Costs and Benefits of Preventing Animal Diseases: A review focusing on endemic diseases Funded through SAC Advisory Activities budget (211)
Author: A.W.Stott Animal Health Economics Team Research and Development Division SAC Craibstone Estate Aberdeen AB21 9YA Tel: 01224 711218 E-mail:
[email protected] October 2003
Not for quotation/distribution without prior discussion with the above author.
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Summary The aim of this study was to use evidence from the literature to assess the best ways to measure the real benefits of improvements in farm animal health. With such evidence it will be possible to estimate the full relative investment potential of animal health and use such information to encourage farmers to adopt good practice in support of the vision set out in Government’s strategy for animal health and welfare in Great Britain (Defra, 2003a). The study focused on endemic sheep and cattle (dairy and beef) diseases. This was because associated livestock systems are important in Great Britain, vulnerable to policy reform and concentrated in fragile regions. There is also evidence that they offer most scope for benefit from increased investment in disease prevention. Furthermore, the responsibility for controlling endemic diseases rests with farmers rather than the State. However, it was not always possible to separate the private interests of farmers in the control of endemic diseases from the public interest. In order to assess the true potential benefits from improvement in disease prevention it is necessary to assess the avoidable losses (as opposed to total costs) from disease as advocated by McInerney (1996). Few studies have adopted this approach due to lack of information (Bennett, 2003). Simulation modelling can provide a solution to this problem. The approach has the added potential advantage of allowing results to be adapted to the individual circumstances of decision-makers. Several mechanistic models of the epidemiology of important endemic diseases of cattle and sheep have been published that exploit scientific advance to provide the necessary adaptability. However, few have been fully integrated within a proper economic framework. There is therefore a need for interdisciplinary systems research in this area. Static deterministic evaluations of avoidable losses are useful at regional or sectoral level but do not reflect the main impact of disease as experienced at the farm level. Here it is important to capture the risk that disease represents to farm business viability as it varies over the course of an epidemic. The interactions between diseases and between disease control and other farm management activities are also important. As well as establishing the opportunity cost of investing in animal health and welfare, more holistic long-term studies will reveal the impact of disease on the sustainability of an agricultural business. This aspect of animal health economics requires focus on the impact of animal disease on the land and hence the environment. Initial studies suggest that this may be an important benefit of animal health to both farmer and society, mediated partly through the impact of animal disease on fertility but also indirectly through the benefits of animal health on whole-farm risk management (Stott et al., 2003). The use of decision analysis techniques in animal health economics has helped to progress this aspect of research and should therefore be encouraged. Knowledge transfer to farmers should emphasise the role of animal health in risk management. Voluntary health schemes, particularly those that offer certified health status provide a ready-made system to enhance the health and welfare of farm animals in Great Britain. However, there seem to be few studies of the potential benefits they offer to
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farmers and to society (due to potential improvements in animal welfare, food safety, environmental protection and public health) yet the costs of membership and associated strategies to avoid re-infection once certified disease free will be clear and in some cases substantial. Such costs could be considerably reduced if farmers in a region co-operated with each other. However, the problem of 'free-riders' (Holden, 1999) makes it difficult to achieve the consensus necessary. This situation indicates a need to re-assess the role of Government in such schemes. While Great Britain has been busy with foot-and-mouth disease and BSE, competitor nations have been eradicating other diseases that are still endemic in the UK. For example, Sweden is stamping out paratuberculosis (OIE, 2003), has cleared bovine virus diarrhoea virus (BVDV), infectious bovine rhinotracheitis (IBR) virus and enzootic bovine leucosis (EBL) (Swedish National Veterinary Institute, 2003). Other Scandinavian countries also operate national disease monitoring and control programmes (Greiser-Wilke et al., 2003; Olsson et al., 2001). New Zealand enjoys freedom from important sheep diseases such as enzootic abortion (ovine chlamydiosis) and maedi visna (OIE, 2003). Major food producing countries in Europe (Holland, Germany and France) are also taking initiatives against endemic diseases (G.Gunn, personal communication). This trend could put British agriculture at a competitive disadvantage and perhaps threaten free trade.
Introduction SEERAD commissioned SAC in August 2003 to carry out this study as part of Advisory Activity 211. The objective was to review the literature on studies of the costs and benefits of preventing farm animal disease. This was done in the context of the document entitled ‘Outline of an Animal Health and Welfare Strategy for Great Britain’ published in July 2003 by Defra, Scottish Executive and the Welsh Assembly Government (Defra, 2003a). An important aspect of this strategy is the promotion of farm animal health and welfare through disease prevention rather than cure. In it, the authors recognise that to encourage animal keepers to adopt good practice in this regard, there is a need to identify and assess the real benefits of improvements in animal health and welfare. The aim of this study was therefore to use evidence from the literature to assess the best ways in which this might be done. In pursuit of this aim, it was expected that policy relevant information would emerge. For example, how best to measure the economic impact of farm animal disease, gaps in evidence, areas for future research and implications for future farm management practice. In order to carry out this work within constraints it was necessary to focus on the diseases and farming systems likely to be most affected by the new approach described in the outline animal health and welfare strategy. Holden (1999) explains why epidemic and zoonotic disease control should remain the responsibility of Government while endemic disease prevention and control is a private matter. The latter are considered private goods because vets and farmers who chose to control them can easily exclude others from any benefits gained. Also there is strong competition (rivalry) for the services concerned. Neither of these attributes applies to the control of epidemic or zoonotic diseases. This report is therefore confined to the literature on endemic disease prevention. However, overlap exists where endemic disease gives rise to significant externalities or control efforts for specific diseases
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interact. Such overlap is dealt with in this report where it is thought to be significant with respect to the above aim. Cattle and sheep farmers are generally slow to adopt disease prevention (biosecurity) strategies (Barrett, 2001). There is evidence that they are less aware of or less responsive to the benefits of biosecurity than their pig and poultry farming colleagues (van Schaik et al., 1998, De Verdier Klingenberg et al., 1999). Furthermore, sheep and cattle farming is the mainstay of livestock farming in Great Britain (Defra, 2003b), particularly in Scotland and in areas of high economic and environmental fragility. For these reasons, this report is mainly confined to diseases of cattle and sheep.
