Pathogen survival trajectories: an eco-environmental approach to the modeling of human campylobacteriosis ecology.

Campylobacteriosis, like many human diseases, has its own ecology in which the propagation of human infection and disease depends on pathogen survival and finding new hosts in order to replicate and sustain the pathogen population. The complexity of this process, a process common to other enteric pathogens, has hampered control efforts. Many unknowns remain, resulting in a poorly understood disease ecology. To provide structure to these unknowns and help direct further research and intervention, we propose an eco-environmental modeling approach for campylobacteriosis. This modeling approach follows the pathogen population as it moves through the environments that define the physical structure of its ecology. In this paper, we term the ecologic processes and environments through which these populations move "pathogen survival trajectories." Although such a modeling approach could have veterinary applications, our emphasis is on human campylobacteriosis and focuses on human exposures to Campylobacter through feces, food, and aquatic environments. The pathogen survival trajectories that lead to human exposure include ecologic filters that limit population size, e.g., cooking food to kill Campylobacter. Environmental factors that influence the size of the pathogen reservoirs include temperature, nutrient availability, and moisture availability during the period of time the pathogen population is moving through the environment between infected and susceptible hosts. We anticipate that the modeling approach proposed here will work symbiotically with traditional epidemiologic and microbiologic research to help guide and evaluate the acquisition of new knowledge about the ecology, eventual intervention, and control of campylobacteriosis. Key words: aquatic environments, Campylobacter, campylobacteriosis, disease ecology, eco-environmental modeling, ecologic filters, feces, pathogen survival. Environ Health Perspect 111:19-28 (2003). [Online 7 November 2002] doi: 10.1289/ehp.5312 available via http://dx.doi.org/

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Campylobacteriosis has been the most common enteric disease in New Zealand since at least the early 1990s (1), with national notification rates currently exceeding 230 cases per 100,000 persons and regional notification rates exceeding 300 per 100,000 (2). In the United States, the Campylobacter spp. pathogens are responsible for the highest incidence of disease of any of the enteric pathogens under surveillance, with between 17 and 25 cases per 100,000 persons across FoodNet sentinel sites from 1996 to 2000 (3). California is the FoodNet site with the highest incidence of campylobacteriosis, with between 30 and 60 cases per 100,000 persons over the same period (3). However, these are probably conservative estimates of disease incidence, as enteric disease surveillance is known to underestimate incidence considerably (4). Despite its widespread incidence, the ecology of campylobacteriosis remains elusive, and suitable ecologic or environmental transport models have yet to be developed. In this review we attempt to lay a foundation for the eco-environmental modeling of human campylobacteriosis.

An eco-environmental model of human disease is an attempt to describe both the ecology (dynamics of the disease, including pathogen filters) and environments (including structure and vectors) through which pathogens must traverse to obtain new hosts. A system, as in the usage "ecosystem" has been defined in the biologic and climatologic literatures as interlinked flows of energy, momentum, and matter (5,6). However, within the public health literature, the term "ecology" is used in different ways, most notably in the context of ecologic analysis, which describes an aggregated scale of analysis, usually in terms of geographically defined human populations (7,8). Other researchers apply the terms "ecology" and "ecosystem" to the construction of conceptual models of the interdependency between human disease and the natural environment (9,10). The linkage between human society and natural ecosystem health is used as a means to broaden the scope of policy development with respect to assessing public health risk, e.g., cutting down trees in a water catchment will decrease water quality and worsen enteric disease outcomes (11,12). However, these models are devised entirely for the purpose of conveying linkage concepts and not of simulating possible outcomes or the mechanisms of exposure that these linkages might entail.

Modeling must eventually move beyond conceptualization to support quantitative assessment of environmental linkages. Eco-environmental disease modeling is the quantitative approach proposed here to assist in the development of understanding with regard to the interdependencies between human health and natural systems. Of the few researchers who have begun to move in this direction, most have been associated with disease vector modeling, the impacts of climate change, or a combination of these two modeling efforts (13-16). Although the desirability of incorporating natural ecosystems into the study of human disease ecology has been identified (17,18), little progress appears to have been made with regard to model development. An eco-environmental model of campylobacteriosis needs, at a minimum, to capture a) the ecologic dynamics that act to filter Campylobacter pathogens from the environment, b) the temporal movement of pathogens through various environments, and c) environmental temperature that influences pathogen survival time.

The predominant focus of disease modeling of the past has been centered on the concepts of host population dynamics and the mechanisms of person-to-person spread (19). This is discussed further in the next section. However, in this paper we propose that an alternative modeling approach focusing on eco-environmental processes is required if we are to apply computer-modeling techniques to the understanding of enteric pathogens such as Campylobacter. This approach attempts to pull together the epidemiologic and microbiologic literature to provide insight into the ecology of campylobacteriosis. After a brief review of the major ecologic dynamics and environmental vectors of this disease, we propose the structure of an eco-environmental model for campylobacteriosis. In this paper, we plan to lay the necessary groundwork for the future development of quantitative eco-environmental models for campylobacteriosis and other enteric diseases.

Approaches to Modeling Human Disease Ecologies

Models are human constructs that are superimposed upon real-world complexity. Therefore, a model can be described as a device used to simplify an object of study. Models come in all shapes and sizes and are used for many purposes. However, researchers have become used to the idea of using quantitative computer-based simulation models, particularly stochastic models used to describe relationships, e.g., regression models. Another type of model that may be less familiar to epidemiologists and public health professionals is the dynamic or process model.

Dynamic modeling underlies the proposed eco-environmental approach to campylobacteriosis. This type of modeling focuses on the components of the system, which drive important ecosystem dynamics. For example, changes in environmental temperature appear to play an important role in the survival of Campylobacter outside the animal host. Consequently, temperature is a variable important to the process of pathogen survival (20,21). This dynamic modeling approach is well entrenched in the earth sciences (6) and disease vector modeling (13). Within the practice of dynamic modeling, a number of tools have been used, including deterministic modeling, stochastic modeling, and more recently, fuzzy logic, neural networks, genetic algorithms (22), implementation of Markov Chain Monte Carlo in application of Bayesian methods (23), and quantitative risk assessment methodologies (24). Regardless of the tools used, the focus should be on those components known to contribute to the dynamics of the system being modeled. Ultimately, modeling should lead to the development of a better understanding of those dynamics and refocus attention on areas where knowledge is lacking. The use of dynamic modeling in the study of human disease ecology has led to significant gains in knowledge that can be used to tackle new problems such as understanding potential impacts from climatic change (25). However, the modeling of disease ecology has yet to be applied to campylobacteriosis or other enteric diseases.

Anderson and May (19) have written what is arguably the single most important text on the modeling of human disease dynamics. Yet, enteric diseases such as campylobacteriosis are not covered in this important work. The reason for this omission can be surmised from their recollection that

   Our interest in the work described in this book
   originally grew out of our attempts to understand
   the extent to which parasites--broadly defined to
   include viral, bacterial, protozoan, and fungal
   pathogens along with the more conventionally
   defined helminth parasites--regulate the numerical
   abundance or geographical distribution of non-human
   animal populations (19).

