First Expert Elicitation of Knowledge on Drivers of Emergence of Bovine Besnoitiosis in Europe

Abstract

Bovine besnoitiosis (BB) is a chronic and debilitating parasitic disease in cattle caused by the protozoan parasite Besnoitia besnoiti. South European countries are affected and have reported clinical cases of BB. However, BB is considered as emerging in other countries/regions of central, eastern and northern Europe. Yet, data on drivers of emergence of BB in Europe are scarce. In this study, fifty possible drivers of emergence of BB in cattle were identified. A scoring system was developed per driver. Then, the scoring was elicited from eleven recognized European experts to: (i) allocate a score to each driver, (ii) weight the score of drivers within each domain and (iii) weight the different domains among themselves. An overall weighted score was calculated per driver, and drivers were ranked in decreasing order of importance. Regression tree analysis was used to group drivers with comparable likelihoods to play a role in the emergence of BB in cattle in Europe. Finally, robustness testing of expert elicitation was performed for the seven drivers having the highest probability to play a key role in the emergence of BB: i.e., (i) legal/illegal movements of live animals from neighbouring/European Union member states or (ii) from third countries, (iii) risk of showing no clinical sign and silent spread during infection and post infection, (iv) as a consequence, difficulty to detect the emergence, (v) existence of vectors and their potential spread, (vi) European geographical proximity of the pathogen/disease to the country, and (vii) animal density of farms. Provided the limited scientific knowledge on the topic, expert elicitation of knowledge, multi-criteria decision analysis, cluster and sensitivity analyses are very important to prioritize future studies, e.g., the need for quantitative import risk assessment and estimation of the burden of BB to evidence and influence policymaking towards changing (or not) its status as a reportable disease, with prevention and control activities targeting, firstly, the top seven drivers. The present methodology could be applied to other emerging animal diseases.

1. Introduction

Bovine besnoitiosis (BB) is a chronic and a debilitating parasitic disease of cattle caused by a protozoan parasite called Besnoitia besnoiti [1]. Besnoitia spp. belong to the phylum Alveolata, subphylum Apicomplexa, a diverse group of largely parasitic protozoa of considerable veterinary and medical importance. Members include the genera Plasmodium, Cryptosporidium, Eimeria, Isospora, Sarcocystis, Toxoplasma, Neospora, Theileria and Babesia [2]. There are several recognized species (B. besnoiti, B. caprae, B. bennetti and B. tarandi) in the genus Besnoitia [3] infecting domestic and wild ungulates (cattle, goats, equids and cervids, respectively). This study focuses exclusively on drivers of emergence of BB due to B. besnoiti in Europe. Cattle are the predominant intermediate hosts of B. besnoiti in Europe and it is also reported to be able to infect antelopes [4] and roe deer (Capreolus capreolus) [5]. The life cycle of the parasite species infecting ruminants is not completely known, especially with regard to the definitive host [6]. In addition, a recent survey reported a B. besnoiti-like sequence (99.57% homology) from 4 out of 187 red foxes (Vulpes vulpes) feces tested in Spain, necessitating further investigation to confirm or refute the role of this species in the epidemiology of B. besnoiti in Europe [7].

There is evidence that biting insects or the re-use of a contaminated needle for group injections can mechanically transmit B. besnoiti [8,9,10]. In Europe, different species of blood-sucking insects, such as tabanid species and stable flies (Stomoxys calcitrans), may transmit B. besnoiti mechanically from chronically or asymptomatic infected cattle. The vector insects remain infectious for only a few hours after their blood meal on a carrier animal [11,12]. The time period during which vectors remain infectious after blood meal on an infected animal is short and varies according to species. It is 1 h in the case of S. calcitrans, 3 h for Glossina brevipalpis and 24 h for tabanids [11]. Although transmission by mosquitoes (Culex simpsoni and other unidentified Culex spp.) has not been demonstrated, it has been shown that mosquitoes are able to ingest B. besnoiti from bovine skin and that the parasites remain infectious for hours (50 h). Thus, their role as vectors remains not well established yet [11]. Another study showed that Stomoxys were able to transmit in vitro parasites 48 h after their last meal on infected cattle [12].

