Amal Bouejla
email: amal.bouejla@mines-paristech.fr2xavier.chaze@mines-paristech.fr

Xavier Chaze

Franck Guarnieri
email: franck.guarnieri@mines-paristech.fr

Aldo Napoli
email: aldo.napoli@mines-paristech.fr

A Bayesian Network to Manage Risks of Maritime Piracy against Offshore Oil Fields

Keywords: Oil platforms, offshore oil fields, pirate attacks, Bayesian networks, quantitative and qualitative knowledge

In recent years, pirate attacks against shipping and oil field installations have become more frequent and more serious. This article proposes an innovative solution to the problem of offshore piracy from the perspective of the entire processing chain: from the detection of a potential threat to the implementation of a response. The response to an attack must take into account multiple variables: the characteristics of the threat and the potential target, existing protection tools, environmental constraints, etc. The potential of Bayesian networks is used to manage this large number of parameters and identify appropriate counter-measures.

Introduction

Currently there are over seven thousand oil platforms scattered throughout the world, each of which requires on the one hand, equipment for the extraction, processing and temporary storage of petroleum, and on the other hand shipping capable of transporting crude oil between production and consumption sites.

Modern piracy is currently the major threat to the security of these energy production sites and maritime crude oil transport. In 2011, 552 attacks on ships and platforms were registered with the International Maritime Bureau 5 compared to 487 reports in 2010. At production sites, monitoring methods are a major weakness in the detection of a threat, and the procedures to be applied in the event of an attack are often inefficient and inappropriate. It is therefore essential to have a system that ensures the security of oil fields and offers them appropriate protection and effective crisis management. The SARGOS6 system, funded by the National French Research Agency7 (L'Agence Nationale de la Recherche) and recognised by regional organisations addresses this need by offering a global protection system in the fight against oil infrastructure piracy. This article is organised into three parts. It first addresses the issue of acts of piracy against oil fields. Next the method used for the planning of counter-measures is described in detail. This includes notably, the construction of Bayesian networks from two datasets: the "Piracy and Armed Robbery" database of the International Maritime Organization (IMO) and the collection and formalisation of the knowledge of domain experts. Finally, the article describes how the model was tested using realistic and comprehensive pirate attack scenarios and the results are discussed.

Piracy against Oil Installations: a Serious Threat and Limited Defences

Offshore oil infrastructure is subject to a constantly increasing risk of piracy. The consequences of these actions have repercussions as much at a local level (on operations) as globally (on distribution). This section highlights both the economic and the political implications of pirate attacks and describes an increasingly insecure context where actors in the offshore oil and gas industry, without effective tools to protect themselves, find themselves helpless. Finally, it presents the SARGOS system and describes the contribution that this new system is expected to make to dealing with the problem of maritime piracy.

Economic and political issues

Offshore oil exploration is expanding rapidly. The exploitation of offshore oil resources currently represents about a third of global petroleum production. This energy resource, despite its scarcity, is being explored in many areas some of which are located in dangerous territorial waters, notably the Gulf of Guinea. In the offshore waters of politically unstable countries, attacks on oil field infrastructure generate significant additional costs -caused by, for example the payment of ransoms, increased insurance premiums and the installation of security equipment. The annual cost of piracy is estimated at 7-12 billion United States dollars [START_REF] Bmi | Study: Piracy Costs World Up to $12 Billion Annually[END_REF]. These additional costs directly affect the international price of oil.

Moreover, oil fields form the interface between the maritime world and the oil and gas industry. The heterogeneity of applicable regulation (rather than the absence of law) makes the status of installations a legal headache. Moreover, this complexity can lead to political conflicts between nations; when the nationality of the company operating the platform does not correspond to physical location of the installation, the problem arises of who has responsibility for the protection of the area [START_REF] Schroeder | Ecological and political issues surrounding decommissioning of offshore oil facilities in the Southern California Bight[END_REF]. Oil Offshore Fields

The importance of oil installations in the global economy and industry and the potential political consequences of piracy therefore require that such assets are better protected.

Violent attacks

Although attacks against oil fields are infrequent and mostly low-profile, they are extremely disturbing because of the severe impact on the crew and infrastructure.

The following examples demonstrate the point:

 On 22 nd September, 2010 the tug Bourbon Alexandre located in the Addax oil field off the Nigerian coast was attacked by four speedboats; three French sailors were taken hostage. This was the fourth attack against the Bourbon Company since 2009.

 The attack on the Exxon Mobil platform off the coast of Nigeria, led to the kidnapping of nineteen of its employees and significant damage to the oil facility caused by explosive devices used by the pirates.

 Finally, on 17 th November, 2010 pirates aboard a speedboat attacked a ship owned by the French company Perenco that was carrying Cameroonian security forces near an oil platform in the Gulf of Guinea. The attack killed six people.

Infrastructure managers, employees and safety officers do not want to continue to see their ships or other assets become the subject of substantial ransoms, nor crewmen injured, killed or kept in extreme conditions for days or even weeks. At the same time insurers are unwilling to continue to provide cover for such high risks indefinitely. Finally, nations do not want to continue to see the price of oil affected by such events.

