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The following is a summary of “A comparative analysis of COVID-19 seroprevalence rates, observed infection rates, and infection-related mortality,” published in the January 2025 issue of Infectious Disease by Ford et al. 

Researchers conducted a retrospective study to examine the discrepancies between seroprevalence data and infection rates reported to the Centers for Disease Control and Prevention (CDC), including infection-related mortality (IRM) to assess implications for public health policy.  

They performed a comparative analysis of seroprevalence data from a National Institutes of Health (NIH) study and CDC-reported infection rates across 10 U.S. regions, examining the correlation with IRM calculations. The analysis revised previous IRM estimates using updated seroprevalence rates, with correlations calculated and statistical relevance assessed.  

The results showed that COVID-19 prevalence was 2.7 times higher than CDC-reported infection rates. Using lower CDC infection rates to calculate IRM led to a 2.7-fold overestimation. Combining seroprevalence and CDC data increased the IRM overestimation to 3.79 times.  

Investigators concluded the integrating diverse data sources was crucial for understanding and managing public health emergencies, emphasizing the need for public health agencies to improve the capacity for regular seroprevalence data collection and analysis due to its stronger correlation with infection rates to better inform policy and interventions. 

Source: frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1504524/full