ELSEVIER

PREVENTIVE VETERINARY

MEDICINE Preventive Veterinary Medicine 26 ( 1996) 223-237

A retrospective study on salmonella infection in Danish broiler flocks

0ystein Angen”,*, Marianne N. Skov”,“, Mariann Chri61b, Jens F. Aggerb, Magne Bisgaard”

“De~xrrtrttcnt of Veterinary Microbiology, The Royal Veterinary and Agricultural University. Biilowsvej 13, 1870 Frederiksberg C, Denmark

hDeptrtmerlt of Animal Science and Animal Health, Div. of Ethology and Health, The Royal Veterinaq and Agricultural University, Biilowsvej 13. 1870 Frederiksberg C, Denmark

Accepted 18 August I995

Abstract

A retrospective longitudinal study was conducted to identify risk factors associated with Sulmonellu erzrerica infection in Danish broiler production. The study was based on information in the ante- mortem database (AM database) where data were available for all broiler flocks slaughtered over the

2-year period from 1992 to 1993 in Denmark. The AM database contains information collected by the ante-mortem veterinarians, from the slaughterhouses, and from the salmonella examinations carried out at the National Veterinary Laboratory. The epidemiological unit was the individual broiler

flock. The salmonella status of the flock was determined by examining the caecal tonsils from 16 3-week-old chickens from each flock. This procedure would detect a salmonella-infected flock, with a probability above 95%, if the prevalence is above 20%. Furthermore, the structure and quality of

the collected data have been evaluated. Fourteen variables were selected for analysis by multivariable logistic regression. An increased

risk of salmonella infection in the broiler flocks was associated with the biggest hatcheries and feedmill, with an increasing number of houses on the farm, if the preceding flock was infected, and if the flock was reared in the autumn. Additionally, the main variables of the model were analysed by including a random effect at the house level. This resulted only in minor changes of the parameter estimates.

K~vww~.s: Salmonella; Broiler; Chicken; Epidemiology

*Corresponding authors

0167.5877/96/$15.00 0 1996 Elsevier Science B.V. All rights reserved SSD/Ol67-5877(95)00549-8

224 0. Angen et al. /Preventive Veterinary Medicine 26 (1996) 223-237

1. Introduction

In Denmark the incidence of human salmonellosis has been increasing over the last

decade. Poultry and poultry products have been suspected to represent a major source for human salmonellosis (Lester et al., 1990; Olsen et al., 1992).

In 1993 about 4000 broiler flocks from approximately 350 different producers totalling 120 million broilers were slaughtered. The production system is highly stratified, each stratum being strictly isolated from the others to avoid horizontal transmission of pathogens.

Two breeding companies are represented at the grandparent level in Denmark. However, most of the parent stock is imported as day-old chickens from the UK. Day-old parent stock

chicks are reared on approximately 25 different farms for the first 18 weeks and are

subsequently moved to almost 70 different egglaying farms. The eggs are hatched in eight different hatcheries. The average broiler flock size is about 30 000, and the stock density is 25.5 animals m-*. On average, six to seven flocks (rotations) are produced per house year- ‘. The broilers are slaughtered in 11 different slaughterhouses in Denmark. In 1993 the broilers were slaughtered at an average of 37.7 days of age with an average live weight of 1667 g.

In February 1989, the Danish Poultry Council initiated a voluntary programme in an attempt to reduce the level of salmonella in broilers (Bisgaard, 1992). This programme was designed to eradicate the infection from the top of the production pyramid. Special

programmes were incorporated to monitor the salmonella status in the breeding and parent stocks, the hatcheries, the broiler flocks, the slaughterhouses and the feedmills. The veter-

inary meat-inspection system was also restructured, putting more emphasis on ante-mortem

(AM) control. Specially trained veterinarians (AM veterinarians) visit the farms 34 days before the flocks are slaughtered. A special database named the ante-mortem database (AM database) was established, including data collected by the AM veterinarians, from the slaughterhouses and from the National Veterinary Laboratory.