Emerging operational requirements

The attacks described above are a perfect illustration of the weakness of current antipiracy tools. At the present time, there is no comprehensive system capable of managing the entire threat processing chain. Current systems treat the detection of a threat and the response to it as independent operations. Among the available detection tools, radarbased (pulse) systems8 can spot large or medium-sized cooperative mobile objects but perform poorly in the detection of small craft (e.g. fishing boats and motor boats) in a rough sea; moreover the analysis of a large domain is relatively slow. There are also optronics surveillance systems9 that, despite their ability to detect small targets at longrange, are handicapped by the problem of solar reflection from the sea and are very sensitive to weather conditions. As for the tools used to counter an attack, they are often inadequate or incorrectly used (e.g. water jets, Ship Security Alert System).

In terms of the threat response, the targets in danger can currently send alert messages to other units in the area but this diffusion is restricted to a very small geographic area. Moreover, even if a security vessel is alerted to a threat, it cannot be assumed that it will be able to intervene, particularly if it is not close to the location of the attack.

Therefore, the aim of the SARGOS system is to offer a new method that is able to both detect threats and plan a response. The response implements a graduated series of non-lethal counter-measures (sonic cannons, barring infrastructure access, etc.) that can be applied in order to eliminate the danger.

The contribution of the SARGOS system

The SARGOS system addresses the need to protect civilian infrastructure that is vulnerable to acts of piracy or terrorism at sea. It is a global system that takes into account the whole threat processing chain, from the detection of a potential danger to the implementation of the response. It can be integrated into the operations of the installation and takes into account regulatory and legal frameworks at both national and international level. The creation of the system, which involved the development of an overall protection method, automatic threat detection and identification, risk assessment and management of an appropriate response, required professional skills from many domains.

The functional diagram of the SARGOS system (Figure 1) describes the threat processing cycle. The overall system operates as follows: when the detection module instruments (Frequency Modulated Continuous Wave radar, infrared cameras, etc.) identify a vessel in an area near to the oil field, the system evaluates the threat and the potential danger and generates an alert report containing comprehensive data describing the scenario. This information includes details such as visibility, time of day, the speed, longitude and latitude of the detected vessel and its potential target, etc. The distance between the two entities and the theoretical response time of the security vessel is also calculated from this data. If the threat is identified as suspicious or hostile, the system generates an alert report every second. The alert report is used in the planning stage where external and internal means to respond to the attack are mobilised. This paper particularly addresses this aspect of response planning and the management of internal and external resources available on the installation (such as searchlights or sonar alarms).

Figure 1: from the information detected by the FMCW radar, the system identifies the threat and then calculates the ranking and generates an alert report containing all the information necessary to assess the situation in order to use internal and external resources to manage the threat. The ranking is calculated in corresponding to the time required (in seconds) to the threat to go the distance to CPA asset considered taking into account the assumption that at any time the threat may change course and coming in on the target constant radial. The terms are: [ranking <300 s], [300 <ranking <900 s] or [s 900 <ranking <1800 s].

A Bayesian Network to Manage Risks of Maritime Piracy against Oil Offshore Fields

Elements of threat analysis and principles for resource management

There are significant obstacles inherent in addressing the problem of maritime piracy. An initial difficulty concerns how to manage the large number of parameters necessary to describe an attack. These parameters, which form the inputs and outputs of the system, characterise the asset in danger (type, criticality, vulnerability, on-board safety tools, etc.), the threat (the type of ship used by the attackers, its speed, their weapons, etc.) and the environment (the time of day, visibility, sea state, etc.). A second problem lies in the fact that these parameters may interact with each other. For example, whether it is relevant to request the intervention of the security vessel will depend not only on the time required for it to reach the asset under attack but also how well armed the attackers are and how fast they are moving. Therefore, the management of the multiple interrelations between system variables presents another major challenge. These first two constraints suggest that the system be based on graph theory, which would make it possible to translate and exploit, using a graph, the large number of variables, their interdependencies and interrelationships, etc.

However, an additional concern is uncertainty in the information describing the threat. The SARGOS system generates an alert report that contains on the one hand, data issuing from various detection instruments (type of ship detected, number of crew, potential weapons, etc.) and on the other hand, mathematical calculations based on dynamic variables (the distance between the target and its attackers, time available before the attackers are able to board the asset, etc.). Despite the improving performance of radars, this data is known to be unreliable. This situation is only made worse as the distance between the target and the threat increases, or if the sea state deteriorates, etc. This uncertainty is a constraint that emphasises the need to use a system based on probability theory and probabilistic calculations.

With these constraints in mind, a solution based on Bayesian networks was explored [START_REF] Leray | Causal graphical models with latent variables : learning and inference[END_REF]. A Bayesian network is a system for the representation of knowledge and the calculation of conditional probabilities (Naim and al., 2007). The tool is based on Thomas Bayes' theorem, which is one of the foundations of probability theory [START_REF] Nielsen | Bayesian networks and decision graphs[END_REF]. Widely used in medical and industrial diagnosis [START_REF] Lee | Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal[END_REF], Bayesian networks make it possible to capitalise on, and exploit knowledge and are particularly suitable when uncertainty must be taken into account [START_REF] Hudson | An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners[END_REF], [START_REF] Martín | A Bayesian network analysis of workplace accidents caused by falls from a height[END_REF].

The aim was to automate the preparation of response plans that are tailored to the nature of the detected intrusion and can provide an appropriate, graduated and progressive response to a threat. Information concerning attacks on shipping and petroleum installations was gathered from a specialist database, and experts in the maritime domain who offered their knowledge and expertise. The data from each of these two sources was modelled with Bayesian networks. The network was built using BayesiaLab 10 software; this powerful network modelling tool provides an intuitive graphical interface.