Although without doubt their contributions are now focused on animals of the two-legged variety, enteric diseases have not in general "regulated the abundance or geographical distribution" of animals, particularly humans, at least in the developed world. Even in developing countries where mortality from enteric disease is high compared with other health issues, the availability of enteric disease interventions such as oral rehydration therapy (26) and engineering solutions that provide clean water have perhaps reduced interest in developing models to improve knowledge. There might be a greater sense of urgency in understanding the campylobacteriosis disease ecology if relatively simple interventions were known to be available and morbidity and mortality more severe. The modeling of human disease ecology has instead focused on the person-to-person spread of diseases between susceptible and infective hosts using mathematical devices such as the basic reproductive rate, [R.sub.0], for diseases with more dire outcomes (19).

[R.sub.0] is the principal mechanism used to model the number of secondary host infections that are produced from one primary infection. This is a particularly useful approach to take when the mechanism(s) of spread and environmental pathways of the pathogen are well understood, as is the case in person-to-person spread and some vector-borne diseases. However, it poses a problem when the environmental pathways of the pathogen are only partially understood, as is the case with most enteric disease (27). Modeling of enteric disease has consequently been limited to those enteropathogens whose primary mechanism of spread is person to person rather than those where animal reservoirs are the primary source of pathogens (26-29). Indeed, we have been unable to find any evidence of an attempt to model campylobacteriosis or similar enteropathogen zoonoses.

The way forward may be to construct a model disease ecology from the perspective of pathogens trying to survive until they find their next host, rather than the more traditional host-centric models typified in population dynamics-based modeling. Modeling pathogen survival as pathogen survival trajectories with various ecologic filtering processes (e.g., cooking food), intermediate environmental vectors (e.g., flies or food preparation surfaces), and associated constraints to survival (e.g., environmental temperature) constitutes an eco-environmental approach. This might also be termed a "Lagrangian" model that follows the flow of a particular group of pathogens through the environment, whereas traditional models calculate fluxes at fixed points within the model (e.g., time or space). The advantage of this Lagrangian approach is that it directly focuses on the unknowns of greatest interest (i.e., the survivability ,Of pathogens as they move through the environment to their next host).

The Ecology of Campylobacteriosis

Although campylobacteriosis is the most common known cause of gastrointestinal illness, our understanding of its ecology is incomplete. Overall, what we do know is that the Campylobacter spp. pathogens replicate almost exclusively within the intestinal tract of warm-blooded animal hosts (20), within a narrow temperature range of approximately 32-45[degrees]C (21). Pathogen dispersal from its animal host is through the excretion of feces or the contamination of an animal's carcass by the intestinal contents during slaughter. It is this contamination of meat products that is believed to be the major source of campylobacteriosis in the human population (30). This belief is supported by case-control studies that have identified the consumption of untreated water, unpasteurized milk, and certain meats, often poultry, with the additional risk of campylobacteriosis (31-35).

Even if we accept that the major mechanism for the spread of campylobacteriosis is through food contamination, our understanding is incomplete. What is it about animal management practices and food processing systems that allow this pathogen to be so widely spread? Food preparation practices, which are the final safety check, do not appear to effectively remove Campylobacter pathogens. Why? The seasonal oscillation in human disease incidence is one of the most remarkable features of campylobacteriosis that we observe through human disease surveillance systems (Figure 1). This seasonal pattern of raised summer incidence appears in all countries where human campylobacteriosis is under surveillance (36).

[FIGURE 1 OMITTED]

There are at least two possible foodborne explanations for the observed seasonal oscillation in incidence. The first explanation is that during the warmer summer months, human exposure to the pathogens increases through outdoor grilling. It is thought that this might reduce the likelihood of thorough cooking and also increase the possibility of cross-contamination on relatively crowded barbecue grills (35,37). A second explanation is seasonality in the number of animals with campylobacteriosis, which in turn drives the seasonality of human disease incidence (38). However, there is contradictory evidence indicating that in some animal industries, carcasses may be contaminated throughout the year (38). Additionally, the seasonal increase in human cases has been acknowledged to precede seasonal rises in animal infections in some instances (38).

Non-food-related explanations for the seasonality of campylobacteriosis include the survival and consequent prevalence of Campylobacter in environmental reservoirs (39,40) and the seasonal effectiveness of the human immune system response (41). For a pathogen that is so difficult to culture in a laboratory, Campylobacter have been shown to have a rather remarkable capacity for survival in aquatic environments (42). Indeed, it appears that greater numbers of pathogens are found in aquatic environments during winter and spring periods because of the relatively lower water temperatures in winter (43). A potential relationship can be hypothesized between the seasonal accumulation of pathogens in the environment and the eventual rise in human disease during the following summer season. However, this is speculative, as we know of no direct studies of this potential relationship, and there are additional confounders such as the potential for increased human exposure through summer recreational contact with water.

The human immune system is another variable with a seasonal oscillation, and is impaired with greater exposure to ultraviolet (UV) radiation (41). This establishes another possible explanation that variation in immune response supports a component of the seasonality in human disease incidence, where the annual summer peak in notifications coincides with the population's greatest exposure to UV radiation. The potential credibility for this mechanism has been established through the study of the role of the immune system in responding to campylobacteriosis and other enteric pathogens in a group of captive primates (41).

The viable but nonculturable (VNC) form of the Campylobacter pathogen has presented a major hurdle in developing an understanding of the disease ecology by limiting our ability to detect, classify, and quantify the Campylobacter pathogens in the environment. The literature on this topic of microbiologic techniques development is vast, and it is not reviewed here. Nonetheless, models will need to capture or account for our lack of knowledge stemming in part from our inability to see Campylobacter in the environment. The physiologic role of the VNC form of Campylobacter is not clear, but its ability to use this form to survive in cold water has been demonstrated (44). Furthermore, there is some evidence that after surviving for considerable periods of time in aquatic environments, the passage of VNC Campylobacter through an animal host will restore it to its culturable form (45).

The dose-response relationship may also be an important component of the disease ecology of campylobacteriosis and may differ for various types and strains of the Campylobacter pathogen (46). The dose-response relationship for the number of pathogens required to cause disease is less certain than that required to cause infection. In one study, the greatest dose studied (1 x [10.sup.8] pathogens) produced no disease in five nonimmune human subjects (46,47). A beta-Poisson model of the challenge studies of Black et al. (46) has been used to define a dose-response relationship for humans (48) and to assess the risk of consuming mussels (49). Although only a few challenge studies of human campylobacteriosis have been conducted, these studies do provide a starting point from which to base a modeling approach.

