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Spatio-temporal cluster analysis of the incidence of Campylobacter cases and patients with general diarrhea in a Danish county, 1995-2004.

Jepsen MR, Simonsen J, Ethelberg S - Int J Health Geogr (2009)

Bottom Line: We used a modified form of local Moran's I to test if features with similar incidence rates occurred next to each other in space and time, and compared the observed clusters with simulated clusters.The results showed a significant persisting clustering of Campylobacter incidence rates in the Western part of Funen.Results were visualized using the Netlogo software.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of policy analysis, National Environmental Research Institute, University of Aarhus, Roskilde, Denmark. mrj@dmu.dk

ABSTRACT
Campylobacter infections are the main cause of bacterial gastroenteritis in Denmark. While primarily foodborne, Campylobacter infections are also to some degree acquired through other sources which may include contact with animals or the environment, locally contaminated drinking water and more. We analyzed Campylobacter cases for clustering in space and time for the large Danish island of Funen in the period 1995-2003, under the assumption that infections caused by 'environmental' factors may show persistent clustering while foodborne infections will occur randomly in space. Input data were geo-coded datasets of the addresses of laboratory-confirmed Campylobacter cases and of the background population of Funen County. The dataset had a spatial extent of 4.900 km2. Data were aggregated into units of analysis (so-called features) of 5 km by 5 km times 1 year, and the Campylobacter incidence calculated. We used a modified form of local Moran's I to test if features with similar incidence rates occurred next to each other in space and time, and compared the observed clusters with simulated clusters. Because clusters may be caused by a high tendency among local GPs to submit stool samples, we also analyzed a dataset of all submitted stool samples for comparison. The results showed a significant persisting clustering of Campylobacter incidence rates in the Western part of Funen. Results were visualized using the Netlogo software. The underlying causes of the observed clustering are not known and will require further examination, but may be partially explained by an increased rate of stool samples submissions by physicians in the area. We hope, by this approach, to have developed a tool which will allow for analyses of geographical clusters which may in turn form a basis for further epidemiological examinations to cast light on the sources of infection.

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Related in: MedlinePlus

Incidences per 10,000 of diarrhea (dotted line) and Campylobacter (solid line).
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Figure 3: Incidences per 10,000 of diarrhea (dotted line) and Campylobacter (solid line).

Mentions: In the study period there was a secular trend that, if not corrected for, in itself would affect the analyses. The input data for the analyses shows two similar "inverted U"-shaped trends with initial increases in the number of Campylobacter cases and also, although to a lesser extend, in the number of diarrhea cases followed by a decrease in numbers toward the end of the study period (Figure 3). Uncorrected, this temporal trend could lead to identification of clusters in the middle of the study period when the incidences peak. As we considered this peak a global phenomenon within the study area we therefore de-trended the data during the analysis by replacing of Equation 1 with the mean incidence for a given year ( and ) (Equation 2).


Spatio-temporal cluster analysis of the incidence of Campylobacter cases and patients with general diarrhea in a Danish county, 1995-2004.

Jepsen MR, Simonsen J, Ethelberg S - Int J Health Geogr (2009)

Incidences per 10,000 of diarrhea (dotted line) and Campylobacter (solid line).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654880&req=5

Figure 3: Incidences per 10,000 of diarrhea (dotted line) and Campylobacter (solid line).
Mentions: In the study period there was a secular trend that, if not corrected for, in itself would affect the analyses. The input data for the analyses shows two similar "inverted U"-shaped trends with initial increases in the number of Campylobacter cases and also, although to a lesser extend, in the number of diarrhea cases followed by a decrease in numbers toward the end of the study period (Figure 3). Uncorrected, this temporal trend could lead to identification of clusters in the middle of the study period when the incidences peak. As we considered this peak a global phenomenon within the study area we therefore de-trended the data during the analysis by replacing of Equation 1 with the mean incidence for a given year ( and ) (Equation 2).

Bottom Line: We used a modified form of local Moran's I to test if features with similar incidence rates occurred next to each other in space and time, and compared the observed clusters with simulated clusters.The results showed a significant persisting clustering of Campylobacter incidence rates in the Western part of Funen.Results were visualized using the Netlogo software.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of policy analysis, National Environmental Research Institute, University of Aarhus, Roskilde, Denmark. mrj@dmu.dk

ABSTRACT
Campylobacter infections are the main cause of bacterial gastroenteritis in Denmark. While primarily foodborne, Campylobacter infections are also to some degree acquired through other sources which may include contact with animals or the environment, locally contaminated drinking water and more. We analyzed Campylobacter cases for clustering in space and time for the large Danish island of Funen in the period 1995-2003, under the assumption that infections caused by 'environmental' factors may show persistent clustering while foodborne infections will occur randomly in space. Input data were geo-coded datasets of the addresses of laboratory-confirmed Campylobacter cases and of the background population of Funen County. The dataset had a spatial extent of 4.900 km2. Data were aggregated into units of analysis (so-called features) of 5 km by 5 km times 1 year, and the Campylobacter incidence calculated. We used a modified form of local Moran's I to test if features with similar incidence rates occurred next to each other in space and time, and compared the observed clusters with simulated clusters. Because clusters may be caused by a high tendency among local GPs to submit stool samples, we also analyzed a dataset of all submitted stool samples for comparison. The results showed a significant persisting clustering of Campylobacter incidence rates in the Western part of Funen. Results were visualized using the Netlogo software. The underlying causes of the observed clustering are not known and will require further examination, but may be partially explained by an increased rate of stool samples submissions by physicians in the area. We hope, by this approach, to have developed a tool which will allow for analyses of geographical clusters which may in turn form a basis for further epidemiological examinations to cast light on the sources of infection.

Show MeSH
Related in: MedlinePlus