Recently, Bayesian networks are used in risk assessment because the model can perform forward or predictive analyses as well as backward or diagnostic analyses. Some methodologies have been proposed to structure Bayesian networks and perform risk assessment.

Several authors have already used Bayesian networks in order to solve problems in offshore. Among these authors, [START_REF] Baoping | Application of BayesianNetworks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations[END_REF] who modeled a Bayesian network for the quantitative evaluation of the preventive operation underwater eruption wells. The choice of using Bayesian networks has been done because they are models to perform predictive analytics and diagnostics systems. Another application described in the article of (Eleye- [START_REF] Eleye-Datubo | Marine and offshore Safety Assessment by Incorporative Risk Modelling in a Fuzzy-Bayesian Network of an Induced Mass Assignment Paradigm[END_REF] is the use of a Bayesian network to provide an intuitive and vital representation that mimics the real world. The integration of the human element in a model based on probabilistic risk requires integrated appropriate technical and essential contributions of the linguistic nature. For this reason, the author proposed a Fuzzy Bayesian network as fuzzy logic is an excellent tool for such integration and Bayesian networks can make a probabilistic framework and cross the boundaries of possibility theory. The implementation of this method was demonstrated in a study of human performance at sea. [START_REF] Khakzad | Quantitative risk analysis of offshore drilling operations: A Bayesian approach[END_REF] looked at preventing the risk of blowouts during drilling operations. The authors demonstrate the application of both the "bow-tie" and Bayesian network methods. In the first method, fault trees and an event tree are developed for potential accident scenarios. In the second method, individual Bayesian networks are created for accident scenarios and an object-oriented Bayesian network is constructed by connecting the individual networks. The dynamic Bayesian network method is a better approach than the "bow-tie" model because it can take into account common cause failures and conditional dependencies along with performing probability updates and sequential learning based on accident precursors. Ren et al. (2007) also addressed the contribution of Bayesian networks when taking into account human factors. The authors designed and developed a methodology based on the "Swiss cheese" model developed by James Reason [START_REF] Reason | Human Error[END_REF]). Reason's model provides a generic framework for risk assessment linked to human factors. Five levels are used to characterize latent failures within the causal chain of events: root causes, trigger events, incidents, accidents and consequences. The detailed characterization of each level made it possible to build the Bayesian network. A range of events was specified, and the prior and conditional probabilities of the model were assigned based on the inherent characteristics of each event. [START_REF] Trucco | A Bayesian Belief Network modelling of organisational factors in risk analysis : A case study in maritime transportation[END_REF] presented an approach to integrate human and organizational factors into risk analysis. This approach has been developed and applied to a case study in the maritime industry, but it can be also be utilized in others sectors. A Bayesian Belief Network has been developed to model the maritime transport system, by taking into account its different actors ship-owner, shipyard, port and regulator and their mutual influences. [START_REF] Vinnem | Risk modeling of maintenance work on major process equipment on offshore petroleum installations[END_REF] addressed the issue of hydrocarbon releases at sea during the exploitation or maintenance phases of a platform. A generic model, based on risk 10 BayesiaLab software is developed by the French company Bayesia (http://www.bayesia.com/).

A Bayesian Network to Manage Risks of Maritime Piracy against

Oil Offshore Fields influencing human factors was developed and adapted to specific failure scenarios. The authors describe a full Bayesian network model and two implementations are outlined. The probability of human error, importance measurement of consequences and common causes and interactions are analysed. The authors demonstrate that the model is able to reflect human and organizational factors and safety culture.

These references highlight the wealth of work that has been carried out into both the assessment of technical risks and human and organizational factors in order to prevent the threats that face platforms at sea.

Coupling of quantitative and qualitative knowledge for the construction of a Bayesian network for response planning

The creation of the Bayesian network used for response planning relied on the coupling of quantitative information from the IMO's "Piracy and Armed Robbery" database and qualitative knowledge offered by experts in the maritime domain. Development was divided into two stages. The first step was to construct a Bayesian network from database records of attacks against shipping and oil installations across the globe, while the second step was to exploit the knowledge of experts in order to refine the results and to add counter-measures.

Construction of a Bayesian network from quantitative data

This first step involved the extraction of data from the IMO's "Piracy and Armed Robbery" database. This is the only database currently in existence that contains historic data (dating from 1994) of pirate attacks at sea. On 15 th July, 2011 the database contained records of 5,502 attacks and provided detailed information on the name of the asset under attack, the number of attackers, the weapons used, the measures taken by the crew to protect themselves, the impact on the crew and the pirates, etc.

In the Duty crew onboard the anchored bulk carrier found that the lock of the forward store had been broken. After checking, he saw the ship's stores lying on the deck and the robbers escaping in their two boats empty-handed.

Table 1. Examples of recent attacks and armed robberies

To classify this information, we applied a method of textmining to the database using the software RapidMiner 11 .

The BayesiaLab software made it possible to automatically generate a bayesian network and describe the interdependencies between the principal basic elements. Among the unsupervised learning methods available (data segmentation algorithms or characterisation of the target node for examples), an algorithm for finding associations was chosen as it offered the most appropriate modelling.

