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A language-matching model to improve equity and efficiency of COVID-19 contact tracing [Computer Sciences]
Proceedings of the National Academy of Sciences of the United States of America ( IF 11.1 ) Pub Date : 2021-10-26 , DOI: 10.1073/pnas.2109443118
Lisa Lu 1 , Benjamin Anderson 1 , Raymond Ha 1 , Alexis D'Agostino 2 , Sarah L Rudman 2 , Derek Ouyang 1 , Daniel E Ho 3
Affiliation  

Contact tracing is a pillar of COVID-19 response, but language access and equity have posed major obstacles. COVID-19 has disproportionately affected minority communities with many non–English-speaking members. Language discordance can increase processing times and hamper the trust building necessary for effective contact tracing. We demonstrate how matching predicted patient language with contact tracer language can enhance contact tracing. First, we show how to use machine learning to combine information from sparse laboratory reports with richer census data to predict the language of an incoming case. Second, we embed this method in the highly demanding environment of actual contact tracing with high volumes of cases in Santa Clara County, CA. Third, we evaluate this language-matching intervention in a randomized controlled trial. We show that this low-touch intervention results in 1) significant time savings, shortening the time from opening of cases to completion of the initial interview by nearly 14 h and increasing same-day completion by 12%, and 2) improved engagement, reducing the refusal to interview by 4%. These findings have important implications for reducing social disparities in COVID-19; improving equity in healthcare access; and, more broadly, leveling language differences in public services.



中文翻译:

一种提高 COVID-19 接触者追踪公平性和效率的语言匹配模型 [计算机科学]

接触者追踪是 COVID-19 应对措施的支柱,但语言准入和公平性构成了主要障碍。COVID-19 对拥有许多非英语成员的少数族裔社区造成了不成比例的影响。语言不一致会增加处理时间并妨碍有效接触者追踪所需的信任建立。我们展示了将预测的患者语言与接触追踪器语言相匹配如何增强接触追踪。首先,我们展示了如何使用机器学习将来自稀疏实验室报告的信息与更丰富的人口普查数据相结合,以预测传入病例的语言。其次,我们将这种方法嵌入到加利福尼亚州圣克拉拉县大量病例的实际接触者追踪的高要求环境中。第三,我们在一项随机对照试验中评估了这种语言匹配干预。我们表明,这种低接触干预导致 1) 显着节省时间,将案件从开立到完成初次面谈的时间缩短了近 14 小时,并将当天完成的时间提高了 12%,以及 2) 提高了参与度,减少了拒绝面试的比例为 4%。这些发现对于减少 COVID-19 中的社会差异具有重要意义;改善医疗保健服务的公平性;更广泛地说,是平衡公共服务中的语言差异。改善医疗保健服务的公平性;更广泛地说,是平衡公共服务中的语言差异。改善医疗保健服务的公平性;更广泛地说,是平衡公共服务中的语言差异。

更新日期:2021-10-24
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