Although it is thought that many or even most cases of campylobacteriosis arise through the consumption of contaminated food and water, it is less clear how the pathogens move through the environment. The majority of campylobacteriosis cases appear sporadically and not in outbreaks, but there is an acknowledged occurrence of common-source milk (50,51) and waterborne disease outbreaks (52-54), which does nothing to clarify the disease ecology. The one mechanism of spread that can be modeled using established techniques is person-to-person spread, but this mechanism has not been widely implicated in the literature (55). Despite the many studies already published, uncertainty dominates our understanding of the ecology of campylobacteriosis, and this justifies an evaluation of the potential for applying dynamic modeling.

Pathogen Survival Trajectories: An Eco-Environmental Modeling Approach

An eco-environmental model for campylobacteriosis must focus on the environmental transmission pathways of the Campylobacter pathogen (Figure 2). This approach describes the ecology of campylobacteriosis from the perspective of a pathogen's survival as it moves through the environment--a survival trajectory. We use the pathogen's survival in the environment as the mechanism to quantify human exposure. Our lack of knowledge about pathogen survival hinders our ability to understand the ecologic dynamics and potential public health intervention points.

[FIGURE 2 OMITTED]

The Campylobacter pathogens replicate primarily within the intestinal tract of warm-blooded animals. Therefore, it is the postexcretion of pathogens that defines the ecology of campylobacteriosis, and all survival trajectories begin with pathogens being dispersed from animals through feces (Figure 2). Feces then serve as the primary environmental dispersal mechanism (Figure 2), including animals slaughtered for food processing, where the carcass is often contaminated by their Campylobacter-laden intestines. Secondary mechanisms of dispersion may occur, and all successful survival trajectories end with the new exposures (Figure 2).

There are three general nonoccupational pathogen survival trajectories: a) direct exposure to feces; b) exposure through food consumption, food processing, or food preparation activities; or c) exposure through aquatic environments (Table 1). These mechanisms, which all describe oral routes for pathogen exposure, have associated ecologic filters that provide natural public health intervention points. In turn, the pathogen burden with which each of these filters must cope depends upon environmental factors that control the size of the pathogen load that arrives at a filter point (e.g., rainfall, stocking density, speed and magnitude of runoff, proximity to water body). Grouping pathogen survival trajectories according to common environmental elements (Table 1) facilitates comparative ecologic risk assessments of pathogen survival and human exposure routes (Figure 2). It is worth noting that the above ordering of ecosystem components is in order of increasing risk, identified through case-control studies. It also appears, at least at face value, to be in increasing order of the complexity of the pathogen survival trajectory. In other words, what might appear to be the best survival prospect for Campylobacter pathogen, from epidemiologic studies, appears to be the least survivable. This is a consequence of the relatively greater number of ecologic filters a pathogen would need to traverse to find a new host and replenish its numbers.

From an ecologic perspective, direct exposure to feces or indirect exposure to feces through human-to-human contact or animal-to-human contact would appear to be the most likely survival trajectory (Figure 2). Feces, as the direct excretion of materials from the intestines of warm-blooded animals, must be the environments of greatest Campylobacter concentration outside their animal hosts. From feces through the exposure of the next animal host, Campylobacter numbers must decrease. Their survival depends only on reaching their next host before their number declines below what is required for an infective dose.

Feces of warm-blooded animals must therefore represent the environmental site of greatest hazard. Animal hosts are many and varied, including birds and wild, domestic, and domesticated animals of all shapes and sizes as well as humans [Table 2 (55-82)]. There does not seem to be a shortage of Campylobacter-contaminated feces in the environment. From a modeling perspective, we might well consider the supply of pathogens from feces to be infinite. Why is it, then, that epidemiologic studies and disease surveillance information do not suggest that either direct feces contact or indirect (human-to-human or animal-to-human) feces contact to be a major component of the disease ecology?

There may be at least two reasons why neither direct nor indirect contacts with feces are the major pathogen survival trajectories of Campylobacter. First, most human contact with animal feces is probably of an occupational nature in the agricultural and food processing sectors. Therefore, the number of humans exposed in this way is relatively small in developed countries, and there is some evidence that persons exposed in an occupational setting build up an immunity to these enteric pathogens (56). Second, there is evidence that the ecologic filter represented by personal hygiene is very effective (83). Even in situations where people are very exposed, such as changing babies's diapers, washing one's hands is likely to remove virtually all pathogens. Where hygiene is poor, as is expected of the very young (under 5 years of age), our disease surveillance information has shown an increased incidence of disease (1).

In terms of modeling human exposure to Campylobacter through direct and/or indirect contact with feces and to simulate the pathogen survival trajectories, we need to know the following:

* How many Campylobacter pathogens are excreted with feces?

* How long do Campylobacter survive in feces?

* How many can be transferred onto fingers, skin, fur, or other relevant intermediate environmental vectors?

* How long does at least an infective dose survive on intermediate environmental vectors?

* How often does an infective dose makes it to an oral exposure?

These questions reduce to the following eco-environmental modeling problems: a) pathogen survival in feces; b) pathogen survival on human hands, fingers, and other intermediate environmental vectors; and c) the ecologic filter of personal hygiene (especially important in occupational settings, diaper changing, and among children under 5 years of age).

On the face of it, the pathogen survival trajectories through aquatic environments are more complex and more uncertain than trajectories of direct exposure to feces or through human-to-animal, animal-to-human, human-to-human, or animal-to-animal contact. However, observational studies suggest that waterborne outbreaks are a more important component of the disease ecology than human-to-human spread. We are as yet unable to account for this apparent contradiction, but environmental, behavioral, and possibly immunologic factors (of which the latter is likely to be important in occupational settings of repeated exposure) are the mechanisms which the modeling will explore. However, although less complex trajectories might be more capable of delivering more hazardous exposures to Campylobacter, the risk of any particular trajectory must also incorporate the number of possible exposures, which is likely to be much greater with respect to food and water-exposure mechanisms.

There are three major issues: a) complexity of the survival trajectories in terms of the environmental constraints and ecologic filters; b) uncertainty in terms of the VNC state of Campylobacter, and c) the variability among strains of Campylobacter. Many studies have found Campylobacter in aquatic environments; in fact, its recovery is common and widespread [Table 3 (38,39,83-104)]. Sewage treatment (86-91) and water-treatment processes (84,85) are less than perfect ecologic filters. Animal access to drinking water catchments and proximity to rivers would seem to be an important factor in determining exposure through aquatic ecosystems. The role of birds in the survival trajectory of the pathogens may be especially important 5n the longer-distance movement of Campylobacter (Table 2).

The VNC state of Campylobacter, also referred to as a coccoid form, introduces additional uncertainty into pathogen survival trajectories through aquatic environments. Uncertainty arises in the interpretation of many observational studies of the disease ecology by increasing the number of false-negative analyses where Campylobacter exists but is not found because it is in the VNC state. Additionally, infectivity after recovery of Campylobacter in the intestinal tract of animal hosts appears possible, but is not well documented (105). Changes in virulence after its recovery may also be possible (45), and the modification of its genotypic structure may be obfuscating attempts to observe flows in the environment (106).