Direct costs of farm animal diseases in Great Britain Cattle diseases Bennett et al. (1999) carried out the only systematic comparative study of the direct costs of animal diseases. The choice of diseases and conditions selected for study reflected past and current public expenditures on research into endemic livestock diseases in Great Britain. Their work is available on the World Wide Web at: http://www.rdg.ac.uk/livestockdisea/
It overcomes the difficult problem of making comparisons between many other 'economic' studies of specific diseases that affect farm animals (Bennett, 2003). These previous studies use a variety of different methodologies, valuation bases, time periods and livestock populations. Most consider only one or two diseases and may not include treatment and control costs incurred. Some go beyond the farm-gate to include effects on markets and the distribution of benefits to different sectors of society (e.g. Andersson et al., 1997). Costs of the main endemic diseases of cattle in Great Britain as estimated by Bennett et al. (1999) are shown in Figure 1. The total cost estimates in Figure 1 are averages of high and low values based on alternative assumption sets, reflecting the range in key parameters reported in the literature. In the case of mastitis for example, the range is from £46/animal at risk to £116/animal at risk. For well-researched diseases like mastitis this range is probably a fair reflection of the between-farm variation observed in practice. However, uncertainty and variation of estimates concerning the incidence of diseases and their effects are the major reasons for the differences between low and high estimates of the output losses (Bennett et al., 1999).
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Figure 1: Total annualised cost (output loss plus control expenditure) of endemic diseases of cattle in Great Britain (http://www.rdg.ac.uk/livestockdisea/)
A comparison of the mastitis costs of Bennett et al. (1999) with those of Kossaibati and Esslemont (1997) highlights some of the issues that must be considered before disease cost estimates are used to encourage animal keepers to adopt good disease prevention practice. The latter paper estimates clinical mastitis costs at just £24 per cow at risk. This is considerably lower than the estimate shown above. However, the figure of Bennett et al. (1999) includes subclinical mastitis costs as well. As Bennett et al. (1999) provide their spreadsheets on the World Wide Web, allowance can be made for this difference. Even so, a large discrepancy (at least £16/cow at risk) remains. This is due primarily to a higher milk price assumption in Bennett et al. (1999) (£0.25/litre compared to £0.20/litre) which governs the value of milk output lost due to the disease. Given that current milk price is about £0.17/litre (SAC, 2003a) and likely to fall still further due to reform of the Common Agricultural Policy (European Commission, 2003), the relative importance of mastitis shown in Figure 1 is likely to decline in future. However, both studies draw on prevalence estimates from a small (90) selected sample of herds on the 'DAISY' system (Esslemont and Kossaibati, 1996). Herds enrolled on this and other such voluntary health recording schemes on which many disease incidence studies are based may not be representative of the wider population. Estimates of disease costs based on the output of epidemiological models can draw on scientific knowledge of the mechanisms that govern disease spread. They may thereby escape any bias inherent in studies based on empirical observation. More likely, they will be used in the absence of data necessary for economic analysis (Dijkhuizen et al., 1991). A recent example is that of Gunn et al. (2003) who used a simulation model to estimate that the expected cost of a 10-year Bovine Viral Diarrhoea (BVD) epidemic 5
was £37/cow/year for a 100-cow Scottish beef suckler herd. As expected, this was much lower than figures from case studies of BVD infection in dairy herds (Duffel et al., 1986 and Pritchard et al., 1989). However, all estimates were much greater than that of Bennett et al., 1999 shown in Figure 1 above. The main cause of this difference was the assumption in Bennett et al. (1999) that losses due to BVD were confined to those herds free of the disease. As only 5% of UK dairy herds were considered antibody negative to BVD (based on Harkness et al., 1978), estimated losses were low. In contrast, the model of Gunn et al., (2003) demonstrated how losses in antibody positive herds could be substantial due to the presence of some susceptible individuals throughout the time course of most simulated epidemics. This example demonstrates the importance of a sound scientific basis as well as a sound economic basis to any disease cost estimates. It also demonstrates the importance of accounting for time (dealt with later in this review). An important feature of BVD is that it causes immunosuppression (Charleston et al, 2001) in cattle. This means that part of the total costs associated with other endemic diseases (such as those in Figure 1) may in fact be due to BVD infection. Failure to account for this in a comparative financial assessment may lead to undue emphasis on diseases other than BVD and hence failure to achieve expected returns on the investment due to persistent BVD. Alternatively, if the immunosuppressive effects of BVD are accounted for (as was done by Gunn et al., 2003) and the results aggregated with other studies there is a risk of double counting and hence an over-estimate of the potential benefits of investment in disease prevention. This example illustrates the general need for a systems approach to agricultural research in order to ensure that interactions between component problems (in this case diseases) are not overlooked when results of scientific research are aggregated in order to solve complex problems (Dillon, 1976). Another important feature of BVD is its negative effect on fertility (Fray et al., 2000). Fertility in UK dairy herds is low in comparison with some competitor nations and declining (Royal et al., 2000). However, it varies greatly between farms (Kossaibati and Esslemont, 1995), suggesting that there is considerable scope for improvement. Stott et al. (1999) estimated that this scope for