ORIGINAL ARTICLE

Campylobacter Monitoring in German Broiler Flocks:An Explorative Time Series AnalysisS. Hartnack1, M. G. Doherr1, T. Alter2, K. Toutounian-Mashad2 and M. Greiner2

1 Department of Clinical Research and Veterinary Public Health, Vetsuisse Faculty, University of Bern, Bern, Switzerland2 BfR, Federal Institute for Risk Assessment, Berlin, Germany

Impacts

• Data generated in the Campylobacter monitoring programme of broiler

flocks in Germany are suitable for a time series analysis.

• The seasonal peak of notified human campylobacterioses precedes the

peak in broiler prevalence.

• The potential influence of climatic factors on seasonal changes in

campylobacteriosis cases should be considered.

Introduction

Campylobacter, a major zoonotic pathogen, is the most

commonly reported, food-borne bacterial cause of human

enteritis in Europe (Anonymous, 2006). Poultry meat is

consistently considered to be the most important risk

factor for human campylobacteriosis, and the reduction

in the number of poultry products contaminated with

Campylobacter is suggested as a means to improve public

health (Rosenquist et al., 2003; Wagenaar et al., 2006).

Other risk factors for human infection include drinking

untreated water, consuming raw and unpasteurized milk,

handling and cooking food, having contact with food-

producing animals and pets, and swimming in natural

bodies of water (Kapperud et al., 2003; Uyttendaele et al.,

2006). Although studies have provided insight into the

epidemiology of Campylobacter infections, our under-

standing is still incomplete (Wingstrand et al., 2006).

Campylobacter spp. can be isolated from pork, beef and

veal, but the highest contamination rates are found in

poultry (Ghafir et al., 2007). Numerous studies based on

questionnaires, cross-sectional or case–control studies

have investigated the epidemiology of Campylobacter spp.

and potential risk factors in poultry flocks. Most of these

studies have focussed on static risk assessment models

without taking into account the fact that some factors

might influence the occurrence of Campylobacter through

seasonal or other time-dependent patterns. The spread of

Campylobacter within the flock is rapid (Berndtson et al.,

1996; Evans and Sayers, 2000) and virtually all animals

will be colonized with this commensal within 6 days

after introduction of the agent (Van Gerwe et al., 2005).

Keywords:

Campylobacter; time series; broiler;

monitoring programme; campylobacteriosis

Correspondence:

Sonja Hartnack. Department of Clinical

Research and Veterinary Public Health,

Vetsuisse Faculty, University of Bern,

Bremgartenstr 109a, CH-3001 Bern,

Switzerland. Tel.: +41 31 631 2648;

Fax: +41 31 631 2538;

E-mail: sonja.hartnack@itn.unibe.ch

Received for publication May 16, 2008

doi: 10.1111/j.1863-2378.2008.01184.x

Summary

Campylobacter, a major zoonotic pathogen, displays seasonality in poultry and

in humans. In order to identify temporal patterns in the prevalence of thermo-

philic Campylobacter spp. in a voluntary monitoring programme in broiler

flocks in Germany and in the reported human incidence, time series methods

were used. The data originated between May 2004 and June 2007. By the use

of seasonal decomposition, autocorrelation and cross-correlation functions, it

could be shown that an annual seasonality is present. However, the peak

month differs between sample submission, prevalence in broilers and human

incidence. Strikingly, the peak in human campylobacterioses preceded the peak

in broiler prevalence in Lower Saxony rather than occurring after it. Significant

cross-correlations between monthly temperature and prevalence in broilers as

well as between human incidence, monthly temperature, rainfall and wind-

force were identified. The results highlight the necessity to quantify the trans-

mission of Campylobacter from broiler to humans and to include climatic

factors in order to gain further insight into the epidemiology of this zoonotic

disease.

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Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128 117

However, several questions about the dynamics of the

colonization process and the transmission routes remain

unanswered (Rivoal et al., 2005; Conlan et al., 2007).