Drinking water contaminated with Campylobacter pathogens appears to be an efficient exposure mechanism. The degree to which water is regularly contaminated appears to be the largest unknown, but from disease surveillance, small waterborne outbreaks appear to be common. The identification of the ratio of identified to unidentified outbreaks might be a key piece of epidemiologic information that could help resolve the relative proportion of the population regularly being exposed to contaminated water. In terms of modeling the pathogen survival trajectories through aquatic environments, we need to know a) how many Campylobacter pathogens are entering aquatic environments; b) how long they survive in various aquatic environments; c) how effective sewage treatment and drinking-water treatment are at removing pathogens from contaminated water; and d) how often an infective dose makes it to an oral exposure?

Consequently, there are three primary modeling problems: a) survival during the period required to get into an aquatic environment, b) survival during the residence time in an aquatic environment, and c) the ecologic filters concerning sewage treatment, water treatment, and human behaviors in the use of water. All of the modeling issues associated with the survival of Campylobacter in water appear to indicate that water temperature may be the primary determinant of pathogen longevity [Table 4 (21,42,43,98,99,105-111)].

Food preparation and consumption of certain meats, particularly chicken, are identified through observational studies as the greatest risk of campylobacteriosis, despite the number of ecologic filters between source and exposure (Figure 2). From a perspective of pathogen survival, food preparation is the final ecologic filter before human exposure. It appears that food derived from poultry, cattle, and sheep are regularly, if not perpetually, contaminated with Campylobacter (Table 2). Animal management and the subsequent slaughtering and food processing represent ecologic filters, but they do not appear to be entirely effective in removing Campylobacter from the environment (Table 3). However, these filters may be important both in reducing the pathogen load and in providing identifiable break points for future interventions.

In terms of modeling pathogen survival trajectories through food processing, preparation, and consumption exposures, we need to know a) how many Campylobacter pathogens are transferred onto foodstuffs destined for human consumption; b) how long they survive on food and food preparation surfaces; c) how many pathogens can be transferred between these intermediate environmental vectors (e.g., kitchen implements); d) how many are transferred between foodstuffs in the process of cross-contamination; e) how effective the food-related ecologic filters of food processing and food preparation are; and f) how often an infective dose makes it to an oral exposure.

These considerations reduce to the following modeling problems: a) survival on food surfaces, b) survival during the transfer between intermediate environmental vectors, and c) the ecologic filters of food processing, food preparation, and personal and kitchen hygiene.

Discussion

An examination of the literature dealing with the environmental constraints of pathogen survival does not clearly indicate how the Campylobacter pathogens are moving through the environment, but only ample evidence of its occurrence. The examination of animals, aquatic environments, and food-related surfaces indicates that there is an abundance of animal reservoirs (Table 2) and a variety of survival trajectories through the environment (Table 3) with rather common environmental survival parameters (Table 4). The ecologic model for campylobacteriosis we propose in this paper (Figure 2, Table 1) is not intended to predict how Campylobacter moves through the environment, but will provide a tool to study the dynamics of this system.

The proposed eco-environmental modeling will attempt to do three things. First, it will assist in bringing together the existing information on the survival of Campylobacter in the environment. In this paper, we have labeled the organizing principle "pathogen survival trajectories." This Lagrangian approach seeks to define the relative survival potential for pathogens moving through the environment. Two key aspects define the movement of pathogens outside their animal hosts: a continuous decay in population numbers and movement of a proportion of the population into the VNC state, and passage through ecologic filters, which further reduces the pathogen population size. Although the first component is shaped by environmental factors such as temperature and nutrient availability, the ecologic filters are also defined by a number of factors, including behavioral factors such as personal hygiene or culinary practices.

Second, the proposed eco-environmental modeling provides an alternative perspective on the ecology of this disease. Three types of information shape our current understanding of human campylobacteriosis: human disease surveillance, epidemiologic case-control studies, and microbiologic investigations. Disease surveillance information has shown, for the last decade at least, a high incidence of campylobacteriosis within developed countries and large seasonal swings in the incidence of human disease. However, disease surveillance information is limited by the nature of the surveillance systems. It is generally accepted that the incidence is many times higher than we can observe and that there is no clear explanation for the seasonality. Case-control studies point to food, particularly poultry and undercooked poultry, raw water, and unpasteurized milk, to name a few, as sources of elevated risk. Case-control studies are not infallible, however. Case-control studies rely on recall ability for food and other activities and particularly on the ability of the control group to recall diarrhea events. This is problematic, particularly where controls may have had only minor symptoms or no symptoms. There have been hundreds of microbiologic studies of Campylobacter pathogens that have examined survival in a myriad of environments (Tables 2, 3, and 4) (e.g., pathogenesis, virulence, and strain typing). Despite the immense effort, there does not appear to be any overarching structure into which these new pieces of knowledge are being organized. It is difficult to see where this accumulation of knowledge is taking us. The model proposed here operates from a pathogen perspective that would be at home in the microbiologic discipline, but that also allows epidemiologic studies of human and animal disease incidence to be integrated into and evaluated against microbiologic knowledge.

Third, and perhaps most importantly, the proposed ecologic model for campylobacteriosis may provide a tool to help us better identify what we do not know and to evaluate how important these unknowns are likely to be in the context of the overall ecology. For example, our disease surveillance systems do not provide strong evidence of human-to-human spread. It is difficult to assess the effectiveness of human-to-human contact as an exposure route compared with the relatively more complex trajectories through water and food. A working mathematical model may allow us to replicate enough of the dynamics to assess whether our lack of knowledge is really important in this area. In short, it is a tool that can assist in the building and evaluation of knowledge.

Whether the proposed ecologic model provides a useful alternative perspective on the ecology of campylobacteriosis will be judged in part on the success of this perspective in fostering new insight. Consequently, the ultimate success of the proposed model, beyond whatever conceptual attractiveness the model may hold, will lie in its implementation. Modeling should work symbiotically with empiric research to help guide and evaluate the acquisition of new knowledge, as has been ably demonstrated with the effort of the climate research community to bring these two approaches together (112).

Table 1. Components of the eco-environmental model required to explore
the disease ecology of campylobacteriosis in humans exposed by means
of the three general nonoccupational pathogen survival trajectories
direct contact with feces, through food consumption and handling, and
through the consumption of water and/or contact with aquatic
environments.

