Spatial Distribution and Registry-based Case-Control Analysis of Campylobacter Infections in Denmark, 1991–2001

Steen Ethelberg, Jacob Simonsen, Peter Gerner-Smidt, Katharina E. P. Olsen and Kåre Mølbak

From the Statens Serum Institut, Copenhagen, Denmark

Correspondence to Dr. Steen Ethelberg, Department of Bacteriology, Mycology and Parasitology, Statens Serum Institut, Artillerivej 5, DK-2300 Copenhagen S, Denmark (e-mail: set{at}ssi.dk).

Received for publication January 17, 2005. Accepted for publication June 15, 2005.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Using data from an 11-year period (1991–2001), the authors analyzed available information on location of residence for all registered, laboratory-confirmed, domestically acquired cases of campylobacteriosis in Denmark. Patient data were merged with data from a national register on housing and addresses, and a population density index was constructed using the Danish population register. The study was performed as a register-based case-control study; 15 age-matched controls for each case were selected from the national population register. A total of 22,066 cases were compared with 318,958 controls in logistic regression analysis. Living in types of housing found in rural areas and living in areas with a low population density were both associated with an increased risk of infection. This relation concerned children in particular and explained one third of cases among children in the countryside. Furthermore, in some counties there was an association between infection and type of drinking-water company serving the home. This study indicated that contact with animals or the environment is the source of a substantial proportion of sporadic Campylobacter infections in the Danish countryside, particularly among children.

Campylobacter; case-control studies; registries; risk factors; water


Abbreviations: CPR, Central Person Register


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Campylobacter species are an important cause of gastroenteritis worldwide. In most developed countries, the number of infections has risen severalfold over the past decade or two (1Go, 2Go). The causes of this development are still not fully understood, but a consensus seems to have emerged that the rise in the number of infections is largely due to consumption of poultry (2Go, 3Go). However, several other risk factors have been identified through a number of case-control studies of sporadic cases. These include traveling abroad, drinking untreated water or unpasteurized milk, eating at restaurants, consuming pork, using antibiotics, barbecuing, living on a farm, and having contact with production animals and pets (2Go–13Go). Furthermore, although outbreaks of campylobacteriosis are relatively rare, routes of transmission typically include unpasteurized milk, drinking water, and various types of foods, including poultry (3Go).

In Denmark, Campylobacter is the most frequent cause of bacterial gastroenteritis, and the incidence of campylobacteriosis rose fourfold between 1991 and 2001 (14Go, 15Go). An estimated 15–30 percent of cases are imported, and most of the remaining cases are believed to result from consumption or handling of fresh chickens (14Go). Most likely other sources exist as well, but their importance is uncertain. A recent Danish case-control study of sporadic infections found consumption of fresh chicken and foreign travel to be the major risk factors (A. Wingstrand et al., Danish Institute for Food and Veterinary Research, unpublished manuscript).

Sources of Campylobacter infection have been difficult to pinpoint, for a number of reasons. In contrast to Salmonella, Campylobacter causes almost exclusively sporadic disease, and subtyping is of limited epidemiologic value. The many case-control studies of sporadic cases have provided no simple explanation for the rise in the number of infections, and conflicting results have been presented. For instance, some studies find consumption of chicken at home to be a risk factor (4Go, 5Go, 7Go), whereas others find it to be protective (8Go–10Go). Although the case-control study is a strong analytical tool, it may face difficulties if, for instance, exposure to a source is extremely common (eating chicken), requiring very large studies to reach sufficient power; if cross-contamination of foods is an important means of transmission; if a number of separate sources of infection exist; or if, as has been suggested (10Go, 13Go), varying degrees of immunity exist among controls.

For this reason, we have searched for additional means of analyzing sporadic cases of Campylobacter infection. Here we present an analysis based on the characteristics and locations of patients' homes. Using geographic information systems and a register-based case-control study, we analyzed all cases registered in Denmark over an 11-year period. We found the risk of Campylobacter infection to vary with municipality, type of water company, and urbanicity.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Registers
Three different registers were used for this study: a patient register, a population register, and a building register, as detailed below.