Using the information kept in the AM database, our aims were to identify factors asso- ciated with salmonella infection in broiler flocks, and to evaluate the structure and quality of the data collected.

2. Material and methods

2.1. The ante-mortem database

The AM veterinarian collects flock data by filling in a questionnaire designed for this purpose. This is done in collaboration with the farmer. The reports are sent to the Danish Poultry Council where the data are stored in the AM database.

In addition, 60% of the farms participate in the Flock Economy Control where economic parameters related to the production are recorded. These data are also stored at the Danish Poultry Council and were available for our study. From this database we only extracted the size and age of the houses, which were, accordingly, only available for 60% of the farms representing 80% of the flocks.

0. Angen et al. /Preventive Veterinary Medicine 26 (I 996) 223-237 225

2.2. Determination of salmonella status

The genus Salmonella consists of two species: S. enterica and S. bongori (Popoff and

Le Minor, 1992). Serotypes isolated from domestic animals and humans normally belong to S. enterica. This nomenclature will be used in the present publication.

The results of two different examinations for S. enterica are recorded in the AM database:

one based upon examination of caecal tonsils from 3-week-old chickens and the other based

upon examination of neck skins taken at the slaughterhouses. Since a positive result from examination of the neck skin could be the result of cross contamination from salmonella- positive chickens belonging to other flocks, we decided to use only the results from the examination of the 3-week-old chickens to determine the salmonella status of the flock.

This examination requires the submission of 16 3-week-old chickens from each flock to the National Veterinary Laboratory. The chickens are selected by the farmer by convenience. The sample size is sufficient to detect a flock with a prevalence level above 20% with a

probability of > 95% (Martin et al., 1987). No differentiation was made between the different serotypes of S. enterica in this study.

The flock was regarded as salmonella positive regardless of which serotype was present.

2.3. Methods usedfor bacteriological examination

Bacteriological examinations were carried out at the National Veterinary Laboratory, Aarhus. Caecal tonsils taken from the 16 broilers were pooled to make eight samples.

Isolation of S. enterica was performed according to standard procedures (Marthedal, 1960)) i.e. selective enrichment in selenite broth (Oxoid) at 37°C for 16-18 h followed by sub- cultivation on BLSF agar (Oxoid CM 329) with 0.2 % Lutensit AB-A at 37°C for 16-18

h. Serotype identification was carried out according to Kauffmann ( 1972)) using one suspect colony from each plate for biochemical and serological characterization.

2.4. Selection sf variables

Fourteen variables were initially chosen for the study (Table 1) . This selection was based

on literature studies (for references, see Discussion) as well as knowledge of the infrastruc- ture of the Danish broiler industry.

2.5. Dejinition of the dataset

The database, which was provided by the Danish Poultry Council initially contained 9494 flock observations. This also included the data available for the members of the Flock Economy Control.

We excluded all flocks which had identical journal numbers, indicating that the farmer had pooled and sent chickens from more than one flock to the National Veterinary Labo- ratory. This made an individual determination of the salmonella status of these flocks impossible. This affected 1224 flocks ( 12.9% of the total number). In addition we excluded all flocks where no chickens had been sent to the National Veterinary Laboratory or where the chickens were decomposed on arrival making an investigation impossible (466 flocks,

226 8. Angen et al. /Preventive Veterinary Medicine 26 (1996) 223-237

Table 1 Variables selected for initial analysis including distribution of the continuous variables for 7108 Danish broiler

chicken flocks, I992- 1993

Variables Min. Mean Max. SD

Hatchery

Feedmill

Salmonella status of the preceding flock

Season (date of AM inspection)

Slaughterhouse of the preceding flock

AM district

Appearance of the bedding

Beetles observed during the cleaning process

Number of chicken houses on the farm

Animal density (chickens m-‘)

Flock size (number of chickens placed)

Area of the chicken house (m’)

Age of the chicken house (construction year)

Days between disinfection and replacement

“Not applicable.

