ABSTRACT
SVEPM Annual Conference 2015
(Last date for submission of abstracts: Sunday 31st August 2014)
Title:
Author(s):
Prevalence, risk factors and spatial analysis of
infections with liver flukes in Danish cattle herds
Halasa, T., Frankena, K., Olsen, A., Bødker, R., &
Toft, N.
Full contact details for main
author (please complete all
sections below):
Full name of main contact:
Postal address:
District
Tariq Halasa
Bülowsvej 27
Frederiksberg C
Town/City
County/State
Postal/Zip Code
Telephone 1:
Telephone 2:
Denmark
1870
+4535886102
+4530709439
Fax:
E-mail:
tahbh@vet.dtu.dk
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ABSTRACT
SVEPM Annual Conference 2015
(Last date for submission of abstracts: Sunday 31st August 2014)
Title:
PREVALENCE, RISK FACTORS AND SPATIAL ANALYSIS OF
INFECTIONS WITH LIVER FLUKES IN DANISH CATTLE
HERDS
Word count:
(max 600)
499 (including the interpretive summary)
Interpretative summary (5 lines max):
This study assesses the extent of liver fluke infection in Danish cattle herds. In addition, it identifies
factors that contribute to the spread of this parasite between herds. This will help the Danish Cattle
Federation in building up control programs against fasciolosis in Danish cattle herds.
Main abstract:
Liver fluke infection, also known as fasciolosis, is a world-wide prevalent zoonotic parasitic disease
infecting a wide range of host species and is caused by Fasciola hepatica. Despite of the substantial
economic and animal welfare effects of the disease, knowledge on its prevalence and the factors related to its
occurrence and distribution are scarce in Denmark.
A retrospective study was performed using liver inspection data of approximately 1.5 million cattle for the
period 2011 to 2013. Spatial analysis was carried out to explore whether the data on F. hepatica infection
was clustered in space as this could help understand whether or not the infection was driven by local
environmental factors. Both global and local spatial autocorrelation techniques were used. Herd level and
environmental risk factors were evaluated for their association with prevalence of F. hepatica infection using
Generalized Estimating Equations.
Risk factor
Production type (non-dairy vs. dairy)
Herd type (organic vs. conventional)
Herd size, for organic farms (≥ 100 vs. <30)
Herd size for organic farms (≥ 30 & <100 vs. <30)
Herd size, (conventional farms) (≥ 100 vs. <30)
Herd size, (conventional farms) (≥ 30 & <100 vs. <30)
Buy animals from infected herds, (organic farms) (yes vs. no)
Buy animals from infected herds, (conventional farms) (yes vs. no)
Streams (present vs. absent)
Pasture (present vs. absent)
Wetlands (present vs. absent)
Odd ratio (95% CI)
1.26 (1.16-1.51)
1.88 (1.16-3.04)
8.95 (6.03-13.29)
4.75 (3.11-7.24)
5.13 (4.52-5.82)
3.01 (2.75-3.30)
1.88 (1.16-3.04)
2.03 (1.82-2.26)
1.21 (1.11-1.33)
1.52 (1.30-1.78)
1.33 (1.23-1.44)
The herd prevalence of fasciolosis increased from 25.64% in 2011 to 28.44% in 2012 and again to
29.26% in 2013. Over the period 2011 till 2013, the animal and herd level prevalence were 4 and 38%,
respectively.
Among herd factors, production-type (non-dairy vs. dairy) was significantly associated with the
occurrence of F. hepatica infection (see table). And among environmental variables, presence of streams,
wetlands and pastures within 500 meters of farm centers showed a significant association with the presence
of F. hepatica infection in herds (see table). The effect of farm-type (organic vs. conventional) on F.
hepatica prevalence was significantly modified by buying cattle from infected farms and by herd-size.
Buying from positive herds significantly increased the odds of being infected on both conventional and
organic farms (see table). The effect of herd-size was modified by farm-type where the odds of having F.
hepatica infection were highest in large organic herds compared to small organic herds (see table).
Both global and local spatial analysis suggested clustering of positive and negative herds. Mapping of
local clusters showed clustering of infected herds in North Jutland. The developed risk factor model was able
to predict this northern cluster suggesting the model captured the key local drivers of infection.
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