improvement represented about £120/cow/year on UK dairy farms. This figure exceeds the cost of all bovine endemic diseases studied by Bennett et al., (1999), suggesting that infertility should be a top priority in any campaign to persuade cattle farmers of the benefits of investment in disease prevention. The problem of infertility in dairy cows (and even some disease problems) is in part due to negative genetic correlations with milk yield and therefore progressive deterioration in response to selection for milk yield (Pryce et al., 1999). It must therefore be tackled at national level by widening breeding goals (Kadarmideen et al., 2000). However, there are many diseases that affect fertility in addition to BVD and these must be dealt with at farm-level (Farin and Slenning, 2001). It will be important to include the benefits of improved fertility in any economic evaluation of such diseases. These are often seen in the short-term as greater output of milk per unit time and reduced replacement costs (Esslemont and Peeler, 1993). However, fertility has a long-term benefit at whole-herd level as it allows the productive animals to be supported by a smaller replacement rearing herd (Santarossa, 2003). Production targets can therefore be met from fewer animals, putting less pressure on the land and
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resulting in a more sustainable agricultural system. It also facilitates the maintenance of a closed-herd which in turn reduces the risk of introducing disease (Radostits, 2001b). By incorporating the long-term effects of fertility on land value, the apparent benefits of fertility will be much enhanced (Logue et al., 2003). It is important to put the cattle disease costs summarised in Figure 1 into the context of returns from the associated farm enterprises. By doing so, the relative economic importance of animal disease to farmers can be gauged. The power of such figures to persuade farmers to invest in disease prevention strategies can then be assessed. Gross margin per cow for a herd of average yield (7,000kg) is about £600 (SAC, 2003a). The total cost of all diseases in Figure 1 is about £140/cow. So if all these diseases could be wiped out at no additional expense, gross margins would rise by 23%. The same effect could be achieved through a 2p per litre increase in the price of milk (SAC, 2003a). For beef suckler cows, gross margins vary according to system. However, £200/cow is a mid-range figure. Excluding mastitis and lameness (predominately dairy cow problems), the total cost of diseases in Figure 1 is about £25/cow i.e. about 12% of the gross margin and equivalent to less than10p/kg on the sale price of suckled calves. Sheep diseases The comparative economic impact of endemic sheep diseases in Great Britain as presented on the Reading University website and summarised in Bennett (2003) are shown in Figure 2. 1.60
Cost (£/animal at risk)
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Figure 2: Total annualised cost (output loss plus control expenditure) of endemic diseases of sheep in Great Britain (http://www.rdg.ac.uk/livestockdisea/) As for the cattle diseases in Figure 1, the above figures are a 'best' estimate based on the average of a low and high figure. Bennett (2003) also evaluated orf (contagious pustular dermatitis) but a lack of survey data on clinical incidence precluded a low 7
and high estimate. Orf and the two most important diseases in figure 2 (toxoplasmosis and enzootic abortion) are zoonotic (Jones, 2003). Diseases such as blowfly strike (Rugg et al, 1998) and orf (Reid, 1995) also have serious implications for animal welfare. These issues imply that important externalities arise from sheep diseases, strengthening the case for greater public intervention (Ekboir, 1999). Apart from Bennett (2003), very few systematic economic assessments of sheep disease have been carried out. A possible reason for this is apparent in the study of Raadsma and Egerton (1993) who state that for a high risk environment the annual cost of footrot per 1000 sheep may range from Aus$14,000 to almost 0, depending on the virulence of the transmitting agent. However, footrot is one of the most important contagious diseases affecting sheep (Scott, 2001). Also, plasma cortisol concentrations in the study of Ley et al. (1994) were significantly higher in sheep with footrot and remained so for up to three months after apparent resolution of clinical lesions, suggesting that footrot is a serious animal welfare problem. In addition, the prevalence of footrot is high in the UK (86% of farmers in the survey of Wassink et al. (2003) reported footrot in the year 1999/2000). These features suggest that footrot should be a high priority for farm-level economic analysis and associated knowledge transfer to farmers. Milne and Dalton (1989) found that purchase of accredited gimmers was the best way to deal with enzootic abortion in sheep. They found that this option cost £1/ewe/year (comparable with Figure 2) but the worst case scenario was to take no action (£19/ewe/year). Though variable from year to year and from farm to farm, lamb mortality is often as high as 20 to 25 per cent (Clarkson, 1992). Such losses must make a significant impact on farm incomes and in some cases threaten farm business viability. Conington et al. (2001) have published economic values for selection index traits of UK hill sheep. For extensive systems, they estimate that a 1% reduction in lamb mortality is worth about £22/100 ewes. This is very similar to the gross margin per lamb in such systems (SAC, 2003a). These figures make a 7% improvement in lamb survival about equivalent in financial terms to the impact of enzootic abortion in Figure 2. Well within the bounds of normal variation (the mean and phenotypic standard deviation of lamb losses reported by Conington et al. (2001) were 0.24 and 0.10 per ewe respectively). The main risk factors for lamb mortality are low birth-weight and low serum immunoglobulin concentration (Christley, 2003). Both factors will be influenced by disease in the pregnant ewe and in the newborn lamb (Henderson, 1997). Rather like fertility in cattle, lamb mortality may be a useful key performance indicator on which to base a knowledge transfer programme to farmers aimed at disease prevention.
