Diverse Epidemiology of Leptospira Serovars Notified in New Zealand, 1999–2017

Nisa, Shahista; Wilkinson, David A.; Angelin-Bonnet, Olivia; Paine, Shevaun; Cullen, Karen Weber; Wight, Jackie; Baker, Michael G; Benschop, Jackie

doi:10.3390/pathogens9100841

Cited by 17 publications

(24 citation statements)

References 33 publications

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“…For example, we assumed that all notified cases had equal probability of producing one of the serovars in the database, irrespective of time of year, location or occupation. However, there is heterogeneity in both the use of diagnostic tests for leptospirosis and in the occupations of notified cases across New Zealand [ 4 ]. The choice of which of the two tests (MAT and PCR) [ 18 ] to use is driven by variation in clinician preference.…”

Section: Discussionmentioning

confidence: 99%

“…In New Zealand, the number of incident cases have reduced significantly since the time of ‘dairy farm fever’ in the 1970s after the introduction of dairy cattle and pig vaccination programmes; however, there is still a high incidence of leptospirosis compared with other high-income countries [ 3 ]. The burden falls largely on rural communities and on Māori, the indigenous people of New Zealand [ 4 ]. From 1 January 2017 to 31 December 2019, 421 new patients were notified (average of 140 cases per annum, with incidence annualized for 2019).…”

Section: Introductionmentioning

confidence: 99%

“…From 1 January 2017 to 31 December 2019, 421 new patients were notified (average of 140 cases per annum, with incidence annualized for 2019). This represents an approximate 89% increase in annual incidence compared with the 5 years prior (2012–2016, average of 74 cases per annum) [ 4 ]. This increase coupled with a long-term change in serovar trends [ 4 , 5 ] and more frequent reporting of clusters of cases [ 6 , 7 ], point to a changing epidemiology.…”

Section: Introductionmentioning

confidence: 99%

“…This represents an approximate 89% increase in annual incidence compared with the 5 years prior (2012–2016, average of 74 cases per annum) [ 4 ]. This increase coupled with a long-term change in serovar trends [ 4 , 5 ] and more frequent reporting of clusters of cases [ 6 , 7 ], point to a changing epidemiology. Despite these changes, farmers and meatworkers remain most at risk with at least 75% of notified cases from these two occupational groups.…”

Section: Introductionmentioning

confidence: 99%

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Still ‘dairy farm fever’? A Bayesian model for leptospirosis notification data in New Zealand

Benschop

Nisa

Spencer

2021

J. R. Soc. Interface.

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Still ‘dairy farm fever’? A Bayesian model for leptospirosis notification data in New Zealand

Benschop

Nisa

Spencer

2021

J. R. Soc. Interface.

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“…In the last 3 years, incidence has been steadily increasing. However, 30% of cases are reported as a positive or negative for Leptospira , with no serological data associated with them due to the recent implementation of DNA testing for diagnosis [ 22 ]. This can have serious implications for surveillance and mitigation strategies as different serovars have different reservoir distributions [ 23 ] and the absence of infecting-lineage data means that likely sources of infection cannot be accurately attributed.…”

Section: Introductionmentioning

confidence: 99%

Identification of pathogenic Leptospira species and serovars in New Zealand using metabarcoding

et al. 2021

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Leptospirosis is a zoonotic disease of global importance. The breadth of Leptospira diversity associated with both human and animal disease poses major logistical challenges to the use of classical diagnostic techniques, and increasingly molecular diagnostic tools are used for their detection. In New Zealand, this has resulted in an increase in positive cases reported nationally that have not been attributed to the infecting serovar or genomospecies. In this study, we used data from all pathogenic Leptospira genomes to identify a partial region of the glmU gene as a suitable locus for the discrimination of the infecting species and serovars of New Zealand-endemic Leptospira. This method can be used in culture and culture-independent scenarios making it flexible for diagnostics in humans, animals, and environmental samples. We explored the use of this locus as a molecular barcoding tool via the Oxford Nanopore Technology (ONT) sequencing platform MinION. Sequences obtained by this method allowed specific identification of Leptospira species in mixed and enriched environmental cultures, however read error inherent in the MinION sequencing system reduced the accuracy of strain/variant identification. Using this approach to characterise Leptospira in enriched environmental cultures, we detected the likely presence of Leptospira genomospecies that have not been reported in New Zealand to date. This included a strain of L. borgpetersenii that has recently been identified in dairy cattle and sequences similar to those of L. mayottensis. L. tipperaryensis, L. dzianensis and L. alstonii.

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“We don't really do doctors.” messages from people diagnosed with occupational leptospirosis for medical professionals on infection, hospitalisation, and long-term effects

Prinsen,

Baker,

Benschop

et al. 2023

Heliyon

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Diverse Epidemiology of Leptospira Serovars Notified in New Zealand, 1999–2017

Cited by 17 publications

References 33 publications

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

Identification of pathogenic Leptospira species and serovars in New Zealand using metabarcoding

“We don't really do doctors.” messages from people diagnosed with occupational leptospirosis for medical professionals on infection, hospitalisation, and long-term effects

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