Still 'dairy farm fever'? A Bayesian model for leptospirosis notification data in New Zealand.
Still 'dairy farm fever'? A Bayesian model for leptospirosis notification data in New Zealand.
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DOI：
10.1098/rsif.2020.0964
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Spencer SEF
中科院分区：
文献类型：
--
作者：
Benschop J;Nisa S;Spencer SEF
政府收集的公共卫生监测数据往往是部分完整的，但仍然是监测发病率和跟踪疾病干预进展的有用来源。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”，这种疾病经常与血清型Hardjo和Pomona有关。为了减少感染，实施了干预措施，如用这两种血清型对奶牛进行疫苗接种。这些干预措施与钩端螺旋体病发病率的显著降低有关，然而，以畜牧业为基础的职业仍然占通报的绝大多数。近年来，越来越多地通过核酸检测进行诊断，目前核酸检测不提供血清型信息。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，这可以反馈到干预策略的设计中。在这项研究中，1999年1月1日至2016年12月31日的确诊和可能钩端螺旋体病通报数据被用于建立一个模型，以根据血清型和发生月份估算来自不同职业群体的病例数。我们在贝叶斯框架内估算了缺失的职业和血清型数据，假设通知病例的发生遵循泊松过程。该数据集包含1430例通报病例，其中927例具有特定职业（181名奶农，45名干牲畜农民，454名肉类工人，247名其他），其余503例具有非特定职业。在1430例病例中，1036例具有特定血清型（231例Ballum，460例Hardjo，249例Pomona，96例Tarassovi），而其余394例具有未知血清型。因此，47%（674/1430）的观察结果既有血清型又有特定职业。结果表明，尽管所有职业都有一定程度的漏报，但干牲畜饲养者受到的影响最大，并被推断为与奶农一样多的病例对疾病负担的贡献，尽管奶农的记录频率要高得多。而不是丢弃一些缺失的记录，我们已经说明了如何数学建模可以用来利用这些部分完成的情况下的信息。我们的发现为重新评估目前在干库存中最低限度使用动物疫苗提供了重要证据。改进病例报告表中具体农业类型的记录是下一步的重要工作。
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.
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DOI：
10.3390/pathogens9100841
发表时间：
2020-10-14
期刊：
Pathogens (Basel, Switzerland)
影响因子：
--
作者：
Nisa S;Wilkinson DA;Angelin-Bonnet O;Paine S;Cullen K;Wight J;Baker MG;Benschop J
通讯作者：
Benschop J
影响因子：
2.5
作者：
GOODMAN, LA
通讯作者：
GOODMAN, LA
影响因子：
2.6
作者：
Yupiana, Y.;Vallee, E.;Heuer, C.
通讯作者：
Heuer, C.
影响因子：
3.8
作者：
Costa F;Hagan JE;Calcagno J;Kane M;Torgerson P;Martinez-Silveira MS;Stein C;Abela-Ridder B;Ko AI
通讯作者：
Ko AI
影响因子：
4.4
作者：
Berger, James O.;Bernardo, Jose M.;Sun, Dongchu
通讯作者：
Sun, Dongchu