OPTIMAL TRACKING AND TESTING OF US AND CANADIAN HERDS FOR BSE:  A VALUE-OF-INFORMATION (VOI) APPROACH

 ABSTRACT

 The USDA tests a subset of cattle slaughtered in the US for BSE.  Knowing the origin of cattle (US versus Canadian) at testing could enable new testing or surveillance policies based on the origin of cattle testing positive.  For example, if a Canadian cow tests positive for BSE, while no US origin cattle do, the US could subject Canadian cattle to more stringent testing.  This paper illustrates the application of a value-of-information (VOI) framework to quantify and compare the economic costs to the US of implementing tracking cattle origins to the potential costs of not doing so.  The potential economic value of information from a tracking program is estimated to exceed its costs by more than 5-fold, since such information can reduce future losses in export and domestic markets and to reduce future testing costs required to reassure or win back customers.  Sensitivity analyses indicate that this conclusion is somewhat robust to many technical, scientific, and market uncertainties, including the current prevalence of BSE in the US and/or Canada and the likely reactions of consumers to possible future discoveries of BSE in the US and/or Canada.  Indeed, the potential value of tracking information is great enough to justify locating tracking Canadian cattle already in the US when this can be done for a reasonable cost.  If aggressive tracking and testing can win back lost exports, then the VOI of a tracking program may increase to over half a billion dollars per year.

Key Words:  Value of information (VOI); BSE risk management

 INTRODUCTION:  A RISK MANAGEMENT DILEMMA

             For the past several years, Canada has tested thousands of cattle per year for BSE – for example, 3377 animals in 2002.  To date, this testing has found only one cow with BSE (Canadian Food Inspection Agency, 2004).  In the province of Alberta, “The brains of 2769 targeted cattle were tested from October 1996 to March 31, 2004. One cow, condemned at slaughter (did not enter the human food chain), was confirmed positive for BSE in May 2003… Brain tissue samples from the remaining 2768 cattle had no evidence of BSE” (Government of Alberta, 2004).  It is assumed that this prevalence level is representative of and consistent with recent Canadian practices with regards to minimizing BSE in the Canadian herd.  Targeted Canadian cattle included animals with neurological signs and/or emaciation, submitted through provincial slaughter facilities and by field veterinarians, as well as samples from cattle submitted to provincial diagnostic laboratories for post-mortem examination.  If, based on European experience, targeted animals are about 60 times more likely to have BSE than non-targeted animals as a base case (e.g., Doherr et al., 2001) then a prevalence rate of BSE among non-targeted cattle of (1/2768)*(1/60) = 6.0E-6 might be estimated from this case.

In December, 2003, a second dairy cow from Alberta, imported into the US to the state of Washington, was also diagnosed with BSE.  Following a prompt, thorough investigation by The United States Department of Agriculture (USDA) and the Canadian Food Inspection Agency (CFIA), USDA’s APHIS Veterinary Services (VS) issued an “Explanatory Note” in February, 2004, concluding that its previous risk analysis of the risks from Canadian cattle and beef products imported into the US remained unchanged by the new case, and that the risks remained low.  As stated in the note:

“Both of the BSE cases of Canadian origin occurred in cattle born before the feed ban was implemented. They were both older than 30 months of age when they were diagnosed as infected. Infection presumably occurred prior to or around the time the Canadian feed ban was enacted. The finding of an imported case in a cow greater than 30 months of age has little relevance to an analysis of risk under the proposed mitigation measures, beyond the implications for BSE prevalence in Canada. The proposed rule was not in effect in 2001 when the imported case, which was more than 4 years old at the time, entered the United States. Under the proposed conditions, the animal would not have been allowed entry into the United States. Therefore, we continue to consider the import controls in the proposed rule to be effective and the results of the analysis unchanged.” (USDA, 2004)