Figure 2. The Bayesian network based on IMO data

The Bayesian network constructed from data related to attacks held in the International Maritime Organization's database lacks many values related to the modalities of the different nodes because of a lack of detail in the description of pirate attacks. BayesiaLab makes it possible to impute missing values by adding a state to the variable. Moreover, the k-means algorithm applied to the data made it possible to 11 RapidMiner is unquestionably the world-leading open-source system for data mining. It is available as a stand-alone application for data analysis and as a data mining engine for the integration into own products A Bayesian Network to Manage Risks of Maritime Piracy against Oil Offshore Fields estimate the independence relations between database variables and thus to obtain the best "cause and effect" structure. For each attack scenario, the network performs a statistical calculation by applying the parameters given as input to simulations of similar cases. The large amount of missing data in the database therefore does not impact any of the parameters for the simulation of attack scenarios.

Figure 2 shows the Bayesian network constructed from the information contained in the IMO database. Some information, such as the longitude, latitude, name of the asset attacked, etc. is not included. This is due to the fact that this data was not specified for all attacks. The network contained around twenty nodes that described the type of vessel under attack, the location of the attack, the type of weapons used by the pirates, their numbers, etc. The interrelationships between these variables were also identified through a machine learning process.

A classical statistical analysis of this data provided some initial findings, which included the observation that most ships coming under attack are bulk carriers or tankers; 48% of attacks take place in international waters (due to the absence of security patrols); and pirates prefer to attack in numbers (68% of attacks are organised by teams of more than five pirates). The network therefore provides a very clear view of the tactics of pirates, the weapons they use, and above all the number of individuals involved.

In the example below, specific modalities were set for nodes that characterise the threat in order to identify the counter-measures used by the crew of the asset under attack. Figure 3 The Bayesian network indicates that in this case (as in most cases) the assailants fired shots at the potential target and that the crew, to protect themselves from the threat, tried to apply evasive manoeuvres and aimed water hoses at the attackers.

Figure 3. Hypothetical attack against a tanker

The network created from the IMO database therefore helped to define the principal steps taken by the crew of attacked entities in order to protect themselves, namely: initiate evasive manoeuvres, activate the Ship Security Alarm System (SSAS), contact the security vessel, secure the crew, turn on searchlights, etc. It also made it possible to assess the effectiveness of these tools and to define the probability of occurrence of certain types of attacks.

It is necessary to carry out an initial analysis of the IMO database to establish the challenges posed by these threats to the crew, the platform, the economy and national security. It makes it possible, in a second step, to identify the frequency of attacks, risk zones, types of ships used to carry out attacks, etc. and to list the most commonly used and effective counter-measures.

Coupling the Bayesian network based on IMO data with the qualitative knowledge of marine experts

The Bayesian network created from the modalities and conditional probabilities found in the IMO database provided an initial formal framework. Domain experts were then able to enrich this initial network by integrating their knowledge and expertise in order to create the final SARGOS network [START_REF] Hudson | An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners[END_REF].

The second step of the approach was for experts in the maritime and petroleum industries to analyse the information provided by the Bayesian network that had been constructed from the IMO data. As the information contained in the IMO database related primarily to attacks on shipping, experts were able to contribute their knowledge of attacks on oil fields in order to extend the results: nodes and arcs were added to the model in order to make it as versatile as possible. Consequently, the Bayesian network was able to model both main target categories (shipping and fixed installations). While the inputs to the network (type of vessel used by the attackers, its movements, etc.) are identical regardless of the nature of the target, the counter-measures recommended by the Bayesian network are tailored to the type of target under attack (for example, evasive manoeuvres are not proposed when a fixed installation is the subject of the attack).

The design of this enhanced Bayesian network, adapted to the constraints and conditions associated with fixed installations came about as a result of many brainstorming sessions during which various maritime security experts shared their experiences and discussed the modalities and probabilities of the network.

The combination of information from the IMO database and the knowledge and experience of experts in marine and offshore safety made it possible to create the SARGOS response planning network, which consisted of four modules and five submodules (Figure 4). In the SARGOS Bayesian network, each module or sub-module consists of one or more nodes that receive input from and/or output to other nodes. Each node is a matrix of conditional probabilities that are calculated from an assessment of the interactions between nodes and the reality represented by the node itself. For example, the probability distribution of the node to activate searchlights ('ActivateSearchlight') is the direct result of interactions with other nodes that describe visibility, time of day and technical constraints such as availability and remote control. The probabilities of the basic nodes are initially standardised as no specific attack characteristics are set.

A Bayesian Network to Manage Risks of Maritime Piracy against Oil Offshore Fields

The definition of the scope of each module is directly related to the composition of its constituent nodes. The module classification included: basic parameters, the overall danger level of the situation, aggravating factors and constraints, counter-measures and nodes related to communication and the request for assistance. These modules are described in detail below.

Basic parameters

The basic parameters module comprises static or dynamic physical data that characterise the threat and the target. These data are the direct result of, or are derived from the intermediate calculations of the alert report. Basic parameters represent the minimum, but sufficiently detailed level of modelling required for a full understanding of the threat and the target when assessing potential responses to an attack. They include, for example, threat identification (the node 'IdentityClass' which has two values: suspicious or hostile), the distance between the threat and the target (the node 'DTGThreat/Asset'), and the criticality of the target (the node 'AssetAssessment' that takes four values: critical, major, significant or otherwise).