During the study period, from 1991 to 2002, all diagnosed cases of Campylobacter were reported to the Statens Serum Institut (Copenhagen, Denmark) and entered into the Register of Enteric Pathogens. The registration included the patient's Civil Registration System number and information on whether the infection had been acquired while traveling. Each case-patient could be registered only once within a 180-day period. All laboratory diagnoses of Campylobacter in Denmark were performed at the Statens Serum Institut or at one of 10 regional clinical microbiology laboratories, depending on which of the 16 Danish counties the requesting physician resided in. Campylobacter isolates were identified to the genus level only (16Go, 17Go), but previous results indicated that isolates comprised 94 percent Campylobacter jejuni and 6 percent Campylobacter coli (18Go).

All residents of Denmark are listed in the Civil Registration System by their Central Person Register (CPR) number, a unique 10-digit identifier that contains information on date of birth and gender (19Go). In this register, the CPR number is linked to the address identification code, a unique code given to all addresses in Denmark. Information on county and municipality can be deduced directly from the address code.

The Danish Register of Buildings and Addresses is a complete, continuously updated register of all buildings and residences in Denmark that is maintained for administrative purposes. All residential addresses are registered using the unique address code, and each entry contains up to 200 variables. Examples of the types of variables present for residential addresses include type and size of housing, number of rooms, numbers of bathrooms and kitchens, type of heating system, and type of water supply.

Geographic analysis
For administrative purposes, Denmark is divided into 16 counties and further divided into 274 municipalities. Coordinates of Danish counties and municipalities and calculated coordinates of all Danish address codes were obtained from the National Survey and Cadastre (Copenhagen, Denmark).

For construction of the population density variable, the address codes of all persons listed in the Civil Registration System (i.e., all Danes) on January 1, 2000, were merged with the geographic coordinates. A grid of 500 x 500-m cells covering the entire country was constructed. Using this grid, each case and control was positioned in a 1 x 1-km cell in such a way that the distance to the border of the cell was at least 250 m. The number of persons living in each 1-km2 cell was counted, and the cells were divided into five population density levels. The cutoff points of these levels were chosen so as to balance both population and geography. The five levels were >2,000, 1,001–2,000, 351–1,000, 26–350, and 1–25 persons per km2. They represented 40.1 percent, 22.4 percent, 17.6 percent, 14.7 percent, and 5.2 percent of the population, respectively, and 2.4 percent, 3.0 percent, 4.6 percent, 40.4 percent, and 49.7 percent of the area (i.e., inhabited 1-km squares) of the country, respectively.

Data set used
We searched for all episodes of Campylobacter infection occuring in Denmark from January 1, 1991, to June 15, 2002, that had intact CPR numbers in the Register of Enteric Pathogens (27,039 episodes). A total of 3,589 infections (13.3 percent) registered as having been acquired abroad and 65 episodes that were part of a recognized outbreak (20Go) were excluded. We linked the remaining patients to the Civil Registration System to obtain the address codes of their addresses at the time of infection. We obtained a set of control subjects from the Civil Registration System by randomly choosing 15 controls for each case, individually matched on week of birth, gender, and county of residency at the time of infection. This meant that all Danish residents, including the registered cases, were eligible to serve as controls. Cases and controls were then linked to the Danish Register of Buildings and Addresses. After subjects with missing values for geocoordinates had been discarded, the final data set contained 22,066 cases and 318,958 controls. This data set was used throughout this paper, unless otherwise stated. A second, smaller data set containing only the larger half of the municipalities, excluding that half of municipalities served by a small number of primary physicians, was also created; this was done by excluding all cases and controls living in municipalities with fewer than 10,000 residents. This data set contained 141 (51 percent) of the municipalities and 82.5 percent of the persons in the large data set. In 2000, the ratio of general practitioners to the population in Denmark was 1:1,375 (21Go).

Statistical analyses
All variables from the Danish Register of Buildings and Addresses with possible relevance for infection were considered one at a time in logistic regression analysis, and those associated with infection (p < 0.1) were examined further in multivariate analyses. All logistic regression analyses were performed using SAS, version 8.2 (SAS Institute, Cary, North Carolina). Because of the large number of levels for the municipality variable, matched analyses could not be performed (the PHREG procedure in SAS could not be used), and unmatched analyses were performed throughout using the GENMOD procedure. Comparison of the results from matched and unmatched models, where the municipality variable was not included, showed only very minor differences. All models were adjusted for the matching variables age and gender. Percent population attributable risks were calculated using Levin's formula, and 95 percent confidence intervals were determined using the delta method.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Population density
Table 1 shows the odds ratios for campylobacteriosis for each of the five levels of population density. The odds of infection were found to be increased in areas with a low population density. Strong effect modification between age group and population density was found (p < 0.001). The increased odds of infection seen in areas with a low population density was particularly pronounced for children; in fact, most of the increase in odds was generated by the younger age groups (0–14 years), as shown in table 2. Use of alternative cutoff points for the classification of population density levels produced similar results (data not shown).