Campylobacter are fragile organisms, susceptible to a

number of environmental conditions (temperature, pres-

ence of oxygen, pH, UV, humidity) but may survive in a

viable but non-cultivable form (VBNC) in the environ-

ment (Talibart et al., 2000). In water samples from a

broiler farm with chickens colonized with Campylobacter,

the agent itself was not detected by different culture

methods, but evidence for its presence in 60% of 167

samples was given by an indirect fluorescence antibody

test (Pearson et al., 1993). The VBNC state leads to

uncertainty in the interpretation of observational studies

of the disease ecology because false negative test results

may occur if Campylobacter is only present in its VBNC

state (Skelly and Weinstein, 2003). The potential role of

the VBNC state in broiler prevalence and in human

campylobacteriosis is controversially discussed (Oliver,

2005; Murphy et al., 2006). Besides this, prior to flock

colonization, Campylobacter spp. were detected in the

environment (Rivoal et al., 2005; Bull et al., 2006). A

case–control study indicates that among children, contact

with animals or the environment is the source of a sub-

stantial proportion of sporadic Campylobacter infections

(Ethelberg et al., 2005). Transmission by flies is discussed

as a contributing factor to broiler prevalence and human

campylobacteriosis (Ekdahl et al., 2005; Nichols, 2005). A

systematic review evaluating the major contributing fac-

tors and sources of Campylobacter occurrence in broilers

in Great Britain highlights the importance of hygienic

measures in the primary production stage for decreasing

the risk, while the highest disagreement between study

results was in factors related to vertical transmission and

carry-over (Adkin et al., 2006).

Seasonality with a peak of broiler prevalence and

campylobacteriosis in summer and autumn has been

found in several studies (Wedderkopp et al., 2001; Nylen

et al., 2002; Miller et al., 2004; Meldrum et al., 2005).

Seasonal pattern and a dependence on latitude indicate

that climate factors might play a role either directly or

indirectly. A seasonal pattern in Campylobacter contami-

nation was found in a river, and a decrease in notified

cases of campylobacteriosis was observed when levels of

Campylobacter were low (Eyles et al., 2003). Ambient

temperature has been described as a factor that

might contribute to the seasonal variation of campylo-

bacteriosis (Louis et al., 2005; Fleury et al., 2006).

Specific climate conditions that lead to an increase in

broiler prevalence and in campylobacteriosis after differ-

ent time lags have been hypothesized (Patrick et al.,

2004; Kovats et al., 2005; Tam et al., 2006). Significant

differences in Campylobacter colonization rates were

observed between chickens raised under high and low

relative humidity conditions (Line, 2006). Rainfall was

found to be a risk factor for Campylobacter infection

(Sandberg et al., 2006).

In order to fulfil the requirements prescribed by the

zoonosis directive 2003/99/EC, the broiler flocks were

sampled in Germany. Whereas the European Food Safety

Authority summary report describes trends in human

incidence and in animal prevalence on a yearly basis,

information associated to pattern within the years may

remain undetected.

The first aim of this study was to identify temporal

patterns in the prevalence of Campylobacter in a voluntary

monitoring programme in broiler flocks and in the inci-

dence of human campylobacterioses cases in Germany. A

second aim was to investigate relationships among climate

pattern, prevalence in poultry and human cases. Time

series techniques were used to address both the aims.

Materials and Methods

Broiler-monitoring-related data

The raw data used in this study was from the German

Campylobacter monitoring programme in broiler flocks

between May 2004 and June 2007. This programme con-

sists of convenience sampling in 14 larger poultry abatt-

oirs in different geographical regions in Germany. Each

abattoir processes at least 2000 broilers per slaughter-

batch from a given farm and in these 14 abattoirs more

than 80% of the broiler flocks in Germany are processed

(Peters et al., 2006). Within the programme, cloacal

samples from ten broilers were selected from each

slaughter-batch, pooled and tested for Campylobacter

spp. in compliance with ISO 10272/1 in one of eight

regional labs. Test results and all identified Campylobac-

ter isolates were submitted to the Federal Institute for

Risk Assessment (BfR) for further analysis and evalua-

tion. The information required for our study was

extracted from the monitoring database, checked for

consistency, and analysed using the statistical software

package spss (spss� version 14.0 SPSS Inc., Chicago, IL,

USA).

