Weinberg J
City University, London, UK.
Clin Microbiol Infect. 2005 Apr;11 Suppl 1:12-4. doi: 10.1111/j.1469-0691.2005.01083.x.
New emerging and re-emerging threats, the weight of public opinion and new technology for surveillance and treatment are likely to impact on how, and if, effective surveillance can be performed in the future. If surveillance fails to address the needs of practitioners and policy-makers, it is likely that there will be loss of confidence. Current surveillance systems are reasonably effective at detecting significant events that are localised in time and space. It is more difficult to detect diffuse and progressive events with a slow increase over time or sporadic and widespread events without obvious links to time, place or person. Detection of these events relies on good data collection, comparative background data and sophisticated analytical tools. To improve surveillance systems, we need methods with the appropriate sensitivity and specificity for the outputs desired. Targeted surveillance should enable better ascertainment of those cases which must be considered and those which can be dismissed. New methods, such as mathematical modelling and geographical information systems, support conventional surveillance in moving events into the known and predictable category. It is important to integrate surveillance across local, regional and international levels and to base surveillance on local public health structures. The purpose and value of data aggregation at each level and the amount of detail needed at each level must be carefully evaluated. The key to all these improvements is developing the workforce. Surveillance needs individuals with a broad range of skills: clinical, epidemiological, anthropological, and mathematical; in particular, people who can think laterally. These individuals must be encouraged through effective training courses, good mentorship, networking and clear career structures.
新出现和再次出现的威胁、公众舆论的影响力以及用于监测和治疗的新技术,可能会对未来能否以及如何开展有效的监测产生影响。如果监测无法满足从业者和政策制定者的需求,很可能会导致信心丧失。当前的监测系统在检测时间和空间上具有局限性的重大事件方面相当有效。但要检测随着时间缓慢增加的扩散性和渐进性事件,或与时间、地点或人物没有明显关联的零星且广泛传播的事件则更加困难。检测这些事件依赖于良好的数据收集、可比的背景数据以及复杂的分析工具。为了改进监测系统,我们需要具备针对所需产出的适当灵敏度和特异性的方法。有针对性的监测应能更好地确定哪些病例必须予以考虑,哪些可以排除。数学建模和地理信息系统等新方法有助于传统监测将事件纳入已知和可预测的范畴。整合地方、区域和国际层面的监测,并以地方公共卫生结构为基础进行监测非常重要。必须仔细评估每个层面数据汇总的目的和价值以及每个层面所需的详细程度。所有这些改进的关键在于培养专业人员。监测需要具备广泛技能的人员:临床、流行病学、人类学和数学方面的技能;尤其是能够横向思维的人员。必须通过有效的培训课程、良好的指导、网络建设和清晰的职业架构来鼓励这些人员。