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TABLE 1. Association between campylobacteriosis and population density, Denmark, 1991–2001

 

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TABLE 2. Association between campylobacteriosis and population density, by age group, Denmark, 1991–2001

 
Next, the attributable risk of living in a rural area was calculated. To simplify this, we used only two levels for the population index, based on the odds ratio differences. Table 3 shows the attributable risks for the exposed and the population attributable risks for all patients and for children only.


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TABLE 3. Association between campylobacteriosis and urbanicity* and attributable risks of disease, Denmark, 1991–2001

 
Type of housing
The variable "type of housing" from the Danish Register of Buildings and Addresses was found to be associated with disease (p < 0.0001). Table 4 shows the odds ratios for the most frequently occurring levels of this variable. The most frequent category was living in a one-family house, and it was chosen as the reference category. The table shows increasing risks of infection with the transition from housing types typical of urban settings to those typical of rural settings. This effect persisted, although it was somewhat attenuated, when results were adjusted for population density (table 4). Therefore, we included both population density and type of housing in the models described below to adjust for the rural-urban effect as completely as possible.


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TABLE 4. Association between campylobacteriosis and type of housing, Denmark, 1991–2001

 
Type of water supply
The Danish Register of Buildings and Addresses contained information on the general type of water supply but not on individual drinking-water providers. Large public water companies and privately owned water companies supplied water to 57 percent and 30 percent of the addresses, respectively. Overall, the same ratios of cases and controls used each of these two types of suppliers, but there was effect modification by municipality (p = 0.004). We found a similar result in an analysis using the data set from which the smaller half of municipalities had been excluded. In a model stratified by county, an association with disease—positive or negative—was found for three of the 14 counties for which the effect could be assessed, as shown in table 5 (the two counties in central Copenhagen were served by a single company only).


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TABLE 5. Association between campylobacteriosis and type of residential water-supply company (private vs. public), by county, Denmark, 1991–2001

 
Effect of municipality
We then examined the effect of municipality of residence and found a marked difference in the association with disease (p < 0.0001) in a model adjusted for urbanicity in addition to age and gender. Municipalities with high and low risks appeared to be randomly distributed when visualized on a map (data not shown). This analysis was repeated with only the 43 percent of patients who lived in one of the six counties where diagnostic practices were similar throughout the study period because all samples were examined in one laboratory (at the Statens Serum Institut). For this subanalysis, controls were matched on age and gender and resided in one of those six counties at the time of infection. Again, clear differences in the association of infection with municipality were seen (p < 0.0001), and again there was no apparent order in the locations of high- and low-risk municipalities (data not shown). In a third analysis of the effect of municipalities, we considered the possibility that the differences were largely an effect of variation arising from municipalities with few residents. Using the data set which contained only the larger half of the municipalities, we once again observed clear differences in the association of infection with municipality (p < 0.0001).


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
In this analysis, four factors were found to be associated with campylobacteriosis in Denmark: degree of urbanization, type of housing, type of drinking-water supply, and municipality of residence.

We constructed a five-level index of population density based on the number of people living in the 1-km2 area containing the address of each case and control. Using this index, we detected a strong association between disease and living in a scarcely populated area. Furthermore, the increase in risk in rural areas was primarily carried by children. Calculation of the attributable risks indicated that 11 percent of all registered case-patients under 15 years of age became ill because of the increased risk of living in a rural area, and that 35 percent of children and 15 percent of all patients living in areas with a population density less than 350 persons per km2 would not have become ill had they lived in a more populated area. These attributable risk calculations reflect the degree to which the true, unknown sources of infection are more abundant in rural areas than in urban areas; they do not measure the total contribution to the infection load made by such sources. If the underlying sources of infection also operate in urban areas, they will account for a larger proportion of the total number of infections.