For the time series analysis, four variables of the data-

base were selected: the status of the pool faecal sample for

Campylobacter (positive/negative), the federal state (Bun-

desland) in which the primary production is located, the

date of arrival of the samples from the slaughterhouses at

the regional labs and the number of animals per slaugh-

ter-batch. The whole dataset contained 3382 records. For

the sample submission and prevalence time series 3360

records and for the weighted prevalence time series 2888

records were taken into account. Main exclusion criteria

were missing or implausible data.

Campylobacter Monitoring S. Hartnack et al.

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118 Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128

Human campylobacterioses data

Human campylobacterioses cases data, based on the man-

datory notification system according to the Act for Pro-

tection against Infectious Diseases, were obtained from

the Robert Koch Institute website (http://www3.rki.de/

SurvStat, accessed on 18.04.2007). As output variables,

the total monthly number of campylobacterioses and the

monthly incidence per 100 000 inhabitants for the period

January 2001 to December 2006 were selected. In Ger-

many, the total recorded number of human campylobac-

terioses comprised 346 427 cases within 6 years ranging

from 50 614 to 64 610 cases per year. In Lower Saxony,

the number of campylobacterioses totalled 28 286 cases

(4007–5443 per year).

Climate data

Climate data were obtained from the German National

Meteorological Service website, (the Deutscher Wetterd-

ienst, DWD) (http://www.dwd.de/de/FundE/Klima/KLIS/

daten/online/nat/ausgabe_monatswerte.htm, website accessed

on 02.09.2007) for the station 10338 Hanover Langenha-

gen in Lower Saxony. The following variables (summa-

rized by month) were included in the database: the mean

temperature (TMM) and the maximum temperature

(TXX) two metres above ground in degrees Celsius, the

total sunshine duration (SOS) in hours, the total precipi-

tation (RSS) and the maximum diurnal precipitation total

(RSX) in millimetres and the mean wind-force (FMM) in

(Bft), Beaufort, for the time span January 2001–June

2007.

Preparation of time series data sets

Time series is a sequence of data points measured at

equal time intervals. The time series for the study were

generated by cross-tabulation of the original data of the

monitoring programme to obtain aggregated data on a

monthly and a weekly basis. Three different time series

data sets were generated for weekly and monthly time

windows. The outcome variables for the time series were

first the total number of samples, second the proportion

of positive samples and third the weighted for the batch-

size proportion of positive samples. The weighting was

done in Excel for Windows by summing the number of

animals in each of the time periods (week or month),

normalizing this sum to one and calculating the fraction

of the positive samples. To generate time series for cam-

pylobacterioses, the data from the Robert Koch Institute

and the climate data were directly transferred into spss.

Time series plots were performed for all series to visualize

possible trends and seasonality.

Statistical analysis of data

Autocorrelation function

Autocorrelation of all time series data sets was assessed by

empirical autocorrelation function (ACF), indicating a

correlation of data at current time (t0) with data from

previous time points, i.e. lagging 1, 2, or more time

points. ACF values range between )1 and +1, with values

near to these borders indicating strong positive or nega-

tive correlation between current values and previous

observations. A plot of the ACF (or correlogram) is a use-

ful check for seasonality and other time series patterns

(Makridakis et al., 1998). The x-axis represents the differ-

ent time lags and the y-axis the ACF values. The approxi-

mate standard errors for the autocorrelation coefficients

were calculated on the assumption that the underlying

process is either independent or white noise (Chatfield,

2004). For testing the entire set of autocorrelation coeffi-

cients for significance, the Box–Ljung test based on the

asymptotic chi-square approximation was used.

