Neisseria meningitidis - Eurosurveillance

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Figure 2 Human cases of campylobacteriosis caused by poultry- and ruminant-associated Campylobacter jejuni MLST types, as well as a ubiquitous ST in a sentinel surveillance site, New Zealand, 2005–2011 Number of cases Number of cases Number of cases 30 25 20 15 10 5 0 9 8 7 6 5 4 3 2 1 0 12 10 8 6 4 2 0 Apr Apr Apr A Jun B Jun C Jun Aug Aug Aug Oct Oct Oct Dec Dec Dec Feb Feb Feb MLST: multilocus sequence typing; ST: sequence type. Apr Apr Apr Jun Jun Jun Aug Aug Aug Oct Oct Oct Dec Dec Dec Feb Feb Feb Apr Apr Apr Jun Jun Jun Aug Aug Aug Oct Oct Oct Dec Dec Dec Feb Feb Feb Apr Apr Apr Panel A shows the time series of human campylobacteriosis cases with two poultry-associated genotypes, ST-474 and ST-48 and illustrates the drop in the number of cases following interventions in the poultry production chain. Panrel B shows the trend in human campylobacteriosis cases with ruminant-associated genotypes ST-61, ST-42, ST-422 and ST-53. Panel C shows the time series of human campylobacteriosis cases with the ubiquitous ST-45. 112 www.eurosurveillance.org Jun 2005 2006 2007 2008 2009 2010 2011 Jun 2005 2006 2007 2008 2009 2010 2011 Jun 2005 2006 2007 2008 Date (month, year) 2009 2010 2011 Pre-intervention period Post-intervention period Aug Aug Aug Oct Oct Oct Dec Dec Dec Feb Feb Feb Apr Apr Apr Jun Jun Jun Aug Aug Aug Oct Oct Oct Dec Dec Dec Feb Feb Feb ST-474 ST-48 ST-61, ST-42, ST-422 and ST-53 ST-45 Apr Apr Apr Jun Jun Jun Aug Aug Aug Oct Oct Oct Dec Dec Dec Feb Feb Feb Apr Apr Apr Jun Jun Jun Aug Aug Aug Oct Oct Oct
categorise molecular approaches to disease surveillance. Approaches that are suitable for control-focused surveillance, such as those used in an outbreak setting, are potentially of lesser value for strategy-focused surveillance, where the aim is often to monitor long-term changes in epidemiology [24,25], and vice versa. The purpose of control-focused surveillance is ‘to identify each occurrence of a particular disease, hazard, or other health-related event that requires a specific response, and to support the delivery of an effective intervention’ [23]. Such surveillance requires methods that have a high degree of timeliness, sensitivity and security (i.e. that can be maintained on an ongoing basis) [23]. The molecular typing tools and associated modelling approaches required for this objective need to be capable of identifying genotypes that indicate a common source of disease or highlight a particular transmission pathway. Often, but not always, this is achieved through highly discriminatory typing tools. Using a recently developed model-based tool for identifying clusters of campylobacteriosis cases related in space and time [26], eight cases in a small area of New Zealand’s North Island were identified as having a high probability (>0.8) of being part of an anomalous cluster (i.e. they were more spatially and temporally localised than would be expected given the average temporal and spatial patterns). Inspection of the epidemiological information linked to each case revealed that they were reported within a single two-week period and typing data showed that they were all the same MLST sequence type (ST-520). When compared with a larger database of over 3,000 sequence types isolated from humans, animals and food in New Zealand it was shown that this type was associated with ruminants in New Zealand, but was a relatively uncommon cause of human infection. This finding triggered a more detailed investigation into the cases, requiring further contact with some affected individuals, which revealed that all cases had consumed unpasteurised (raw) milk – a relatively rare risk factor – and that 7/8 cases reported purchasing the milk from the same source farm. This combination of epidemiological information and typing data lead to a local response and also informed the ongoing debate on the national policy concerning the sale of raw milk. The purpose of strategy-focused surveillance is ‘to provide information to support prevention strategies to reduce population health risk, such as describing the epidemiology of the annual influenza season and the characteristics of the seasonal influenza viruses’ [23]. Such surveillance requires methods that have a high degree of representativeness, completeness (referring to the data recorded with each event) and validity [23]. Different molecular and modelling approaches are required in this instance, with the optimal tools providing information on the long-term epidemiology of a pathogen rather than short-term changes. An example is the recent emergence of new approaches to source www.eurosurveillance.org attribution using molecular subtyping, which has been used successfully in several countries to understand the relative contribution of different sources to the burden of human campylobacteriosis and salmonellosis [27-30]. Source attribution models based on microbial subtyping were initially developed in Denmark as a tool for salmonellosis risk management [31]; they provide estimates of the