In the Bayesian network, we take into account the longitude and latitude of the pirate ship for the calculation of the kinematics of the vessel to determine the distance between the graft vessel and the platform. These two variables are passed in the alert report but not included in the network nodes.

The overall level of danger of the situation

The overall danger level of the situation is arrived from the basic parameters. The node 'ShowGradationLevel' is used to formalise this module in the Bayesian network. The grading system runs from level 1 (least serious) to 4 (most serious). This level and the planning of counter-measures are constantly adapted to the situation.

Aggravating factors and constraints

The aggravating factors and constraints module consists of elements that are both internal and external to the system. Aggravating factors make it possible to take into account a potential deterioration in the situation and thus to anticipate potential planning options. The nodes in this module represent the environment, for example visibility (the node 'Visibility') and time of day ('PeriodOfDay'). Constraints are represented by parameters which reflect the effectiveness of the response both technically and operationally. Technical constraints are directly related to the use of counter-measures, and include nodes that represent their availability ('ImmediateReadiness') or the potential for remote control ('RemoteControlled').

Counter-measures

Counter-measures include all defences that are mobilised by a target under attack in order to protect itself against an identified threat. They are the concrete realisation of the response plan and constitute the set of means and actions intended to normalise, as quickly as possible, the situation. Counter-measures are divided into five sub-modules, which reflect the concept of a graduated response through increasingly forceful measures that correspond to the nature of the detected threat. Measures range from communication and a request for assistance, through deterrence and small-scale repulsion, repulsion, antiboarding measures and neutralisation, to procedure management and securing the facility. They are described in detail below.

Communication and the request for assistance are two key responses to a threat. Internal communication can be used to alert all relevant personnel on the target (e.g., the node 'InformOIM' which represents informing the crew master), while external communication makes it possible at various levels to alert the different actors involved in maritime security -for example to request the intervention of the security vessel (represented by the node 'RequestSecurityVessel') or to activate the Ship Security Alarm System (represented by the node 'RaiseSSAS') etc. Both of these types of communication enable fixed installations and shipping to prepare their response plan and to establish if external intervention is available.

From the position of the ship security (BIR), the system calculates the time required for the intervention on the location of the threat. If the estimated response time is greater than 300 seconds, the ship security may be required, in which case a request must be sent.

Deterrence and small-scale repulsion measures are intended to inform the attacker that the target is aware of the attacker's intentions, can follow the attacker and that it is not in the attackers' interest to continue. These measures include the ability of the target to repel an attack with low-impact devices such as searchlights, fire hoses or sonic cannons (Long Range Acoustic Devices), represented by the node 'ActivateLRAD'.

A Bayesian Network to Manage Risks of Maritime Piracy against Oil Offshore Fields

Repulsion, anti-boarding and neutralisation are high-impact counter-measures whose main function is at least to mitigate an attack, if not neutralise the attackers. The node 'EngageRepellentEquipment' represents a growing number of tools available on the antipiracy market that are designed for the repulsion of an assault at long-range (while remaining within the bounds of legitimate, non-lethal defence). Like repulsion equipment, the main function of anti-boarding tools is to prevent attackers from gaining access to the facility or vessel. The function of the 'SetCrowdControlMunition' node is to delay the progress of the attackers in order to exhaust or even neutralise them and thereby provide the crew with maximum time to mobilise other safety measures.

Procedure management is composed of two counter-measures. On the one hand, the node 'CrewManagement' represents the sounding of crew Action Stations and the reporting of crew to their pre-assigned post or station. On the other hand, the 'AssetAssaultManagement' node represents activities related to securing the target of the attack. The modalities of this node are: activate the Citadel, engage evasive manoeuvres (for mobile units and shipping), and declare the security post (a set of individual procedures to be applied by each crew member as necessary). Like procedure management, the SARGOS system offers a way to secure the installation through the planning of actions designed to safely stop production and prevent access to sensitive areas.

Demonstration of the contribution of the Bayesian network and discussion

Once the probability distribution of the various modalities has been established, an interesting exercise is to test the Bayesian network by using it to simulate different attack scenarios through the selection of certain criteria. An examination of these scenarios made it possible to finalise the network before integrating it into the SARGOS system.

The integrated data that provides the input to the network is interpreted from images captured by cameras and various sensors. The uncertainty of this information increases with the distance between the target to be protected and the pirate ship. In its current form, the Bayesian network cannot handle the temporal evolution of the attack and there is no connection between response reports generated for the same attack. This issue is addressed in other research based on dynamic Bayesian networks [START_REF] Dabrowski | Maritime piracy situation modelling with dynamic Bayesian networks[END_REF].

Attack scenarios

The example below (Figure 5) shows the results of setting parameters to simulate an attack on a Floating Production, Storage and Offloading (FPSO) unit by an unknown vessel. In this example the danger level of the situation is 2 with a 64.68% probability of occurrence and the counter-measures to be applied are: inform the crew master; request the intervention of the security vessel; broadcast a strong, clear message by loudspeaker; activate the searchlight; activate the security post; and engage repulsion equipment. Figure 5 shows that the planning of the response corresponds to the danger level of the situation and is able to adapt to changes in parameters representing the threat and the target. Setting parameters to represent the threat, the target, the environment, etc. creates different attack scenarios that make it possible to refine the probability of an attack and test the response of the Bayesian network.