Seasonal decomposition

All time series data were decomposed into a trend com-

ponent, a seasonal component and a residual error. As

the magnitude of the seasonal fluctuations did not vary

proportionally to increases or decreases of the time series

by visual inspection and assuming that the size of the sea-

sonal effect is constant from year to year, seasonal

decomposition was done in an additive way in spss, lead-

ing to trend-cycle series, seasonally adjusted series, sea-

sonal factors and errors for the human campylobacteriosis

and the climate data. In this procedure, the seasonal fac-

tors estimate the seasonal component, which contribute

to the time series pattern. If subtracted from the original

time series, the seasonally adjusted series is obtained. As

the data from the broiler monitoring programme ranged

only over 3 years to estimate both deseasonalized time

series and seasonal factors, the following procedure was

applied. First, a centred moving average with a time span

of 12 was calculated for all time series (12MA). The

resulting time series represents then a smoothed trend-

cycle. By subtraction of the 12MA series from the original

time series resulting in a detrended time series and calcu-

lation of the mean monthly differences the seasonal fac-

tors were obtained. Subtracting the seasonal factors from

the original data generated a deseasonalized series. For

estimating the errors, the seasonal factors were subtracted

from the detrended time series.

Cross-correlation

In order to evaluate the relationship between Campylo-

bacter isolates in broiler, human campylobacterioses

and climate, cross-correlation functions using the

S. Hartnack et al. Campylobacter Monitoring

ª 2008 The Authors

Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128 119

cross-correlation function of the Trends module in spss

were calculated among the prevalence time series in broi-

lers, the incidence time series in humans and the climate

time series. The lag time for analysis of cross-correlation

refers to a shift between the two time series. For example,

at lags )1, 0 and 1, the correlation of the human incidence

with the prevalence in broilers 1 month earlier, during the

same month, and 1 month later is estimated. This was

done with the original time series and the deseasonalized

time series for the relationship between prevalence in

broiler and human campylobacterioses. The approximate

standard error for the cross-correlation coefficients was

calculated on the assumption that the series are not cross-

correlated and that one of the series is white noise.

Results

Broiler monitoring data

Sample submission and prevalence

Because of data quality, time series for sample submission

and prevalence were determined solely on a monthly

basis.

The sample submission and the prevalence of Campylo-

bacter are shown in Table 1. During the years of the study

period, which comprised for 2004 the period from May

to December and for 2007 the period from January to

June, a decrease in sample submission was found.

Samples from animals originating from Lower Saxony

contributed to 62% of the total number of samples, while

the other 13 federal states each accounted for less than

10% of the total sample size. Therefore, further analyses

were performed both for Germany as a whole and for

Lower Saxony separately.

The pattern of sample submission throughout the year

is quite similar in Germany as a whole and in Lower

Saxony. The highest number of samples was submitted in

May and June, whereas distinctly lower numbers were

found in April, July and October, corresponding to the

prime holiday periods. Time plots for the weekly and

monthly prevalence and weighted prevalence for Germany

and Lower Saxony showed a high variability with values

(range 0–100%) (graph not shown). The highest preva-

lence was detected during the summer months June to

September. The prevalence of Campylobacter in broiler

was considered to be slightly higher in Lower Saxony as

compared with Germany as a whole. The weighted weekly

and monthly prevalences are lower for Germany as a

whole and Lower Saxony as compared with the preva-

lence without weighting. The variable number of animals

per slaughter-batch varied between 500 and 220 001 with

a median of 20 100. No significant difference in the num-

ber of animals per slaughter-batch size could be found

between Campylobacter positive or negative tested slaugh-

ter-batches by a non-parametric test (Wilcoxon P =

0.054). Autocorrelation plots for the sample submissions

and the prevalence in Lower Saxony show distinct pat-

terns: Whereas the sample submission shows significant

positive autocorrelation for time lags 6 and 12, the preva-

lence in broilers displays sinusoidal behaviour with signif-

icant positive autocorrelation for time lags 1, 2 and 12

and significant negative autocorrelation for time lags 5, 6

and 7 (Fig. 1a and b).

Seasonal decomposition

The incidence rates of human campylobacterioses per

100 000 inhabitants display a strong seasonal pattern but

no clear increase or decrease during 2001–2006 is visible.

