Biomedical subjects

Olaf Berke

Publications and source records attributed to Olaf Berke.

6 recordsLinked to original sources

PubMed2006

Risk factors for Campylobacter spp. colonization in broiler flocks in Iceland.

We sampled 1,091 Icelandic broiler flocks at slaughter from May 2001 to December 2003 to determine the prevalence of, and investigate risk factors for the presence of, Campylobacter spp. at the flock level. Approximately 15% of the flocks were positive for Campylobacter spp.; most (95%) of the infected flocks being raised during the months of April-September. Based on the data from the latter months, and using multivariable logistic regression with random effects for herd, we found that the odds of a flock being positive for Campylobacter spp. increased with age and flock size. Additionally, vertical ventilation systems were strongly associated with positive flocks (OR=5.3). After controlling for these variables, we found no evidence of an effect of: year; company; Campylobacter being carried over from one flock to the next; time interval between flocks; using (at the hatcheries) eggs laid on the floor; density of bird housing, or the number of catch lots a flock was divided into for slaughtering purposes on the risk of a Campylobacter-positive flock.

Animal Husbandry↗

PubMed2006

Spatial analysis of sleeping sickness, southeastern Uganda, 1970-2003.

Sleeping sickness re-emerged in southeastern Uganda in the 1970s and remains a public health problem. It has continued to spread north into new districts, and gaps remain in the understanding of the causes of its spread and distribution. We report the distribution and magnitude of sleeping sickness in southeastern Uganda from 1970 to 2003. Data were collected from records of the Ugandan Ministry of Health, individual sleeping sickness treatment centers, and interviews with public health officials. Data were used to develop incidence maps over time, conduct space-time cluster detection analyses, and develop a velocity vector map to visualize spread of sleeping sickness over time in southeastern Uganda. Results show rapid propagation of sleeping sickness from its epicenter in southern Iganga District and its spread north into new districts and foci.

Animals↗

PubMed2005

Exploratory spatial relative risk mapping.

The many faces of disease mapping include maps of disease case locations, regional counts of cases, and disease risk. Another approach is that of mapping the relative risk. Previous methods to map the relative risk were based on regression models of relative risk, given information about geographical locations and established risk factors. However, spatial epidemiological investigations are often exploratory with limited knowledge about the putative risk factors. Indeed, often the primary motivation for the analysis is to identify unknown geographically varying risk factors. An exploratory approach to mapping the spatial relative risk is to scale the risk map using the background risk in the unexposed (or less-exposed) population. Exposure to unknown spatial risk factors is defined via specific cluster analysis. Identification of spatial disease clusters separates the population into those inside and those outside high risk areas (the exposed and unexposed populations). This exploratory approach to relative risk mapping gives the investigator an impression about the importance and geographical distribution of the unknown spatial risk factors. Two examples illustrate the exploratory relative risk mapping approach using a spatial point data set on pseudorabies in pig-herds and a regional count data set on small fox tapeworm infections in red foxes.

Animals↗

PubMed2004

Spatial risk assessment of herd sero-status of Aujeszky's disease in a county in Hungary.

A Geographic Information System (VetEpiGIS) was used to analyze the ADV (Aujeszky's disease virus) sero-status in large-scale pig units regarding certain geographical features in a county of southern Hungary. The ADV sero-statuses were collected from all swine units in Csongrád county in 1998-2000. The units' coordinates were combined with a vector graphical digital map of the county, with a resolution of 1:100,000. Logistic regression tested the associations between sero-status of large-scale units and presence of topographical features, other units and villages in the neighborhood. "Neighborhood" was defined by circular zones with radius 1-10km around the unit (in 1km increments; one logistic regression for each radius). The following topographical features showed significant positive association with the ADV seropositivity: lake (3km OR: 5.7; 5km OR: 7.5; 6km OR: 6.1; 10km OR: 5.4) and highway (5km OR: 4.2; 6km OR: 5.3). Other features had negative association with ADV seropositivity: forest (3km OR: 0.13; 4km OR: 0.15; 5km OR: 0.15; 6km OR: 0.10; 7km OR: 0.10; 8km OR: 0.23) and uninfected large-scale unit (4km OR: 0.07; 5km OR: 0.27; 6km OR: 0.32; 7km OR: 0.31).

Animal Husbandry↗

PubMed2004

Exploratory disease mapping: kriging the spatial risk function from regional count data.

BACKGROUND: There is considerable interest in the literature on disease mapping to interpolate estimates of disease occurrence or risk of disease from a regional database onto a continuous surface. In addition to many interpolation techniques available the geostatistical method of kriging has been used but also criticised. RESULTS: To circumvent these critics one may use kriging along with already smoothed regional estimates, where smoothing is based on empirical Bayes estimates, also known as shrinkage estimates. The empirical Bayes step has the advantage of shrinking the unstable and often extreme estimates to the global or local mean, and also has a stabilising effect on variance by borrowing strength, as well. Negative interpolates are prevented by choice of the appropriate kriging method. The proposed mapping method is applied to the North Carolina SIDS data example as well as to an example data set from veterinary epidemiology. The SIDS data are modelled without spatial trend. And spatial interpolation is based on ordinary kriging. The second example is included to demonstrate the method when the phenomenon under study exhibits a spatial trend and interpolation is based on universal kriging. CONCLUSION: Interpolation of the regional estimates overcomes the areal bias problem and the resulting isopleth maps are easier to read than choropleth maps. The empirical Bayesian estimate for smoothing is related to internal standardization in epidemiology. Therefore, the proposed concept is easily communicable to map users.

Journal Article↗

PubMedDate not supplied

[The occurrence of Echinococcus multilocularis in red foxes in lower Saxony: identification of a high risk area by spatial epidemiological cluster analysis].

There is considerable interest in the spatial distribution of Echinococcus multilocularis in red foxes (Vulpes vulpes L.), because this parasite causes the zoonoses of alveolar echinococcosis which is potentially of high fatality rate. High risk areas are known from France, Switzerland and the Swabian Alb in Germany for a long time. In this work, the spatial scan statistic is introduced as an instrument for identification and localisation of high risk areas, so called disease clusters in spatial epidemiology. The use of the spatial scan statistic along with data about the distribution of the parasite in 5365 red foxes in Lower Saxony, that were collected during 1991 to 1997, led to the identification of another high risk area. The relative risk for this disease cluster is approximated by RR = 5.03 (CI0.95(RR) = [4.27; 6.58]) for the period of 1991 to 1994 and by RR = 4.45 (CI0.95(RR) = [3.53; 5.59]) for the period of 1994 to 1997, respectively.

Animals↗