This disease presents two distinct phases: a first acute phase followed by a chronic phase. In the acute phase, clinical signs are non-specific and do not really help in the diagnosis. Otherwise, in the chronic phase, clinical signs could be very suggestive of BB [13], but this is only true for a small proportion of animals. Indeed, a large proportion of concerned animals are sub-clinically infected without clinical signs. Those animals represent a huge risk for parasite transmission on farm [14,15,16]. Relying only on clinical signs for detection can lead to misdiagnosis, as BB can be confused with other infectious diseases characterized by similar clinical signs [13]. It is therefore essential to carry out confirmatory laboratory tests.

Different laboratory diagnostic methods (histopathology, polymerase chain reaction (PCR), immunofluorescence antibody test (IFAT), Western blot (WB) and enzyme-linked immunosorbent assay (ELISA)) are available for detection of B. besnoiti infection in cattle. The use of one of these methods or their combination depends on the animal’s and corresponding herd’s clinical status [14].

Concerning BB control, there is no effective drug or vaccine available, although in South Africa and Israel, a live-attenuated vaccine has been used [17]. Detection followed by culling is also a management option. However, the lack of detection/regulation in animals’ movements within a country and between countries has led to its rapid spread from Western Europe towards eastern countries and to the north [14,17]. There are few references available concerning control measures against insects (vectors) related specifically to BB. In 1968, Bigalke demonstrated the possibility of vector control [11]. Some pyrethroids are active on stomoxes, but controlling the latter becomes difficult because of insecticide resistance development [18,19]. For tabanids, only a short time effect of pyrethroid insecticides has been cited [20] and they are considered far less effective than similar applications targeting other vectors (e.g., mosquitoes).

Outbreaks of BB have been reported in Africa, mainly in the South [14]. BB has also been reported in Israel, Kazakhstan, the People’s Republic of China, India and Venezuela [21,22]. In Europe, the disease is endemic (referring to a clinically expressed or non-expressed disease that occurs regularly in animals in a given area) in Spain, Portugal, Italy and France [21,23,24]. In endemic areas, there are very few studies on the economic impact of BB. However, recently, a paper has revealed an association with a higher milk somatic cell count and BB, which can induce important economic losses [25].

Several outbreaks have been reported in European non-endemic countries [22] such as Germany [26], Switzerland [27], Croatia [28], Hungary [29], Ireland [30] and Belgium [31,32]. A range of reasons could explain this new scenario, including the animal trade, management practices such as sharing pastures, and climate change by modification of the vector activity [33]. Indeed, disease emergence is related to the joint presence of several factors, called “drivers”. The knowledge of these drivers is crucial to properly understand host–pathogen–environment interactions [34].

The aim of this study was to investigate, for the first time, the drivers of emergence of BB in Europe using expert elicitation. Multi-criteria decision analysis (MCDA) was chosen to allow systematic integration of information from a range of sources [35] and improve repeatability and transparency [36].

2. Results

2.1. Response Rate and Field of Expertise Mobilised by the Experts

Eleven European professionals with recognized scientific knowledge and/or field knowledge or experience on BB in cattle were contacted and all agreed to participate. The fields of expertise were summarized in Appendix

Table A1. Profile of experts involved in the elicitation of knowledge (N = 11).

Last Name	First Name	Gender	Institution	Country	Field of Expertise
Álvarez-García	Gema	Female	Complutense University of Madrid	Spain	Animal health: Parasitology and parasitic diseases
Alzieu	Jean-Pierre	Male	Veterinary Laboratory of the department of Ariège	France	Animal parasitology
Delooz	Laurent	Male	Regional Association for Animal Registration and Health	Belgium	Animal disease epidemiology
Evrard	Julien	Male	Regional Association for Animal Registration and Health	Belgium	Animal disease project management
Gentile	Arcangelo	Male	Department of Veterinary Medical Sciences, University of Bologna	Italy	Bovine internal medicine
Houtain	Jean-Yves	Male	ARSIA	Belgium	Animal disease management
Jacquiet	Philippe	Male	Ecole Nationale Vétérinaire de Toulouse	France	Parasitology, parasitic diseases, applied zoology and tropical parasitology
Gazzonis	Alessia Libera	Female	Università degli Studi di Milano	Italy	Veterinary parasitology, parasitic diseases
Liénard	Emmanuel	Male	Ecole Nationale Vétérinaire de Toulouse	France	Parasitology, parasitic diseases and applied zoology
Schares	Gedeon	Male	Friedrich-Loeffler-Institut	Germany	Animal health
Villa	Luca	Male	Università degli Studi di Milano	Italy	Veterinary parasitology, parasitic diseases

.