_a _ _ _

_ _ _

_ _ _

_ _ _ _

_ _ _ _

_ _

_ _ _

_ _ _

_ _ _

4.2 24.6 77.3 2.8

1600 28298 77553 13731

151 1185 3010 535

1955 1978 1993 10.2

0 14.8 99 6. I

4.9% of the flocks). Finally, the study was limited to the flocks that were visited by the AM veterinarian between 1 /l/92 and 31112193. This dataset ended up consisting of 7 108 flock

observations and will in the following be referred to as Dataset A. As logistic regression is unable to handle missing values, all observations with missing

values were deleted. All hatcheries and feedmills with less than 200 flock observations

during these 2 years were pooled to make calculation of parameter estimates for the variables possible. This affected four hatcheries ( 185 flocks) and 10 feedmills (637 flocks). Dataset B, therefore, consisted of 5921 flock observations. This dataset was compared to Dataset A

to evaluate if the elimination process had introduced any bias. We found no significant difference between the datasets regarding the flock size (t-test, P = 0.47) and salmonella

frequency (X2-test, P = 0.61).

2.6. Statistical methods

A retrospective longitudinal study design was used (Kleinbaum et al., 1982). The indi- vidual flock was the unit of study.

Dataset A was initially analysed using the FREQ procedure in SAS” (SA!?’ Institute

Inc., 1990) and for the bivariable analysis the continuous parameters were divided into intervals (Table 2). Bivariable chi-square analyses compared each variable to the salmo- nella-infection status of the flock. Variables were selected for further analysis if the P-value

was less than 0.20. A multivariable logistic regression analysis was performed using the CATMOD proce-

dure in SAS”” on Dataset B. The best model fit was found by a combined forward and backward selection process where we used the log likelihood ratio chi-square statistic for testing the significance (P < 0.05) of adding or subtracting a variable or a two-way inter- action from the model (Hosmer and Lemeshow, 1989).

0. Angen et al. /Preventive Veterinary Medicine 26 (1996) 223-237 227

Table 2

Descriptive statistics for variables being significant in the bivariable analysis but not included in the final logistic

regression model; 7108 Danish broiler chicken Rocks, 1992-1993

Variables”

Slaught

Minimum

Maximum

Levels

n= 12

% of flocks

0.4

15.8

Salm. %”

6.4

26.7

Density <5 0.1 0.0

S-10 0.1 12.5

IO-15 0.4 16.7

15-20 2.1 14.3

20-25 47.6 14.1

25-30 48.4 19.0

>30 0.7 23.7

Flock size

Area

< 10000 1.4 14.8

10-20000 24.4 13.9

20-30000 26.8 15.2

30~0000 15.6 19.7

40-50000 21.3 19.6

> 50000 4.6 21.3

<500 10.6 14.7

500-1000 26.6 14.3

1000-1500 29.2 16.9

1500-2000 29.3 18.5

> 2000 4.3 19.7

AM dist n=6 2to23 16to23

“Slaught, slaughterhouse of the preceding flock; Density, number of chickens placed m-‘; Flock size, number of

placed chickens in flock; Area, area of house in m’; AM dist, AM district.

“Salmonella prevalence in % for the actual covariate level.

2.7. Random effects

In our data, a dependence might be expected among all flocks reared in the same house

(Curtis et al., 1988, 1993). Therefore we used the computer package EGRET (EGRET Reference Manual, 1993) for ana\lysing distinguishable data by a logistic-binominal random

effects model. However, limitations in the programme make it impossible to consider

interactions between variables with too many levels. Neither is it possible to take random variation on more than one level into consideration. We therefore had to restrict our analysis to the five main variables obtained through ordinary logistic regression, omitting the inter-

action terms. We estimated the parameters for the variables with and without a term related to random effects on the house level. The significance (P < 0.05) of the random effect was

assessed by comparing the square root of the log likelihood ratio statistic against a one-

tailed normal distribution.