For Germany as a whole, the highest rates are found in

July and August (ranging from 83.2 to 129.5) and the

lowest rates in March and April (ranging from 36.3 to

60.9). Similarly in Lower Saxony, the highest rates are

found in July and August (ranging from 68.3 to 112.6)

and the lowest rates in March and April (ranging from

29.6 to 45.8). An autocorrelation plot for the human inci-

dence in Lower Saxony indicates a sinusoidal behaviour

with significant positive autocorrelation for time lags 1, 2

and 10–13 and significant negative autocorrelation for

time lags 4–8 (Fig. 1c).

A decomposition plot for human incidence of campy-

lobacterioses in Lower Saxony including the original time

series, the monthly seasonal factors, a seasonally adjusted

series, a trend-cycle component and the errors, supports

the strong seasonality (Fig. 2a–e). The errors of decompo-

sition vary around 0 ranging from )13.7 to 10.1 and are

normally distributed. The autocorrelation plot of the

errors indicates significant positive autocorrelation coeffi-

cients at time lags 3, 6 and 14 (not shown). The estima-

tion of the seasonal factors and the deseasonalized series,

principally based on a centered moving average of 12,

display a similar pattern as compared with the classical

decomposition obtained by spss (not shown). The 12MA

resembles the trend-cycle component of the classical

decomposition but it is smoother. The seasonally adjusted

series of the classical decomposition are similar to the

Table 1. Number of positive tested broiler samples and total number

of submissions for Germany as a whole and Lower Saxony separately

during the years of the study period

Year

Germany as a whole

(no. positives/total)

Lower Saxony

(no. positives/total)

2004 442/988 308/625

2005 515/1208 332/761

2006 269/901 168/522

2007 96/263 61/167

Total 1322/3360 869/2075

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120 Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128

estimated deseasonalized series. The errors of the esti-

mated deseasonalized and detrended time series are larger,

varying from )16.1 to 17.1, and are normally distributed.

Decomposition of the prevalence data of the broiler

monitoring programme was not possible, as data for at

least four seasonal periods or years are required. Estima-

tion of deseasonalized time series was therefore performed

with a moving average of 12 and several subtractions

(Fig. 3a–e). The autocorrelation plot of the errors indi-

cates positive and significant autocorrelation at time lags

1, 6 and 8 lags.

Cross-correlation

Campylobacteriosis and Campylobacter prevalence

in broilers

As the human incidence is calculated annually for the

number of inhabitants, the data used for calculation of

cross-correlation comprised the period January 2004–

December 2006.

Plots of the time series broiler prevalence, human cam-

pylobacterioses and temperature are similar with regard

to their seasonality (Fig. 4a–c). Cross-correlation func-

tions plots find strong cross-correlations between the inci-

dence in humans and the prevalence and weighted

prevalence in broilers at different time lags )1 to 2 with

positive correlation (Fig. 5). The plot shows that the posi-

tive coefficients are not symmetric around the lag 0 with

higher values for positive lag times 1 and 2 months as

compared with )1 month. This indicates that the peak in

campylobacterioses precedes the peak in broiler preva-

lence rather than following it. This result needs to be

carefully interpreted as the autocorrelations present in the

original time series might cause spurious cross-correla-

tions. If the same but deseasonalized series are examined

for cross-correlation, there is still some cross-correlation

found but to a lesser extent at time lag 0 (Fig. 6).

Climate data

For Campylobacter prevalence in broilers, significantly

positive or negative cross correlations were detected for

the mean monthly temperature, the maximum monthly

16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Lag number

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AC

F

Lower confidence limit

Upper confidence limit

Coefficient

Lower confidence limit

Upper confidence limit

Coefficient

Lower confidence limit

Upper confidence limit

Coefficient

Fig. 1. Coefficients of the autocorrelation function of different time

lags corresponding to different months for (a) the sample submission,

(b) the prevalence of Campylobacter spp. in broilers in Lower Saxony

and (c) the human incidence of campylobacteriosis in Lower Saxony.

The upper and the lower confidence limits refer to the 95% confi-

dence limits.

S. Hartnack et al. Campylobacter Monitoring

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Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128 121

120(b)

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Fig. 2. Seasonal decomposition plot of the human incidence of campylobacterioses in Lower Saxony including a time plot of the original data

(a), a trend-cycle component (b), seasonal factors (c), a seasonal adjusted time plot (d) and the residuals (e).

