Simulating Facebook Advertisements to Establish Cost per New HIV Diagnosis Using Routine and Targeted Models in a Local Population

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Undiagnosed human immunodeficiency virus (HIV) infection remains a public health challenge. We explore Facebook (FB) advertisement (Ads) cost per new HIV diagnosis using non-targeted Ads, a routine testing model against targeted Ads, and a focused testing model in Texas. Methods: On 14 October 2021, we created (without launching) Texas-based, USD 10 targeted (using criteria matching HIV populations at risk) and non-targeted FB Ads for 10 days. In the process of creating the Ads, we collected estimated audience size, daily reach, and daily clicks. We estimated Ad cost for each new HIV diagnosis for targeted and non-targeted Ads using new HIV diagnosis rates from focused and routine testing campaigns. Results: The Ad costs per new HIV diagnosis from the targeted model were 4.74, 2.86, 5.28, and 2.88 times lower for men, Black, Hispanic, and all age groups, respectively, when compared to the non-targeted model. The wider the gap was between new HIV diagnosis rates in a population for focused and routine testing, the more cost-effective targeted Ads became. Conclusions: Among HIV populations at risk, targeted FB Ads are more cost-effective for detecting new HIV infections than non-targeted Ads. This cost-effectiveness increases in locations where focused testing increases new HIV diagnosis rates, compared to routine testing.

Original languageEnglish (US)
Article number1195
JournalHealthcare (Switzerland)
Volume10
Issue number7
DOIs
StatePublished - Jul 2022

Keywords

  • acquired immunodeficiency syndrome
  • consumer health informatics
  • diagnosis
  • Facebook advertisements
  • human immunodeficiency virus
  • personalized advertisements
  • population health
  • precision medicine
  • public health communications
  • public health informatics
  • social media

ASJC Scopus subject areas

  • Leadership and Management
  • Health Policy
  • Health Informatics
  • Health Information Management

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