- Research article
- Open Access
- Open Peer Review
Geographic determinants of reported human Campylobacter infections in Scotland
© Bessell et al; licensee BioMed Central Ltd. 2010
- Received: 20 November 2009
- Accepted: 15 July 2010
- Published: 15 July 2010
Campylobacteriosis is the leading cause of bacterial gastroenteritis in most developed countries. People are exposed to infection from contaminated food and environmental sources. However, the translation of these exposures into infection in the human population remains incompletely understood. This relationship is further complicated by differences in the presentation of cases, their investigation, identification, and reporting; thus, the actual differences in risk must be considered alongside the artefactual differences.
Data on 33,967 confirmed Campylobacter infections in mainland Scotland between 2000 and 2006 (inclusive) that were spatially referenced to the postcode sector level were analysed. Risk factors including the Carstairs index of social deprivation, the easting and northing of the centroid of the postcode sector, measures of livestock density by species and population density were tested in univariate screening using a non-spatial generalised linear model. The NHS Health Board of the case was included as a random effect in this final model. Subsequently, a spatial generalised linear mixed model (GLMM) was constructed and age-stratified sensitivity analysis was conducted on this model.
The spatial GLMM included the protective effects of the Carstairs index (relative risk (RR) = 0.965, 95% Confidence intervals (CIs) = 0.959, 0.971) and population density (RR = 0.945, 95% CIs = 0.916, 0.974. Following stratification by age group, population density had a significant protective effect (RR = 0.745, 95% CIs = 0.700, 0.792) for those under 15 but not for those aged 15 and older (RR = 0.982, 95% CIs = 0.951, 1.014). Once these predictors have been taken into account three NHS Health Boards remain at significantly greater risk (Grampian, Highland and Tayside) and two at significantly lower risk (Argyll and Ayrshire and Arran).
The less deprived and children living in rural areas are at the greatest risk of being reported as a case of Campylobacter infection. However, this analysis cannot differentiate between actual risk and heterogeneities in individual reporting behaviour; nevertheless this paper has demonstrated that it is possible to explain the pattern of reported Campylobacter infections using both social and environmental predictors.
- Foreign Travel
- Gaussian Markov Random Field
- Integrate Nest Laplace Approximation
- Postcode Sector
Infection with bacteria of Campylobacter spp is the leading cause of human bacterial gastroenteritis in most developed countries . In Scotland in 2006 there were 95.3 reported cases per 100,000 , although this figure is likely to represent only one in eight cases, as has been demonstrated in England . Further studies in England and Wales show that approximately 10% of reported cases were admitted to hospital for treatment .
Infection with Campylobacter is thought to occur principally via the consumption of contaminated, under-cooked meat (mainly chicken) and cross-contaminated foods [5, 6]. However other modes of transmission include direct and indirect contact with animal faeces (especially ruminant faeces)  and consumption of contaminated water [8–10]. Human exposure to these sources is spatially heterogeneous and therefore the spatial pattern of infection is heterogeneous.
Previous studies have identified risk factors that include eating chicken, eating in restaurants and eating from fast food outlets [6, 11]. Additionally, those who live in rural areas and have regular contact with livestock are at greater risk of infection [12–15], as are individuals with private water supplies [10, 11]. Further variations in Campylobacter incidences caused by either physiology or differences in exposure relate to the age and gender of the individual. For example, male children are at around 1.5 times greater risk of infection than their female counterparts [16, 17].
In addition to heterogeneity in infection there will be heterogeneity in reporting. Infections may be under ascertained by a factor of 8 , but this may not necessarily be distributed evenly throughout the population. Reporting may be influenced by the age and gender of the patient [14, 16], use of primary health care facilities [18, 19] and the socio-economic status of the patient .
To quantify the importance of deprivation in determining Campylobacter infections given that deprivation may influence food consumption, environmental contact and propensity to seek medical attention or submit a stool sample.
To identify rural-urban differences in Campylobacter infections and whether such differences may be explained by proximity to livestock.
To identify differences in Campylobacter infections between NHS Health Board areas.
To establish whether these differences are age dependent.
