Salmonella enteritidis: clinical epidemiological approaches for prevention and control of S. enteritidis in poultry production

International Journal of Food Microbiology, 21 ( 19~4) i3 i - "t,*.~ © 1994 Elsevier Science B.V. All rights reserved 0168-1605/94/$07.00 FOOD

131

00664

Salmonella enteritidis" clinical epidemiological

approaches for prevention and control of S. enteritidis in poultry production J.P. Noordhuizen and K. Frankena Department of Animal Husbandry, Animal Health Section, Wageningen Agricultural Unic'ersity, Wageningen, The Netherlands

Salmonella enteritidis infections in poultry appear to be of major public concern. Prevalence levels in veal calves and pigs are rather low. Because of the complex of socio-psychological, welfare, economic and public health aspects great emphasis should be put on prevention and control. This paper deals with some clinical epidemiological approaches for prevention and control of S. enteritidis. Emphasis is set on multifactorial background of infection occurrence, epidemiological methods and features of monitoring and surveillance for evaluation of measures taken during a follow-up period. Finally, it is stated that the application of Risk Assessment & Analysis principles in this problem area, integrating the concepts previously addressed, might prove to be a valuable perspective.

Key words: S. enteritidis; Clinical epidemiology; Poultry production; Monitoring/Surveillance; Control

Introduction Due to the publicity around cases of S. enteritidis infections in man originating from, e.g., infected eggs in primarily the UK since 1988, public opinion often regards this infection as the major foodborne disease (Anonymous, 1988a,b,c; Notermans et al., 1992). The pathogen has subsequently been detected in poultry and poultry products in other countries too (Ament et al., 1993). The concept Council Directive 9 2 / 1 1 7 / E C (Council Directive EC, 1993) of 17 December 1992, dealing with specified zoonoses and specified zoonotic agents, points to the need for accurate, reliable and internationally comparable disease data. The latter is even more important given the abolishment of the internal borders in the EC and the increase of international trade. Correspondence address: J.P. Noordhuizen, Department of Animal Husbandry, Animal Health Section, Wageningen Agricultural University, PO Box 338, 6700 AH Wageningen, The Netherlands. Tel. 08370 83120/83187; Fax 08370 85006. SSDI 0 1 6 8 - 1 6 0 5 ( 9 3 ) E 0 0 8 0 - B

132 In The Netherlands, the number of reported cases of salmonellosis in man varied from 8000 to 10000 per year between 1974 and 1984 (Edel and Visser, 1989). Since 1988 the proportion of S. enteritidis in human Salmonella isolates in The Netherlands has increased from 7.6 to 34.4%. At the same time, the estimated number of suspect Salmonella cases in man was assessed to be a multiple of this, resulting in a corrected incidence estimate of 5000 cases per million inhabitants (Hoogenboom-Vergedaal et al., 1989). Components of economic loss directly related to the disease in man are disease treatment and death in high risk groups, extra health care, loss of productivity and impaired welfare, all cumulating upto 50 million US$ per year (Krug, 1984; Roberts, 1989). Additionally, the animal production sectors suffer from economic losses due to market changes and image loss, and productivity losses. For the veal calf industry in Germany, Krug (1984) calculated a total yearly loss of 15 million US$. At the farm level this is translated into costs related to disease symptoms, production losses and costs for measures to be taken in case of positive diagnosis such as combat or eradication. However, a reliable insight in the prevalence of the infection throughout the sector is not always available. Yet, the combat or eradication of these infections is relevant because of public health relationships, animal health implications and potential export limitations (Ament et al., 1993). In The Netherlands, it has been stated that the Salmonella problem situation in poultry is enhanced by a lack of control on import animals, a lack of sufficient control on the breeder farms, a lack of monitoring in the production sector and a deficient hygiene strategy on the farms (Van den Wijngaard, 1993). Since it appears that S. enteritidis is primarily a poultry sector problem and less a problem in pigs and veal calves (National Salmonella Centre, RIVM Bilthoven, The Netherlands) focus will be on poultry. The complex of economic, welfare and socio-psychological effects should be sufficient for the individual farm and sector to design and apply a proper programme of preventive measures. This paper addresses clinical epidemiological approaches for prevention and control of S. enteritidis infections in poultry. Emphasis is set on the multifactorial background of such infections, methodological principles and quantitative epidemiological features of monitoring and surveillance.