In this case it is necessary to inform the master of the crew of the FPSO, request the intervention of ship safety and security since the probability of their action is equal to 80% (close to the ship attacked infrastructure). Several counter-measures can be activated as the speakers, bright lights and water jets. Following the evolution of the situation a few moments later and the increased level of danger that follows, it should then alert the crew to use the security station.

Figure 5. Result of response planning using the scenario of an attack from an unknown vessel Integration of the Bayesian network into the SARGOS system

In order to integrate the Bayesian network into the SARGOS system, a prototype was developed that included an alert report as input and a planning report (which listed all the counter-measures to be applied either by the crew or automatically by the system) as output. The BayesiaEngine software provides a module that makes it possible to select and set attack parameters. This module consists of an application programming interface (API) and a Java library. Intermediate calculations are carried out on the basis of these parameters and the results are fed into the enhanced Bayesian network created from expert knowledge.

The resulting list of counter-measures varies according to the attack scenario. Consequently, a threshold must be set in order to only activate those measures that provide the most relevant response at a particular time, and in a particular situation. This threshold was set at 70%. In other words, only those counter-measures where one of the modalities had a probability greater than 70% were selected for further processing. This threshold was arrived at by domain experts as it reflects actual events in more than twothirds of real-life cases. Following an extensive period of testing, the selected countermeasures were found to correspond to realistic and reliable responses.

Once the counter-measures had been selected, they were added to the planning report in a specific order. The main factors determining this order of priority were: the action mode of the counter-measure, its ease of implementation, the degree of automation or the A Bayesian Network to Manage Risks of Maritime Piracy against Oil Offshore Fields need for a large number of crew members to activate it, the time required for it to become effective and its potential additional functions.

The SARGOS system can handle multiple threats contained in a single alert report. Consequently, priorities must be established. In the system, the first threat to be treated is always the one where time available to react is the shortest for the target that is most exposed.

The system detected several potential threats heading towards the oil field and has classed them into 'Enemy', 'Unknown' or 'Friend'. An alert is only generated following a classification of Enemy or Unknown. Once a threat has been detected and analysed, the response planning report is prepared. It is divided into two parts: the first concerns communication and a general request for assistance directed at the entire oil field; the second concerns the specific asset at risk. The response planning report also displays the counter-measures to be activated in chronological order (Figure 6).

The representation of the probability that a particular measure will be implemented can be seen in the counter-measure 'Security Vessels', where the proportion of the blue segment suggests a 60-70% probability that this method will be called upon.

Conclusion

Acts of maritime piracy against oil field infrastructure present a complex problem. The effectiveness of current measures designed to protect infrastructure is significantly affected by the vast terrain and environmental constraints. Moreover, it is difficult to assess a potential threat given the constantly changing nature of a dangerous situation and the huge number of parameters that must be managed.

The implementation of a Bayesian network therefore offers a significant advantage for the SARGOS system as this type of network is able to handle all possible combinations of parameters. These include not only the characteristics of the threat and the target under attack, but also the environment and variables related to crew and facility management. Most importantly, the system is able to adapt in real-time to changes in the danger level of the situation. The SARGOS system offers a response planning solution that manifests in the preparation of a planning report created from an intelligent assessment of successive alert reports, and which can react to an evolving situation.

The activation threshold of counter-measures has been determined by experts. Most counter-measures are against-enabled manually by the crew. Some of them are not systematically exploited if their activation requires setting a crew danger. The Bayesian network was developed specifically for the protection of static targets (platforms) and is therefore not suitable for ensuring the safety of ships.

The network can be continuously improved through the integration of feedback from attacks that have already been managed. It is therefore possible to continue to enhance and tailor the planning module iteratively.

Finally, an interesting approach that may improve the modelling of knowledge embedded in the Bayesian network could be to establish an appropriate ontology. The use of a suitable ontology would make it possible to formalise knowledge upstream of the Bayesian network in order to consolidate the threat detection and identification steps.

The use of dynamic Bayesian networks is a way to explore. These networks have been an interesting development as a generalization of models hidden Markov models or Kalman filters for applications such as speech recognition, state estimation of a dynamic system, etc. A dynamic Bayesian network is a factored representation of a Bayesian network whose nodes are indexed by time on a discrete scale. The Bayesian network is represented by nodes and indexed by generic no time and two types of links: links classical Bayesian networks and so-called temporal relationships that define the conditional probability tables of the nodes according to their parents located to lower time indices. The application of a dynamic Bayesian network would integrate the notion of time on decisions to be taken in case of attack and its influence on the evolution of the threat.

Figure 1 .

 1 Figure 1. Functional diagram of the SARGOS system

  illustrates the following assumptions:  The asset under attack: a tanker  The location of the accident: international waters  The type of attackers: thieves  Type of weapons: armed personnel

Figure 4 .

 4 Figure 4. Structure of the SARGOS Bayesian Network

Figure 6 .

 6 Figure 6. The user interface of the SARGOS system showing global countermeasures on the left (in order: inform the crew master, request the intervention of the security vessel and inform other installations in the field) and specific countermeasures on the right (assemble crew, block access to infrastructure, activate searchlights and activate the sonar cannon).

  table below are listed some examples of recent attacks and armed robberies.