The different types of housing among patients could be arranged in order of high risk to low risk, going from farmhouses, which are found in rural areas, to one-family houses, which may be found in both low- and moderate-population-density areas, to townhouses (terrace houses) and apartments (flats), which are typically found in suburbs and within cities. In other words, types of housing typical of a rural environment were associated with high risk, and types of housing typically found in cities were associated with low risk. Therefore, we believe that these differences in risk reflect the typical locations of these different types of housing rather than differences in their inherent risk. Type of housing and population density index were both statistically significant in a combined analysis and thus independently described the increased risk associated with rural areas.

The two major well-recognized sources of Campylobacter infection in Denmark—foreign travel and consumption of fresh chicken—did not seem to explain the increased risk of infection associated with living in the countryside. The degree to which chicken is contaminated is not believed to vary among different shops/supermarkets or different areas of the country. Likewise, the frequency of foreign travel is not known to be higher among residents of rural areas, and in addition, cases known to be associated with travel were excluded from the analyses. Several case-control studies have found an increased risk associated with consumption of chicken in restaurants as opposed to at home (8Go–10Go, 13Go). However, people in rural areas are less likely to frequent restaurants than urban people. A governmental analysis of food intake in Denmark found little variation with degree of urbanization, although urban residents consumed slightly more chicken, pork, and beef (22Go). Thus, the urban-rural gradient of risk detected here must indicate that additional sources of infection exist in the countryside as compared with the city.

Intuitively, this makes sense, as people in the countryside are more exposed to farm animals and more likely to drink unpasteurized milk or water from small suppliers or their own supply. In addition, rural residents may have more direct contact with the natural environment, including contact with surface water and with animals such as farm-raised poultry, wild birds, pets, and insects. Previous studies have provided good support for a hypothesis of increased risk of Campylobacter infection in nonurban settings. Increased incidences of Campylobacter have been noted in rural areas (23Go, 24Go); in several case-control studies, contact with farm animals (5Go, 7Go, 9Go) or pets (9Go–12Go) was among the risk factors found, and Campylobacter infection has also been associated with small-scale chicken husbandry in rural areas (25Go, 26Go). In Sweden, an ecologic study on the geography of Campylobacter cases was recently conducted (27Go). Using data aggregated on municipality, the investigators noted an association between the incidence of campylobacteriosis and both the average number of ruminants and the average length of water pipes (27Go).

The finding that children in the countryside were predominantly at risk is intriguing. One straightforward explanation is immunity acquired during childhood towards local sources of Campylobacter. Children may also be more at risk because of differences in behavior. Finally, it is possible that adults living in rural areas are less likely to consult a doctor than their urban counterparts, which would give rise to statistical bias.

Analysis of the type of water company serving the home suggested that infections are also transmitted via drinking water. The odds associated with the two major categories of water company (public and private) varied by municipality and by county—each category conferring both increased and decreased odds—after adjustment for rural/urban status. This indicates that a subset of individual companies (of either type) confers an increased risk of infection. If one or a few individual water companies (data on which were not available) within a county were associated with an increased risk of infection, this would probably be seen as a higher risk for the entire group to which these companies belonged (private or public).

Is drinking water a plausible source of Campylobacter infection? Drinking water has been the source of a number of reported outbreaks (20Go, 28Go–32Go) and has also been suggested as a significant cause of sporadic disease (3Go). A Norwegian case-control study found untreated drinking water to be an important risk factor for infection (5Go). Drinking water derived from various sources has also been implicated as a cause of sporadic infections in several other case-control studies (4Go, 6Go, 10Go, 33Go, 34Go), and even bottled water has been incriminated (35Go). However, the water supply in Denmark differs from that found in most countries, since it consists almost exclusively of untreated groundwater. For this reason, Danish drinking water is generally considered free of infectious agents. Nevertheless, because the groundwater is supplied untreated, even a small or isolated contamination event may affect a large number of people or may even lead to prolonged contamination of the water system. This was clearly illustrated in the only large-scale Campylobacter outbreak that has occurred in Denmark so far (20Go). It resulted from one-time contamination of drinking water with sewage in a small town, but the same Campylobacter strain persisted in the drinking water for a 6-week period and made an estimated 2,400 people ill (20Go). Recently, it has also been shown that Campylobacter can live and multiply within protozoa, which suggests a means by which Campylobacter may persist for longer periods in water (36Go). The Danish requirements for drinking-water quality control follow the European Union Drinking Water Directive, with periodic testing for fecal indicator bacteria. The control system is not set up to detect Campylobacter or low-dose or intermittent contamination, or contamination affecting part of the network only. Thus, although much remains to be learned about the water-Campylobacter relation in terms of both ecology and epidemiology, we regard it as likely that differences in numbers of sporadic cases are due to differences between water companies (of which there are more than 2,000 in Denmark) with regard to quality of equipment, water pipes, procedures, etc.

