JOURNAL ARTICLE

(In)sufficient institutionalization? Norm articulation in the World Health Organization and infectious disease prevalence across the global South.

  • Published In: International Journal of Comparative Sociology (Sage Publications, Ltd.), 2024, v. 65, n. 6. P. 752 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Shorette, Kristen; Phillips, Nolan Edward 3 of 3

Abstract

This article examines how the diffusion of global health norms from international nongovernmental organizations (INGOs) to nation-states varies according to the degree of norm articulation within the World Health Organization (WHO). Focusing on four infectious diseases with differing levels of WHO prioritization—HIV, tuberculosis, leprosy, and Guinea-worm disease—the study finds that stronger national ties to health INGOs are associated with significantly lower prevalence of HIV and tuberculosis, diseases highly institutionalized by the WHO. In contrast, the relationship is weak or nonexistent for leprosy and Guinea-worm disease, which receive less WHO attention. These findings support neoinstitutional theory's claim that norm diffusion depends on the prominence of specific norms within prevailing global institutions, highlighting the conditional nature of international organizational influence on health outcomes in the global South.

Additional Information

  • Source:International Journal of Comparative Sociology (Sage Publications, Ltd.). 2024/12, Vol. 65, Issue 6, p752
  • Document Type:Article
  • Subject Area:Diplomacy and International Relations
  • Publication Date:2024
  • ISSN:0020-7152
  • DOI:10.1177/00207152241226449
  • Accession Number:180522540
  • Copyright Statement:Copyright of International Journal of Comparative Sociology (Sage Publications, Ltd.) is the property of Sage Publications Inc. 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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