Campylobacter Monitoring S. Hartnack et al.

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122 Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128

(b)

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Fig. 3. Seasonal decomposition plot of the prevalence of Campylobacter in broilers in Lower Saxony including a time plot of the original data

(a), a trend-cycle component (b), seasonal factors (c), a seasonal adjusted time plot (d) and the residuals (e).

S. Hartnack et al. Campylobacter Monitoring

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Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128 123

temperature and the hours of sunshine (Fig. 7) indicating

that temperature is correlated with the prevalence of

Campylobacter in broilers in the same month and the two

following months. For the human campylobacterioses

incidence, additionally significant cross-correlations were

found the rainfall and the wind-force (not shown). Gen-

erally, the data pertaining to human campylobacterioses

exhibit a stronger cross-correlation with the climate data

than the Campylobacter prevalence in broilers.

NOV 2006MAY 2006NOV 2005MAY 2005NOV 2004MAY 2004Date

NOV 2006MAY 2006NOV 2005MAY 2005NOV 2004MAY 2004Date

NOV 2006MAY 2006NOV 2005MAY 2005NOV 2004MAY 2004Date

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Fig. 4. Time plots for the monthly human incidence (cases per

100 000 inhabitants) of campylobacterioses (a), the prevalence in

broilers (b) and the mean temperature in Lower Saxony (c).

7 6 5 4 3 2 1 0 –1 –2 –3 –4 –5 –6 –7 Lag number

0.9

0.6

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F

Lower confidence limit

Upper confidence limit

Coefficient

Fig. 5. Coefficients at different positive and negative time lags, corre-

sponding to different months, for the cross-correlation between

human campylobacterioses incidence and weighted broiler prevalence.

The upper and the lower confidence limits refer to the 95% confi-

dence limits.

76543210–1–2–3–4–5–6–7Lag number

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F

Lower confidence limit

Upper confidence limit

Coefficient

Fig. 6. Coefficients at different positive and negative time lags, corre-

sponding to different months, of the seasonal adjusted time series for

the cross-correlation between human campylobacterioses incidence

and broiler prevalence. The upper and the lower confidence limits

refer to the 95% confidence limits.

Campylobacter Monitoring S. Hartnack et al.

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124 Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128

