Assigning the source of human campylobacteriosis in New Zealand: A comparative genetic and epidemiological approach

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Abstract

Integrated surveillance of infectious multi-source diseases using a combination of epidemiology, ecology, genetics and evolution can provide a valuable risk-based approach for the control of important human pathogens. This includes a better understanding of transmission routes and the impact of human activities on the emergence of zoonoses. Until recently New Zealand had extraordinarily high and increasing rates of notified human campylobacteriosis, and our limited understanding of the source of these infections was hindering efforts to control this disease. Genetic and epidemiological modeling of a 3-year dataset comprising multilocus sequence typed isolates from human clinical cases, coupled with concurrent data on food and environmental sources, enabled us to estimate the relative importance of different sources of human disease. Our studies provided evidence that poultry was the leading cause of human campylobacteriosis in New Zealand, causing an estimated 58–76% of cases with widely varying contributions by individual poultry suppliers. These findings influenced national policy and, after the implementation of poultry industry-specific interventions, a dramatic decline in human notified cases was observed in 2008. The comparative-modeling and molecular sentinel surveillance approach proposed in this study provides new opportunities for the management of zoonotic diseases.

Introduction

Emerging zoonotic pathogens, including Campylobacter spp., are commonly associated with a wide taxonomic and ecological host range, and are more likely to infect both domestic and wild animals (Woolhouse, 2002). Studies of the population structure and biology of pathogens with multiple hosts can improve our knowledge of their complex transmission ecology, advance models of infectious diseases, and thereby inform control strategies. In general, disease transmission, and thereby the exposure of susceptible hosts to potential pathogens, is affected by changes in the host pathogen ecology. New opportunities for pathogen transmission may arise following changes in, for example, land use, climate, host demography and food production practices (Woolhouse, 2002). Pathogen characteristics such as virulence and host association are the result of complex interactions involving evolutionary, ecological and epidemiological processes. Merging epidemiology with evolutionary ecology can therefore improve our understanding of the evolution and emergence of pathogens and help guide public health policy (Galvani, 2003).

Knowledge of the proportion of human cases of zoonotic disease that are caused by a particular exposure source is critical for the prioritization of public health resources and the successful implementation of control measures (Batz et al., 2005). Bacterial source tracking has been identified as a tool to link people who are ill to the sources of bacterial contamination (Foley et al., 2009), and new molecular tools are being increasingly applied to study transmission patterns within populations at the strain level, and to evaluate host- and strain-specific risk factors (Murray, 2002). However, when there are many risk pathways and multiple hosts and sources of infection, estimating the relative contribution of different pathogen reservoirs to human infection is challenging.

Recently several advances have been made in our understanding of the evolution and population structure of Campylobacter spp., most notably following the development and application of a multilocus sequence typing (MLST) scheme for this pathogen. These advances include the identification of potentially environmentally adapted Campylobacter jejuni strains (Sopwith et al., 2008), and comparisons of the population biology and molecular biology of C. jejuni and C. coli, which have provided evidence for the ecologically driven convergence of these two pathogens (Dingle et al., 2005, Sheppard et al., 2008).

The availability of molecular typing schemes, combined with new modeling tools based on different underlying assumptions, have provided a platform for understanding the origin of human infections and informing public health policy. For example the model described by Hald et al. (2004) is an associative simulation model for salmonellosis that utilizes the distribution of bacterial subtypes in potential sources of disease to estimate the contribution of each source to the human disease burden. The model accounts for differences in the ability of sources to transmit disease and differences in virulence, pathogenicity and survival of pathogen subtypes. Subsequently this tool has been modified and applied to other pathogens including C. jejuni (Mullner et al., 2009). In addition a new generation of genetic attribution tools have been developed and applied to campylobacteriosis (Wilson et al., 2008, Sheppard et al., 2009, Strachan et al., 2009). These models use the relative frequency and relatedness of isolates from different sources to infer attribution estimates. By taking a population-genetics approach the evolutionary relationships between pathogen populations can be better understood and key reservoirs can be identified.