Epidemiology as an aid to prevention

According to Humphrey et al. (1989) S. enteritidis can spread through both vertical and horizontal transmission. In the former case, breeders and egg-laying lines are involved, often without clinical signs. In the latter case, infection spreads in hatcheries, broiler farms and slaughter plants by aerosol, contact (e.g., via egg shell) and poor desinfection (Cox et al., 1993; Rigby et al., 1980b; Williams, 1984). An overview of major components contributing to Salmonella prevalence in livestock and products is given in Fig. 1 (after Bolder, 1981). In multifactorial

133 feed ~mpor'-s

l

birds

rodents

anima'~ feed

¢

crops

i~sects

litter material

anima~l

/

surface wa-_er (

liveszock (pig; chicken e~c)

offals <

man

meat, eggs and egg products, milk

Fig. 1. Overview of the major components contributing to the prevalence of Salmonella in livestock and products (after Bolder, 1981).

infection situations like this, prevention must be focused on the respective components. If the contribution of these components to infection occurrence could be quantified according to modern epidemiological methods, an even better basis for prevention could become available. In observational-analytic studies such risk factors could be identified and their contribution quantified through multivariate analysis like logistic regression or survival analysis. Parameters could be Odds Ratios or Relative Risk with confidence interval (Hosmer and Lemeshow, 1989). The importance of this quantification is in the fact that, from both an epidemiological and economic point of view, these risk factors can thus be ranked and measures prioritized. H e n k e n et al. (1992) reported on this multivariate risk quantification for salmonellosis in general in broiler breeder flocks. In this way they established in their study that major risk factors were related to disinfection, hygiene barriers and feedmill. In the worst case of these factors, the risk of being in the salmonella-positive group was 46-times greater than in the best situation of the factor states. Small feed mills alone increased this risk with a factor 5.3. In this way, these methods make visible what high risk flocks or situations are and on what factors prevention should be focused first. Follow up studies in the same population then can reveal the extent to which risk reduction has been achieved after measures taken.

134

Another potential application of epidemiology regards infection/disease modelling. In simulation models, contributing factors and infection state transition probabilities are mathematically computed into a model. This model describes or predicts patterns of infection occurrence or predicts certain disease outcomes with or without economic valuation (Huirne et al., 1991; Dijkhuizen, 1993; Dijkhuizen et al., 1993; Hurd and Kaneene, 1993). The model can be validated by using data from the field. Other techniques using the so-called basic reproductive ratio R o can determine spread of infection without simulation techniques (De Jong and Diekmann, 1992). For Salmonella no such model could be found by the authors in the literature. One of the advantages of such a model would be that it can indicate the areas where action is warranted. At the same time the model can present the economic consequence of certain decision alternatives for prevention or control at the farm level or the sector level. Examples of such an approach are presented by Ament et al. (1993) and Hurd and Kaneene (1993).

Monitoring & Surveillance Systems (MOSS) Given the fact that EC regulations will induce a strict control of S. enteritidis infections in Member States, it could be worthwhile to investigate the potentials and limitations of a MOSS for this purpose. The general objective of a MOSS is to provide reliable and accurate information about infection/disease occurrence and spread over time and spatially which is not or hard to obtain otherwise. Hence, this information may be used for surveillance and infection prediction, or during a follow-up period for evaluating the effectivity of control measures. The basis of a MOSS is a network of sampling units (all farms or sample of farms) in a region, chosen according to a strict protocol (sampling procedure; diagnostic sampling protocol; for both see elsewhere). The first requirement is to have a good estimate of the population at risk (number of farms; number of animals per farm), the second is to estimate the relative number of infection cases in this population (Martin et al., 1987). For an optimum insight in the different prevalence rates both the broiler sector and the egg laying sector have to be sampled separately because of different infection courses. For the broiler sector a slaughterhouse sampling is also possible, although more contamination may occur and hence a false prevalence estimate would be obtained. In addition, sampling there would technically be more difficult (Fanelli et al., 1969; Eibers, 1991). The next alternative would be the design of continuous screening programmes on production farms to detect Salmonella infections. The general outcomes of a MOSS should be that: (1) vertical transmission is prevented on breeding farms and egg-laying farms; (2) horizontal transmission is prevented on all farms; (3) current infections on all types of farms are combatted adequately. Table I comprises elements for consideration about a MOSS meant for the poultry sector.