	Date	Ship name	Ship	Incident details
			type
	2012-	ASSO	Supply	Pirates armed with guns attacked and boarded the
	12-23	VENTUNO	ship	offshore supply ship underway and kidnapped four
				crew members. The ship sailed to a safe port after
				the incident. The other crew members did not
				sustain any injuries.
	2012-	SANKO	Bulk	Robbers boarded the anchored ship while waiting to
	12-29	MERCURY	carrier	commence loading operations. They broke into the
				forward bosun store, stole the ship's stores and
				property and escaped unnoticed. The incident
				occurred between 29.12.2012, 2300 LT and
				30.12.2012, 0400 LT and was reported to the local
				agent and the port authorities.
	2012-	NORD	Bulk
	12-29	DISCOVERY	carrier

The Offshore Warning and Graduated Response System (Système d'Alerte et de Réponse Graduée OffShore).

The SARGOS project includes participants from private sector organisations such as DCNS (a French naval shipbuilder) and SOFRESUD (a supplier of high-tech equipment to the defence industry), and public research centres including ARMINES (a French contract research organisation) and TéSA (Telecommunications for Space and Aeronautics).

In these systems, a radar antenna emits microwave pulses towards the target. These signals are reflected back, and then intercepted by the radar receiver, which collects an electrical signal called the echo.

These electronic and electrical systems generally consist of an optical sensor, an image processing system and a data storage, or display device.


Martine Hours
email: martine.hours@ifsttar.fr

Keywords: follow-up studies, trafic crashes, wounds and injuries, outcome assessment, recovery of function, subjective health

Objective:

The aim of this study was to examine the selfreported health status of road trafic accident victims and the predictors of self-assessed recovery 1 year after major trauma in a French population.

Design:

A follow-up study. Methods: The cohort comprised 276 seriously injured victims of road trafic accidents, aged > 16 years from the Rhône administrative department, France. Victim characteristics at the time of the crash and self-reported health status 1 year after trauma were collected. Predictive factors for self-assessed recovery were examined using a Poisson regression approach.

Results:

The majority of victims were male (76%); most had severe injuries (76%), involving mainly the lower limbs and the head (68% and 55%, respectively). At 1-year follow-up, 80% reported being not fully recovered. Self-reported health status was not signiicantly associated with age, gender, being in employment, type of road user, or health status during the year preceding the accident, but rather with low socioeconomic status, high injury severity, and presence of lower limb injury.

Conclusion:

Care for subjects who are at high risk of not fully recovering (manual workers, the very seriously injured, and those with lower limb injury) needs to be extended and improved. Longer follow-up studies on the risk factors for not fully recovering are needed in order to reduce harmful consequences for victims.

INTRODUCTION

Previous studies have shown that road trafic accidents were the main cause of injury in most countries (1, 2) and that major trauma resulting from road trafic accidents may induce long-term negative effects, either objective or subjective, in victims' lives, such as reduction in quality of life (2, 3), functional limitations (1, 4-6) and psychological problems (2, 7). Soberg et al. (5) found that the mental and general health of subjects with a New Injury Severity Score (NISS) >15 did not improve signiicantly between discharge home and 1 year after the crash. Studying quality of life after major trauma in subjects aged 18 years or over, Holbrook & Hoyt (2) reported that 169 out of 1048 subjects (16%) showed poor recovery at 12 months. Barnes & Thomas (3) reported that, 12 months after injury, 58% of hospitalized subjects had not fully recovered and still had pain, healing problems, and reduced leisure and work activity. Schluter & McClure (6) found that 119 (61%) of 195 subjects hospitalized for at least 6 days still had major functional limitations 12 months after the crash. Rates of non-return to work at 1-year follow-up varied between studies, from 10% to 36% (3,8,9). These studies assessed the consequences of trauma in terms of various outcomes: quality of life, post-traumatic stress disorder, and return to work. In France, since 2002, road safety has been 1 of the 3 most important government programmes. Although an unprecedented decrease in mortality has been obtained through police measures, such as increased controls on alcohol and speed, the decrease in severe casualties has not been as marked. Reducing the prevalence and impact of major trauma has therefore become a prime goal of the road safety authorities (10). Most victims of major trauma caused by road trafic accidents now have a good chance of survival, but there have been few French studies of late outcome in victims of road trafic accidents. Outside of France, studies have shown that adult victims of road trafic accidents experience far more severe health problems than do children, with signiicant impairment of well-being (11), and that various baseline factors, such as gender, age, type of injury and length of hospital stay, may predict longterm health status (1,2,(4)(5)(6)8). Such studies on French victims are still lacking.

ESPARR (Etude et Suivi d'une Population d'Accidentés de la Route dans le Rhône: Rhône area road trafic accident victim follow-up study) ( 12) is a study conducted in the Rhône administrative department of France. It involves a 5-year follow-up of a cohort of victims of road trafic accidents, and assesses the medical, physical, social, and psychological consequences of the crashes on the victims and their families. The aim of the present study, which is part of ESPARR, was to investigate predictive factors of self-assessed health status in victims of major road trauma at 1-year's follow-up in a French population.

METHODS

Participants

The ESPARR cohort is linked to the Rhône Road Trauma Registry, which records all road trafic accident casualties seeking medical care in public or private health facilities of the Rhône Département. Inclusion criteria are: having been in a road trafic accident, involving at least one mechanical means of transport, and occurring in the Rhône administrative area; having been admitted to one of the area's hospital emergency departments; having survived the crash until hospital admission; being resident in the same area (to facilitate follow-up).

