Craighead Laura, Gilbert William, Subasinghe Dynatra, Häsler Barbara
Royal Veterinary College, North Mymms, Hertfordshire, United Kingdom.
Royal Veterinary College, North Mymms, Hertfordshire, United Kingdom.
Prev Vet Med. 2015 Oct 1;121(3-4):206-14. doi: 10.1016/j.prevetmed.2015.06.016. Epub 2015 Jul 7.
Surveillance systems for rabies in endemic regions are often subject to severe constraints in terms of resources. The World Organisation for Animal Health (OIE) and the World Health Organisation (WHO) propose the use of an active surveillance system to substantiate claims of disease freedom, including rabies. However, many countries do not have the resources to establish active surveillance systems for rabies and the testing of dead dogs poses logistical challenges. This paper explores the potential of using a scenario tree model parameterised with data collected via questionnaires and interviews to estimate the sensitivity of passive surveillance, assessing its potential as a viable low-cost alternative to active surveillance systems. The results of this explorative study illustrated that given a large enough sample size, in this case the entire population of Colombo City, the sensitivity of passive surveillance can be 100% even at a low disease prevalence (0.1%), despite the low sensitivity of individual surveillance components (mean values in the range 4.077×10(-5)-1.834×10(-3) at 1% prevalence). In addition, logistic regression was used to identify factors associated with increased recognition of rabies in dogs and reporting of rabies suspect dogs. Increased recognition was observed amongst dog owners (OR 3.8 (CI, 1.3-10.8)), people previously bitten by dogs (OR 5.9 (CI, 2.2-15.9)) and people who believed they had seen suspect dogs in the past (OR 4.7 (CI, 1.8-12.9)). Increased likelihood of reporting suspect dogs was observed amongst dog owners (OR 5.3 (CI, 1.1-25)). Further work is required to validate the data collection tool and the assumptions made in the model with respect to sample size in order to develop a robust methodology for evaluating passive rabies surveillance.
在狂犬病流行地区，狂犬病监测系统往往在资源方面受到严重限制。世界动物卫生组织（OIE）和世界卫生组织（WHO）建议使用主动监测系统来证实包括狂犬病在内的无病声明。然而，许多国家没有资源建立狂犬病主动监测系统，而且对死亡犬只进行检测存在后勤方面的挑战。本文探讨了使用一种情景树模型的潜力，该模型通过问卷调查和访谈收集的数据进行参数化，以估计被动监测的敏感性，评估其作为主动监测系统可行的低成本替代方案的潜力。这项探索性研究的结果表明，在样本量足够大的情况下，在本案例中即科伦坡市的全部人口，即使疾病患病率较低（0.1%），被动监测的敏感性也可以达到100%，尽管各个监测组成部分的敏感性较低（患病率为1%时平均值在4.077×10⁻⁵ - 1.834×10⁻³范围内）。此外，使用逻辑回归来确定与犬只狂犬病识别增加以及狂犬病疑似犬只报告增加相关的因素。在犬主中（比值比3.8（置信区间，1.3 - 10.8））、曾被犬只咬伤的人（比值比5.9（置信区间，2.2 - 15.9））以及认为自己过去见过疑似犬只的人（比值比4.7（置信区间，1.8 - 12.9））中观察到识别增加。在犬主中观察到报告疑似犬只的可能性增加（比值比5.3（置信区间，1.1 - 25））。需要进一步开展工作来验证数据收集工具以及模型中关于样本量的假设，以便开发一种用于评估被动狂犬病监测的稳健方法。