2.2. Estimating the Overall Weighted Score and Ranking of Drivers of Bovine Besnoitiosis in Cattle

The medians of the weight between domains of drivers as well as for the different drivers were not equal according to the non-parametric Kruskal–Wallis test (Chi-squared test = 30.1 with 7 d.f. and α = 0.05, p-value = 0.0001; and Chi-squared test = 119.1 with 49 d.f. and α = 0.05, p-value = 0.0001, for the weights between domains and weights of the different drivers, respectively) (Figure 1).

The median of the weight of the domain D6 (wildlife interface) was significantly lower than the median of the other domains (bootstrapped regression; p-value < 0.001).

Ten drivers out of 50 were ranked as having the highest probability to play a key role in the emergence of BB in Europe. Indeed, the following drivers were ranked in a descending order of importance: the most likely influence of (il)legal movements of live animals (i.e., cattle) from neighboring/European Union member states (MS) (D8–4) or Third countries (a country that is not a member of the European Union as well as a country or territory whose citizens do not enjoy the European Union right to free movement) (D8–7) for the disease to (re)emerge in a given country, the risk of showing no clinical sign and silent spread during infection and post infection (D1–5) and as consequence, the difficulty of detecting the emergence (D3–7), the existence of vectors and its potential spread (D1–7), the European geographic proximity of the pathogen/disease to the country (D2–2), the animal density of farms with extensive (small holders with a few animals) versus intensive farming (D4–3), the disease’s last reported case in Europe (D2–3), the mode of transmission of the pathogen (D1–8) and the problem of the ability of preventive/control measures to stop the disease from entering the country or spreading, excluding treatment, vaccination and vector(s)/reservoir(s) control (D3–1) (Figure 2).

2.3. Cluster Analysis

Two significantly different clusters were identified by regression tree analysis (Figure 3) (non-parametric Kruskal–Wallis equality-of-populations rank test; Chi-squared test = 102 with 1 d.f. and α = 0.05; p-value = 0.0001). These two clusters were classified as having “less importance” with 43 drivers and “more importance” with 7 drivers (D1_5, D1_7, D2_2, D3_7, D4_3, D8_4, D8_7), respectively.

2.4. Sensitivity Analysis of the Impact of Experts on the Final Ranking of Bovine Besnoitiosis Top Drivers of Emergence in Cattle

The result of the sensitivity analysis indicated that irrespective of the expert ignored, ignoring an expert only had no effect on the ranking of top 5 or 7 drivers (i.e., drivers included in the cluster with significantly more importance) considering a change of one or two ranks, respectively. These results were confirmed using a Kruskal–Wallis equality-of-populations rank test on experts for the top five or seven drivers identified. Indeed, results were very conclusive and respectively: chi-squared = 9.61 with 10 d.f. and probability = 0.48 (top five drivers) and chi-squared = 11.17 with 10 d.f. and probability = 0.34 (top seven drivers).

3. Discussion

Fifty drivers of BB in cattle were ranked and aggregated into two homogenous groups according to the present expert elicitation. Only the first ten most important ranked drivers will be further discussed with a focus on the seven categorized in the “more importance” node. In addition, for ranking of the first seven drivers, there was no expert effect when assessed by sensitivity analysis, indicating an acceptable robustness of the elicitation for the seven drivers included in the first node.

The first and second most important drivers were the influence of (il)legal movements of live animals from neighboring/European Union MS (D8–4) or from Third countries (D8–7), respectively. In European countries there are currently fewer movements of live animals originating from Third countries (note that BB was also reported in Africa and Asia) than from neighboring/European Union MS, explaining the difference in rank of these two drivers (see also international trade statistics, available at the following URL address: https://www.trademap.org/tradestat/Index.aspx; accessed on 15 December 2021). However, the animal trade from Africa to Europe may explain why the disease appeared in Europe by the end of 19th century and the beginning of 20th century in Portugal and France. In addition, there are very few studies available on the estimation of illegal movements of live animals in the scientific literature (e.g., [37,38]). Nevertheless, a proper estimation of the relative importance of illegal movements of live animals and their introduction pathways is deemed essential to set up risk-based awareness, prevention and surveillance programs that correspond to reality [34]. Direct (isolation of the protozoan parasite and real-time polymerase chain reaction (rtPCR)) and indirect (IFAT, WB and ELISA) diagnostic tests have been set up for BB [14]. Some commercial assays permit to implement a proper testing strategy in order to control the trade of live animals and to certify the sanitary BB status of the herd of origin. In order to identify mitigation measures, we strongly recommend developing a quantitative import risk assessment (QIRA) modelling similar to those developed for Lumpy skin disease that involved live bovines as well as S. calcitrans as a mechanic vector [39,40]. These previous studies can serve as a basis for further modelling development.

