Web-based intervention for improving influenza vaccination in pregnant women: a cost-effectiveness analysis

ABSTRACT

A website with vaccine information and interactive social media was reported to improve maternal influenza vaccine uptake. This study aimed to evaluate cost-effectiveness of a web-based intervention on influenza vaccine uptake among pregnant women from the perspective of US healthcare providers. A one-year decision-analytic model estimated outcomes in a hypothetical cohort of pregnant women with: (1) website with vaccine information and interactive social media (intervention group), and (2) usual care (usual care group). Primary measures included influenza infection, influenza-related hospitalization, mortality, direct medical cost, and quality-adjusted life-year (QALY) loss. In base-case analysis, intervention group reduced cost (by USD28), infection (by 28 per 1,000 pregnant women), hospitalization (by 1.226 per 1,000 pregnant women), mortality (by 0.0036 per 1,000 pregnant women), and saved 0.000305 QALYs versus usual care group. Relative improvement of vaccine uptake by the intervention and number of pregnant women in the healthcare system were two influential factors identified in deterministic sensitivity analysis. The intervention was cost-effective in 99.5% of 10,000 Monte Carlo simulations (at willingness-to-pay threshold 50,000 USD/QALY). A website with vaccine information and interactive social media to promote influenza vaccination for pregnant women appears to reduce direct medical costs and gain QALYs from the perspective of US healthcare providers.

KEYWORDS:

• Influenza vaccination
• vaccine uptake
• pregnant women
• website
• social media
• cost-effectiveness analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All the data related to this study are available in the manuscript.

Additional information

Funding

The author(s) reported work featured in this article was funded by Direct Grant for Research, The Chinese University of Hong Kong (grant number 2022.079).