                             Eco-environmental model variables

                                                Intermediate
Human exposures         Ecologic filters        environmental vectors

Direct exposure         Personal hygiene        Survival in feces
    through feces
  Direct contact with                           Survival on skin and
    human and/or                                  hair surfaces
    animal feces
  Person to person
  Animal to person
Food
  Eating contaminated   Personal hygiene        Survival on human
    meat or cross-      Kitchen hygiene           skin and hair
    contaminated        Pathogen removal via      surfaces
    foods                 effective storage     Survival on metallic,
                          and cooking             wood, and plastic
                          processes               surfaces
                        Pathogen removal        Survival on prepared
                          during meat             food
                          processing
                        Infection control
                          during animal
                          rearing
Aquatic environments    Safe recreational       Transport and
                          behaviors               deposition into
  Untreated             Reduction in non-         aquatic
    Swallowing            point source            environments
      contaminated        pollution (domestic   Survival in aquatic
      water during        grazing management)     environments
      recreation        Sewage treatment
      (unintentional)
      or drinking
      untreated water
      (intentional)
  Treated
    Drinking            Water treatment         Survival in treated
      contaminated                                water environments
      water through a
      community
      reticulation
      system

Table 2. Animal reservoirs of human Campylobacter pathogens from
intestinal samples, fresh feces, or surfaces of freshly
slaughtered carcasses.

                    Proportion of animals
Animal reservoirs   infected                        Pathogen load

Humans
  United Kingdom    Poultry workers: short-               --
                      term ([less than or
                      equal to] 1 month)
                      employees, one tested
                      positive (n = 43);
                       long-term employees,
                      seven tested positive
                      (n = 78)
                    Clearance from food
                      handlers averaged 17-19
                      days
  Chile             Children with diarrhea                --
                      16% (n = 190); healthy
                      children 6% (n = 157)

Wild animals and domesticated pets

  United States     Thirteen orders of wild               --
                      and domesticated birds
                      10% (n = 445)
                    Young dogs with diarrhea
                      infected with C. jejuni
  Norway            Urban Oslo: crows 90%                 --
                      (n = 48); gulls 50%
                      (n = 54); pigeons 4%
                      (n = 71)
                    Nonurban coastal area:
                      puffin 51% (n = 76);
                      common tern 6%
                      (n = 36); common gull
                      19% (n = 37); black-
                      headed gull 13%
                      (n = 53)
                    Migratory water fowl 35%              --
                      (n = 445)
  Japan             Crows 34% (n = 87); blue              --
                      magpies 20% (n = 10);
                      gray starlings 14%
                      (n = 35); domestic
                      pigeons 13% (n = 16);
                      bulbuls 11% (n = 36);
                       eastern turtledoves 2%
                      (n = 62)
                    Seashore crows (feces)                --
                      63% (n = 270); cemetery
                      crows 46% (n = 230)
  United States     Migratory birds: sandhill             --
                      cranes 81% (n = 91);
                      ducks 73% (n = 113);
                      Canada geese 5%
                      (n = 94)
  Sweden            Migratory passerines 3%               --
                      (n = 101)
  Portugal          Poultry 60% (n = 59);
                      swine 59% (n = 65);
                      black rats 57%
                      (n = 31); sparrows 46%
                      (n = 61); ducks 41%
                      (n = 21); cows 20%
                      (n = 32); sheep 15%
                      (n = 27)
Swine
  Norway            100% (n= 114)                         --
  Netherlands       At slaughter (SI                (4,000 cfu/g)
                      contents) 79%;                      ND
                      carcasses postslaughter
                      9%
  United States     Gilts 76% (n = 50);
                      pregnant sows 100%
                      (n = 9)
                    Newborn piglets 58%
                      (n= 73); weaned piglets
                      100% (n = 20)
  Netherlands       Pigs during fattening            4.1 log N/g
                      (feces), 85% (n = 7,
                      n= 10)
Poultry broilers
  United States     50-100%                            4-16,000
  United Kingdom    Birds 27% (n= 12,233)
  Netherlands       Flocks 82% (n = 187)
Dairy herds
  United Kingdom    Feces, 10 of 12 herds                 ND
                      positive, 10-72% of         Feces, 70 MPN/gfw
                      test cows                  (SE = 2, n = 1,080)
                    4 average size dairy
                      herds
  United States     Feces 38% (n= 2,085)                  --
  New Zealand       Feces, summer 24%                     --
                      (n = 72); autumn 31%
                      (n = 106); winter 12%
                      (n = 95)
Beef cattle
  United Kingdom    At slaughter (SI            6.1 x [10.sup.2] MPN/g
                      contents) 89% (n= 360)     (SE = 2; n = 1,080)
  United Kingdom    Calves (SI contents), ND    3.3 x [10.sup.4] MPN/g
                                                   (SE= 180, n=32)
Sheep
  Norway            Feces 8%, (n = 197)                   --
  United Kingdom    SI contents 92%; feces                --
                      30-46%
  United Kingdom    Lambs at slaughter (SI         4 log 10 MPN/gfw
                      contents) 92% (n = 360)   (n = 1,080, SD = 0.16)
                                                   to 7 log MPN/gfw
  United Kingdom    Sheep (feces) 29%                     ND
                      (n = 420)

                    Proportion of animals       Seasonality and other
Animal reservoirs   infected                    notable study features

Humans
  United Kingdom    Poultry workers: short-     Only one short-term
                      term ([less than or         worker had symptoms
                      equal to] 1 month)          indicating longer-
                      employees, one tested       term immunity in
                      positive (n = 43);          workers
                       long-term employees,
                      seven tested positive
                      (n = 78)
                    Clearance from food
                      handlers averaged 17-19
                      days
  Chile             Children with diarrhea      Associated analysis of
                      16% (n = 190); healthy      domestic pets: dogs
                      children 6% (n = 157)       43% (n = 214); hens
                                                  67% (n = 150); ducks
                                                  73% (n = 100);
                                                  pigeons 11%
                                                  (n = 104)