The method employed here has both strengths and weaknesses. The register-based case-control approach provides considerable statistical power because of the large number of study subjects, and the population-based sampling of controls means that calculated odds ratios will approximate relative risks. In addition, as opposed to an interview-based case-control study, there are no potential problems with recall bias in a register-based case-control study, and it has major advantages in terms of feasibility and cost-effectiveness. In contrast to some other studies of geographic factors, this study did not involve comparisons of groups of people and was therefore not subject to the ecologic fallacy; it relied on direct comparisons of individuals, allowing for complete adjustment for differences in age and gender. By exploring the relation between campylobacteriosis and the residential locations of cases and controls, we were able to extract circumstantial but strong evidence about the sources of infection.

The most obvious limitation of this study was the unavailability of many types of exposure data. In particular, data on food exposures were not available, and therefore we could not examine the degree to which food consumption confounded geography. Secondly, we assumed that cases became infected where they lived, although they may also have been infected at work, restaurants, the premises of family members and friends, etc. However, this bias in correct classification of the geography of exposure will have been nondifferential and thus will have worked to diminish the observed association between cases and geography. Thirdly, some travel-related cases were probably misclassified as domestic cases, because registration of travel-acquired infections is believed to be incomplete. However, this will also have been nondifferential. Both biases appear to have been partly outweighed by the high statistical power of the study, but most likely we have not measured the full strength of the urban-rural risk difference.

The analysis concerning municipalities deserves a few additional comments. The reason for studying municipalities within counties as the geographic units rather than the counties themselves was the possible existence of variations in diagnostic and reporting practices between counties, since the diagnostic systems are administrated at the county level. The odds of infection were found to vary by municipality of residence after adjustment for rural/urban status. There may be several explanations for this, including differences in risk associated with different water suppliers, since—with very few exceptions—each water company in Denmark supplies only addresses in one municipality. However, two types of biases could have affected this part of the analysis. First, differences in the tendency among physicians in small municipalities to request a stool sample from patients with gastroenteritis could have played a role. To address this problem, we performed an analysis of only the larger half of the Danish municipalities and still found the same result. However, many of the same municipalities had a high risk in a comparable analysis for Salmonella (S. Ethelberg, J. Simonsen, and K. Mølbak, unpublished data), suggesting that differences in the tendency to test patients do indeed exist among municipalities. Second, unrecognized outbreaks not caused by drinking water among the registered cases may have led to localized geographic clustering of cases. However, it is unlikely that larger outbreaks would have passed unnoticed by the Danish surveillance system, and although small, local community outbreaks could in theory have produced some of the registered cases, we regard it as unlikely that such outbreaks would have been sufficiently frequent to explain the variations in risk between municipalities. A previous study showed 3.2 percent of registered Campylobacter cases in Denmark to be part of household outbreaks (37Go), but these very small outbreaks appear to be distributed in the same way as the singular sporadic cases and are therefore unlikely to create bias.

In conclusion, this nationwide Danish study including a large number of cases and controls found that persons—particularly children—living in areas of the country with a low population density had an increased risk of sporadic campylobacteriosis. This indicates that sources of infection other than chicken consumption and travel are responsible for this excess of cases. Furthermore, the risk varied with water company and municipality of residence, which may suggest that the water supply is responsible for a portion of sporadic cases.


    ACKNOWLEDGMENTS
 
This work was supported by grant 22-02-0462 from the Danish Medical Research Council.

The authors thank Dr. Anne Wingstrand for fruitful comments on the manuscript.

Conflict of interest: none declared.


    References
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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