Division of Infectious Diseases and Geographic Medicine, Stanford University, California.
Typhoid Programs, Sabin Vaccine Institute, Washington, District of Columbia.
J Infect Dis. 2018 Nov 10;218(suppl_4):S268-S276. doi: 10.1093/infdis/jiy494.
Cohort studies and facility-based sentinel surveillance are common approaches to characterizing infectious disease burden, but present trade-offs; cohort studies are resource-intensive and may alter disease natural history, while sentinel surveillance underestimates incidence in the population. Hybrid surveillance, whereby facility-based surveillance is paired with a community-based healthcare utilization assessment, represents an alternative approach to generating population-based disease incidence estimates with moderate resource investments. Here, we discuss this method in the context of the Surveillance for Enteric Fever in Asia Project (SEAP) study. We describe how data are collected and utilized to adjust enteric fever incidence for blood culture sensitivity, facility-based enrollment, and healthcare seeking, incorporating uncertainty in these parameters in the uncertainty around incidence estimates. We illustrate how selection of surveillance sites and their coverage may influence precision and bias, and we identify approaches in the study design and analysis to minimize and control for these biases. Rigorously designed hybrid surveillance systems can be an efficient approach to generating population-based incidence estimates for infectious diseases.
队列研究和基于机构的哨点监测是描述传染病负担的常见方法，但存在权衡取舍；队列研究资源密集，可能改变疾病的自然史，而哨点监测则低估了人群中的发病率。混合监测是一种将基于机构的监测与基于社区的医疗保健利用评估相结合的方法，是一种用适度资源投入生成基于人群的疾病发病率估计的替代方法。在这里，我们结合亚洲肠热病监测项目（SEAP）研究来讨论这种方法。我们描述了如何收集和利用数据来调整肠热病发病率，以适应血培养敏感性、基于机构的入组和医疗保健寻求，并在发病率估计的不确定性中纳入这些参数的不确定性。我们说明了监测地点的选择及其覆盖范围如何影响精度和偏差，并确定了在研究设计和分析中最小化和控制这些偏差的方法。精心设计的混合监测系统可以成为生成传染病基于人群的发病率估计的有效方法。
