Trends in Campylobacter incidence in broilers and humans in six European countries, 1997–2007
Introduction
Campylobacter are ubiquitous bacteria, frequently found in the alimentary tracts of animals, especially birds, and commonly contaminate the environment, including water. It is, however, less clear how the bacteria are distributed in the environment, and in what numbers. Since the 1970s, Campylobacter have been shown to be an important cause of enteritis in humans and regarded as the most common cause of reported bacterial gastroenteritis in the EU (Anonymous, 2007). Most Campylobacter infections occur as sporadic cases and consumption of undercooked broiler meat is regarded as a significant source of human infections (Reiersen et al., 2001, Vellinga and van Loock, 2002, Stern et al., 2003, Wingstrand et al., 2006, Anonymous, 2007). Possible sources of Campylobacter for broiler flocks are: low levels of biosecurity; presence of rodents on farms; age of broilers (Hansson et al., 2007, McDowell et al., 2008), drinking water that has not been disinfected (Kapperud et al., 1993); or other livestock on the farm/neighbourhood (van de Giessen et al., 1996, Bouwknegt et al., 2004). In the Netherlands, it has been estimated that 20–40% of all laboratory-confirmed cases are attributable to consumption of undercooked chicken. Nearly the same estimate (40%) was found in Belgium (Vellinga and van Loock, 2002, Janssen et al., 2008). Other identified sources are contaminated drinking water, raw milk and contact with pets (Kapperud et al., 2003, Ethelberg et al., 2005, Olson et al., 2008). Although these potential sources have been identified, the routes of transmission and their relative importance are still uncertain.
In wild birds and chickens the Campylobacter carriage rate (the number of birds carrying the bacteria), and the numbers of Campylobacter in the small intestine and caeca of broilers, have a seasonal pattern, with a distinct peak in prevalence in summer and a low prevalence during winter (Jacobs-Reitsma et al., 1994, Wallace et al., 1997, Wedderkopp et al., 2001, Hansson et al., 2004, Hofshagen and Kruse, 2005, Olson et al., 2008). This seasonal fluctuation in Campylobacter numbers is reflected in the increased risk of human infection from carcass contamination and environmental sources at certain times of the year. Similar seasonal pattern of raised summer incidence is also seen in the occurrence of human campylobacteriosis in Northern Europe (Nylen et al., 2002, Miller et al., 2004, Meldrum et al., 2005, Tam et al., 2006, Heier et al., 2006, van Hees et al., 2007, Olson et al., 2008). Several studies suggest that climate may play a role in colonization of both animals and humans (Patrick et al., 2004, Kovats et al., 2005, Louis et al., 2005, Valérie et al., 2005, Tam et al., 2006, Fleury et al., 2006). Seasonal variation in infection pressure may be dependent on climatic variables like temperature, length and intensity of daylight and precipitation, as well as changes in host social behaviour and in host immune system (Altizer et al., 2006). Seasonal variation in the incidence of infectious diseases is a well-known phenomenon worldwide, although the mechanisms behind are poorly understood in many instances (Grassly and Fraser, 2006).
One possible explanation for the seasonality of human campylobacteriosis, besides a documented seasonal variation in several food sources, include prevalence of Campylobacter in environmental reservoirs, which varies greatly with season (Jacobs-Reitsma et al., 1994, Stanley and Jones, 2003). The seasonal effectiveness of the human immune system response and the possible role acquired immunity plays, are also important factors in the epidemiology of campylobacteriosis (Mann et al., 2000, Skelly and Weinstein, 2003, Havelaar et al., 2009).
The objectives of this study were to compare whether the seasonal variation observed is similar in the six different countries, and to describe long-term trends in Campylobacter incidence for broilers and humans, as a way of generating hypotheses and to give a thorough description of the existing situation.
Section snippets
Data collection
The incidence data for Campylobacter in broilers originated from the countries’ surveillance data. The incidence estimation was based on the assumption that flocks are all expected to be negative at the beginning of the rearing period. The calculation of the annual/monthly incidence was based on date of slaughter, i.e. flock lifetime incidence. A slaughter group was regarded as positive if at least one of the cloacal or the caecal samples proved positive for Campylobacter. Details for each
Results
Campylobacteriosis showed a gradual rise in spring, which peaked in late-summer (July–August), before the incidence returned to baseline level in late autumn. The seasonality seen for campylobacteriosis was most pronounced for Finland. The colonization of Campylobacter-positive broiler flocks had an equivalent spring-rise which cumulated in July–August before returning to baseline level late autumn (Fig. 1, Fig. 2). For the Netherlands the peaks of incidence were less pronounced for both humans
Discussion
This study analyses data from Campylobacter monitoring programs from six European countries. The data generated from the Campylobacter monitoring programs for broilers represents an active, “targeted surveillance”, whilst the human data are collected from a passive surveillance system. The modes of data collection and data sources are therefore different. This represents potential sources of bias in the study. The surveillance programs for broilers and humans in the six countries differ, and
Conclusion
When comparing incidence of human domestic campylobacteriosis and incidence of Campylobacter positive broilers at slaughter from six countries in Europe, distinct and similar seasonal variations were found both within and between countries. By seasonally adjusting and de-trending the incidence time series, the strong and similar seasonal pattern seen between human and broiler incidences within and between countries were removed. A strong association between the mean temperature in the sampling
Acknowledgements
Thanks to Anja Kristoffersen for assistance with the data analysis, Bruce David and Sonja Hartnack-Wilhelm for critically reviewing the paper. Thanks to Petter Hopp for always helpful assistance. Thanks to Ketil Isaksen for providing temperature data. Product Boards for Livestock, Meat and Eggs (Rijswijk, the Netherlands) for providing the Dutch poultry data. Thanks to all personnel at farms, abattoirs, laboratories, and others that have contributed to the data used in this paper.
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