228 0. Angen et al. /Preventive Veterinary Medicine 26 (1996) 223-237

3. Results

3.1. Univariate and bivariate analysis

The uni- and bivariable analyses were based on Dataset A consisting of 7108 flock observations. S. enterica was found in 16.8% of the flocks. Descriptive statistics of the

continuous variables are shown in Table I. The density of chickens showed a very small

standard deviation. The prevalence of S. enterica in the different levels of the variables and

the corresponding number of flocks are shown in Tables 2 and 3.

Table 3

Prevalences and odds ratios for variables included in the final multivariable logistic regression model of flock-

level Sulmonell~~ entericu infections in 5921 Danish broiler chicken flocks, 1992-1993

Variable“ Flock %h Salm. %’ Adj. OR 95% CI

26

24

22

IO

II

5

2

14 1.0 16 1.2 0.9-I .4

15 1.0 OS-I.3

22 1.5 I .2-2.0

22 I .6 I .2-2.0

23 I .6 I .2-2.2

17 I .o 0.6-I .9

20 I .I 0.7-3.9

Lagsalm _

+

Season

Mar-May

Jun-Aug

Sep-Nov

Dee-Feb

Feedmill“

n= II

Hatchery4

I, = 5

85 15 1 .o

15 30 1.9

25 13 I.0

26 15 1.2

26 23 2.0

22 17 I .3

4

to

19

5

to

43

IO 1.0 (ref)

to to

26 2.3

5

to

21

I .6-2.3

I .0-l .5

I .6-2.4

1.0-1.6

I .8-3.0

I.0 (ref)

to

4.0 3.0-5.4

“Houses. number of chicken houses on the farm; Lagsalm, salmonella status of the preceeding flock.

“% of flocks in the study (7108).

‘Salmonella prevalence in % for the actual covariate level (7 108 flocks).

‘lOnly minimum and maximum values shown

Table 4

8. Aqen et al. /Preventive Veterinary Medicine 26 (1996) 223-237 229

Comparison of random effects and ordinary multiple logistic regression model of Salmonella enterica infections

in 5921 Danish broiler chicken flocks, 1992-1993

Variable” Ordinary logistic regression Logistic regression with random

effects

P SE(P) P P SE(P) P

Intercept houses

Lagsalm -

+

Mar-May 0.00 _

Jun-Aug 0.17 0.11 Sep-Nov 0.69 0.10 Dee-Feb 0.24 0.12

Feedmill

A

B

c

J>

E

F

G

H

I J

K”

Hatchery

A

B

C

D

E”

