Ford Eric W, Patel Kunal N, Baus Holly Ann, Valenti Shannon, Croker Jennifer A, Kimberly Robert P, Reis Steven E, Memoli Matthew J
School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States.
College of Health and Human Sciences, Northern Illinois University, Dekalb, IL, United States.
Front Public Health. 2025 Jan 28;13:1504524. doi: 10.3389/fpubh.2025.1504524. eCollection 2025.
The COVID-19 pandemic highlighted the need for data-driven decision making in managing public health crises. This study aims to extend previous research by incorporating infection-related mortality (IRM) to evaluate the discrepancies between seroprevalence data and infection rates reported to the Centers for Disease Control and Prevention (CDC), and to assess the implications for public health policy.
We conducted a comparative analysis of seroprevalence data collected as part of an NIH study and CDC-reported infection rates across ten U.S. regions, focusing on their correlation with IRM calculations.
The analysis includes a revision of prior estimates of IRM using updated seroprevalence rates. Correlations were calculated and their statistical relevance assessed.
Findings indicate that COVID-19 is approximately 2.7 times more prevalent than what CDC infection data suggest. Utilizing the lower CDC-reported rates to calculate IRM leads to a significant overestimation by a factor of 2.7. When both seroprevalence and CDC infection data are combined, the overestimation of IRM increases to a factor of 3.79.
The study highlights the importance of integrating multiple data dimensions to accurately understand and manage public health emergencies. The results suggest that public health agencies should enhance their capacity for collecting and analyzing seroprevalence data regularly, given its stronger correlation with IRM than other estimates. This approach will better inform policy decisions and direct effective interventions.
2019年冠状病毒病（COVID-19）大流行凸显了在管理公共卫生危机中基于数据进行决策的必要性。本研究旨在通过纳入感染相关死亡率（IRM）来扩展先前的研究，以评估血清流行率数据与向疾病控制和预防中心（CDC）报告的感染率之间的差异，并评估其对公共卫生政策的影响。
我们对作为美国国立卫生研究院（NIH）一项研究的一部分收集的血清流行率数据与CDC报告的美国十个地区的感染率进行了比较分析，重点关注它们与IRM计算的相关性。
分析包括使用更新的血清流行率对先前的IRM估计值进行修订。计算相关性并评估其统计相关性。
研究结果表明，COVID-19的实际流行率约为CDC感染数据显示的2.7倍。利用CDC报告的较低感染率来计算IRM会导致高估2.7倍。当血清流行率和CDC感染数据相结合时，IRM的高估增加到3.79倍。
该研究强调了整合多个数据维度以准确理解和管理突发公共卫生事件的重要性。结果表明，鉴于血清流行率数据与IRM的相关性比其他估计更强，公共卫生机构应提高定期收集和分析血清流行率数据的能力。这种方法将为政策决策提供更好的信息并指导有效的干预措施。