Wild animals and domesticated pets

  United States     Thirteen orders of wild
                      and domesticated birds
                      10% (n = 445)
                    Young dogs with diarrhea
                      infected with C. jejuni
  Norway            Urban Oslo: crows 90%       11 of 40 species
                      (n = 48); gulls 50%         tested positive for
                      (n = 54); pigeons 4%        C. fetus spp. jejuni
                      (n = 71)
                    Nonurban coastal area:
                      puffin 51% (n = 76);
                      common tern 6%
                      (n = 36); common gull
                      19% (n = 37); black-
                      headed gull 13%
                      (n = 53)
                    Migratory water fowl 35%    Another study found no
                      (n = 445)                   evidence of C.
                                                  jejuni in lakewater
                                                  inhabited by over
                                                  600,000 water fowl
  Japan             Crows 34% (n = 87); blue    25% of gut contents of
                      magpies 20% (n = 10);       crows was human
                      gray starlings 14%          refuse
                      (n = 35); domestic
                      pigeons 13% (n = 16);
                      bulbuls 11% (n = 36);
                       eastern turtledoves 2%
                      (n = 62)
                    Seashore crows (feces)      Considerable monthly
                      63% (n = 270); cemetery     variation in
                      crows 46% (n = 230)         isolation rates was
                                                  found
  United States     Migratory birds: sandhill
                      cranes 81% (n = 91);
                      ducks 73% (n = 113);
                      Canada geese 5%
                      (n = 94)
  Sweden            Migratory passerines 3%     C. jejuni found in
                      (n = 101)                     three birds with
                                                    low antibiotic
                                                    resistance
  Portugal          Poultry 60% (n = 59);         Also looked at
                      swine 59% (n = 65);           antimicrobial
                      black rats 57%                resistance
                      (n = 31); sparrows 46%
                      (n = 61); ducks 41%
                      (n = 21); cows 20%
                      (n = 32); sheep 15%
                      (n = 27)
Swine
  Norway            100% (n= 114)               More than 1,200 wild
                                                  and domestic mammals
                                                  were surveyed
  Netherlands       At slaughter (SI            Dry cool conditions
                      contents) 79%;              thought responsible
                      carcasses postslaughter     for rapid Campy-
                      9%                          lobacter die-off,
                                                  while Salmonella
                                                  persisted
  United States     Gilts 76% (n = 50);         76% C. jejuni, 21% C.
                      pregnant sows 100%          coli, and 3% C. lari
                      (n = 9)
                    Newborn piglets 58%         87% C. jejuni, 13% C.
                      (n= 73); weaned piglets     coli
                      100% (n = 20)
  Netherlands       Pigs during fattening       ND
                      (feces), 85% (n = 7,
                      n= 10)
Poultry broilers
  United States     50-100%                     Spring has lowest
                                                  flock positivity
  United Kingdom    Birds 27% (n= 12,233)       In 5-year study, 36%
                                                  of 251 shed flocks
                                                  infected, but only
                                                  9% of shed flocks
                                                  infected between
                                                  successive flocks
  Netherlands       Flocks 82% (n = 187)        Flock positivity was
                                                  seasonal, with 100%
                                                  in June-September
                                                  and 50% in March
Dairy herds
  United Kingdom    Feces, 10 of 12 herds       ND
                      positive, 10-72% of       Spring and autumn
                      test cows                   peaks, "evidence of
                    4 average size dairy          true seasonality"
                      herds
  United States     Feces 38% (n= 2,085)        Extensive study of
                                                  farm practice
                                                  including whether
                                                  herds consumed
                                                  chlorinated water,
                                                  which appears to
                                                  have no predictive
                                                  effects
  New Zealand       Feces, summer 24%           Rectal swabs taken in
                      (n = 72); autumn 31%        summer, autumn, and
                      (n = 106); winter 12%       winter, with a
                      (n = 95)                    fairly even split
                                                  between C. jenjuni
                                                  and C. coli
Beef cattle
  United Kingdom    At slaughter (SI            No significant
                      contents) 89% (n= 360)      seasonal periodicity
  United Kingdom    Calves (SI contents), ND    ND
Sheep
  Norway            Feces 8%, (n = 197)         More than 1,200 wild
                                                  and domestic mammals
                                                  were surveyed in
                                                  this study
  United Kingdom    SI contents 92%; feces      Survived in feces for
                      30-46%                      up to 4 days;
                                                  shedding is
                                                  seasonal, peaking
                                                  with lambing,
                                                  weaning, and
                                                  movements to new
                                                  pasture
  United Kingdom    Lambs at slaughter (SI      Seasonality evident;
                      contents) 92% (n = 360)     time series not
                                                  indicative of annual
                                                  peaks
  United Kingdom    Sheep (feces) 29%           NF
                      (n = 420)

                    Proportion of animals
Animal reservoirs   infected                    References

Humans
  United Kingdom    Poultry workers: short-        (56)
                      term ([less than or
                      equal to] 1 month)
                      employees, one tested
                      positive (n = 43);
                       long-term employees,
                      seven tested positive
                      (n = 78)
                    Clearance from food            (55)
                      handlers averaged 17-19
                      days
  Chile             Children with diarrhea         (57)
                      16% (n = 190); healthy
                      children 6% (n = 157)

Wild animals and domesticated pets

  United States     Thirteen orders of wild        (58)
                      and domesticated birds
                      10% (n = 445)
                    Young dogs with diarrhea     (59,60)
                      infected with C. jejuni
  Norway            Urban Oslo: crows 90%          (61)
                      (n = 48); gulls 50%
                      (n = 54); pigeons 4%
                      (n = 71)
                    Nonurban coastal area:
                      puffin 51% (n = 76);
                      common tern 6%
                      (n = 36); common gull
                      19% (n = 37); black-
                      headed gull 13%
                      (n = 53)
                    Migratory water fowl 35%       (62)
                      (n = 445)
  Japan             Crows 34% (n = 87); blue       (63)
                      magpies 20% (n = 10);
                      gray starlings 14%
                      (n = 35); domestic
                      pigeons 13% (n = 16);
                      bulbuls 11% (n = 36);
                       eastern turtledoves 2%
                      (n = 62)
                    Seashore crows (feces)         (64)
                      63% (n = 270); cemetery
                      crows 46% (n = 230)
  United States     Migratory birds: sandhill      (65)
                      cranes 81% (n = 91);
                      ducks 73% (n = 113);
                      Canada geese 5%
                      (n = 94)
  Sweden            Migratory passerines 3%        (66)
                      (n = 101)
  Portugal          Poultry 60% (n = 59);          (67)
                      swine 59% (n = 65);
                      black rats 57%
                      (n = 31); sparrows 46%
                      (n = 61); ducks 41%
                      (n = 21); cows 20%
                      (n = 32); sheep 15%
                      (n = 27)
Swine
  Norway            100% (n= 114)                  (68)
  Netherlands       At slaughter (SI               (69)
                      contents) 79%;
                      carcasses postslaughter
                      9%
  United States     Gilts 76% (n = 50);            (70)
                      pregnant sows 100%
                      (n = 9)
                    Newborn piglets 58%
                      (n= 73); weaned piglets
                      100% (n = 20)
  Netherlands       Pigs during fattening        (71-73)
                      (feces), 85% (n = 7,
                      n= 10)
Poultry broilers
  United States     50-100%                        (74)

  United Kingdom    Birds 27% (n= 12,233)          (75)
  Netherlands       Flocks 82% (n = 187)           (75)
Dairy herds
  United Kingdom    Feces, 10 of 12 herds          (77)
                      positive, 10-72% of          (78)
                      test cows
                    4 average size dairy
                      herds
  United States     Feces 38% (n= 2,085)           (79)
  New Zealand       Feces, summer 24%              (80)
                      (n = 72); autumn 31%
                      (n = 106); winter 12%
                      (n = 95)
Beef cattle
  United Kingdom    At slaughter (SI               (78)
                      contents) 89% (n= 360)
  United Kingdom    Calves (SI contents), ND       (78)
Sheep
  Norway            Feces 8%, (n = 197)            (68)
  United Kingdom    SI contents 92%; feces         (81)
                      30-46%
  United Kingdom    Lambs at slaughter (SI         (82)
                      contents) 92% (n = 360)
  United Kingdom    Sheep (feces) 29%              (82)
                      (n = 420)

Abbreviations: cfu, colony-forming units; gfw, gram fresh weight;
MPN, most probable number; ND, not done; NF, significant association
not found; SI, small intestine.

