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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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(A) Spatio-temporal distribution of features identified as diarrhea clusters using Equation 3 with a Z-score of 2.76. (B) Distribution of diarrhea cluster features passing the tests for outbreaks and cold spots.
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Figure 6: (A) Spatio-temporal distribution of features identified as diarrhea clusters using Equation 3 with a Z-score of 2.76. (B) Distribution of diarrhea cluster features passing the tests for outbreaks and cold spots.

Mentions: Of the 1,881 features in the data set, 818 (44%) contained at least one Campylobacter case and only four of the 818 did not have any other features containing Campylobacter cases in their neighborhood (Figure 4). The mean number of Campylobacter positive features in the neighborhood was 13.5. Applying the modified local Moran's I (Equation 3) to the Campylobacter dataset resulted in 19 features being identified as clusters (Table 2). Passing these Campylobacter clusters through the logical tests reduced the result list to 12 features; two cluster features are located in the central Western part of the study area while the remaining ten forms a larger aggregation of cluster cells in the North-Western part of the study area. The cluster aggregation spans four adjacent stacks or a spatial extend of 100 km2, and covers the period 1996; 1998–2003 (Figure 5). We performed the same analysis on the diarrhea cases resulting in 57 features before the logical tests and 18 features after passing the clusters through the logical tests. Eight of these are located in the same stack, covering the central part of Odense, the provincial capital, for the period 1995; 1997–2002. Four of the diarrhea cluster features are placed South-West of the center of the study area and the remaining six are found near or in the Campylobacter cluster aggregation in the North-Western part of the study area (Figure 6). Of these six diarrhea cluster features one overlaps spatio-temporally with the Campylobacter cluster features (Figure 7).


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)

(A) Spatio-temporal distribution of features identified as diarrhea clusters using Equation 3 with a Z-score of 2.76. (B) Distribution of diarrhea cluster features passing the tests for outbreaks and cold spots.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: (A) Spatio-temporal distribution of features identified as diarrhea clusters using Equation 3 with a Z-score of 2.76. (B) Distribution of diarrhea cluster features passing the tests for outbreaks and cold spots.
Mentions: Of the 1,881 features in the data set, 818 (44%) contained at least one Campylobacter case and only four of the 818 did not have any other features containing Campylobacter cases in their neighborhood (Figure 4). The mean number of Campylobacter positive features in the neighborhood was 13.5. Applying the modified local Moran's I (Equation 3) to the Campylobacter dataset resulted in 19 features being identified as clusters (Table 2). Passing these Campylobacter clusters through the logical tests reduced the result list to 12 features; two cluster features are located in the central Western part of the study area while the remaining ten forms a larger aggregation of cluster cells in the North-Western part of the study area. The cluster aggregation spans four adjacent stacks or a spatial extend of 100 km2, and covers the period 1996; 1998–2003 (Figure 5). We performed the same analysis on the diarrhea cases resulting in 57 features before the logical tests and 18 features after passing the clusters through the logical tests. Eight of these are located in the same stack, covering the central part of Odense, the provincial capital, for the period 1995; 1997–2002. Four of the diarrhea cluster features are placed South-West of the center of the study area and the remaining six are found near or in the Campylobacter cluster aggregation in the North-Western part of the study area (Figure 6). Of these six diarrhea cluster features one overlaps spatio-temporally with the Campylobacter cluster features (Figure 7).

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