135 TABLE I Major elements for considering a general MOSS for S. enteritidis control/eradication Specified objectives Prevalence or incidence data Individual or group observations On-farm versus slaughterhouse measuring Unit of time for observation Diagnostic procedures Sampling procedures and unit selection Costs and benefits

Specified objectives For eradication purposes a test-and-cull screening procedure can be followed based on a reliable diagnostic test. Cost-benefit analyses will determine the feasibility of p r o g r a m m e s ( A m e n t et al., 1993). For control purposes it is rather a matter of following patterns and trends, and take action in case certain thresholds are passed. For evaluation of measures during follow up a MOSS can be suitable.

Prevalence/incidence data Prevalence data would provide the basis for economic cost-benefit calculation about alternative strategies. The n u m b e r of new or existing cases in a population and the frequency of infection occurrence are major disease information elements. Data about prevalence are easier to gather than incidence data. The former, however, are a poor approximation of the latter if infection is frequent and of short duration (Martin et al., 1987).

Observation of individuals versus group Several sampling procedures are applied in the field of epidemiology, e.g., to determine prevalence rates or to define a flock as negative (Cannon and Roe, 1982; Martin et al., 1987). For an S. enteritidis MOSS meant for evaluation during follow up, it appears most appropriate to design a two-way sampling protocol: sampling of f a r m s / f l o c k s and sampling of individual animals within these farms. This sampling serves to define a farm as being infected or not. A mean per farm can be calculated and hence a prevalence. This procedure would not be technically feasible since large numbers of individuals would then be needed for testing. Therefore, pooled animal samples replace the individual samples. True prevalence cannot be calculated, but rather the number of infected samples. This pooled sample comprises 20 individual sampled animals (Van der Giessen et al., 1991). False-negative farms, however, were found. For detection of various serotypes it could be worthwhile to take more pooled samples per farm. In addition to this

136 procedure it should be borne in mind that diagnostic test characteristics like sensitivity and specificity are paramount features in control.

Obsercations on the farm cersus slaughterhouse It could be indicated to sample at different parts of the entire production chain, especially since the correlation between results will decrease, the more distant the primary production (sample) is from the consumer. Therefore, it appears adequate to sample specifically in the production sector, both laying and broiler. Although vertical transmission of S. enteritidis has been proven, its clinical incidence still is very low. In The Netherlands, contaminated breeder flocks are only incidentally found (Ament et al., 1993). Within-egg infection has been determined in eggs from clinically diseased hens and in this group only rarely (VIAl, 1990). On the other hand, transmission through egg shells is considered of substantial relevance.

Unit of obserc'ation time Because the study of trends can be part of the MOSS there must be a time unit defined. Observation cycles of about 1 year seem adequate because a cycle for laying hens is about 1 year while that for broilers is about 2 months. Age at sampling must be defined since this can influence the outcome in later phases. A sampling just before delivery seems most adequate because then the correlation with outcomes of the end product stage is highest and contamination risk on the farm is lowest.

Diagnostic and sampling procedures (unit of selection) For diagnostic methods we refer to other papers. Major issues are sensitivity and specificity of tests applied, since sample size is affected by, e.g., test sensitivities less than 100%; most diagnostic tests do not reach the 100% level. When we would design a MOSS for, e.g., Dutch conditions based on proper sampling

TABEL II Number of broiler farms and broilers to be sampled for the estimation of the prevalence of Salmonella organisms Confidence level Estimated

0.10 (0.90) 0.20 (0.80) 0.30 (0.70) 0.40 (0.60) 0.50

At farm level 0.90

0.95

At animal level 0.90 0.95

96 169 221 251 261

137 239 311 354 368

97 171 224 256 267

prevalence 138 244 319 364 379

137

principles and comprising a two-way procedure (sampling both flocks and animals separately), the following statements could be made (Table II).

