical formula for the rapid evaluation of the accessibility of health advice among vulnerable people in China. Results Our study provides needed research tools to fill in a persistent gap in Chinese public health research on accessible, inclusive communication of infectious diseases' prevention and management. For the probabilistic prediction, using the optimised Bayesian machine learning classifier (GNB), the largest positive likelihood ratio (LR+) 16.685 (95% confidence interval: 4.35, 64.04) was identified when the probability threshold was set at 0.2 (sensitivity: 0.98; specificity: 0.94). Conclusion Effective communication of health risks through accessible, inclusive, actionable public advice represents a powerful tool to reduce health inequalities amidst health crises and emergencies. Our study translated the best-practice public health advice developed during the pandemic into intuitive machine learning classifiers for health authorities to develop evidence-based guidelines of accessible health advice. In addition, we developed adaptive statistical tools for frontline health professionals to assess accessibility of public health advice for people from non-English speaking backgrounds.
Highlights
From Ebola, Zika, to the novel coronavirus pandemic, outbreaks of highly infectious diseases continue to reveal severe consequences of social and health inequalities in both developing and developed countries [1, 2]
Higher classifier sensitivity is indicative of higher precision with the prediction of regular public health advice which have restricted public accessibility; and higher classifier specificity implies increased accuracy with the detection vulnerable people-oriented health advice which were designed by experienced health professionals to maximally enhance the language, cognitive accessibility, informational actionability, and communicative effectiveness of these emergency pieces of advice among diverse vulnerable populations with limited education, health literary, socioeconomic abilities, and varying intellectual/cognitive capabilities
Retrospective Assessment of Accessibility of Public Health Advice in Other Languages. Another major strength of our study was that we translated the best practices of accessible health advice on infectious diseases developed by English health authorities during the pandemic into adaptive analytical instruments that can be used for the retrospective assessment of existing health advice in other languages. is was achieved by translating the English materials that we collected to the Chinese language using the forward and backward translation method recommended by World Health Organisation (WHO) and comprehensively annotated the translations using Chinese natural language processing tools to allow automatic feature engineering and machine learning classifier development
Summary
From Ebola, Zika, to the novel coronavirus pandemic, outbreaks of highly infectious diseases continue to reveal severe consequences of social and health inequalities in both developing and developed countries [1, 2]. A key lesson that governments have taken from the ongoing coronavirus pandemic is the importance of developing disseminating highly accessible, inclusive, actionable, coherent public health advice [5,6,7,8], Computational Intelligence and Neuroscience which represents a critical tool to help people with diverse cultural, educational backgrounds and varying abilities to effectively implement health policies at the grassroots level. A key lesson that governments have taken from the ongoing coronavirus pandemic is the importance of developing and disseminating highly accessible, actionable, inclusive, coherent public health advice, which represent a critical tool to help people with diverse cultural, educational backgrounds and varying abilities to effectively implement health policies at the grassroots level. We developed adaptive statistical tools for frontline health professionals to assess accessibility of public health advice for people from non-English speaking backgrounds
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