Extra-binominal variation

-4.41

0.00

0.14

0.03

0.43

0.45

0.46

0.04

0.52

0.00 0.64

0.00

055

0.84

0.49

0.06

0.60

0.44

0.26

0.03

0.27

0.46

0.00 _

I .35 0.16

I .39 0.15

0.86 0.20

0.76 0.22

0.22 <O.Ol 0.23 <O.Ol _ _

0.11 0.18

0.11 0.77

0.13 <O.Ol

0.13 <O.Ol

0.16 <O.Ol

0.3 I 0.91

0.43 0.22

-4.37

0.00

0.15

0.03

0.45

0.46

0.49

0.04

0.57

_ _

0.12 0.21

0.13 0.78 0.15 <O.Ol

0.15 < 0.01

0.19 <O.Ol

0.35 0.9 I 0.46 0.22

_

0.09 <O.OI

0.00

0.50

_

0.10

_

< 0.01

_ 0.00 _ _

0.1 I 0.17 0.11 0.11

<O.Ol 0.7 I 0.10 <O.OI

0.03 0.26 0.12 0.03

_ 0.00 _ _ _

0.18 <O.Ol 0.56 0.20 <O.OJ

0.13 <O.Ol 0.87 0.14 <O.Ol

0.19 0.0 1 0.52 0.2 I 0.0 I 0.19 0.74 0.06 0.20 0.98

0.19 <O.Ol 0.63 0.2 1 <O.OI

0.14 <O.Ol 0.43 0.16 <O.Ol

0.17 0.13 0.25 0.19 0.18

0.23 0.89 0.04 0.24 0.86

0.22 0.21 0.27 0.24 0.26

0.17 <O.OI 0.44 0.18 0.02

_

<O.Ol

<O.Ol

<O.Ol

10.01

_

0.00 _

I .39 0.17

I.45 0.16

0.9 I 0.22

0.84 0.23

0.43 0.08

_

< 0.0 I < 0.01 <O.OI <O.Ol

_‘

“Houses. number of houses at the farm; Lagsalm, salmonella status of the preceding flock.

“Hatcheries and feedmills with less than 200 flock observations are pooled in these covariate levels

‘Not applicable.

230 0. Angen et al. /Preventive Veterinary Medicine 26 (I 996) 223-237

3.2. Multivariate analysis

Our final model obtained by multivariate logistic regression on Dataset B included the following five significant variables (P < 0.05 by log likelihood ratio X*-test):

- Salmonella status of preceding flock (Lagsalm) . - Number of houses on the farm (Houses). - Hatchery. - Season. - Feedmill.

The model also included the following interactions: Season X Houses, Season X Hatchery,

Season X Feedmill, Lagsalm X Feedmill and Feedmill X Hatchery. Because of the large

number of interaction levels (173 in all) the parameter estimates are not shown. Sea-

son X Houses is the only interaction term where both main effects can be generally applied outside Denmark. Having four to six houses in the autumn (September-November) increased the odds ratio (OR) at least two-fold compared with having fewer than four

houses in the rest of the year. The model was improved significantly (P < 0.001) by adding a random effect term to

the model, but this resulted only in minor changes of the estimates of the covariates (Table 4). The biggest difference was observed for the salmonella status of the preceding flock. Generally the standard deviation was slightly increased for all covariates in the random

effects model. Additionally, odds ratios and 95% confidence intervals for the ORs were computed for

the five main variables based upon the estimates from the ordinary logistic regression model

(Table 3).

4. Discussion

In our logistic regression model we found that five of the selected variables were asso- ciated with the salmonella status of the broiler flocks. However, none of the variables were independently associated with the salmonella status as they all were involved in interaction terms.

We will for convenience divide the variables into those being included in the multivariate

model and those being excluded from this model.

4. I. Signijcant factors

4.1.1. Hatcheries

We found a notable difference in odds ratios ( ORs) between the hatcheries (Table 3). The high OR-values found for some hatcheries might be due to management factors asso- ciated with large-scale production as there is an increased probability for big hatcheries to receive eggs from an infected egglaying flock. The two hatcheries with the largest ORs delivered chickens to 42.7% and 25.8% of the flocks. If the parent stock is infected with S. enterica, the organism can be vertically transmitted (Lahellec and Colin, 1985; Mcllroy et al., 1989). Eggs from different egglaying flocks are sent to the hatcheries where horizontal

0. Anpn et al. /Preventive Veterinary Medicine 26 (1996) 223-237 231

infection can take place. The wet meconium and hatchery fluff and debris from the hatching

process are probably important factors in the transmission of bacteria between chickens. If cleaning and disinfection procedures are not followed, there is also the possibility of per-

sistence and, thereby, transmission from one hatch to the next.