Data on cases of Campylobacter infection were collected by staff at Health Protection Scotland (HPS) from the public health teams at the 12 mainland NHS Health Boards that existed in Scotland prior to 2006. Ethical approval for the collection and use of the data was obtained from the Multi-Centre Research Ethics Committee (MREC) in Scotland; approval for the research was also obtained from the Research and Development Committee in each of the NHS Health Boards. Data for the island NHS Health Boards of the Western Isles, Orkney and Shetland were not collected due to their small populations and small numbers of cases. Data were collected for the years 2000 to 2006 (inclusive), with the exception of the Ayrshire & Arran NHS Health Boards for which only the years 2003 to 2006 (inclusive) were available. Data were anonymised and included the age, gender and postcode sector of residence of the case, and the date of reporting. Subsequently all analysis was at the level of the postcode sector (median population = 5,977, 25th, and 75th percentile = 3,788 and 7,847; median area = 12.4 km2, 25th,and 75th percentile = 2.23 and 75.2 km2).
Data on the human population were collected from the 2001 Scottish census  along with data on the Carstairs index of deprivation; cattle, sheep and poultry numbers were obtained from the Scottish agricultural census  (2004 estimates). Data on recent travel was available for the Lothian and Grampian NHS Health Board areas from the Health Protection Scotland (HPS) enteric disease reporting forms.
The following risk factors were included for initial screening:
The Carstairs deprivation score .
Easting and northing of the postcode sector centroid.
Population density (people/km2) of the postcode sector (log10 transformed to linearise its relationship with the response mean on the log-scale).
Density of cattle, sheep and poultry (head/km2) in the postcode sector.
where H i represents the effect of health board i; V ij the spatially structured variation associated with being in postcode sector j in health board i and U ij the corresponding unstructured variation. X ij represents the vector of risk factors in each postcode sector in each health board. The mean, 2.5% and 97.5% quantiles of the estimated coefficients were used to calculate mean relative risks (RRs) and the 95% confidence intervals of the RRs. INLA was implemented in the INLA package  for the R statistical environment . The model fit was checked by inspecting the mean, 2.5% and 97.5% quantiles of the posterior distributions of the standard deviations (sd) of the random effects. Large, or asymmetrical 95% intervals would indicate poor model fit.
Summary statistics for NHS Boards.
NHS Health Board
Number of postcode sectors
Total number of cases
Argyll & Clyde (AC)
Ayrshire & Arran (AA)
Dumfries & Galloway (DG)
Forth Valley (FV)
Greater Glasgow (GG)
To allow for the differences in the ages of cases  and to test for the age dependent differences in the effect of rurality noted in Denmark , separate models were constructed for those aged under 15 years and those 15 and over (the cut-off at 15 was selected due to five year age groupings in the Scottish census data). The relative risk estimates for the final model including population density (irrespective of whether it a was included in the final model) were compared for those aged under 15 years, 15 and over, and all data.
Further sensitivity analysis was conducted by building models for just the Lothian and Grampian NHS Health Board areas and running the model with and without the cases that had travelled overseas in the previous 14 days. The Lothian and Grampian NHS Health Boards were selected because it was only these Boards for which overseas travel data was available. Evaluation of the relative change in the model coefficients indicates whether the model results were a result of foreign travel. The RRs for the risk factors in these models were compared.
NHS Board differences
Risk factor analysis
Univariate poisson GLM analysis of risk factors.
3.533 × 10-3
2.457 × 10-4
9.412 × 10-4
1.835 × 10-4
-6.401 × 10-4
3.854 × 10-4
-4.655 × 10-6
1.631 × 10-4
-2.460 × 10-6
3.654 × 10-6
Posterior distributions of the fitted terms in the reduced spatial GLMM for Campylobacter risk.
Mean (95% CIs)
-6.893 (-7.209, -6.582)
0.965 (0.959, 0.971)
0.945 (0.916, 0.974)
0.249 (0.213, 0.296)
0.082 (0.060, 0.116)
0.502 (0.356, 0.788)
Acquired immunity through exposure to household sources of infection at a young age amongst the more deprived. The level of exposure among younger age groups to bacterial sources of infection within the household may increase with deprivation. However, Figure 3 demonstrates that there is no significant difference in the Carstairs deprivation score in the age-stratified analysis. If acquired immunity were the explanation then the younger groups would be more commonly infected in more deprived areas whilst older age groups would be more commonly infected in less deprived areas, however, Figure 3 does not support this. These findings are supported by other studies that suggest that there is no difference between age and deprivation [18, 20].
