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部分完成的，但仍然可以用作在疾病干预期间监测发病率和跟踪进展的有用来源。在1970年代，新西兰的钩端螺旋体病被称为“奶牛场发烧”，该病通常与血清型Hardjo和Pomona有关。为了减少感染，已实施了干预措施，例如用这两种血清型疫苗接种奶牛。这些干预措施已使钩端螺旋体病的发病率显着降低，但是，以牲畜为基础的职业仍占主导地位。近年来，通过核酸检测的诊断越来越多，核酸检测目前不提供血清学信息。血清素信息可以帮助链接公认的维护主机，例如牲畜和野生动植物，感染人类病例中的血清，可以反馈到干预策略的设计中。在这项研究中，使用1999年1月1日至2016年12月31日的确诊和大概的钩端螺旋体病通报数据建立模型，根据血清型和发生月份估算不同职业组的病例数。我们在贝叶斯框架内估算了丢失的职业和血清数据，并假设发生已知病例的是泊松过程。该数据集包含1430例通报病例，其中927例具有特定职业（181个奶农，45个干畜农，454个肉类工人，247个其他职业），其余503个具有非特定职业。在1430例病例中，有1036例具有特定的血清型（231 Ballum，460 Hardjo，249 Pomona，96 Tarassovi），其余394例血清型未知。因此，47％（674/1430）的观察既有血清型又有特定职业。结果表明，尽管所有职业的漏报率都有一定程度，但干奶农受到的影响最大，据推测，尽管奶农的记录频率更高，但其对疾病负担的贡献与奶农一样多。我们已经说明了如何使用数学建模来利用这些部分完整案例中的信息，而不是丢弃某些缺失的记录。我们的发现为重新评估目前在干畜中最少使用动物疫苗提供了重要的证据。下一步，重要的是要改进病例报告表中特定农业类型的捕获。结果表明，尽管所有职业的漏报率都有一定程度，但干奶农受到的影响最大，据推测，尽管奶农的记录频率更高，但其对疾病负担的贡献与奶农一样多。我们已经说明了如何使用数学建模来利用这些部分完整案例中的信息，而不是丢弃某些缺失的记录。我们的发现为重新评估目前在干畜中最少使用动物疫苗提供了重要的证据。下一步，重要的是要改进病例报告表中特定农业类型的捕获。结果表明，尽管所有职业的漏报率都有一定程度，但干奶农受到的影响最大，据推测，尽管奶农的记录频率更高，但其对疾病负担的贡献与奶农一样多。我们已经说明了如何使用数学建模来利用这些部分完整案例中的信息，而不是丢弃某些缺失的记录。我们的发现为重新评估目前在干畜中最少使用动物疫苗提供了重要的证据。下一步，重要的是要改进病例报告表中特定农业类型的捕获。尽管奶农的记录频率更高。我们已经说明了如何使用数学建模来利用这些部分完整案例中的信息，而不是丢弃某些缺失的记录。我们的发现为重新评估目前在干畜中最少使用动物疫苗提供了重要的证据。下一步，重要的是要改进病例报告表中特定农业类型的捕获。尽管奶农的记录频率更高。我们已经说明了如何使用数学建模来利用这些部分完整案例中的信息，而不是丢弃某些缺失的记录。我们的发现为重新评估目前在干畜中最少使用动物疫苗提供了重要的证据。下一步，重要的是要改进病例报告表中特定农业类型的捕获。