             From a statistical perspective, the detection of two BSE cases from Alberta in less than eight months raises the question of what the true prevalence of BSE in Canadian cattle may be at present.  The statistical inference problem is complicated by the fact that the cow in Washington State was not detected as part of Canada’s routine sampling program, and the probability that such cattle will be detected once they have been imported into the US is not known.  From a risk management perspective, the key question is what actions, if any, the US should take now in light of uncertainty about the true prevalence rate of BSE among Canadian cattle now and in the future.  This decision problem is made more challenging by high economic stakes and by scientific uncertainties regarding BSE sources, reservoirs, and dynamics.  As noted by the USDA’s Animal and Plant Health Inspection Service in a February, 2004 Position statement entitled “Official diagnosis of Chronic Wasting Disease (CWD) should be performed exclusively by Federal and State regulatory agency laboratories”, even false positives might be economically damaging:  “In the case of a disease like BSE, a false positive could be devastating, costing the U.S. economy billions of dollars in unnecessary domestic and international market disruption from which it could take years to recover.”  Subsequent reporting by USDA of unconfirmed BSE cases that turned out to be false positives, starting in July, 2004, suggest that such market impacts can occur quickly.  Examples of pertinent scientific uncertainties include:

·         Roles of horizontal and vertical transmission (if any) within herds

·         Existing and potential BSE reservoirs in Canada and the US and how these are affected by respective ongoing imports

·         Transmission dynamics within and between different reservoirs

·         Differences in susceptibility among individual cattle of the same age

·         The shape of the age versus infectivity curve for cattle

·         Distribution of infectivity and differences in virulence among new BSE cases

·         Latency period until clinical expression; possibility of subclinical cases (Thackray et al., 2003; Hill and Collinge, 2003;); common definition of clinical BSE expression

·         Potential for clustering of rare events within geographic areas, processing plants, affected populations etc.

·         Error and compliance rates (such as mislabeling, etc.) in Canada and the US

·         Possible heterogeneity of the basic reproductive rate R₀ for BSE in different geographic areas or for different strains of BSE, different types of cattle, etc.

·         Detection probabilities per case, given the target and sampling schemes used

·         Uncertainty of inferred cattle age measurements (e.g., from dentition, etc)

·         Variability and accuracy in testing methods for BSE detection 

With so many unknowns, predictive modeling can be highly uncertain.  Real-world data on observed cases of BSE can therefore potentially be especially valuable for improving estimates of true BSE prevalence.  However, the two BSE cases from Alberta detected in 2003 support alternative interpretations, ranging from (a) the first beginnings of a wave of BSE cases to;  (b) the last remnants of a problem from the 1980s and 1990s that has already been fixed and that, by chance, escaped detection until 2003 and (c) possibly scenarios in between.  The data do not reveal a unique correct interpretation.

This creates a dilemma for both health and economic risk management.  On the one hand, experience since 2003 has shown that discovery of BSE cases in the US can dramatically reduce US beef exports, even if the infected animals originated in Canada.  If the true prevalence of BSE in Canadian cattle shipped to the US were known to be as high as 6.0E-6, then continued prevention of cattle imports from Canada might be expected.  On the other hand, if the prevalence of BSE in Canadian cattle were known to be much smaller or zero, then the advantages of resumed trade could be gained by allowing unrestricted imports, without incurring a substantial risk of additional BSE cases.  Given the high economic stakes and the uncertainties about the prevalence of BSE in Canadian cattle (and, for that matter, US cattle), it has been difficult to determine what policies would best promote US and international interests – what policies would be optimal based on a solid analytic foundation.  Options range from maintaining the status quo (e.g., preserving current import restrictions and testing programs) to tightening or loosening current import policies to gathering more information first – for example, by tracking all imported cattle and testing all Canadian cattle in the US – and then using this information and the results of future sampling to decide when and whether to change import restrictions.  To discover which of these (or other) options is most desirable, it is necessary to compare their conditional probability distributions of gains and losses.

This paper applies constructive decision-analytic techniques, including value-of-information (VOI) calculations (Yokota and Thompson, 2004), to quantify and compare the potential economic values of different risk management and information-seeking options available to the US for managing the uncertain risks of BSE originating in Canada.  The analysis focuses mainly on a near-term decision – whether to require Canadian cattle in the US to be identified, permanently marked, and tracked to provide information about their origins in case future BSE cases are found – and on the economic consequences of different potential futures whose probabilities can be affected by these near-term decisions.  This focus reflects the facts that economic consequences will probably dominate near-term policy decisions, are easier to estimate from available information than possible human health risks, and provide an analytic framework that can later be extended to include health risk considerations.  By explicitly representing key uncertainties and assessing the probable consequences of alternative current decisions under several scenarios, the decision-analytic framework presented here may prove useful to policy analysts and decision-makers in considering how best to assess and manage the highly uncertain risks of BSE in the US from imported cattle.

