Back

Global prevalence of anemia among women of reproductive age, 2000–2019.

  • Published In: European Journal of Haematology, 2024, v. 113, n. 2. P. 253 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: DeLoughery, Emma P. 3 of 3

Abstract

A common disease with significant impacts on health and quality of life, anemia is particularly prevalent in women of reproductive age due to blood losses during menstruation and pregnancy. Data from the World Health Organization (WHO) was analyzed to compare trends in prevalence of anemia in women aged 15‐49 among countries and over time with the goal of identifying regions both successful and in need of assistance in combatting anemia. Worldwide from 2000 to 2013 the prevalence of anemia among women aged 15‐49 decreased, and then increased from 2013 to 2019; severe anemia decreased throughout the world from 2000 to 2019. Throughout all years, African countries had the highest prevalence of anemia and severe anemia while American and European countries had the lowest. With each decrease in human development index (HDI) category (very high to high, etc.) there was a significant increase in prevalence of total anemia (P < 0.001 for all). This data suggests that although the prevalence of anemia among reproductive age women has decreased over time there is still much work remaining, particularly in low HDI countries. More effort is needed in preventing, recognizing and treating anemia. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:European Journal of Haematology. 2024/08, Vol. 113, Issue 2, p253
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2024
  • ISSN:0902-4441
  • DOI:10.1111/ejh.14227
  • Accession Number:178210575
  • Copyright Statement:Copyright of European Journal of Haematology is the property of Wiley-Blackwell 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.