4.1.2. Feedmills

Salmonella infection through contaminated feed represents a well known risk factor (Bisgaard, 1978; Hinton et al., 1987; Hinton, 1988; Jones et al., 1991; Brown et al., 1992; Henken et al., 1992; Bisgaard and Hansen, 1994). This was also confirmed in our study,

despite the fact that all Danish broiler feed is now heat treated to prevent contamination of the broiler flocks. The largest feedmill had the highest OR (Table 3) and delivered feed to

19.2% of the flocks. In the same way as for the larger hatcheries, the high OR may be due to management factors associated with large-scale production. An interaction between hatchery and feedmill also turned out to be significant in the multivariate model. This could

be due to an ‘area-effect’ as the hatcheries and the feedmills are quite stable in their trading

districts.

4.1.3. Salmonella status of precedingjock

Following the implementation of the control programme in 1989, a decline in salmonella prevalence in broiler flocks was observed, although this decline had ended by 1992 (Bis- gaard and Hansen, 1994). This has led to suggestions that resident bacteria within the farm might be an important factor in addition to the current external contamination (Lahellec et

al., 1986; Baggesen et al., 1992; Brown et al., 1992). From our dataset it was not possible

to evaluate the effectiveness of the cleaning and disinfection procedures in the houses. We were therefore forced to measure these conditions indirectly.

The salmonella status of the preceding flock turned out to be a statistically significant variable in our model. If a broiler flock is infected by S. enterica, the bacteria can persist in the house if the prerequisites and procedures for cleaning and disinfection are not adequate

(Lahellec et al., 1986; Baggesen et al., 1992; Brown et al., 1992). Important factors in this respect could be if the standard of houses does not allow for satisfactory cleaning or if the

bacteria survive in/on beetles hiding in the insulation of the building. Alternatively it could

indicate that the flocks are currently exposed to external contamination. This might be an explanation for the interaction between salmonellastatus of the preceding flock and feedmill turning out to be a significant factor in our study. Contamination of the surroundings during

removal of the birds and manure and subsequent recontamination of the house might represent other factors.

4.1.4. Season Season of the year turned out to be an important factor in the model, with the highest risk

for salmonella infection in the autumn (September-November). This is in accordance with other investigations where a higher probability of chickens getting infected during the wet and cold season was found (Soerjadi-Liem and Cumming, 1984; Annan-Prah and Jane,

1988). We also found season to be a part of three interactions: with hatchery, feedmill and

number of houses at the farm. This could be explained by enhanced problems with cleaning,

232 0. Angen et al. /Preventive Veterinary Medicine 26 (1996) 223-237

disinfection and drying during the cold season - a problem aggravated on farms with many houses and in certain feedmills (Bisgaard, 1978) and hatcheries. In periods where there is

an important internal/external temperature differential, there could be an increased risk of condensed water which would favour growth conditions for S. enterica and consequently

the risk of persistence if introduced.

4.1.5. Number of houses on the farm

There was a significantly increased risk of salmonella contamination of the broiler flocks if there were more than three houses on a farm (Table 3). The low risk recorded for farms of ‘seven or eight houses’ may be regarded as spurious because of the low number of flocks in these covariate levels (Tables 3 and 4). An increased number of houses on the farm

might increase the possibilities of transmission of S. enterica between houses. Furthermore an increased number of houses would mean a reduction of the time left for cleaning and disinfection before new stock is introduced to the farm - making it more difficult to follow

the ‘all out-all in’ principle.

4.2. Random effects

Adding a random variation on the house level improved the simplified model significantly

(P<O.OOl) without altering the estimates much. This could indicate that the ordinary logistic regression model is not intolerably biased in spite of not including the random variation. The p-value for the extra-binominal variation was 0.43 which indicates a moderate ‘house-effect’. One reason for this relatively low value might be that the random effect is

already partly accounted for through the variable ‘Lagsalm’, the salmonella status of the preceding flock in the same house. This is supported by the fact that the largest deviation

in p-value (although all deviations are small) between the two models in Table 4 is found for ‘Lagsalm’.