2.  METHODS AND DATA

            The decision-analytic approach to risk management developed in this paper proceeds through the following steps:

1.       Identify a set of alternative decision rules or options to be compared.  A decision rule specifies the actions to be taken at each time, given the information available at that time.  It may be thought of as a plan that specifies what to do under different contingencies.

2.      Identify the consequences of concern, which the actions may affect.

3.      Identify the probabilities of different consequences, for each decision rule.  This typically requires considering different scenarios or assumption sets describing alternative ways in which current uncertainties might be resolved.  These are also called “states of nature”.  Often, there is no objective, uniquely correct way to determine the prior probabilities of alternative scenarios.  Then, conservative assumptions (tending to favor the status quo) and sensitivity analyses (in which various prior probabilities of scenarios are assumed) may be used to determine how robust the conclusions and decision recommendations from the analysis are to variations in scenario probabilities.

4.      Identify the optimal decision rule, defined as the one with the most desirable probability distribution of consequences, given current information and assuming that future actions will be made optimally given future information. 

5.      Identify and recommend an optimal current action, determined by the optimal decision rule.

This framework is explained in detail in Raiffa, 1968 and Clemen, 1996. 

Formulation of the Risk Management Decision Problem as a Decision Tree

The decision rules compared in this paper are structured as follows (see Figure 1).  First, an initial (“Stage 1”) decision must be made either to track Canadian cattle in the US (“Track CA imports”) or not to track them (“Do not track CA imports”).  The main purpose of the decision analysis is to compare the probable consequences to the US of these two alternative initial actions.  Following this Stage 1 decision, additional information will be obtained from ongoing sampling programs in the US and Canada that perform tests for BSE on both symptomatic (e.g., “downer” cattle) and randomly selected healthy-appearing cattle at slaughter.  If the Stage 1 decision was “Track CA imports”, then any of the following informative events may be observed over a specified following time period (e.g., 1 year): 

·         No new BSE cases detected

·         BSE case of Canadian origin detected in US

·         BSE case of US origin detected in US

·         BSE case of Canadian origin detected in Canada

(If several of the last three events occur in a year, we focus on the first to occur as the event of interest.)  The probabilities of these events depend on both the unknown true prevalence rates of BSE in the US and Canadian herds (i.e., on which scenario or state of nature is correct) and also on the sampling plans and tests used to examine the herds.  If the Stage 1 decision is “Don’t track CA imports”, then the four possible observations for the next period are aggregated to only the following three: 

·         No new BSE cases detected

·         New BSE case detected in Canada

·         New BSE case detected in US

In reality, as in the case of the Washington state cow, forensic efforts might successfully identify the origin of a BSE case even without new tracking measures.  The effect of a Stage 1 decision to track imports is then to increase the probability that the origin of a new case can be determined.  The formal analysis treats the Track vs. Do not track decisions as providing vs. failing to provide, respectively, the information needed to identify the origin (Canadian or not) of any new BSE case, while recognizing that partial tracking via ear tags, brands, and tattoos may already be available.  (Indeed, the tracking issue is confined to Canadian cattle because Mexican cattle in the US are already well identified.)

After the Stage 1 decision, and given updated information about any new BSE cases, a subsequent (“Stage 2”) decision must be made about whether to sell and process healthy-appearing cattle without first requiring them to be tested for BSE (“No required test”) vs. requiring all US cattle to be tested for BSE before being sold or processed (“Test all”) vs. requiring only all Canadian cattle in the US to be tested for BSE before being sold or processed (“Require testing for CA cattle only”.)  The latter option is available only if the Stage 1 decision was “Track CA imports”.  In addition to any required testing, some cattle will continue to be sampled and tested according to a USDA test program, and this is not affected by the Stage 1 and Stage 2 decisions.  The Stage 2 decision presumably will be made to obtain the most desirable outcome possible, given the information available then.  For example, if a new BSE case is detected in the US and its origin cannot be ascertained, then the Stage 2 decision might be “Test all” US cattle at slaughter, to reduce export and domestic consumption losses (if the economic benefits outweigh the costs of testing); while if the origin of the case is known to be Canadian and the Stage 1 decision was to “Track CA imports”, then the best Stage 2 decision might be “Require testing for CA cattle only”.

