School of Veterinary Science, Massey University, Palmerston North, New Zealand.
Department of Statistics, University of Warwick, Coventry, UK.
J R Soc Interface. 2021 Feb;18(175):20200964. doi: 10.1098/rsif.2020.0964. Epub 2021 Feb 17.
Routinely collected public health surveillance data are often partially complete, yet remain a useful source by which to monitor incidence and track progress during disease intervention. In the 1970s, leptospirosis in New Zealand (NZ) was known as 'dairy farm fever' and the disease was frequently associated with serovars Hardjo and Pomona. To reduce infection, interventions such as vaccination of dairy cattle with these two serovars was implemented. These interventions have been associated with significant reduction in leptospirosis incidence, however, livestock-based occupations continue to predominate notifications. In recent years, diagnosis is increasingly made by nucleic acid detection which currently does not provide serovar information. Serovar information can assist in linking the recognized maintenance host, such as livestock and wildlife, to infecting serovars in human cases which can feed back into the design of intervention strategies. In this study, confirmed and probable leptospirosis notification data from 1 January 1999 to 31 December 2016 were used to build a model to impute the number of cases from different occupational groups based on serovar and month of occurrence. We imputed missing occupation and serovar data within a Bayesian framework assuming a Poisson process for the occurrence of notified cases. The dataset contained 1430 notified cases, of which 927 had a specific occupation (181 dairy farmers, 45 dry stock farmers, 454 meatworkers, 247 other) while the remaining 503 had non-specified occupations. Of the 1430 cases, 1036 had specified serovars (231 Ballum, 460 Hardjo, 249 Pomona, 96 Tarassovi) while the remaining 394 had an unknown serovar. Thus, 47% (674/1430) of observations had both a serovar and a specific occupation. The results show that although all occupations have some degree of under-reporting, dry stock farmers were most strongly affected and were inferred to contribute as many cases as dairy farmers to the burden of disease, despite dairy farmer being recorded much more frequently. Rather than discard records with some missingness, we have illustrated how mathematical modelling can be used to leverage information from these partially complete cases. Our finding provides important evidence for reassessing the current minimal use of animal vaccinations in dry stock. Improving the capture of specific farming type in case report forms is an important next step.
常规收集的公共卫生监测数据通常是不完整的，但仍然是监测疾病干预期间发病率和跟踪进展的有用来源。在 20 世纪 70 年代，新西兰（NZ）的钩端螺旋体病被称为“奶牛场热”，该疾病常与血清型 Hardjo 和 Pomona 相关。为了减少感染，对奶牛接种这两种血清型的疫苗等干预措施得到了实施。这些干预措施与钩端螺旋体病发病率的显著降低有关，但基于牲畜的职业仍然是主要的通知来源。近年来，诊断越来越多地通过核酸检测进行，目前该检测方法不提供血清型信息。血清型信息可以帮助将公认的维持宿主（如牲畜和野生动物）与人类病例中的感染血清型联系起来，从而为干预策略的设计提供反馈。在这项研究中，使用了 1999 年 1 月 1 日至 2016 年 12 月 31 日的确诊和可能的钩端螺旋体病通知数据，构建了一个模型，以根据血清型和发病月份推断不同职业组的病例数。我们在贝叶斯框架内假设通知病例的发生是泊松过程，从而推断出缺失的职业和血清型数据。该数据集包含 1430 例通知病例，其中 927 例有特定职业（181 名奶牛场工人、45 名干牲畜饲养员、454 名肉类工人、247 名其他），而其余 503 例没有特定职业。在 1430 例病例中，1036 例有特定血清型（231 号 Ballum、460 号 Hardjo、249 号 Pomona、96 号 Tarassovi），而其余 394 例血清型未知。因此，47%（674/1430）的观察结果同时具有血清型和特定职业。结果表明，尽管所有职业都存在一定程度的漏报，但干牲畜饲养员受到的影响最大，尽管奶牛场工人的记录频率更高，但他们被推断为疾病负担的贡献与奶牛场工人一样多。我们没有丢弃那些存在部分缺失的记录，而是说明了如何使用数学模型来利用这些部分完整的案例中的信息。我们的研究结果为重新评估当前在干牲畜中使用动物疫苗的最低限度提供了重要证据。改进病例报告表中对特定养殖类型的捕获是下一步的重要工作。