Economics of animal disease; total cost or avoidable losses? “If the published costs of all the animal diseases on my farm were correct, then I would have gone out of business long ago.”
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Comment of a farming client at SAC (G.Gunn, personal communication) This statement demonstrates the importance of establishing credible and relevant estimates of disease cost if they are to be used to persuade farmers to adopt good animal health practices. The main reason for discrepancy between published disease costs and farmer experience is that most published estimates give the total cost and not the avoidable losses (McInerney, 1996). For many endemic diseases, most of the output loss and some of the control costs will be unavoidable even under best practice. For exotic diseases it may be prudent to incur some recurrent expenditure in order to reduce the risk of an outbreak. Such costs are already part of farmers’ income calculations. Total disease costs are therefore inappropriate for direct knowledge transfer to farmers because they give a false impression of the investment potential of disease prevention. McInerney (1996) questions if disease costs have any value at all. However, Perry and Randolph (1999) suggest that they may at least provide a first crude, though imperfect, indication of which diseases might give the greatest potential economic benefit from control. Bennett (2003) argues that total costs are justified because they can be estimated within inherent livestock disease information constraints. McInerney et al. (1992) demonstrated a framework for the economic analysis of disease in farm livestock, capable of establishing the avoidable losses and hence the scope and means of improvement. Yalcin et al., (1999) applied this framework to the case of subclinical mastitis in Scottish dairy herds with high bulk tank somatic cell counts (Figure 3). Each point in figure 3 represents a particular mastitis control strategy. In general, the greater the expenditure on disease control, the lower the output loss from the disease. However, at any given level of disease control expenditure there is a wide range of output losses. Strategies that give the lowest output loss at each level of control expenditure form the loss-expenditure frontier (marked by a solid line in figure 3). The point on this frontier that gives the lowest total cost is found on the iso-cost line tangential to the loss-expenditure frontier (dotted line in figure 3). In this case, the minimum total cost of subclinical mastitis was £66/cow/year. The average total cost was £100/cow/year. This result gave an avoidable cost of £34/cow/year i.e. only 34% of the total costs of subclinical mastitis in this sector could be avoided. The average total cost of the disease therefore overestimated the investment opportunity of improved control by 66%.
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Figure 3: Relationship between output loss and control expenditure for subclinical mastitis in Scottish dairy herds with high bulk tank somatic cell count (BTSCC) (Yalcin et al., 1999) Economics of disease prevention Few studies focus on the cost of farm animal disease prevention. Even fewer do so using a proper economic analysis as described in the previous section. A notable exception is Chi et al. (2002). Some of their results are summarised in Figure 4. Figure 4 is based on a survey of 90 dairy herds in the Maritime Provinces of Canada. Blood samples to test for four diseases (EBL is enzootic bovine leucosis see Chi et al., (2002) for further details) were matched with information on basic disease control practices. The strategy with the lowest aggregate cost for the four diseases was associated with farms purchasing replacements from a dealer or an auction, conducting checks before introducing these animals to the herd and using vaccination. It is interesting to note that in this case, buying replacements from a dealer or auction was part of the optimal strategy, even though this practice had a negative effect on disease prevalence (see figure 4). This was because of the significant ex ante control cost of the best alternative (maintaining a closed herd). However, closed farms were more cost effective in relative terms at the aggregate level than at the individual disease level. This is because the costs of maintaining a closed herd can be spread across the benefits of reduced prevalence of all diseases. Such results emphasise the importance of systems studies in this context (Dillon, 1976). They are also a reminder that the optimal decision for the individual farmer may carry negative externalities for
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his neighbours (due to disease spread across farm boundaries or by meeting/exchange of cattle at auction) and for society (e.g. animal welfare and possible zoonosis in the case of Johnes disease (Scientific Committee on Animal Health and Animal Welfare, 2000)). The survey of Chi et al. (2002) was small and most differences illustrated in Figure 4 were not statistically significant. A series of similar studies were conducted in Holland and built into an economic model for on-farm decision support of management to prevent infectious disease introduction into dairy farms (van Schaik et al., 2001). In contrast to Chi et al. (2002) this study concluded that a more-closed farming system might be a more profitable way to prevent economic losses due to infectious disease. They point out that the economic implications of a more-closed farm system will not always be obvious to farmers. Their strategy (refrain from purchasing cattle, provide protective clothing to visitors and build and maintain double boundary fencing) reduced the probability of disease introduction by 74%. However, cost effectiveness depended on individual farm circumstances, for example how much boundary fencing was required and how great was the local risk from the diseases included in the model. The decision support systems devised by Chi et al. (2002) and van Schaik et al. (2001) for use at farm level against endemic diseases have much in common with the grander systems described by Morris et al. (2002) for control of foot-and-mouth 1 0.9 0.8 No Vaccine No Checks Dealers/Auction Intercept
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Figure 4: Tobit results on the relationship between disease prevention practices and the prevalence of disease within Canadian dairy herds (Chi et al., 2002) disease at national level. A key need of both is for up-to-date and accurate databases linked to appropriate decision support tools. Better biosecurity at farm-level was a key recommendation of the enquiries that took place after the 2001 foot-and-mouth epidemic in the UK (e.g. Royal Society, 2002). The work described above clearly shows that it is also important for endemic diseases. These observations suggest that
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close co-operation between those responsible for taking and supporting endemic disease control decisions (usually individual farmers, veterinary practices and advisory services etc.) and those responsible for equivalent exotic diseases (usually regional and national authorities) will be beneficial. It follows, that emphasis on clear demarcation between these groups to clarify roles and responsibilities (Defra, 2003a) must be tempered if mutual benefits from co-operation are thereby put at risk.
