Use of Random Domain Intercept Technology to Track COVID-19 Vaccination Rates in Real Time Across the United States:...

Area of Science:

• Public Health
• Epidemiology
• Health Informatics

Background:

• Accurate COVID-19 vaccination data is crucial for effective public health strategies and identifying access barriers.
• Current data collection methods are often limited in scope or delayed, hindering rapid response.
• Innovative technologies are needed to improve global vaccination data collection.

Purpose of the Study:

• To validate Random Domain Intercept Technology (RDIT) for real-time tracking of self-reported COVID-19 vaccination rates.
• To assess RDIT's effectiveness at national and state levels in the US.
• To explore RDIT's potential for collecting additional attitudinal and behavioral data.

Main Methods:

• RDIT, an online intercept sampling method, was used to survey US adults (≥18 years) from June 30 to July 26, 2021.
• Self-reported vaccination status was the primary data point for validation.
• RDIT-generated national and state vaccination rates were compared against Centers for Disease Control and Prevention (CDC) data.

Main Results:

• RDIT captured self-reported vaccination status from 63,853 adult web users.
• National RDIT-estimated COVID-19 vaccine coverage (69.7%) was slightly higher than the CDC estimate (67.9%) on July 15, 2021.
• RDIT and CDC state-level estimates showed a strong positive correlation (r=0.90), with RDIT estimates within 5 percentage points for 29 states.

Conclusions:

• RDIT offers a valuable real-time data stream for monitoring vaccine uptake and evaluating interventions.
• The technology can capture demographic, attitudinal, and behavioral data beyond administrative records.
• RDIT enables deeper insights into vaccine uptake predictors, facilitating targeted public health actions.