Discussion

The German broiler-monitoring programme according to

the Zoonoses directive 2003/99/EC is intended to monitor

changes in antibiotic resistance. In order to detect possi-

ble trends in antibiotic resistance, at least 170 Campylo-

bacter isolates should be examined each year. This

demand was largely met during the years 2004–2006. Our

aim was to answer the question whether this database

could be additionally explored by time series analysis in

order to identify temporal patterns in the sample submis-

sion pattern and the infection prevalences in broiler flocks

in Germany, and temporal links to the climate patterns

and human Campylobacter monitoring data. Classical

seasonal decomposition is preferably performed with

longer time series comprising (if possible) observations

over several years or even decenniums to detect seasonal

and trend-cycle patterns. In our study, data were only

available for 6 years for the human campylobacterioses

and for 38 months for the Campylobacter prevalence in

broilers. We consider this time-span long enough to

detect seasonality (annual patterns) in the time series. For

describing reliable trend-cycle patterns, longer time series

would be needed. The question about appropriate length

for time series to be analysed in monitoring programmes

should be carefully addressed, as over a long time period

other influences like changes in diagnostic techniques and

disease awareness might play a role, too. We focussed on

the description of sample submission and prevalence dur-

ing the year and the relation between sample submission

and prevalence. Further, we considered the relationship in

time between prevalence of Campylobacter in broiler and

incidence of campylobacterioses in humans and the cli-

mate data temperature, rainfall and wind-force. The most

striking finding of this study is that the data indicate that

the seasonal peak in human campylobacterioses preceded,

rather than followed, the peak in broiler prevalence. This

result is in accordance with a study comparing the peak

of human Campylobacter infections (early June) with

Campylobacter isolation rates from fresh retail chicken

samples (late June) (Meldrum et al., 2005). If the effect of

season is removed by seasonal decomposition in the time

series human campylobacterioses and in the time series

broiler prevalence data, the cross-correlation between

these two time series is substantially reduced but still

present at time lag 0. It should be noted that the time of

notification of human campylobacteriosis and not the

date of infection or disease onset is recorded. This raises

the question about the relative importance of Campylo-

bacter in poultry for human campylobacterioses and leads

to the hypothesis that poultry may not be the main

source of infection for humans, but humans and poultry

may be infected by a common source (Hudson et al.,

1999; Skelly and Weinstein, 2003). The relative impor-

tance of human campylobacterioses because of contami-

nated poultry is considered to be between 20–40%

(Havelaar et al., 2007) in the Netherlands to 80%

frequency of food-borne transmission in the USA (Mead

et al., 1999). Several authors agree that there is a need for

quantifying the contribution of different sources to cam-

pylobacterioses (Patrick et al., 2004; Conlan et al., 2007;

Karenlampi et al., 2007). The Dioxin Crisis in Belgium in

1999 led to the complete withdrawal of poultry products

originating from Belgium, and simultaneously a decrease

in notified campylobacterioses was detected. This decrease

was estimated to be 40% as compared with the expected

number (Vellinga and Van Loock, 2002). Another finding

of our study is that the correlation between climate data

and human campylobacterioses seems to be stronger than

between climate data and broiler prevalence. It is difficult

to directly compare the suitability of the broiler and

human campylobacterioses data for time series analysis,

because both data were collected for different purposes

and by totally different data-generating processes. The

broiler-monitoring data can be described in terms of a

sampling process with known sampling frame and sample

sizes while the human data are based on absolute num-

bers of registered cases. Expected or potential sources of

bias (e.g. selection bias for the broiler data and under-

reporting in the human data) were not addressed in this

study. On the other hand, the data for a study investigat-

ing the effects of climate on the incidence of Campylobac-

ter spp. in humans and the prevalence in broiler flocks in

Denmark related to all the broiler flocks slaughtered

76543210–1–2–3–4–5–6–7Lag number

0.9

0.6

0.3

0.0

–0.3

–0.6

–0.9

CC

F

Lower confidence limit

Upper confidence limit

Coefficient

Fig. 7. Coefficients at different positive and negative time lags, cor-

responding to different months, for the cross-correlation between

mean monthly temperature and broiler prevalence. The upper and the

lower confidence limits refer to the 95% confidence limits.

S. Hartnack et al. Campylobacter Monitoring

ª 2008 The Authors

Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128 125

during the period of the study but similarly indicate that

the climate factors with some time lags prior are more

useful predictors for human incidence than for broiler

flock prevalence (Patrick et al., 2004). The underlying rea-

sons for the seasonality in human campylobacterioses and

the Campylobacter prevalence in broiler remain elusive.

Further research, preferably an approach which combines

epidemiological, ecological, microbiological and veterinary

perspectives, seems to be necessary to assess the impact of

bio-security measures on poultry farms and to reduce the

public health burden because of Campylobacter. Data used

for the analysis were considered to be sufficient for an

evaluation on a monthly but not on a weekly basis. As

most of the countries, with the exception of Lower Sax-

ony, contributed less than 10% to the total sample sub-

mission, we decided to generate time series for Lower

Saxony and for Germany as a whole. No marked differ-

ence could be found between the situation of Campylo-

bacter in broilers in Lower Saxony and in Germany as a

whole in the database. In case of limited resources, it

could be advisable for future research to focus on Lower

Saxony instead of trying to investigate different regions.