4.3. Non-signijcantfactors

4.3. I. Animal density

Animal density might influence the infection pressure within the flock (Martin et al., 1987) and could consequently be associated with an increased salmonella prevalence. We therefore included the number of chickens m P2 in our investigation. This variable was

based upon data from the Flock Economy Control. It was consequently only available from 60% of the farms and 80% of the flocks. The flock size and the area of the house were also included as separate variables. None of these were significant in our multivariable model. This might be due to the small standard deviation in animal density in our data (Table I 1. Additionally, no correlation was found between the animal density and the number of

houses at the farm, a parameter in the model which might also be related to production intensity, making collinearity a less probable explanation for the non-significance of this variable.

4.3.2. Age of house This factor might represent a risk factor because old buildings are difficult to clean

properly. These data came from the Flock Economy Control but proved to be non-significant and were therefore not included in the final model.

0,. Angen et al. /Preventive Veterinury Medicine 26 (1996) 223-237 233

4.3.3. Slaughterhouse of the precedingjock

The broilers are transported to the slaughterhouses in large crates of different forms and

sizes which belong to the slaughterhouses. Cleaning and disinfection of the crates and the lorries represent a weak link due to the lack of efficient equipment. In addition, beetles have

been observed inside the metal tubing of the crate racks (M. Bisgaard, unpublished results, 1992). Consequently S. enterica can be transferred from farm to farm through the slaugh-

terhouse. We therefore decided to test the slaughterhouse used to slaughter the preceding flock in our investigation. This was a significant factor in the bivariable analysis, but turned

out to be non-significant in the multivariate modelling. An explanation might be that the variable is collinear with other area-related variables in the model, e.g. hatchery and feedmill.

4.3.4. AM district

As all flock data are collected by the AM veterinarians the individual reporting traditions might cause information bias. Detection of systematic differences in this respect might be

an indication of the reliability of the reporting system, and consequently the database. This

variable was also tested to control if the AM veterinarian could be a confounder in the analyses. No association between AM district and salmonella prevalence was found in the

multivariate analyses, but there was a significant association between AM district and some

of the recorded data, indicating a possible difference in reporting routines. Collinearity with the other area-related variables in the model might also exist.

4.3.5. Days between disinfection and replacement

This is an obvious parameter if we want to assess the efficiency of decontamination. The decimation of bacteria is correlated with the time that the disinfectant is allowed to act and the temperature in the house (Jawetz et al., 1989). S. enterica survive in dust and other

organic material not removed, and these factors also protect against disinfectants. This variable was not significant in the bivariable analysis. The number of days is recorded according to the farmer’s information at the AM veterinarian’s visit, and may also be biased by the AM veterinarian’s reporting tradition. This is indicated by the observed difference between the AM districts in the number of days reported.

4.3.6. Bedding

The condition of the bedding (wet, dry, hard) could be regarded as an indicator of the

hygienic standard and management in the house. Different researchers have studied the

effect of bedding upon salmonellacontamination with conflicting results (Dougherty, 1976; Lahellec, 1987; Jones, 1990; Waldroup et al, 1992). The condition of the bedding is evaluated by the AM veterinarian. It was not associated with salmonella in the bivariable analysis. However, we noted a small difference between the AM districts in the reporting of this variable.

4.3.7. Beetles

Salmonella has been isolated from beetles (Jones et al., 1991; Brown et al., 1992) which might explain the persistence of S. enterica on these farms. However, beetles were reported only occasionally prior to January 1993. Furthermore, the precision of the observations could be questioned. If the farmer observes beetles this should be reported to the AM

234 0,. Angen et al. /Preventive Veterinary Medicine 26 (1996) 223-237

veterinarian at his next visit. Due to supposed lack of precision we decided to regard a house as contaminated with beetles if their presence had been reported once or more during 1993. This variable was not significant in our bivariable analysis. We found, however, a significant

difference between the AM districts in reporting of this variable.

4.4. AM database

In addition to identifying factors associated with salmonella contamination in broiler

flocks, the objective of the study was also to evaluate the structure of the AM database and the data included. The data stored in the AM database were not primarily gathered for epidemiological purposes. This has affected the relevance and quality of the variables available for the study.