The third and the fourth most important drivers were related to the risk of showing no clinical sign and silent spread during infection and post infection (D1–5) and as consequence, the difficulty to detect the emergence (D3–7). The disease is expressed only in the most susceptible animals [9,41,42]. In the chronic phase, cutaneous lesions and patognomnic scleral cysts may be helpful for diagnosis and also surveillance. However, many animals are sub-clinically infected with low parasite loads and they may act as parasite carriers which can only be diagnosed by serological tools [43]. In endemic areas, clinical cases are observed between 1–10% of the new infections but between 15–20% in the case of B. besnoiti infections in areas where the disease is emerging [44]. Moreover, during the first weeks following infection, acutely infected animals may be difficult to be clinically diagnosed due to non-specific signs [21]. In addition, several other diseases should be considered in the differential diagnosis according to the stage of the BB such as malignant catarrhal fever, bovine granulocytic ehrlichiosis, bluetongue, bovine respiratory disease, photosensitization, scabies or zinc deficiency [13]. Indeed, clinical surveillance of BB is not fully efficient, and it is essential to carry out confirmatory laboratory tests [21]. There is no formal gold standard test for BB but four tests are frequently used to confirm a clinical suspicion of BB: rtPCR and serological tests (IFAT, ELISA and WB). For WB, the sensitivity (Se) and specificity (Sp) are close to 100%. The Se and Sp of the IFAT are close to 100% and 95%, respectively. Depending on the ELISA used, their Se and Sp are generally > 97% and >93%, respectively [45]. The Se and Sp of the rtPCR are around 90% and >99% [46]. A previous study recommended also a mandatory active surveillance system via a systematic analysis of all imported animals originating from areas at risk [32]. Research is recommended to develop more commercial accurate laboratory assays and decision-making trees able to help the diagnostic of BB.

The fifth most important driver was the existence of vectors and their potential spread (D1–7). The knowledge of different species of blood-sucking insects in a country, their distribution and frequency over time, and the time-period during which vectors remain infectious after a blood meal on an infected animal [10,11,12] are of prime importance to develop QIRA modelling [39,40]. In addition, due to the presence of mechanical vectors of BB (i.e., S. calcitrans and tabanids) in Europe, the seasonality of BB was previously reported as playing a major role in disease epidemiology [18]. The inclusion of seasonality should be valuable for further development of a QIRA modelling.

The sixth most important driver was geographic proximity between a specific non-endemic country and a specific endemic country of origin (D2–2). This driver is related to the third and the fourth ones because if a disease is notifiable, it is easier to secure the trade. Threat analysis and QIRA modelling should be appropriate responses to deal with this driver [39,40].

The seventh most important driver is related to the animal density of farms with extensive (small holders with a few animals) versus intensive farming (D4–3). Density of farms is a driver of spread of a disease, especially if mechanical vectors are present and if these vectors are able to transmit parasites for few hours after their last (interrupted) blood meal on infected cattle [12].

The eighth most important driver is the last reported case of the disease in Europe (D2–3). This driver can be related to the fact that BB is currently not a reportable disease in most of the affected countries. Several criteria to include a disease as reportable exist among which the most important is its zoonotic character (that was not the case for BB according to [17]) and its significant health impacts, taking into account the occurrence and severity of the clinical signs, including direct production losses and mortality [47]. Despite several papers reporting economic concerns related to BB (e.g., [28,32]), factual data on the burden of the disease and its translation to monetary losses are completely lacking [17]. We strongly recommend estimating the economic burden of BB in order to convince policy makers to take action (or not) whether to include BB as a notifiable disease based on factual data.

