To develop a method leveraging hospital-based surveillance to estimate influenza-related hospitalizations by state, age, and month as a means of enhancing current US influenza burden estimation efforts. Using data from the Influenza Hospitalization Surveillance Network (FluSurv-NET), we extrapolated monthly FluSurv-NET hospitalization rates after adjusting for testing practices and diagnostic test sensitivities to non-FluSurv-NET states. We used a Poisson zero-inflated model with an overdispersion parameter within the Bayesian hierarchical framework and accounted for uncertainty and variability between states and across time. Model validation included checking the sensitivity of results to input data, as well as model convergence diagnostics and comparing the results with independent data sources. We estimated 379 300 (90% credible interval [CrI] = 305 400, 479 300) influenza-related hospitalizations in the United States for the 2022-2023 season. Median cumulative state rates ranged widely from 23.2 to 249.0 per 100 000 people. Our estimates were comparable to national burden estimates incorporating other approaches while accounting for variations in the timing and geography of disease activity and changes in detection and reporting. Our results provide a complementary framework to calculate estimates at finer geographic scales. (. Published online ahead of print January 30, 2025:e1-e9. https://doi.org/10.2105/AJPH.2024.307928).
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2105/AJPH.2024.307928 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!