number of human cases originating from different sources or reservoirs based on a comparison of genotypes [31,32]. In New Zealand, attribution models were adapted to data from the MLST surveillance site. Two models were used, a population genetics-based attribution model [32] and the microbial subtyping-based model by Hald et al. [31], to quantify the contribution of selected sources to the human disease burden. These studies revealed that between 2005 and 2008, poultry was the leading source of human campylobacteriosis, causing an estimated 58–76%, of notified cases [8]. Contributions by individual poultry suppliers showed wide variation and supplier specific strains were detected [15]. The use of these models to monitor changes over time and to assess the effectiveness of interventions is ongoing [10,11]. Re-defining response and explanatory variables using molecular tools – a new epidemiological approach to inform surveillance? A common starting point of epidemiology is seeking non-random associations between response variables and potential explanatory variables. Regression modelling, for example, may be used to identify statistically significant predictors of increased risk of adverse health effects [33]. However, the use of such traditional methods for quantifying the contribution of different sources of campylobacteriosis to the disease burden in New Zealand (notably case–control studies [9,34], which identified poultry as the major source of human infection) had not provided sufficient compelling evidence for decision-makers to invest in controlling the poultry source. The epidemiology of campylobacteriosis is challenging: as a multi-host pathogen, infection with C. jejuni is associated with a large number of risk factors [35] and human cases arising from exposure to different sources may have very different risk factors, some of which may even be protective for some sources and increase the risk for others. Using molecular tools, pathogen genetics and evolution can be incorporated into epidemiological modelling to make inferences about disease or transmission risks rather than simply relying on the association of response and explanatory variables. Such tools can be used to refine outcome variables, for example by using case–case comparison of poultry- and ruminantassociated cases of campylobacteriosis to identify more subtle associations [14,17] or to investigate the cause of a disease outbreak [24]. However, greater epidemiological gains are likely to be made when models combine pathogen evolution and transmission in 113
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Editorial team Editorial advisors B
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genome sequencing technologies, we
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References 1. Struelens MJ, De Ghel
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e procedures in place to check and
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microorganisms [23]. After PCR, amp
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of the based upon repeat patterns (
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two days. Due to a very high accura
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eported a clinically meaningful app
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18. Chang HL, Tang CH, Hsu YM, Wan
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from: http://www.eurosurveillance.o
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Table 1 Online molecular typing dat
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Table 2 Currently available softwar
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Current concepts and technologies f
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References 1. Hesper B, Hogeweg P.
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Review articles Automated extractio
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diverse organisms, such as the meni
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Figure 2 Extracting antigen and ant
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Figure 3 Relationships of 139 Neiss
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25. Wirth T, Hildebrand F, Allix-B
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Euroroundups Use of multilocus vari
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searches were also repeated using G
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Table 2 MLVA analysis of Salmonella
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Research articles Current applicati
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Table 2 Techniques, time and costs
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common C. difficile variants causin
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References 1. Bartlett JG. Antibiot
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Research articles Within-patient em
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Neisseria meningitidis - Eurosurveillance

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