Deprivation may be associated with differences in dietary habits ; differences in the quality of the available fresh food have been observed elsewhere . If there is greater consumption of processed rather than fresh meat among the more deprived there will be less Campylobacter because the process of freezing reduces the number of Campylobacter organisms . Furthermore, the less deprived may also eat at restaurants more frequently, which has been demonstrated as a risk factor in other studies .
Differences in environmental exposure associated with different leisure activities, differences in access to rural areas or people working in rural areas.
Differences in foreign travel. The sensitivity analysis, however, in the two NHS Health Board areas for which travel data were available indicated that this is not the case.
Further research is necessary to fully understand the processes operating, for example comparing hospitalisation rates; however, it is likely that some combination of these factors is responsible for the relationship with deprivation.
The significance of the protective effect of population density among children confirms findings from Denmark  where significantly higher case rates were found among children in rural areas. This may be the result of differences in the tolerance level that determines whether a patient reports to a doctor, which is likely to be age dependant. Alternatively, or in combination, that children in rural areas are playing outdoors and becoming exposed to environmental reservoirs of infection, and may additionally be compounded if there is poorer hygiene among younger groups.
Whilst one of the greatest sources of Campylobacter in rural areas is likely to be livestock [7, 31, 32], our analysis did not show density of livestock to be associated with Campylobacter infections. Furthermore, contamination of private water supplies [10, 11], which is associated with low population density, may be an additional source of infection. Therefore, these findings suggest that environmental exposures, whilst these may ultimately be the result of contamination from livestock sources, are best characterised by low population densities.
The model incorporated the spatial structure of the data because it can not be assumed that the data are spatially independent as neighbouring postcode sectors may have similar properties. Whilst most exposure to infection is likely to occur in the postcode sector of residence, the incorporation of the spatial structure allows for environmental exposures to infection arising from travel outside of the postcode sector of residence.
Once these predictors were taken into account, differences were noted between NHS Health Board areas, in particular, the Argyll area of the Argyll & Clyde NHS Health Board area and the Ayrshire and Arran Health Board that had RRs significantly lower than 1. This suggests that after the factors in the model have been taken into account there remains some mechanism affecting incidence or reporting of Campylobacter infection. In addition, the Ayrshire & Arran NHS Health Board area also reported less GI disease per head of population than any other NHS Health Board area in Scotland for both Salmonella and Cryptosporidium infections (unpublished data). Furthermore, several NHS Health Boards have significantly higher case incidences, in particular the Grampian, Highland and Tayside NHS Health Boards. This may be the result of some factors not included in the model or reporting differences. However, the Lothian NHS board that has the second highest case incidence is not significantly different from expected in the final model, suggesting that in this Health Board the other factors in the model explain the patterns of reporting in this district.
This study has demonstrated that there are real differences in the geographic distribution of Campylobacter infections within Scotland, which are either caused by differences in exposure to infection, or differences in individuals reporting infection. Variation due to reporting at the level of the Health Board has been accounted for in the model. Those at greatest risk are the less deprived and children in rural environments. The results suggest that the relationship with deprivation is unlikely to result from differences in acquired immunity. Furthermore, those less deprived may be more exposed to infection or may be more willing to seek medical attention. However, large differences remain in reported disease incidences between the deprived and the less deprived as well as differences in ascertainment between the boards administering the health care.
The authors are grateful to the Food Standards Agency of Scotland for funding this study. We are also grateful to Martin Hazleton and Patrick Brown for their comments on this manuscript and to Håvard Rue for their assistance in implementing the INLA package.