The ninth most important driver was related to the mode of transmission of the pathogen (D1–8). There is evidence that several biting insects can mechanically transmit B. besnoiti [8,9,10,11] but the entire life cycle remains unknown and especially the definitive host [6]. The intra-herd transmission of BB is generally intense but weak between herds [48]. However, no information of the basic reproductive number for BB is known. Currently, in Europe, there is no strong evidence of the role of the wildlife in BB [49,50]. More studies are needed.

The tenth most important driver was related to the problem of the ability of preventive/control measures to stop the disease from entering the country or spreading (D2–1). Recent studies recommended the awareness of decision-makers about the need for an appropriate prevention and control policy, law enforcement and the implementation of necessary measures to avoid BB becoming endemic in non-endemic countries [32,51,52,53]. As biosecurity measures, a quarantine and a systematic screening of all imported animals originating from areas at risk can be proposed [32]. In addition, in South Africa and Israel, live-attenuated vaccines were used [17]. Other valuable preventive/control measures should be identified using networking permitting sharing of information and experiences between researchers/veterinarians and literature search, especially systematic review and meta-analyses and using an evidenced-based approach.

As an example of a recent advance, real-time PCR on skin biopsies permitted the detection of super-spreaders in BB [1] and identification/elimination of these super-spreaders contribute to disease control in heavily infected herds. The control of stable flies can be difficult by the development of insecticide resistance [19] and nothing is known about the eventual resistance of horseflies to insecticides (or even their effectiveness). In addition, the lack of repellents with long lasting activity in livestock hampers ecto-parasite control. Moreover, regular treatments are not feasible in extensive husbandry systems.

Considering the European spread in time and space of the BB, the importance of live-animal trade between European countries (endemic versus non-endemic), the fact that notification of the disease is currently not mandatory, the large proportion of sub-clinically infected animals (but at risk), the need for affordable confirmatory tests, and the climatic changes that affect and alter the habitats and population dynamics of vectors, the BB is becoming a concern and needs more collective efforts to limit its spread and its impacts.

4. Materials and Methods

The methodology followed in this expert elicitation of knowledge is the same as previously published [33,34] for other emerging diseases but is adapted for BB. For transparency, the method is detailed below.

4.1. Species Included

The objective was to prioritize the drivers of BB in Europe. Using the following algorithms on 12 December 2021 (((bovine besnoitiosis [Title/Abstract]) OR (Besnoitia besnoiti [Title/Abstract]) OR (B. besnoiti [Title/Abstract])) AND (cattle [Title/Abstract]) AND (Europe [Title/Abstract])), search strings were conducted in PubMed (US National Library of Medicine, National Institutes of Health). The results of the search (N = 47 articles from 2009 through 2021) showed that three review paper were produced; other papers were related to field/epidemiological surveys (N = 20), biology studies (N = 10), diagnosis (N = 8), experimental studies (N = 3), treatments (N = 2), and vectors (N = 1).

4.2. Questionnaire Design

To determine the main drivers of BB emergence, a questionnaire was used. A driver was defined as a factor that has the potential to directly or indirectly precipitate (“drive”) or lead to the emergence of BB in cattle. A former questionnaire made to rank (re-)emergence of animal diseases based on drivers [33] was adjusted for bovine besnoitiosis in cattle. Fifty drivers were established and classified in eight different domains (Appendix B). The domains (D) were: (D1) disease/pathogen characteristics (N = 9 drivers); (D2) distance of Europe (spatial-temporal scales) (N = 3 drivers); (D3) ability to monitor, treat and control the disease (N = 7 drivers); (D4) Farm/European characteristics (N = 7 drivers); (D5) changes in climatic conditions (N = 3 drivers); (D6) wildlife interface (N = 6 drivers); (D7) human activities (N = 6 drivers); and (D8) economic and trade activities (N = 9 drivers).

These were formatted in an Excel^® (Microsoft, Redmond, WA, USA, 2016) file with one spreadsheet per domain, each domain harbouring its respective drivers. Each driver had a score with its definition, which could range from 0 to 4 or 1 to 4 and an intra driver weight point. A last spreadsheet was added, in which the 8 domains were listed, with an inter-domain weight.