Health Schemes Private health and productivity schemes were developed in the 1960’s in response to the realisation that most diseases were multifactorial in nature and therefore required a more holistic approach based on herd health rather than ‘fire brigade’ attention to individual animals by the veterinary surgeon (Thrusfield, 1995). Early initiatives in the dairy sector involved regular planned health visits from the veterinary surgeon to individual farmer clients. More recent initiatives have extended into the sheep and beef sectors and may involve more formal planning, perhaps as part of a multi-farmer group (G.Gunn, personal communication). The importance of these schemes is likely to grow given the emphasis on disease prevention in Great Britain (Defra, 2003a) and the shortage of large animal veterinary practices to carry out ‘fire brigade’ work. (The number of veterinary surgeons in large animal practice halved between 1998 and 2001 (Royal College of Veterinary Surgeons, 2002)). The goals of these schemes are to identify disease problems, prioritise them and then plan, implement, monitor and control a programme of remedial actions that are technically and economically efficient (Radostits, 2001a). The activities associated with this type of health scheme are referred to as 'farm health planning' in the outline health and welfare strategy for Great Britain (Defra, 2003a). The economic justification of these schemes is well documented (Thrusfield, 1995). For example, Pharo et al., (1984) reported a 3:1 benefit: cost ratio for a dairy herd health/productivity scheme and Williamson (1987) confirmed similar values for the USA. Stott and Elston (1991) demonstrated that in the dairy cow at least, early signs of disease may be very subtle, involving disruption of the normal association between weight change and production rather than a sudden drop in productivity. By using developments in information technology (Cox, 2002) to spot such changes, health scheme members may be able to react more quickly and hence more effectively to impeding health/welfare problems. As well as or instead of 'farm health planning' as described above, some health schemes aim to detect, eradicate and then demonstrate continued freedom from specific diseases in the members' herd/flock. If such schemes deal with specific epidemic or zoonotic diseases they are the proper preserve of the public sector (Holden, 1999) and therefore outside the scope of this review. However, in recent years, many schemes associated with specific endemic diseases have been privatised in the UK. A good example is the Premium Cattle Health Scheme (SAC, 2003b). The objective of this scheme is to eliminate specific diseases (BVD, IBR, Johnes and leptospirosis) from members’ cattle herds through a test and cull policy. By doing so, a supply of breeding stock of certified health status becomes available to all farmers. The process also allows timely control or preventive action to be taken. Similar
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schemes exist for sheep and goats. An important aspect of these schemes is biosecurity; i.e. ensuring that once a disease is eliminated it is not re-introduced into the herd/flock. This is a painstaking and potentially expensive aspect of scheme membership. Studies similar to those of Chi et al. (2002) and van Schaik et al. (2001) reported above are therefore very important in this context. They demonstrate the value of biosecurity measures to scheme members and indicate the best way to allocate resources to biosecurity activities. The costs of preserving health status may be greatly reduced if all farmers in a region are committed to an appropriate health scheme. This point has been exploited in remote areas of Scotland (e.g. the Shetland Islands where BVD has been eradicated (Clark et al., 2002)). However, as long as health schemes are voluntary, the problem of ‘free-riders’ (Holden, 1999) makes it difficult to achieve eradication in more extensive regions. Nevertheless, some countries are committed to eradication of endemic cattle diseases such as BVD (Greiser-Wilke et al., 2003). For example, Scandinavian countries have instigated compulsory disease recording programmes and affiliation to associated health schemes may be demanded by organisations within the agricultural industry (Olsson, 2001). As a result Sweden has eradicated several cattle diseases endemic in the UK (Swedish National Veterinary Institute, 2003) and is stamping out Johne's disease (OIE, 2003). Differentiation between countries in terms of animal health status could therefore arise and impede progress towards global free trade in livestock (Leslie and Upton, 1999). Defra (2003a) raise the problem of number and diversity of health schemes in UK agriculture. Health schemes may exist to provide farm assurance, improve animal welfare, protect public health, reduce dependence on pharmaceuticals and facilitate administration as well as improve productivity or eliminate specific diseases as described above. This situation must be reviewed so that the considerable benefits of disease prevention and health planning illustrated in the previous section can be delivered and clearly seen to be delivered in the best way for all stakeholders. Given that the best interests of all stakeholders will not coincide there will be a need for some intervention to strike an appropriate balance. A promising initiative that addresses some of the issues raised by Defra (2003a) is 'HI-Health'. This is a health scheme that combines health planning and surveillance with disease eradication. It is described by Gunn et al. (2001). HI-Health is a farmerdirected business formed in 1998 that aims to create a pool of high health status herds for beef and dairy farms in the Highlands and Islands of Scotland. The mandatory core of the programme (level 1) involves annual veterinary inspections and associated health plans. Level 2 involves monitoring and accreditation for BVD, Johne's disease, IBR and Leptospira hardjo. A key element according to Gunn et al. (2001) is an educational component for farmers to encourage uptake. The programme is centred on participating veterinary practices linked by computer network and e-mail to a central database. Details of farm visits are recorded on this database and made available for outside evaluation on a 'need to know' basis. Another data network involves a novel sample labeling system linked to the British Cattle Movement Service Database. These features ensure credibility through transparency and traceability. The next step is to link the whole system to other quality assurance programmes via a partnership with Quality Meat Scotland and the SAC.