- Blaser MJ: Epidemiologic and clinical features of Campylobacter jejuni infections. Journal of Infectious Diseases. 1997, S103-105. 10.1086/513780. 176Google Scholar
- Locking M, Browning L, Smith-Palmer A, Brownlie S: Gastro-intestinal and foodborne infections. HPS Weekly Report. 2007, 41: 3-4.Google Scholar
- Wheeler JG, Sethi D, Cowden JM, Wall PG, Rodrigues LC, Tompkins DS, Hudson MJ, Roderick PJ: Study of infectious intestinal disease in England: rates in the community, presenting to general practice, and reported to national surveillance. The Infectious Intestinal Disease Study Executive. BMJ. 1999, 318 (7190): 1046-1050.View ArticlePubMedPubMed CentralGoogle Scholar
- Gillespie IA, O'Brien SJ, Frost JA, Adak GK, Horby P, Swan AV, Painter MJ, Neal KR: A case-case comparison of Campylobacter coli and Campylobacter jejuni infection: a tool for generating hypotheses. Emerg Infect Dis. 2002, 8 (9): 937-942.View ArticlePubMedPubMed CentralGoogle Scholar
- Altekruse SF, Stern NJ, Fields PI, Swerdlow DL: Campylobacter jejuni--an emerging foodborne pathogen. Emerg Infect Dis. 1999, 5 (1): 28-35. 10.3201/eid0501.990104.View ArticlePubMedPubMed CentralGoogle Scholar
- Gormley FJ, Macrae M, Forbes KJ, Ogden ID, Dallas JF, Strachan NJ: Has retail chicken played a role in the decline of human campylobacteriosis?. Appl Environ Microbiol. 2008, 74 (2): 383-390. 10.1128/AEM.01455-07.View ArticlePubMedGoogle Scholar
- Horrocks SM, Anderson RC, Nisbet DJ, Ricke SC: Incidence and ecology of Campylobacter jejuni and coli in animals. Anaerobe. 2009, 15 (1-2): 18-25. 10.1016/j.anaerobe.2008.09.001.View ArticlePubMedGoogle Scholar
- Sandberg M, Nygard K, Meldal H, Valle PS, Kruse H, Skjerve E: Incidence trend and risk factors for campylobacter infections in humans in Norway. BMC Public Health. 2006, 6: 179-10.1186/1471-2458-6-179.View ArticlePubMedPubMed CentralGoogle Scholar
- Savill MG, Hudson JA, Ball A, Klena JD, Scholes P, Whyte RJ, McCormick RE, Jankovic D: Enumeration of Campylobacter in New Zealand recreational and drinking waters. J Appl Microbiol. 2001, 91 (1): 38-46. 10.1046/j.1365-2672.2001.01337.x.View ArticlePubMedGoogle Scholar
- Sopwith W, Birtles A, Matthews M, Fox A, Gee S, Painter M, Regan M, Syed Q, Bolton E: Identification of potential environmentally adapted Campylobacter jejuni strain, United Kingdom. Emerg Infect Dis. 2008, 14 (11): 1769-1773. 10.3201/eid1411.071678.View ArticlePubMedPubMed CentralGoogle Scholar
- Danis K, Di Renzi M, O'Neill W, Smyth B, McKeown P, Foley B, Tohani V, Devine M: Risk factors for sporadic Campylobacter infection: an all-Ireland case-control study. Euro Surveill. 2009, 14 (7):Google Scholar
- Devane ML, Nicol C, Ball A, Klena JD, Scholes P, Hudson JA, Baker MG, Gilpin BJ, Garrett N, Savill MG: The occurrence of Campylobacter subtypes in environmental reservoirs and potential transmission routes. J Appl Microbiol. 2005, 98 (4): 980-990. 10.1111/j.1365-2672.2005.02541.x.View ArticlePubMedGoogle Scholar
- Ellis-Iversen J, Pritchard GC, Wooldridge M, Nielen M: Risk factors for Campylobacter jejuni and Campylobacter coli in young cattle on English and Welsh farms. Prev Vet Med. 2009, 88 (1): 42-48. 10.1016/j.prevetmed.2008.07.002.View ArticlePubMedGoogle Scholar
- Ethelberg S, Simonsen J, Gerner-Smidt P, Olsen KE, Molbak K: Spatial distribution and registry-based case-control analysis of Campylobacter infections in Denmark, 1991-2001. Am J Epidemiol. 2005, 162 (10): 1008-1015. 10.1093/aje/kwi316.View ArticlePubMedGoogle Scholar
