Zhang Ying, Arab Ali, Cowling Benjamin J, Stoto Michael A
Department of Health Systems Administration, School of Nursing and Health Studies, Georgetown University, Washington, DC, USA.
BMC Public Health. 2014 Aug 15;14:850. doi: 10.1186/1471-2458-14-850.
Infectious disease surveillance is a process the product of which reflects both actual disease trends and public awareness of the disease. Decisions made by patients, health care providers, and public health professionals about seeking and providing health care and about reporting cases to health authorities are all influenced by the information environment, which changes constantly. Biases are therefore imbedded in surveillance systems; these biases need to be characterized to provide better situational awareness for decision-making purposes. Our goal is to develop a statistical framework to characterize influenza surveillance systems, particularly their correlation with the information environment.
We identified Hong Kong influenza surveillance data systems covering healthcare providers, laboratories, daycare centers and residential care homes for the elderly. A Bayesian hierarchical statistical model was developed to examine the statistical relationships between the influenza surveillance data and the information environment represented by alerts from HealthMap and web queries from Google. Different models were fitted for non-pandemic and pandemic periods and model goodness-of-fit was assessed using common model selection procedures.
Some surveillance systems - especially ad hoc systems developed in response to the pandemic flu outbreak - are more correlated with the information environment than others. General practitioner (percentage of influenza-like-illness related patient visits among all patient visits) and laboratory (percentage of specimen tested positive) seem to proportionally reflect the actual disease trends and are less representative of the information environment. Surveillance systems using influenza-specific code for reporting tend to reflect biases of both healthcare seekers and providers.
This study shows certain influenza surveillance systems are less correlated with the information environment than others, and therefore, might represent more reliable indicators of disease activity in future outbreaks. Although the patterns identified in this study might change in future outbreaks, the potential susceptibility of surveillance data is likely to persist in the future, and should be considered when interpreting surveillance data.
传染病监测是一个其产物既反映实际疾病趋势又反映公众对该疾病认知的过程。患者、医疗保健提供者以及公共卫生专业人员在寻求和提供医疗保健以及向卫生当局报告病例方面所做的决定均受不断变化的信息环境影响。因此，偏差存在于监测系统之中；需要对这些偏差进行特征描述，以便为决策目的提供更好的态势感知。我们的目标是开发一个统计框架来描述流感监测系统，特别是它们与信息环境的相关性。
我们确定了涵盖医疗保健提供者、实验室、日托中心和老年护理院的香港流感监测数据系统。开发了一个贝叶斯分层统计模型，以检验流感监测数据与由HealthMap警报和谷歌网络查询所代表的信息环境之间的统计关系。针对非大流行期和大流行期拟合了不同模型，并使用常见的模型选择程序评估模型的拟合优度。
一些监测系统——尤其是为应对甲型H1N1流感大流行爆发而开发的临时系统——比其他系统与信息环境的相关性更强。全科医生（所有就诊患者中流感样疾病相关就诊的百分比）和实验室（检测呈阳性的标本百分比）似乎按比例反映了实际疾病趋势，且较少代表信息环境。使用特定流感代码进行报告的监测系统往往反映了寻求医疗者和提供者双方的偏差。
本研究表明，某些流感监测系统与信息环境的相关性比其他系统弱，因此，在未来疫情爆发中可能代表更可靠的疾病活动指标。尽管本研究中确定的模式在未来疫情爆发中可能会改变，但监测数据潜在的易感性在未来可能会持续存在，在解释监测数据时应予以考虑。