Table 3. Survival of Campylobacter pathogens in aquatic environments
and on intermediate environmental vectors outside of animal hosts.

                        Positive
                        environmental samples   Seasonality and other
Environmental setting   and pathogen load       notable features

Finished drinking
    water
    environments
  New Zealand           29% (n = 24), median
                          MPN < 0.07 100/mL
  Greece                1% (n = 500)            Occurrence signifi-
                                                  cantly higher when
                                                  coliform bacteria
                                                  present, but no
                                                  difference in
                                                  frequency of
                                                  occurrence between
                                                  chlorinated and
                                                  nonchlorinated
                                                  water
Wastewater
    environments
  Netherlands                                   Review
  United Kingdom        2-50 x [10.sup.2]/
                          100 mL
                        2.2 x [10.sup.3]/100    May-June peak, sewage
                          mL to 5.1 x             effluent from
                          [10.sup.4]/100 mL       abattoir and animal
                                                  processing plants
                        46% of pond samples     Positive samples
                          and 45% of all          year-round; E. coil
                          drain samples were      not indicative of
                          positive                Campylobacter
                                                  presence
  Netherlands           80-1,600 MPN/100 mL     Minimum June-August
  Italy                 630-3,200/100 mL        Maximum May-July
Aquatic environments
  Greece                16% (n = 86), <         Contamination not
                          10/100 mL               predicted by the
                                                  standard indicator
                                                  bacteria
  New Zealand           Rivers 60% (n = 30),
                          median MPN 0.18/100
                          mL
                        Shallow ground water
                          75% (n = 18),
                          median MPN 0.12/100
                          mL
                        Roof water 38%
                          (n = 24), median
                          MPN < 0.06/mL
  United States,        Sampled a number of     Recovery rates
    Washington State      mountain streams        highest in fall/
                          and lakes               winter, lowest in
                                                  spring and summer
  United Kingdom        22% (n = 49), 10-230    Coastal and estuary
                          MPN/100 mL              samples
                        16% (n= 44), 10-36/     River samples
                          100 mL
                        Filtration method 43%   Autumn and winter
                          (n = 312); 21% by       peaks; greatest MPN
                          MPN (< 10               downstream of
                          Campylobacter/          sewage outfalls;
                          100 mL)                 rural and urban
                                                  samples
                        Groundwater spring      Not isolated in the
                          contamination,          absence of fecal
                          isolated from           indicators
                          550-mL and 100-mL
                          filter enrichment
  United Kingdom,                               Higher numbers in
    river, canal,                                 winter; lower or
    estuary                                       none in May, June,
                                                  and July; negative
                                                  correlation with
                                                  infection incidence
                                                  in community
  United Kingdom,       Seasonal variation in
    Morecombe Bay         C. jejuni, C. coli,
                          UPTC, C. lari, with
                          higher numbers
                          found in winter
Human skin and hair
    surfaces
  Fingertips            Suspensions of          Organisms removed by
                          [10.sup.6]-             hand washing with
                          [10.sup.7] C.           either soap and
                          jejuni dried on         water or just water
                          fingertips for 1-4      and drying hands on
                          min                     paper towels
Food surfaces
  United Kingdom        Grown at 37[degrees]C
                          on high-pH meat
                          (pH 6.4), but not
                          on normal pH meat
                          (pH 5.8);
                          population decay
                          rates same for both
                          pHs; very slow
                          decay rate at 1
                          [degrees]C for
                          high-pH meat
                        Kitchen meat samples    83% chicken samples
                          73% (n = 489);          positive; all meats
                                                  had some positives;
                                                  C. jejuni, 57 sero/
                                                  phage types
                        Chicken giblets 41%,    Multiple visits to
                          thawed chicken          four large
                          juices 22%, fresh       commercial kitchens
                          chickens 88%          Internal and external
                          (n = 34)                swabs of various
                                                  meats
Insect vectors
  House flies           Sample of 32 house
                          flies allowed to
                          ingest C. jejuni,
                          20% recovery from
                          feet and ventral
                          surface, 70%
                          recovery from
                          viscera
  House flies           Chicken farm 51%        Authors suggest that
                          (n = ?)                 flies may be an
                        Piggery 43% (n = ?)       important vector
                                                  between animals
Other
  Beach sand            45% (n = 182) and >     Presence greater in
                          30% dry sand            wet sand, but still
                          samples also            30% of dry sand
                          contaminated            samples positive
  Beach sand            Sediment samples
                          showed no
                          seasonality, unlike
                          water samples taken
                          at the same time;
                          sediment samples
                          had greater numbers
                          of Campylobacter
                          than overlying
                          water samples

                        Positive
                        environmental samples
Environmental setting   and pathogen load       Reference

Finished drinking
    water
    environments
  New Zealand           29% (n = 24), median      (84)
                          MPN < 0.07 100/mL
  Greece                1% (n = 500)              (85)
Wastewater
    environments
  Netherlands                                     (86)
  United Kingdom        2-50 x [10.sup.2]/        (87)
                          100 mL
                        2.2 x [10.sup.3]/100      (88)
                          mL to 5.1 x
                          [10.sup.4]/100 mL
                        46% of pond samples       (89)
                          and 45% of all
                          drain samples were
                          positive
  Netherlands           80-1,600 MPN/100 mL       (90)
  Italy                 630-3,200/100 mL          (91)
Aquatic environments
  Greece                16% (n = 86), <           (92)
                          10/100 mL
  New Zealand           Rivers 60% (n = 30),      (84)
                          median MPN 0.18/100
                          mL
                        Shallow ground water
                          75% (n = 18),
                          median MPN 0.12/100
                          mL
                        Roof water 38%
                          (n = 24), median
                          MPN < 0.06/mL
  United States,        Sampled a number of       (93)
    Washington State      mountain streams
                          and lakes
  United Kingdom        22% (n = 49), 10-230      (94)
                          MPN/100 mL
                        16% (n= 44), 10-36/
                          100 mL
                        Filtration method 43%     (39)
                          (n = 312); 21% by
                          MPN (< 10
                          Campylobacter/
                          100 mL)
                        Groundwater spring        (95)
                          contamination,
                          isolated from
                          550-mL and 100-mL
                          filter enrichment
  United Kingdom,                                 (38)
    river, canal,
    estuary
  United Kingdom,       Seasonal variation in    (96-98)
    Morecombe Bay         C. jejuni, C. coli,
                          UPTC, C. lari, with
                          higher numbers
                          found in winter
Human skin and hair
    surfaces
  Fingertips            Suspensions of            (83)
                          [10.sup.6]-
                          [10.sup.7] C.
                          jejuni dried on
                          fingertips for 1-4
                          min
Food surfaces
  United Kingdom        Grown at 37[degrees]C     (99)
                          on high-pH meat
                          (pH 6.4), but not
                          on normal pH meat
                          (pH 5.8);
                          population decay
                          rates same for both
                          pHs; very slow
                          decay rate at 1
                          [degrees]C for
                          high-pH meat
                        Kitchen meat samples      (100)
                          73% (n = 489);
                        Chicken giblets 41%,      (101)
                          thawed chicken
                          juices 22%, fresh
                          chickens 88%
                          (n = 34)
Insect vectors
  House flies           Sample of 32 house        (102)
                          flies allowed to
                          ingest C. jejuni,
                          20% recovery from
                          feet and ventral
                          surface, 70%
                          recovery from
                          viscera
  House flies           Chicken farm 51%          (103)
                          (n = ?)
                        Piggery 43% (n = ?)
Other
  Beach sand            45% (n = 182) and >       (104)
                          30% dry sand
                          samples also
                          contaminated
  Beach sand            Sediment samples          (96)
                          showed no
                          seasonality, unlike
                          water samples taken
                          at the same time;
                          sediment samples
                          had greater numbers
                          of Campylobacter
                          than overlying
                          water samples