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Risk Management Reform of agricultural policy in the European Union is likely to deprive its farmers of market price support exposing them to greater price risk (Harvey, 2001). Martin and McLeay (1998) found that New Zealand farmers faced with this problem adopted a range of risk-management strategies reflecting specific farm and farmer characteristics. Although pest and disease management (preventive measures and monitoring programmes etc.) was an important strategy in this study, it was only part of an integrated approach. It follows that under increased world trade competition; preventive veterinary medicine is likely to be of increasing importance to farmers as part of a wider strategy for risk management. Stott et al. (2003) therefore used a linear programming method (Hazell, 1971) to assess the relative contribution that disease (BVD) prevention could make to wholefarm income and to the variability in farm income (risk) on Scottish beef suckler farms. Using the method of McInerney et al., (1992) discussed above and a computer simulation of BVD epidemics (Gunn et al., 2003), they found that the minimum expected total cost of BVD was similar whether the herd was free of BVD at the start of the simulation (£20/cow/year) or of unknown BVD status (£22/cow/year). However, the control (biosecurity) strategies required to obtain these minimum costs were very different. If the farm was known to be free of BVD, greater investment in biosecurity was justified in order to maintain that freedom. This allowed such farms to achieve a given income target at minimum risk with fewer sheep and cattle and less labour input than a farm where BVD status was unknown. Such results suggest that maintaining healthy cattle has benefits that go beyond the direct improvements in cattle productivity and welfare to encompass benefits for the environment through less intensive production. It also emerged that the control strategy that minimised expected total cost of BVD was different to the one that minimised risk. As farmers are generally considered to be risk averse (Oglethorpe, 1995) the least-cost disease control option might not always be the preferred option. This finding was important in the study of Stott et al. (2003) as a substantial proportion (up to 10%) of variance in farm income (risk) could be attributed to BVD. Risk was greatest for low-income targets (limited funds to invest in biosecurity) and where herds were known to be free of BVD at the start of simulations (more serious consequences if biosecurity fails). The importance of disease in wider farm business risk management and the inherent risks associated with animal disease have led to the use of standard risk management techniques in animal health economics (Rushton et al., 1999). Such techniques are often termed ‘decision analysis’ (Hackett and Luffrum, 1999). A review of such techniques in the context of farm management is provided by Hardaker et al. (1997). Ngategize et al. (1986) provide a critical review of the techniques relevant to animal health. Decision analysis often involves construction of a decision tree as a pictorial representation of the flow of events including both decisions (e.g. treatment options) and possible outcomes (e.g. mortality) (Marsh, 1999). The sum of probability weighted monetary values associated with each possible outcome is known as the expected value of the decision option. Decision options can be selected on the basis of
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their expected values. The approach is explicit, quantitative and prescriptive. It is flexible and easily understood. Although uncertainty often surrounds many of the quantitative assumptions, methods have been developed to elicit subjective values from the decision-maker (Boelhje and Eidman, 1984). The aim is to make the best decision given the available information. This will not always be the right decision but the process can greatly improve the understanding of the decision-maker and his/her advisers. Greater understanding will improve subsequent decisions and raise awareness of key issues such as the relative impact of animal health on farm incomes and farm income risk. These features make decision analysis a useful basis for knowledge transfer in support of the animal health and welfare strategy for Great Britain (Defra, 2003a) introduced above. However, most recent applications in animal health economics have been aimed at decision-makers beyond the farm gate (e.g. Houe et al., (1999), Smith and Slenning, (2002) and Tomassen et al., (2002)). While studies of farmer decision making in this context suggest a more subjective approach
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Figure 5: Distribution of total costs of treating calf enteritis (from Stott and Gunn (1995))
(Vaarst et al., 2002). Care is needed when using average costs of disease and standard measures of dispersion to support farm level decision making under risk. This is because the distribution of such costs is often skewed (Chi et al., 2002, Fourichon et al., 2001). The potential extent of this and hence its significance is illustrated in Figure 5. The average cost of enteritis shown in Figure 5 is £29/calf at risk. However, the mode was just £5/calf at risk, the median £20/calf at risk and the highest cost recorded over £125/calf at risk. In the study of Fourichon et al., (1995) the mean dairy health-control costs reported exceeded the median for all 17 reported conditions, suggesting that this phenomenon is not confined to enteritis in suckled calves. Most farmers will therefore experience disease costs below the mean and so consider reported average costs too high. If so, this provides a second explanation for the farmer’s statement given above
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expressing doubt about the applicability of published animal disease costs. The danger is that this situation may spread undue complacency about animal disease in farmers’ minds. The average cost of disease, although higher than common experience, may include a small proportion of extremely high cost outcomes. If the distribution of costs is highly skewed, as it was in the study of Stott and Gunn (1995) then a minor condition for most farmers may be so severe as to threaten farm business viability for a small minority. This situation must be made clear in any knowledge transfer to farmers, especially given that most farmers are risk averse and would not therefore be expected to base their decisions on the expected value unlike a risk neutral decision maker (Hardaker et al., 1997).