Step I." farms//flocks Broiler population: 41.6 million animals on 1430 farms, six rounds per year and including 2 empty days per round. Sampling population of farms per year: 1430- 6 = 8586 units. The average number of flocks per farm is 1.8. The average flock-population is 8586" 1.8 = 15 455 units. The S. enteritidis prevalence can be determined at both farm/flock level and animal level. For the prevalence estimation several assumptions have to be made. If 90% of the farms//flocks would be infected, then 137 farms//flocks (Table II) should be sampled in order to conclude with 95% confidence that the true prevalence is between 85 and 95% (at a confidence level of 0.95 and a maximum absolute error of 0.05). If prevalence is lower than 90%, then the number of farms to be sampled increases. The worst situation will be at a prevalence of 50% (368 units of farms//flocks to be sampled). In case no information about infection levels is available, it is indicated to choose for the worst case scenario: set the expected prevalence at 50%. It also appears that lowering the confidence limit will decrease the number of units to be sampled; the same is valid for increasing the absolute error.

Step 11: animals For the estimation of prevalence at animal level the second step has to be taken (see Table II). This means that for 41.6 million animals and six rounds per year the sample size is 138, given a prevalence of 10%, an error level of 0.05 and a confidence level of 95%. And for the calculation of the total number of animals involved in sampling, results from both steps I and II have to be multiplied. Thus, selection of farms yields a sample of 137. 138 --- 18 906 animals. Selection of flocks finally results in a

TABLE III Estimation of the number of broilers to be sampled for detection of Salmonella at varying test sensitivity levels, varying prevalence and at a confidence level of 95% Expected prevalence

Test sensitivity levels (%) 100

90

80

1 5 10 15 20 50

297 58 28 18 13 4

330 65 32 21 15 5

371 73 36 23 17 6

138

slightly higher figure of 138. 138 = 19044 animals (still at a 10% infection level) due to a technical adjustment in sample size calculation for finite populations. In the worst case of a 50% infection prevalence the calculated number of samples would be 137-380 (see Table II) equalling 52060 samples. After adjustment for finite populations thus 138-380 equals 52440 samples. The ultimate confidence level would be 0.95 • 0.95 = 0.90 (or 90%), while the ultimate error level can be determined as 1-0.95.0.95 = 1-(1-0.05 error)-(1-0.05 e r r o r ) = 0.975 (or 97.5%). The 138 flocks named for sampling should be randomly chosen. Practically, this means that 138/1.8 = 77 farms will be selected instead of 137 independent farms or 138 independent flocks, because this reduces costs of sampling. However, flocks within a farm will be more alike than flocks between farms; hence prevalence will be biased. Selecting more farms than numbered above would improve the estimate and reliability. Since no information about the variance is currently available, this figure cannot be calculated exactly but can be said to be between 138 and 247 ( = 137" 1.8). The question remains whether or not one is interested in the total number of infected animals (see objectives of a MOSS). It could be far more relevant to gain insight in the prevalence of specific serotypes of Salmonella species at flock level. But for this purpose the sampling for detection is dependent on the diagnostic test characteristics, while the first step remains the same. Examples of how sensitivity can affect sample size are presented in Table III. At decreasing test sensitivity more animals have to be sampled, while at higher confidence levels also more animals have to be sampled (data not shown). For serotypes with a relatively high prevalence less animals have to be sampled as compared to serotypes with a low prevalence. S. enteritidis for example shows prevalences of 1% or less, which causes less reliability and leads to more animals to be sampled. In the latter case a total of 137 flocks • 330 animals = 45210 animals have to be sampled. When it appears that the farms show a skewed distribution of farm size over farm numbers in the population, results obtained from a random selection will not be representative for the population. In that case random selection should be replaced by allocation according to farm size. A farm twice as large as another will then have a twice as higher chance for being sampled. This is called stratification according to farm size. Within each stratum sampling will be done randomly. If geographic location is another relevant factor for disease occurrence a second stratification according to region can be done. When comparing random procedures and stratification procedures it should be borne in mind that each variance is different and that the sample size numbers have to be adjusted for being valid at the same level of accuracy and confidence level. For non-stratification procedures this would mean that sample size increases (e.g., from 138 to 393) since the assumption that distributions are binomial is not correct. The latter results in an underestimation of numbers at random selection. Stratification further leads to a reduced number of samples required, mainly caused by a reduced number of farms involved. Further decrease of numbers could be obtained if more information

139

about infection status or probabilities of subgroups would be available. Different sampling strategies and calculations have been provided by Fleiss (1973), Barnett (1974), Stuart (1984) and Raj (1986).

