Department of Computer Science, City University of Hong Kong, Hong Kong, China.
School of Languages and Cultures, University of Sydney, Sydney, Australia.
Comput Intell Neurosci. 2021 Dec 17;2021:1916690. doi: 10.1155/2021/1916690. eCollection 2021.
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.
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.
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.
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).
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.
从埃博拉、寨卡到最近的 COVID-19 大流行，高传染性疾病的爆发继续揭示出社会和健康不平等的严重后果。社会经济和教育背景较低、健康素养较低的人往往受到公共卫生危机和紧急情况的不确定性、复杂性、波动性和先进性的影响。各国政府从当前的冠状病毒大流行中吸取的一个重要教训是，必须制定和传播易于理解、可操作、包容各方、连贯一致的公共卫生建议，这些建议是帮助具有不同文化、教育背景和不同能力的人在基层有效实施卫生政策的重要工具。
我们旨在将来自英语国家的多文化卫生当局制定的关于 COVID-19 的可及性、包容性公共卫生建议的最佳实践（专门针对社会经济和教育背景较低、健康素养水平较低、英语水平有限以及认知/功能障碍的人）翻译成其他语言的公共卫生建议可及性评估的适应性工具。
我们开发了一个优化的贝叶斯分类器，用于对包括移民和在中国生活的外国人在内的弱势群体中官方卫生建议的可及性进行概率预测。我们为中国弱势群体中卫生建议的可及性快速评估开发了一个适应性统计公式。
我们的研究提供了中国公共卫生研究中急需的研究工具，以填补传染性疾病预防和管理的可及性、包容性沟通方面的持续空白。对于概率预测，使用优化的贝叶斯机器学习分类器（GNB），当概率阈值设置为 0.2 时，最大正似然比（LR+）为 16.685（95%置信区间：4.35，64.04）（敏感性：0.98；特异性：0.94）。
通过可及性、包容性、可操作性的公共建议进行有效的健康风险沟通，是在危机和紧急情况下减少健康不平等的有力工具。我们的研究将大流行期间制定的最佳实践公共卫生建议翻译成直观的机器学习分类器，供卫生当局制定基于证据的可及性健康建议指南。此外，我们还为一线卫生专业人员开发了适应性统计工具，以评估非英语背景人群获取公共卫生建议的可及性。