Variables which were not suspected to influence the occurrence of S. enterica in the 3- week-old chickens were not included in the analysis. Most of the information from the

slaughterhouses and the clinical observations from the AM veterinarian were therefore omitted.

Some of the variables were regarded as biologically irrelevant, e.g. coccidiostatics, pro- biotics and disinfectants. The probiotics used have no effect on Gram-negative bacteria (Friis, 1994). However, an indirect effect by altering the intestinal bacterial ecology cannot

be excluded. In the AM database only the names of the disinfectants are included - not the manner in which they are used.

We suspect that low quality of some data makes it difficult to evaluate their association with the salmonella status. This might, for example, be the case for the condition of the bedding, for the presence of beetles, and the number of days between cleaning and replace-

ment. As mentioned earlier, significant differences were found in the distribution of these variables between the AM districts. This could be explained by actual differences between

the AM districts, but it could also be a result of differences in reporting routines.

In our analysis an unequivocal identification of the flock was requested. However, 12.9%

of the chicken samples sent to the National Veterinary Laboratory consisted of chickens

from more than one flock making the determination of the salmonellastatus of the individual flock impossible. Determination of the salmonella status of a flock could also be improved by random sampling of the 16 3-week-old chickens collected by the farmer.

Bacteriological examination of the faeces of the chickens might be a method to determine the salmonella status of the flock at a lower prevalence level. Alternatively, this could be achieved by increasing the sample size of 3-week-old chickens.

The biological relevance of looking for risk factors associated with a high-grade contam- ination (prevalence> 20%) as opposed to a low-grade contamination might be questionable.

4.5. Prospective view

In our study, all serotypes of S. enterica were pooled in one group. There are indications that the different serotypes have different pathogenesis (Bisgaard and Hansen, 1994). This could be investigated by carrying out a separate analysis for each serotype. However, this

0. Angen et al. /Preventive Veterinary Medicine 26 (1996) 223-237 235

could be problematic because only one colony per isolation is serotyped - probably giving

an underestimate of the diversity of the serotypes found in the hocks. The model could be validated by analysing data collected in the coming year. The risk factors identified in the study could further be investigated by making a pro-

spective study on a random sample of flocks. This has for instance been done studying camphylobacter infection in broilers (Kapperud et al., 1993).

In our analysis hatcheries and feedmills were found to be risk factors associated with

salmonella infection in broiler flocks. It could be relevant to include the actual salmonella status of these sites in analysis. The salmonella status of the egglaying hocks could also be

a relevant variable to integrate in the analysis. Season turned out to be a significant part of our model - suggesting that time-series

analysis might be an appropriate method to analyse the data.

5. Conclusions

Of the 14 variables investigated in the study, five were associated with the salmonella

status of the broiler flocks. There were increased risks for S. enterica infection associated with the largest hatcheries and feedmill, with an increasing number of houses on the farm, if the preceding flock was found infected, and if the flock was reared in the autumn.

Additionally, some data possibly had too low precision to allow a reliable evaluation of their epidemiological importance.

Our study indicates that the contamination problem might be increased in connection

with large-scale production. Obviously, more emphasis should be put into investigating the salmonella status of the different hatcheries and egglaying flocks.

We also found indications that the hygienic conditions inside the farms influence the

salmonella prevalence in the flocks. This might be improved by more stringent cleaning and disinfection procedures, for instance by following a strict all out-all in principle.

Acknowledgements

This research was supported by The Ministry of Agriculture and Fisheries (Grant No. SUN95KVL- 11) and The Danish Research Councils (Grant No. 20-35 10). We thank Dr. Henrik Stryhn and Dr. Derek J. Brown for critical reading of the manuscript and Jakob Bo

Kristensen (Danish Poultry Council) for a thorough introduction to the AM database.

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