Costs and benefits The costs of a MOSS, either as representative or as screening method, are associated with elements such as costs for laboratory examinations of samples, personnel costs for in-field, laboratory and analysis activities, travel costs for sampling of farms. For the detection of S. enteritidis infected laying hen farms with a sampling screening visit once per year and revisit for resampling after a positive test result, Ament et al. (1993) calculated a total annual cost of 1.6 million US$ for 1962 farms. Vermunt (1990) calculated the costs of an intensive monitoring system for the whole poultry and pig sector in The Netherlands, comprising all farms and pooled samples, at 9 million US$ per year. For the veal calf industry these costs could be estimated at 2 million US$ per year. This MOSS would involve 18 840 farm visits, 24 persons, laboratory investigations of five samples per farm and travel expenses. On a pro animal basis these costs would be 2 US$ and on a pro kg product basis 2 dollarcents (unpublished data). The expected benefits of a MOSS are very difficult to estimate. Basically because of indirect elements such as human labour productivity improvement, increase of human welfare and lack of pain (Thompson, 1986; Fisher et al., 1989). Direct benefits such as decrease in death rate, hospital intakes and medical care are easier to estimate. In addition to these elements, the sector itself may benefit from improvement of the Salmonella situation. Elements of benefit are lower animal health care costs, increased productivity, a better image of the sector and its products on the market, and lower risk of contaminated eggs free for consumption. Effectiveness of eradicating positive laying flocks was defined by Ament et al. (1993) as a function of age of the birds at screening, the probability to detect positive flocks and the reliability of tests applied. Effectiveness, meaning the percentage of eggs from contaminated flocks withhold from consumption, was greatest at screening on 50 weeks of age (43.7%). Triple screening at 35, 50 and 65 weeks could increase effectiveness to 65.4% but costs would increase as well. Random screening results in a low effectiveness (28.3%). These authors point to the benefits of eradication procedures in the laying hen sector as well. A whole spectrum of different situations in different countries may lead to different cost-benefit estimations at a national level. The message, however, still remains that the total costs of salmonellosis in both the human and animal sector largely outbalance the costs of a MOSS and associated activities for combat or prevention.

Risk assessment procedures

In the areas of human medicine, toxicology and environmental hygiene the discipline of Risk Assessment & Analysis has been established to a greater extent

140 TABLE IV Rating of food safety risks as calculated and as perceived by consumers (Hudson, 199l) Calculated or actual risk high l

low

Risk factor microbiological contamination packaging failures distribution failures pesticide residues biotechnology food additives food irradiation

Perceived risk

low

high

than in the animal production sector. The discipline can be defined as the technical assessment of the nature and magnitude of risk, determining statistical probabilities of possible outcomes and consequence values (Cohrssen and Covello, 1989). The major components of the Risk Assessment Procedure are: (1) Risk Analysis; (2) Risk Management; and (3) Risk Communication. Risk Analysis comprises the alternative quantitative methodologies of risk estimation and the most optimal procedures to use information. Observational-analytic epidemiological studies, a MOSS and disease models all have their place here. Risk Management mainly regards the decision-making at the sector or (public health) policy level, involving scientific data based on both epidemiological and economic investigations, and comprising socio-economic, welfare, cultural and political issues and information about resources. It further involves the establishment of acceptable risk levels with regard to, e.g., salmonellosis. Risk Communication refers to all exchange of information regarding this risk between all parties possibly involved. It is the tension between scientists at the one hand (actual or calculated risks) and the lay public at the other hand (perceived risks) that at least causes a part of the problems encountered such as in the case of S. enteritidis-infected eggs in the UK. The media play a substantial role in this field. It appears paramount that more attention has to be focused on this aspect with regard to Salmonella infections. Table IV illustrates the possible tensions between groups of human population with regard to actual and perceived risk in case of food safety (Hudson, 1991).

Concluding remarks General preventive measures against Salmonella infections in the animal production sector involve the improvement of hygienic and disinfection standards according to an uniform protocol in all segments of the production chain and, if needed, under strict screening and control. But as in most disease/infection situations with a multifactorial etiology there are more actions to be taken.