Accounting for time in animal health economics The example application of the economic analysis framework of McInerney et al. (1992) described above (Yalcin et al. (1999)) was based on a large sample of dairy farms at one point in time (1993/4). As such it is useful for decision support at national or sectoral level as described by Perry et al. (2001) but fails to take account of the particular circumstances that affect decision making at individual farm level. Here decisions are complicated by the cyclical nature of agricultural productions systems. Fixed assets such as land and breeding livestock are 'harvested' repeatedly so that decisions in one production cycle must be taken with due consideration for their consequences in future cycles. This is particularly important in the case of animal disease where infection in one cycle often impairs performance in subsequent cycles for example with mastitis (Lucey and Rowlands, 1984), maedi visna (Brodie et al., 2001) and paratuberculosis (Whittington and Sergeant, 2001). Susceptibility to disease in later cycles may also be affected by exposure in earlier cycles and in any case, tends to increase with age. Productivity on the other hand may initially rise with age and then decline. All these factors must be taken into account when dealing with the economics of animal health in breeding livestock. Dynamic programming (DP) (Bellman, 1957) provides a framework for the economic analysis of multi-stage decision problems. It has frequently been applied to natural resource management problems (Kennedy, 1986) including some in animal health economics. For example, optimal replacement of mastitic cows (Stott and Kennedy 1993; Houben et al., 1994) and the relative value of different mastitis control procedures (Yalcin and Stott, 2000). The technique has also provided a useful framework for establishing the economic weight of goal traits for use in dairy cattle breeding programmes where benefits accrue over long periods (Veerkamp et al., 1995). Stott et al., (2002) used DP to establish the optimum replacement policy for dairy herds, taking into account subclinical mastitis caused by the bacteria Staphylococcus aureus (S.aureus). This organism is associated with food poisoning and, in human medicine at least, with multiple antibiotic resistance so a maximum concentration is set for milk under EC regulations (EC1992/46). It is therefore particularly important to find physical methods to control this organism as an alternative to antibiotic therapy. The case highlights the need to reduce antibiotic useage in agriculture, which
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is an important aspect of Government policy for sustainable agriculture (Defra (2002a) and SEERAD (2001)). The DP model of Stott et al., (2002) estimated the extent to which the effects of subclinical mastitis might be alleviated by adopting an optimum culling policy that took account of the impact of each cow's future milk yield and somatic cell count (SCC) on the financial performance of the herd in comparison to the cost of replacing the animal with a heifer. The analysis took account of expected trends in milk yield and SCC by lactation number, variation about these trends and the historical performance of existing cows for a herd of given performance (herd average yield approximately 7000 kg/cow/year). Results are summarised in Table 1. The effects of SCC on milk yield and milk price were substantial even in the control herd, prompting 2% more voluntary culling in order to remove older cows with higher SCC. This reduced bulk-tank SCC by 14%. The expenditure on this extra culling, the associated change in herd output and the estimated direct losses from subclinical mastitis reduced expected financial performance by 34%. Corresponding figures for a herd with SCC commensurate with S. aureus infection were 7% more voluntary culling, a 42% reduction in bulk-tank SCC and a 61% reduction in expected financial performance. Table 1: Impact of S.aureus infection under the optimum replacement policy on otherwise identical dairy herds either including or excluding the effects of subclinical mastitis on milk yield and milk price. (Based on Stott et al. 2002) Control Herd S.aureus Infected Herd Effects of subclinical mastitis: Excluded Included Excluded Included ENPV* 224 147 224 88 Culling (%/year) 20 21 20 25 Voluntary Culling (%/year) 4 6 4 11 Bulk-tank SCC (kcounts/ml) 149 128 329 191 * ENPV: annualised expected net present value from milk production (£/cow/year) These figures demonstrate the considerable scope for alleviating the effects of subclinical mastitis through appropriately targeted culling decisions. If culling is the only available method of control, or if other methods have already been applied correctly, then the DP approach provides the economically optimum balance between output loss and control expenditure discussed above. This is only true however at the given levels of SCC. In practice, the culling regime will alter SCC, which in turn will influence subsequent culling decisions. Removing infected, generally older, cows also affects the SCC averages indirectly by removing a source of infection for other cows, indeed that is one of the aims of such culling. To deal properly with these issues requires a version of the DP linked to an epidemiological model of S. aureus spread in the herd so that each culling decision can be based on its consequences for the whole herd, not just the individual cow concerned. Further research is required to establish whether such developments are feasible (Stott et al., 2002). In the meantime it is likely that output from the current model, updated on a regular basis is likely to provide useful decision support for UK dairy farmers when combined with existing provision.
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Progress might be made without recourse to complex bio-economic simulation modelling at individual farm level. Instead Logue et al., (2000) proposed culling guides based on the work of Stott et al., (2002). An example is shown in Figure 6. It shows how trade-offs between SCC and milk yield may be balanced when shortlisting cows for replacement. Of course other criteria will also be important especially at the borderlines. However, Logue et al., (2000) report that the guidelines reflect best practice and trends observed in the field. The diagram illustrates how much more severe culling criteria may have to be when faced with a S. aureus problem. It is likely that farmers will wish to combine targeted culling strategies with a wide range of good management and husbandry practices advocated for use in the control of mastitis. The example of Stott et al. (2002) demonstrates the importance of expressing the costs of disease prevention in a specific decision support context. As advocated by McInerney (1996), this means including the best actions to take in order to eliminate avoidable losses. The work extends that of McInerney (1996) by accounting for both
800 700 SCC (kcounts/ml)
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Yield (kg) Figure 6: Culling guide for cows in lactation 5 based on DP results of Stott et al. (2002). Cows performing in the shaded area (high yield and/or low SCC) should be retained, those in the unshaded area should be considered for replacement. The darker shading refers to herds infected with S.aureus mastitis, lighter shading is for control herds (see Table 1). the dynamic and risky nature of animal disease. Simulation methods are particularly useful for dealing with these issues (Bennett, 1992). There are many examples of such methods relevant to animal health economics. Recent ones include Groenendaal et al. (2003) (Johnes disease), Keeling et al. (2003) (foot-and-mouth vaccination strategy) and Gunn et al. (2003) (BVD). A review of simulation and other techniques in animal health economics is given by Morris and Dijkhuizen (1997).
