Couture Alexia, Iuliano A Danielle, Chang Howard H, Threlkel Ryan, Gilmer Matthew, O'Halloran Alissa, Ujamaa Dawud, Biggerstaff Matthew, Reed Carrie
Alexia Couture, A. Danielle Iuliano, Ryan Threlkel, Matthew Gilmer, Alissa O'Halloran, Dawud Ujamaa, Matthew Biggerstaff, and Carrie Reed are with the National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA. Howard H. Chang is with the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.
Am J Public Health. 2025 Apr;115(4):546-554. doi: 10.2105/AJPH.2024.307928. Epub 2025 Jan 30.
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. (. 2025;115(4):546-554. https://doi.org/10.2105/AJPH.2024.307928).
开发一种利用基于医院的监测方法,按州、年龄和月份估算流感相关住院病例数,以加强美国当前流感负担估算工作。利用流感住院监测网络(FluSurv-NET)的数据,在调整检测方法和诊断测试敏感性后,我们将FluSurv-NET每月住院率外推至非FluSurv-NET的州。我们在贝叶斯分层框架内使用了具有过度分散参数的泊松零膨胀模型,并考虑了各州之间以及不同时间的不确定性和变异性。模型验证包括检查结果对输入数据的敏感性、模型收敛诊断以及将结果与独立数据源进行比较。我们估计2022 - 2023年美国流感相关住院病例数为379300例(90%可信区间[CrI]=305400,479300)。各州累计发病率中位数范围广泛,每10万人从23.2至249.0例不等。我们的估计与采用其他方法的国家负担估计数相当,同时考虑了疾病活动时间和地域的差异以及检测和报告的变化。我们的结果提供了一个补充框架,用于在更精细的地理尺度上计算估计数。(. 2025;115(4):546 - 554. https://doi.org/10.2105/AJPH.2024.307928)