141 There is a variety of other steps that can be taken to free the sector of any potential damage due to infections. First of all there is a great need for obtaining adequate insight into the (serotype) infection level of farms and flocks in both the egg-laying and broiler section. Sensitivity and specificity of diagnostic tests applied are paramount in this respect. A proper insight can only be gained through protocol-based investigations or screenings in the population. A MOSS structured around defined objectives and accepted methodologies of unit selection may be supportive to this as well as to the evaluation of measures taken. It should be more adopted by the sector itself that this is a prerequisite for better marketing perspectives on the longer run that implies costs of investment. Then, the quantification of risk factors contributing to disease occurrence will support the decision-making about preventive measures to be taken at farm and sector level. This type of observational-analytic epidemiological studies can be performed as part of a MOSS activity, and as such against reduced costs. Thirdly, simulation modelling of S a l m o n e l l a infections in poultry production may reveal areas where more research is needed, and at the same time provide alternative strategies based on the quantitative epidemiological infection outcomes named above and on economic considerations. For different situations different simulation outcomes are obtained, which can be supportive for decision-making. Finally, by applying Risk Analysis procedures involving information originating from various sources and obtained through the various routes described above, an integrated approach for the prevention and control of S a l m o n e l l a infection in poultry and man can be achieved.

References Ament, A.J.H.A., Jansen, J., Van der Giessen, A. and Notermans, S. (1993) Cost-benefit analysis of a screening strategy for Salmonella enteritidis in poultry. Vet. Q. 15, 33-37. Anonymous. (1988a) Unprecedented increase in S. enteritidis food poisoning. Vet. Rec. 123 (6), 145. Anonymous. (1988b) S. enteritides in poultry. Vet. Rec. 123 (16), 405. Anonymous. (1988c) A case of over-egging.Vet. Rec. 123 (26/27), 657. Barnett, V. (1974) Elements of Sampling Theory. The English University Press Ltd., London. Bolder, N.M., van der Hulst-van Arkel, M.C. and Mulder, R.W.A.W. (1980) Salmonellae in broiler chicks. Proc. 6th Europaische Geflugelkonferenz, Hamburg, Germany, pp. 271-279. Bolder, N.M. (1981) Mengvoeder als schakel in de Salmonella kringloop. Pluimveehouderij 37, 16-18 (in Dutch). Cannon, R.M. and Roe, R.T. (1982) LivestockDisease Surveys: a field manual for veterinarians. Austr. Governm. Publ. Service, Canberra, Australia. Cohrssen, J.J. and Covello, V.T. (1989) Risk analysis: a guide to principles and methods for analyzing health and environmental risks. US Council on Environm. Quality, NTIS Dept. of Commerce, Springfield, VA. Council Directive 92/117/EC of 17 December, 1992. Off. J. Eur. Comm. No L62/38 5-48, 1993. Cox, N.A., Davis, A.B. and Colmer, A.R. (1973) Salmonella recovery from viscera, feces and eggs following oral inoculation. Poult. Sci. 52, 661-666. De Jong, M.C.M. and Diekmann, O. (1992) A method to calculate for computer simulated infections:

142 the threshold value R 0 that predicts whether or not the infection will spread. Prevent. Vet. Med. 12. 269-285. Dijkhuizen, A.A. (1993) Modelling animal health economics. In: Proc. SVEPM meeting, M.V. Thrusfield (Ed.), Exeter, UK, pp. ix-xx. Dijkhuizen, A.A., Houben, E.H.P., Buytels, J.A.A.M. and Huirne, R.B.M. (1993) Economic simulation of Aujeszky's disease control. In: Proc. SVEPM meeting, M.V. Thrusfield (Ed.), Exeter, UK, pp. 20-33. Edel, W. and Visser, G. (1989) Salmonella enteritidis in Nederland. Tijdschr. Diergeneeskd. 114, 405-410. Elbers, A.R.W. (1991) The use of slaughterhouse information in monitoring systems for herd health control in pigs. PhD thesis, State University Utrecht, The Netherlands. Fanelli, M.J., Sadler, W.W. and Brownwell, J.R. (1969) Preliminary studies on persistence of salmonellae in poultry litter. Avian Dis. 14, 131-141. Fisher, A., Chestnu, L.G. and Violette, D.M. (1989) The value of reducing risks of death: a note on new evidence. J. Policy Anal. Management 1, 123-136. Fleiss, J.L. (1973) Statistical methods for rates and proportions. J. Wiley & Sons, New York, USA. Henken, A.M., Frankena, K., Goelema, J.O., Graat, E.A.M. and Noordhuizen, J.P.T.M. (1992) Multivariate epidemiological approach to salmonellosis in broiler breeder flocks. Poult. Sci. 71, 838-843. Henken, A.M., Ploeger, H.W., Graat, E.A.M., Carpenter, T.E. (1993) Description of a simulation model for the population dynamics of an Eimeria acervulina infection in broilers. Parasitology (submitted). Hoogenboom-Vergedaal, A.M.M., During, M., Leentvaar-Kuypers, A., Peerbooms, P.G.H., Kooij, W.C.M., Van Vlerken, R. and Sobczak, H. (1989) Epidemiologisch en microbiologisch onderzoek met betrekking tot gastro-enteritis bij de mens in de regio's Amsterdam en Helmond in 1987. RIVM rapport 148612001, RIVM Bilthoven, The Netherlands. Hosmer, D.W. and Lemeshow, S. (1989) Applied Logistic Regression. Wiley & Sons Inc., New York, USA. Hudson, C.B. (1991) Risk assessment and risk management: key factors in food safety decision-making. Food Australia 43 (9), 10-12. Huirne, R.B.M,, Dijkhuizen, A.A. and Renkema, J.A. (1991) Economic optimization of sow replacement decisions on the personal computer by method of stochastic dynamic programming. Livest. Prod. Sci. 28, 331-347. Humphrey, T.J., Baskerville, A., Mawer, S., Rowe, B. and Hopper, S. (1989) S. enteritidis phage type 4 from the contents of intact eggs: a study involving naturally infected hens. Epidemiol. Infect. 103, 415-423. Hurd, H.S. and Kaneene, J.B. (1993) The application of simulation models and systems analysis in epidemiology: a review. Prey. Vet. Med. 15, 81-99. Krug, W. (1984) Social costs of Salmonella infections in humans and domestic animals. Proc. Int. Symp. on Salmonella, New Orleans, USA, pp. 304-309. Martin, S.W., Meek, A.H. and Willeberg, P. (1987) Veterinary Epidemiology: principles and methods. Iowa State University Press, Ames, Iowa, USA. Notermans, S., Hoogenboom-Verdegaal, A. (1992) Existing and emerging foodborne diseases. Int. J. Food Microbiol. 15, 197-205. Raj, D. (1986) Sampling theory. McGraw-Hill Inc., New York, USA. Rigby, C.E., Pettit, J.R., Baker, M.F., Bentley, A.H., Salomons, M.O. and Lior, H. (1980) Flock infection and transport as sources of salmonellae in broiler chicken and carcasses. Can. J. Comp. Med. 44, 328-337. Roberts, T. (1989) Human illness costs of foodborne bacteria. Am. J. Agric. Econ. 71,936-943. Rothman, K.J. (1986) Modern Epidemiology. Little, Brown and Co., Boston/Toronto USA, 358 pp. Thompson, M.S. (1986) Willingness to pay and accept risks to cure chronic diseases. Am. J. Public Health 76, 390-396. Van den Wijngaard, J.K. (1993) The usefulness of a Salmonella monitoring system for laying hens based on ELISA of egg or blood samples, In: Proc. Seminar "Epidemiology in animal health", June 8, Animal Health Service, Boxtel, Netherlands (in Dutch).

143 Van der Giessen, A.W., Peters, R., Berkers, P.A.T.A., Jansen, W.H. and Notermans, S.H.W. (1991) Salmonella contamination of poultry flocks in The Netherlands. Vet. Q. 13 (1), 41-46. Vermunt, E.M.C. (1990) Een monitoring systeem voor Salmonella bij pluimvee en varkens. COVP Het Spelderholt, Beekbergen, The Netherlands. VII1 (1990) Report of the Veterinaire Hoofdlnspectie Volksgezondheid. Integrale aanpak Salmonella problematiek bij pluimvee. Tijdschr. Diergeneeskd. 115 (7) 329-331. Williams, J.E. (1984) Paratyphoid infections. In: Diseases of Poultry (8th Edn.). M.S.Hofstad, H.J.Barnes, B.W. Calnek, W.M.Reid and H.W.Yoder (Eds.), Iowa State University Press, Ames, Iowa, USA, 1:)19.91-129.