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Conclusions Most estimates of average farm animal disease costs in the literature lack both economic and scientific rigour. This makes them unsuitable for knowledge transfer to farmers in support of the Government strategy for animal health and welfare in Great Britain (Defra, 2003a). Their use may even be counter-productive. Studies that quote the total cost of diseases rather than the avoidable loss exaggerate the benefits of investment in disease prevention and may thereby come to lack credibility with farmers. Even so, their implied value is small in comparison to normal variation in output prices for cattle and sheep. A different picture emerges from studies of animal disease as a source of risk to farm businesses. Animal disease may represent a significant proportion of the risk (variation in farm income) over which the livestock farmer has some control. There is also evidence that disease costs are positively skewed thus average costs could mask the effects of rare though potentially devastating epidemics. A knowledge transfer campaign based on risk management at whole-farm level and backed by research into the sources of variation in avoidable disease losses therefore holds promise. A more holistic (systems) approach to animal health is also required in this context. This will need an interdisciplinary research and development/extension effort. There are many detailed scientific studies of individual diseases but these must be combined in ways that address the efficient resource allocation (economic) issues relevant to farm business decision-makers. In particular, more epidemiological studies of the type used by Chi et al. (2002) and van Schaik et al. (2001) are needed to establish the effectiveness of alternative biosecurity strategies under different physical and financial circumstances. To be cost-effective such strategies must provide proven protection against a wide range of diseases. It will also be important to include general farm management activities within the biosecurity strategy. For example, replacement policy for breeding livestock has been shown to have a considerable impact on animal health and on farm profits in the long-term. The management of fertility is also a crucial issue, closely associated with animal health and welfare as well as sustainability. By joining-up these disparate issues it should be possible to demonstrate the full potential benefits of establishing and maintaining freedom from specific endemic diseases through membership of appropriate health schemes. The more farmers that join such schemes, the greater the benefit for all as the risks of breeches in biosecurity are reduced. Scheme members may also be able to pool knowledge and resources for mutual benefit. A potential problem arises in the overlap between public and private goods/bads related to animal health at farm level. For example, most of the biosecurity measures highlighted during the foot and mouth outbreak (public benefit) in 2001 would be good standard operating procedures for any farm at all times, to reduce the risk of introducing novel disease (private benefit) (Paterson et al., 2003). Also some of the endemic diseases reviewed here have zoonotic potential thus creating a public interest in how farmers deal with them. Furthermore, how farmers deal with endemic disease may have consequences for animal welfare and the environment. These factors make it difficult to draw the distinctions between public and private interests as described by Holden (1999). This report has concentrated on the farmers' perspective as this will be the necessary theme for knowledge transfer to encourage disease prevention in
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support of the strategy for animal health and welfare in Great Britain (Defra, 2003a). However, a wider economic analysis will also be required to support the wider resource allocation decisions needed at national level to tackle issues of public concern that might otherwise arise such as zoonoses and animal welfare. Such decisions should consider possible use of funds arising from reform of the CAP. Some of the money saved due to decoupling subsidy from production (SEERAD, 2003) could be spent on public concerns related to zoonoses, animal health and animal welfare via the cross-compliance mechanism. Simulation modelling provides a vital basis for systems research in animal health. There are many epidemiological models but so far relatively few combining epidemiology with economics to identify optimal animal health establishment and maintenance strategies. Greater research effort in this area would help overcome the problem of lack of information that hinders the development of proper economic evaluations of animal disease (Bennett, 2003). Such an approach makes the most of what information we have on the science of animal disease and helps to identify important gaps. By using mechanistic models where possible, adaptations can be made to reflect specific decision making circumstances. This will become increasingly important in the more volatile markets that will follow liberalisation of trade in agricultural commodities. However, this does not mean that sophisticated simulation models and associated decision support systems will always be appropriate for use at farm level. Studies of knowledge transfer requirements in livestock agriculture (Defra, 2002b) suggest that many farmers require more traditional approaches. Carefully selected and sensitively communicated output from simulation studies, supported by appropriate local advice, training and information may therefore be the best approach.
Recommendations How best to measure economic impact • • • • • • •
Measure avoidable losses not total costs. Use simulation methods to overcome lack of information and make best use of scientific knowledge. Present uncertainty in cost estimates. Express results relative to alternative investments farmers could make. Adopt a systems approach at whole-farm level to take account of interactions between diseases and to incorporate farm management practices that indirectly affect animal health. Use decision analysis techniques to reflect the contribution animal health can make to risk management. Allow for variations over time in both biological and financial aspects of animal health systems.
Gaps/further research •
Improve sources of information on disease incidence.
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• • • • • • •
Integrate existing epidemiological models into appropriate economic frameworks. Develop methods to usefully express the relative risks from alternative disease threats. Establish the benefits of disease prevention (e.g. biosecurity) strategies. Analyse the flow of public and private goods (e.g. farm profits, animal welfare, public health, sustainability) from animal health and hence establish the best mix of public/private veterinary services needed to promote animal health. Consider public goods associated with animal health for support under the reformed (decoupled) common agricultural policy. Examine the potential economies of scale, co-operation and integration for disease prevention schemes and strategies. Explore the role for lifelong learning in the development and delivery of animal health and welfare strategies.
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