Both sample submission and prevalence showed a strong

seasonal pattern and a decrease over time but the time

pattern was not fully synchronized. Whereas the seasonal-

ity for the sample submission seems to be related to the

Easter, summer and autumn holidays, the seasonality for

the prevalence with a peak in the summer months seems

to be related to climate factors, mainly the temperature in

the same month and the two previous months. In a study

that investigated environmental temperatures and the

incidence of food poisoning by the use of deseasonalized

data, a strong association between a rise in food poison-

ing and temperatures 2–5 weeks earlier (Bentham and

Langford, 2001) was detected. One of the limitations of

this study was that the available data did not allow gener-

ating time series on a weekly basis. The decrease in sam-

ple submission and in prevalence poses no problem for

the antibiotic resistance monitoring, as a sufficient

number of samples were still examined. However, this

decrease could hinder an appropriate estimation of the

prevalence. The extreme values of 0% or 100% prevalence

are mainly because of a very low number of submitted

samples, which were all negative or positive. This variabil-

ity in prevalence is in accordance with the inconclusive

data for prevalence of Campylobacter in broilers in the lit-

erature and underlines the need for a baseline study for

Campylobacter prevalence based on sample sizes which

are representative for the animals, the geographic origin

and the time point during the year when the samples are

taken. It would be useful if a minimum number of sam-

ple submissions per month could be examined. The

demand of appropriate comparable sampling techniques

and enumeration methods, which are important for the

evaluation of quantitative data (Scherer et al., 2006; Rass-

chaert et al., 2007) are presumed to be met by the organi-

zation of the broiler-monitoring programme. Despite the

fact that this monitoring programme is based on conve-

nience sampling in the slaughterhouses and the regional

labs and is not designed for appropriate prevalence esti-

mation, it still allows the description of Campylobacter

prevalence dynamics in broilers over the year. The sam-

pling scheme with a pool of 10 cloacal samples per

slaughter-batch is considered to be justified as the spread

of Campylobacter in a poultry flock is described to occur

in a few days and the probability that all animals will be

either positive or negative or that the within-herd preva-

lence is close to 100% is very high (Berndtson et al.,

1996; Evans and Sayers, 2000). The weightage factor based

on the variable number of animals per slaughter-batch

was calculated and used to generate weighted prevalence

time series. The aim was to account for possible bias in

the prevalence estimation because of the sampling scheme

of a fixed number of 10 cloacal samples pool regardless of

the slaughter-batch size. The slaughter-batch size was

assumed to be identical to the flock size. The effect of

weighting was that the prevalence was generally lower as

compared with the non-weighted time series. However,

slaughter-batch size was not significantly different in

Campylobacter positive or negative slaughter-batches. This

is in accordance with the findings of another study (Reg-

ula et al., 2005) and indicates that correction for slaugh-

ter-batch size does not improve the estimation of

prevalence.

The approach of seasonal decomposition was chosen

with the aims of eliminating seasonal factors and of

examining time-dependent but non-seasonal factors that

explain the relationship between Campylobacter in broilers

and campylobacterioses in humans. After removal of

trend and seasonal effects through decomposition, the

time series did not exhibit any pattern and, thus, it is

suggested that the variability over time can be sufficiently

explained by a trend and a seasonal component. To

describe the time series of incidence in more detail by a

model, which was beyond the scope of this study, it

should be taken into account that some seasonality is left

after decomposition, as is shown by the significant auto-

correlation coefficients of the errors at time lags 3, 6 and

14. The estimation of deseasonalized time series based on

a 12MA was accomplished parallel to the incidence of

campylobacterioses in humans and for the climate data to

compare the results between the two methods, which

were similar and thus reliable. As seasonal decomposition

was not possible for the time series of prevalence in broil-

ers because of a too short observation time, the esti-

mation of deseasonalized time series proved to be a

Campylobacter Monitoring S. Hartnack et al.

ª 2008 The Authors

126 Journal compilation ª 2008 Blackwell Verlag • Zoonoses Public Health. 56 (2009) 117–128

possibility for examining the relationship in time between

the different time series. The seasonally adjusted time ser-

ies for Campylobacter prevalence should be interpreted

carefully, as the estimated seasonal factors were calculated

for only two periods of the 12 months. The overall

pattern of seasonality is considered to be reliable and

significant auto- and cross-correlation coefficients are

meaningful. The data of the climate factors originate from

one meteorological station in Lower Saxony. This intro-

duces uncertainty in the interpretation of the results. For

further research on the interactions between the occur-

rence of Campylobacter in humans and in broilers, the

meteorological data from several stations should be used

to account for the variability in the data.

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