Scholarlink

<p>Many ethno-racial minority populations had disproportionately high rates of COVID-19 infection and mortality during the initial years of the pandemic, but it is currently unclear whether Middle Eastern and North African populations in the USA experienced similar inequalities. This study disaggregates the MENA population in Virginia death records using a long short-term memory machine learning model that can probabilistically identify ethnically-unique names. The results suggest that MENA populations had higher overall excess mortality and a higher percentage of COVID-19 deaths among total deaths, when compared to the non-Hispanic White population of Virginia. The paper highlights the importance of disaggregating the MENA population in health inequalities research and offers a new method that improves upon previous name-based disaggregation methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Disaggregating Middle Eastern and North African Populations in Mortality Records to Better Understand COVID-19 Inequalities in Virginia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月15日

查看全文
  • Elyas Bakhtiari

摘要

Many ethno-racial minority populations had disproportionately high rates of COVID-19 infection and mortality during the initial years of the pandemic, but it is currently unclear whether Middle Eastern and North African populations in the USA experienced similar inequalities. This study disaggregates the MENA population in Virginia death records using a long short-term memory machine learning model that can probabilistically identify ethnically-unique names. The results suggest that MENA populations had higher overall excess mortality and a higher percentage of COVID-19 deaths among total deaths, when compared to the non-Hispanic White population of Virginia. The paper highlights the importance of disaggregating the MENA population in health inequalities research and offers a new method that improves upon previous name-based disaggregation methods.