- Nygard K, Andersson Y, Rottingen JA, Svensson A, Lindback J, Kistemann T, Giesecke J: Association between environmental risk factors and campylobacter infections in Sweden. Epidemiol Infect. 2004, 132 (2): 317-325. 10.1017/S0950268803001900.View ArticlePubMedPubMed CentralGoogle Scholar
- Strachan NJ, Watson RO, Novik V, Hofreuter D, Ogden ID, Galan JE: Sexual dimorphism in campylobacteriosis. Epidemiol Infect. 2008, 136 (11): 1492-1495. 10.1017/S0950268807009934.View ArticlePubMedGoogle Scholar
- Unicomb LE, Dalton CB, Gilbert GL, Becker NG, Patel MS: Age-specific risk factors for sporadic Campylobacter infection in regional Australia. Foodborne Pathog Dis. 2008, 5 (1): 79-85. 10.1089/fpd.2007.0047.View ArticlePubMedGoogle Scholar
- Simonsen J, Frisch M, Ethelberg S: Socioeconomic risk factors for bacterial gastrointestinal infections. Epidemiology. 2008, 19 (2): 282-290. 10.1097/EDE.0b013e3181633c19.View ArticlePubMedGoogle Scholar
- Olowokure B, Hawker J, Weinberg J, Gill N, Sufi F: Deprivation and hospital admission for infectious intestinal diseases. Lancet. 1999, 353 (9155): 807-808. 10.1016/S0140-6736(99)00611-X.View ArticlePubMedGoogle Scholar
- Snel SJ, Baker MG, Kamalesh V, French N, Learmonth J: A tale of two parasites: the comparative epidemiology of cryptosporidiosis and giardiasis. Epidemiol Infect. 2009, 1-10.Google Scholar
- UKBorders Service. [http://www.edina.ac.uk/]
- Carstairs V, Morris R: Deprivation and health in Scotland. Health Bull (Edinb). 1990, 48 (4): 162-175.Google Scholar
- Rue H, Martino S, Chopin N: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Ser B-Stat Methodol. 2009, 71: 319-392. 10.1111/j.1467-9868.2008.00700.x.View ArticleGoogle Scholar
- Besag J, York J, Mollié A: Bayesian computation and stochastic systems (with discussion). Ann Inst Statist Math. 1991, 43: 1-59. 10.1007/BF00116466.View ArticleGoogle Scholar
- INLA: Functions which allow to perform a full Bayesian analysis of structured additive models using Integrated Nested Laplace Approximaxion. [http://www.r-inla.org/]
- R Development Core Team: R: A language and environment for statistical computing. 2008, Vienna, Austria: R Foundation for Statistical ComputingGoogle Scholar
- Cummins S, Smith DM, Taylor M, Dawson J, Marshall D, Sparks L, Anderson AS: Variations in fresh fruit and vegetable quality by store type, urban-rural setting and neighbourhood deprivation in Scotland. Public Health Nutr. 2009, 1-7.Google Scholar
- Ritz M, Nauta MJ, Teunis PF, van Leusden F, Federighi M, Havelaar AH: Modelling of Campylobacter survival in frozen chicken meat. J Appl Microbiol. 2007, 103 (3): 594-600. 10.1111/j.1365-2672.2007.03284.x.View ArticlePubMedGoogle Scholar
- Snel SJ, Baker MG, Venugopal K: The epidemiology of giardiasis in New Zealand, 1997-2006. N Z Med J. 2009, 122 (1290): 62-75.PubMedGoogle Scholar
- Snel SJ, Baker MG, Venugopal K: The epidemiology of cryptosporidiosis in New Zealand, 1997-2006. N Z Med J. 2009, 122 (1290): 47-61.PubMedGoogle Scholar
- Brown PE, Christensen OF, Clough HE, Diggle PJ, Hart CA, Hazel S, Kemp R, Leatherbarrow AJ, Moore A, Sutherst J, et al: Frequency and spatial distribution of environmental Campylobacter spp. Appl Environ Microbiol. 2004, 70 (11): 6501-6511. 10.1128/AEM.70.11.6501-6511.2004.View ArticlePubMedPubMed CentralGoogle Scholar
- French N, Barrigas M, Brown P, Ribiero P, Williams N, Leatherbarrow H, Birtles R, Bolton E, Fearnhead P, Fox A: Spatial epidemiology and natural population structure of Campylobacter jejuni colonizing a farmland ecosystem. Environ Microbiol. 2005, 7 (8): 1116-1126. 10.1111/j.1462-2920.2005.00782.x.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/10/423/prepub