4.3. Expert Elicitation on Drivers Used to Assess the Emergence of Bovine Besnoitiosis in Europe

An expert elicitation of knowledge was conducted, which consisted of gathering the opinion of people with recognized scientific expertise and/or experience in the field of BB in cattle (Appendix A). For guidance purposes, an explanatory letter accompanied the questionnaire that each expert had to fill out (Appendix C). Each expert was contacted personally and responded individually to the questionnaire. Data generated by the elicitation were based on the individual values provided by experts in order to capture the degree of variability of experts’ knowledge. The elicitation was performed in one month.

4.4. Scoring and Weighting System

The elicited experts were asked to provide three types of information. First, they were asked to score the drivers (as established in Appendix B). For each driver, the higher the score, the higher the driver’s chance to contribute to the emergence of BB in cattle. Uncertainty score was not asked due to lack of evidence-based data on BB in cattle at this stage. Secondly, experts were requested to weight each driver within a specific domain (intra-domain weight). This relative weight was determined using the Las Vegas technique [54]. Briefly, experts were given a number of points to be distributed between the drivers according to their importance in the specific domain. If all the drivers of a given domain had been considered as equivalent by experts, each of them would have received the same score. Lastly, the relative importance of each domain was subsequently weighted by experts (inter-domain weight).

4.5. Calculation of an Overall Weighted Score for Each Driver and Ranking Process

To obtain the overall score per driver, an aggregation method that combined the two types of weighting (i.e., the intra- and inter-domain) was used. First, the driver score (coefficients attributed by experts) was standardized by dividing it by the number of possibilities. Indeed, some drivers were allocated coefficients from 0 to 4 (5 possibilities) and others from 1 to 4 (4 possibilities). Afterwards, this standardized score was multiplied by the intra-domain weight and the inter-domain weight, as given by the expert. These results led to an overall weighted score for each driver and per expert:

OWSDri = SDri × WDri × WDoj

(1)

In this formula, OWSDri = overall weighted score for a specific driver; SDri = standardized score for a specific driver; WDri = intra-domain weight for a specific driver; WDoj = inter-domain weight for a specific driver included in a specific domain. Furthermore, all drivers were ranked based on the median overall weighted score obtained for each driver and taking into account the answers of all the experts who answered the questionnaire. The statistical difference of the median, depending on the specific driver or the group of drivers considered, was assessed through a non-parametric Kruskal–Wallis equality-of-populations rank test (State SE 14.2; StataCorp, College Station, TX, USA).

4.6. Cluster Analysis

A cluster analysis was carried out using a regression tree analysis (Salford Predictive Modeler^®, Version 8.2, Salford Systems, San Diego, CA, USA). The median overall weighted score (median OWSDri) being a continuous variable, the aim was to obtain groups of drivers with minimal within-group variance, with comparable likelihood to play a role in the emergence of BB in cattle. In addition, the statistical difference between medians after grouping drivers in clusters was assessed using a non-parametric Kruskal–Wallis equality-of-populations rank test. Indeed, each driver was characterized by a median (based on all experts’ answers), then drivers were grouped. The test allowed highlighting of potential significant differences between groups, in terms of driver medians, after clustering.

4.7. Sensitivity Analysis to Test the Robustness of the Expert Elicitation

In order to identify whether the ranking of BB drivers of emergence was influenced by the choice of experts, a sensitivity analysis was performed on the top five and top seven drivers. First, we started by ranking the drivers using the obtained median OWSDri. Second, an expert was excluded from the analysis and the ranking of the drivers was carried out using the same methodology as previously described. This was done expert by expert. Third, we counted the number of changes in the ranking, for each driver, only considering changes equal or more than one rank. These results were confirmed using a Kruskal–Wallis equality-of-populations rank test on experts.

5. Conclusions

Since scientific knowledge on drivers of emergence of BB in cattle is still incomplete and associated uncertainty is high, expert elicitation of knowledge and multi-criteria decision analysis, in addition with clustering and sensitivity analyses, allowed the identification of seven drivers of more importance on which to focus on future studies. The transport of live cattle asymptomatic carriers seems to be a key factor of introduction and spread of BB. Indeed, further quantitative import risk assessment and estimation of economic burden of BB are highly recommended. This expert elicitation of knowledge should be also refined in the coming years when more evidence data will be available. In this case, addition of an uncertainty index should be recommended during elicitation. The present methodology could be applied to other emerging animal diseases. The application of this methodology to a specific disease also allows highlighting or not the need for more investigations.