JOURNAL ARTICLE

Exploring Racial Disparities in the 1918 Influenza Pandemic: A Case Study of Durham, North Carolina.

  • Published In: Journal of the History of Medicine & Allied Sciences, 2025, v. 80, n. 2. P. 126 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bryant, Mallory; Baker, Jeffrey 3 of 3

Abstract

This article examines the paradox of excess mortality among White Americans during the 1918 influenza pandemic through a detailed case study of Durham, North Carolina, a city notable for its prosperous Black middle class and strong Black health institutions. Despite overall lower mortality in Durham compared to other North Carolina locales, White residents experienced significantly higher influenza death rates than Black residents, a disparity attributed in part to the robust healthcare response led by Black nurses from Lincoln Hospital. The study highlights how segregation delayed the pandemic's impact on Black neighborhoods and how the presence of a skilled Black nursing workforce, amid nationwide nursing shortages due to World War I, enabled Durham to meet every call for care, benefiting both Black and White patients. Comparative analysis with other North Carolina counties underscores the unique role of Black healthcare professionals in mitigating racial disparities in influenza mortality in Durham. This research emphasizes the importance of local context and Black agency, particularly nursing care, in understanding racial health outcomes during the 1918 pandemic.

Additional Information

  • Source:Journal of the History of Medicine & Allied Sciences. 2025/04, Vol. 80, Issue 2, p126
  • Document Type:Article
  • Subject Area:Geography and Cartography
  • Publication Date:2025
  • ISSN:0022-5045
  • DOI:10.1093/jhmas/jrad066
  • Accession Number:184348334
  • Copyright Statement:Copyright of Journal of the History of Medicine & Allied Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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