After Stage 1 and Stage 2 decisions have been made and the future information has been obtained, it becomes possible to evaluate how much beef consumption, if any, has been lost in export and domestic markets due to BSE cases and risk management responses, and how much the Stage 1 and Stage 2 decisions cost to implement.  A goal for rational risk management decision-making today is to anticipate how current decisions change probable future total costs (i.e., the sum of implementation costs and costs from lost domestic and export sales) as they will eventually be assessed in hindsight.  Each Stage 1 decision, in conjunction with optimized Stage 2 decisions given future information, determines a probability distribution for total cost.  Rational risk management requires making the choice today that induces the most desirable probability distribution for total costs, as they eventually will be evaluated in the future.

 Figure 1 presents a decision tree model summarizing the logical structure of the decision problem.  In this tree, a decision rule specifies which outgoing branch to follow at each decision node (represented by a rectangular node in Figure 1.)  “Repeat test” refers to the action of doing nothing other than to continue the routine BSE sampling and testing programs.  The notation for Stage 1 and Stage 2 decisions (“choice sets”) and observed outcome events (“information sets”) listed at the bottom of Figure 1 allows the same framework to be expanded to include additional decisions, scenarios, and information events if so desired to increase the resolution of the problem description.  However, the relatively simple, aggregate descriptions of possible decisions and futures in Figure 1 suffice to carry out the decision analysis calculations; analogous calculations can be performed for more detailed descriptions.

Notation for decision problem components

·         D1 = Stage 1 choice set = {Track Imports, Do Not Track Imports}

·         {Y1 | d1} = information sets of possible outcomes based upon the Stage 1 decision d1 ε D1

·         {D2 | d1} = Stage 2 choice set, given the Stage 1 decision d1 ε D1

·         {Y2 | d1,d2} = information sets of possible outcomes after decisions d1 ε D1 and d2 ε {D2 | d1}

Figure 1.  Decision Tree for BSE Testing Policy

Estimated Economic Consequences of Detecting Additional BSE Cases

            To finish describing the decision problem, it is necessary to estimate the economic costs associated with each terminal node (i.e., “leaf” node) at the tips of Figure 1.  Only the direct costs of implementing the different Stage 1 decisions and of reduced beef sales in case of detection of new BSE cases will be considered, as a first approximation to the full societal costs.  (A refined analysis could estimate economic multiplier effects and reductions in consumer surplus from reduced domestic sales, which would increase their impacts further. However, sensitivity analysis suggests that the main conclusions, which are dominated by loss-of-export-related impacts, would not be changed by these refinements.)  The decision model incorporates the following three types of cost: Tracking Costs, Testing Costs, and Market Costs.  Tracking costs represent the cost of permanently marking each live cow coming into the US, including labor and materials.  Testing costs represent the costs per BSE test, including kits, labor, shipping, holding, laboratory facilities, and expenses. Market costs represent market losses (or gains) associated with each second stage outcome as a function of all that occurred up to that point.  Baseline values for each of these costs are estimated next.  These are then varied to obtain sensitivity analyses.

Market Impacts

The main economic impacts on the US of discovering a new BSE case are assumed to be as follows for the baseline scenario.

·         If a new BSE case of unknown origin is discovered in the US, then both domestic demand and remaining exports of US beef will immediately decline.  Following the discovery of the BSE-positive cow in Washington State in 2003, US exports declined by approximately 50%.  For the baseline scenario, we assume that discovery of a new BSE case of unknown origin in the US will result in a further loss of –12.27B dollars per year in cattle sales, corresponding to a 25% assumed reduction in consumer demand.  The situation where full testing identifies a BSE case of known US origin in the US provides a similar loss.

·         If a new BSE case is found in the US that is not specifically known to be of Canadian origin, but subsequent full testing does not find a similar case, a smaller loss of –6.14B dollars per year will occur.

·         If a new BSE case is discovered in Canada, then US exports may increase to replace decreased Canadian exports.  The magnitude of this effect is estimated as a gain of +1.382B dollars per year in the base case.  

·         If a new BSE case known to be of Canadian origin is discovered in the US, and if Canadian cattle are then removed from US exports and from the food supply, the net impact on the US is a loss of –2.683B dollars per year in the base case, primarily from additional lost exports. (The US domestic markets responded only relatively slightly to the Canadian BSE cases discovered in 2003, suggesting that the main economic impacts come from the closing of export markets to US beef.)

Table 1 summarizes the baseline economic impacts for each of the possible futures (i.e., branches through the decision tree to a leaf node) in Figure 1.  Appendix A provides the supporting rationale and data for the estimated market impacts.

TABLE 1:  Economic Impact Estimates