Abbreviations: ?, unknown; MPN, most probable number; UPTC,
urease-positive thermophilic campylobacters.

Table 4. Environment factors controlling survival of Campylobacter
pathogens outside of the intestinal tract of host animals.

                         Environmental
Factor                   temperature         Campylobacter spp. (a)

Replication conditions
  Minimum                34-36[degrees]C     C. fetus spp. jejuni, 12
                                               strains
                         31-32[degrees]C     C. jejuni, 2 strains
  Optimum                37-42[degrees]C     C. jejuni, 2 strains
  Maximum                45[degrees]C        C. jejuni, 2 strains
Survival conditions
  Review papers          Various             Various
  30-65 days             4[degrees]C         C. jejuni
  18-45 days             12[degrees]C
  4-7 days               25[degrees]C
  2-9 days               20[degrees]C and    C. coli, C. jejuni
                           30[degrees]C
  8-14 days              3[degrees]C and
                           10[degrees]C
  Viable for months      -20[degrees]C and
                           -24[degrees]C
  18-28 days and 16      4[degrees]C         C. jejuni, 4 strains
    weeks to VNC
  > 4 months,            4[degrees]C         C. jejuni
    filtered stream
    water in lab flask
  202 hr (avg)           4[degrees]C         C. jejuni, 17 strains
  176 hr (avg)           10[degrees]C
  43 hr (avg)            22[degrees]C
  22 hr (avg)            37[degrees]C
  7 months, laboratory   4[degrees]C         C. jejuni
  12 hr to               37[degrees]C in     C. lari, C. jejuni, C.
    nonculturability       darkness            coli
  5 days to              4[degrees]C in
    nonculturability       darkness
  30-60 min to           Not temperature
    nonculturability       dependent

                         Environmental
Factor                   temperature         Comments

Replication conditions
  Minimum                34-36[degrees]C     --
                         31-32[degrees]C     --
  Optimum                37-42[degrees]C     --
  Maximum                45[degrees]C        --
Survival conditions
  Review papers          Various             --
  30-65 days             4[degrees]C         Isolated from wastewater
  18-45 days             12[degrees]C          using various culturing
                                               techniques
  4-7 days               25[degrees]C
  2-9 days               20[degrees]C and    Half-shelled and unopened
                           30[degrees]C        oysters
  8-14 days              3[degrees]C and     Survival better at 3
                           10[degrees]C        [degrees]C than at 10
                                               [degrees]C in half-
                                               shelled oysters;
                                               survival better in
                                               bottled oysters at same
                                               temperature
  Viable for months      -20[degrees]C and   Frozen half-shelled
                           -24[degrees]C
  18-28 days and 16      4[degrees]C         VNC recovered for two
    weeks to VNC                               strains using mice (b)
  > 4 months,            4[degrees]C         Filtered stream water in
    filtered stream                            lab flask; shaking and
    water in lab flask                         aeration decrease
                                               survivability;
                                               increasing temperature
                                               decreases
                                               recoverability
  202 hr (avg)           4[degrees]C         Water microcosms and
                                               biofilm studies show
                                               consistency in survival
                                               across C. jejuni strains
  176 hr (avg)           10[degrees]C
  43 hr (avg)            22[degrees]C
  22 hr (avg)            37[degrees]C
  7 months, laboratory   4[degrees]C         Identified viability
                                               through respiration
  12 hr to               37[degrees]C in     C. lari and UPTCs
    nonculturability       darkness            survived longer,
                                               survival in sea water
  5 days to              4[degrees]C in      Slightly better than in
    nonculturability       darkness            river water
  30-60 min to           Not temperature     Exposed to UVB in lab
    nonculturability       dependent           simulating sunny June
                                               day

                         Environmental
Factor                   temperature         Reference

Replication conditions
  Minimum                34-36[degrees]C       (99)
                         31-32[degrees]C      (21,43)
  Optimum                37-42[degrees]C       (21)
  Maximum                45[degrees]C          (21)
Survival conditions
  Review papers          Various             (42,107)
  30-65 days             4[degrees]C           (43)
  18-45 days             12[degrees]C
  4-7 days               25[degrees]C
  2-9 days               20[degrees]C and
                           30[degrees]C
  8-14 days              3[degrees]C and
                           10[degrees]C
  Viable for months      -20[degrees]C and     (108)
                           -24[degrees]C
  18-28 days and 16      4[degrees]C           (109)
    weeks to VNC
  > 4 months,            4[degrees]C           (110)
    filtered stream
    water in lab flask
  202 hr (avg)           4[degrees]C           (111)
  176 hr (avg)           10[degrees]C
  43 hr (avg)            22[degrees]C
  22 hr (avg)            37[degrees]C
  7 months, laboratory   4[degrees]C           (105)
  12 hr to               37[degrees]C in       (98)
    nonculturability       darkness
  5 days to              4[degrees]C in        (98)
    nonculturability       darkness
  30-60 min to           Not temperature       (98)
    nonculturability       dependent

UVB, ultraviolet B radiation.
(a) Spiral forms of thermophilic Campylobacter (i.e., C. jejuni,
C. coli, C. lari, and C. upsaliensis) are gram-negative,
oxidase-positive, S-shaped morphology. (b) NC morphology occurs in
response to unfavorable environmental conditions.

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Chris Skelly (1) and Phil Weinstein (2)

(1) Public Health Intelligence, Public Health Directorate, Ministry of Health, Wellington, New Zealand; (2) Department of Public Health, Wellington School of Medicine, The University of Otago, Wellington, New Zealand

Address correspondence to C. Skelly, Public Health Directorate, Ministry of Health, Old Bank Building, CNR Lambton Quay and Willis Street, PO Box 5013, Wellington, New Zealand. Telephone: [phone]. Fax: [phone]. E-mail: chris_skelly@ moh.govt.nz

We thank R.R. Eyles for comments and suggestions provided on an earlier draft.

This research was supported in part by New Zealand Health Research Council grant HRC 98/507 1198-1099.

Received 26 October 2001; accepted 30 May 2002.