New Zealand is used as a study case to illustrate our approach. Campylobacteriosis has emerged as a major public health problem worldwide and in New Zealand the number of notifications has increased markedly over the last decade. In 2005 and 2006 the incidence exceeded 300 cases per 100,000 people per annum (Baker et al., 2006). In consequence the relatively high prevalence of campylobacteriosis in New Zealand attracted considerable media attention, was regarded as a national epidemic, and raised a public demand for urgent action (Baker et al., 2006). However, the complex epidemiology of campylobacteriosis, and lack of an appropriate subtyping scheme has hindered the development of successful measures to control this emerging pathogen in New Zealand and elsewhere (Mullner et al., 2009). The situation in New Zealand is quite unique (Crump et al., 2001): the country is geographically remote, with extensive and changing agricultural land use, and human, animal and pathogen populations that are relatively isolated. Although there is relatively little importation of animals and animal products into the country as a result of rigid border biosecurity measures, the country is exposed to a large number of international travelers. Of particular relevance to Campylobacter spp., and in contrast to many other countries; the country's poultry suppliers focus almost entirely on the domestic market and, for biosecurity reasons, no raw poultry products are imported into the country. These factors are likely to play an important role in the invasion, dissemination and evolution of multi-host pathogens such as C. jejuni and C. coli.

The use of integrated surveillance, across human, domestic animal and wildlife populations has been identified as a key component of strategies aimed at preventing and controlling emerging pathogens, in particular since the population dynamics of multi-host pathogens is often poorly understood (Woolhouse, 2002). In this study samples from human clinical cases, animal-derived food products and the environment were gathered in a defined geographical area of New Zealand over a 3-year period (French and Molecular Epidemiology and Veterinary Public Health Group Hopkirk Institute, 2008) and genotyped using MLST (Dingle et al., 2002). The resulting dataset contained a total of 969 typed samples of which 502 were from human cases. The temporal and spatial scale of this study allowed for a more complete understanding of local transmission dynamics compared with previous research (Wilson et al., 2008, Sheppard et al., 2009). The objective of this study was to estimate the relative contribution of food and non-food sources to the burden of human campylobacteriosis in New Zealand and test the hypothesis that poultry rather than ruminants, wildlife and water were the predominant reservoir for human infection (Savill et al., 2001, Devane et al., 2005, Nelson and Harris, 2006).

Section snippets

Sampling

Over a 3-year period from March 1st 2005 until February 29th 2008 a total of 2766 human, retail meat, on-farm and environmental samples were collected in the Manawatu region of New Zealand's North Island (French and Molecular Epidemiology and Veterinary Public Health Group Hopkirk Institute, 2008). This included monthly sampling of retail fresh carcasses from different poultry suppliers in the region, which are dominated by two major suppliers named A2

Proportional similarity index

The genotype distribution of Poultry Supplier A was significantly more similar to the distribution of genotypes from human cases than to that from any other source (median estimate 0.58, 95% CI 0.48–0.64) (Table 3). The PSI was similar for the other two poultry suppliers and bovine sources, with median values between 0.32 and 0.34. The least similarity was observed between environmental and human sources (median estimate 0.18, 95% CI 0.12–0.22).

Dutch model

The Dutch model (Fig. 2) estimated that the

Discussion

Estimates of the relative contribution of individual sources to human infection can help public health policy makers decide upon the most appropriate control measures to implement. However, for zoonotic pathogens, such as C. jejuni, identifying the primary animal reservoirs and transmission pathways is complex and challenging, particularly when these include both domestic and wild animal hosts and a background of changing agricultural and food production practices. The integration of molecular

Acknowledgements

The authors are thankful to all members of the Hopkirk Molecular Epidemiology Team, Environmental Science and Research (ESR), MidCentral Health, Public Health Services, NZFSA (Science Group), and MedLab Central (in particular Lynn Rogers) for their contributions. The authors want to acknowledge in particular Sarah Moore (EpiCentre) and Tui Shadbolt (MidCentral Health) for their help with the data collection. The authors are grateful to Jonathan Marshall (Hopkirk Research Institute) for

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    Present address: School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.

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