In addition, the present study included only seriously-to-critically injured subjects: i.e. with at least one injury scored ≥ 3 on the Abbreviated Injury Severity Scale (AIS) (13). The most severe injury was described by its maximum injury severity score (M-AIS). Also, because recovery processes and assessment tools differ between children and adults, participants had to be aged 16 years or over, and they (or their parents or legal representatives) had to give informed consent to the follow-up and complete a self-administered questionnaire 1 year after the crash.

Further details of patient contact, recruitment and interview for collection of baseline and clinical characteristics have been published previously (12).

From October 2004 to July 2006, the Rhône Road Trauma Registry recorded 722 victims. Of these, 324 met the ESPARR inclusion criteria and were initially included; the participants (or their representatives) were interviewed as soon as possible after the crash (median delay: 4 days; 1 st and 3 rd quartiles: 1 and 10.5 days). The subsequent withdrawal of 48 victims resulted in 276 subjects being included in the inal analyses (Fig. 1).

Study data and measurement tools

Two sources of data were used for the present study: the ESPARR cohort data, and data from the Rhône Road Trauma Registry.

Patient characteristics data collected during the initial interview:

• Demographic characteristics: age at crash, gender.

• Socio-economic status: this variable is derived from the "French occupational and social categories, level 1, 8 categories" (14); it was then regrouped into 5 categories according to their frequency.

• Current employment: this variable was divided into 2 categories according to their employment at the time of the accident. Participants were considered to be "in employment" if they were students or had a regular job at baseline. • Type of road user: this variable was divided into 4 categories according to their mode of transport.

Medical data:

• Comorbidity collected during the initial interview: subjects were considered as having a comorbidity if, at the time of the crash, they had at least one chronic disease (excluding vision problems) listed in the French Ministry of Transport decree of December 21, 2005 [START_REF]la liste des affections médicales incompatibles avec l'obtention ou le maintien du permis de conduire ou pouvant donner à la délivrance de permis de conduire de durée de validité limitée[END_REF]. • Initial injury report: were collected from the medical records and coded by a trained physician:

• Injury severity: as measured by the AIS, which standardizes injury data and scores lesions from 1 (minor) to 6 (fatal); M-AIS, the score of the most severe injury; and NISS ( 16), sum of the squares of AIS scores for the 3 most severe injuries, regardless of body region. • Type of injury: injuries were also classiied by body part into subgroups according to their injuries (head, face, neck, spine, abdomen, upper limbs, and lower limbs). Thoracic injuries were divided into 2 subgroups: "severe thoracic injury" for thoracic M-AIS from 3 to 5, and "absence of severe thoracic injury" for no thoracic involvement or thoracic M-AIS of 1 or 2.

Self-reported data: 1 year after the crash, a self-administered questionnaire assessed the health, social, emotional, and inancial status of each participant [START_REF]WHO. Development of the World Health Organization WHOQOL-18. BREF quality of life assessment. The WHOQOL Group[END_REF]. These data were collected by questionnaires, either sent by regular post followed by reminder telephone calls or completed during a face-to-face interview.

• Self-assessed recovery: a standard single-item self-assessed recovery measure was used: "How would you describe your health status today?", with 4 response options ("Fully recovered", "Improved but not recovered", "Stabilized" or "Deteriorated"), dichotomized for analysis as "Fully recovered" vs " Not fully recovered", the latter combining all 3 remaining response options. • Quality of life: quality of life was evaluated with a standard instrument (the World Health Organization Quality of Life Questionnaire-Short Form (WHOQOL-BREF)), which is an abbreviated version (26 items) of the WHOQOL (100 items), and covers 4 domains: physical health (7 items), psychological health (6 items), social relationships (3 items), environment (8 items) and 2 global health-related qualityof-life items assessing overall satisfaction with life and general sense of personal well-being. Responses to each item were coded from 1 to 5, summed, and transformed to a scale from 0 (worst health-related quality of life) to 100 (best health-related quality of life) (18).

Statistical analysis

In order to assess representativeness, our population was compared with a reference group of major road trafic accident trauma victims recorded in the Rhône Road Trauma Registry during the same period (October 2004 to July 2006), i.e Non-ESPARR victims and nonrespondent ESPARR cohort (Fig. 1). Nominal variables were compared using χ 2 test or Fisher's exact test, and quantitative variables by Student's t-test. The correlation between responses to our subjective health question and to the overall health question in the WHOQOL-BREF (a validated instrument used to assess health-related quality of life) was assessed with Spearman's rho. Predictive factors for being not fully recovered 1 year after the crash were sought among the parameters collected at baseline. One of the advantages of cohort studies is that relative risk, instead of odds ratios, which are only approximations of the real relative risk, can be calculated directly; furthermore, odds ratios can be used as an approximation of relative risk only if the prevalence of the health problem in question in the study population is very low, which was not the case in the present study. A Poisson regression approach was used for modelling (19,20) to estimate relative risk directly (20,21)

Complete-data participants n=276

Not fully recovered n=222

Fully recovered n=54

was applied to binomial data, a sandwich estimation of the variance of the estimated relative risk was used (20). In the univariate analysis, the associations between subject characteristics, injury severity and the probability of poor subjective health were assessed with the χ 2 test. For each characteristic, the association was quantiied by a risk ratio. A 95% conidence interval (95% CI) was given for each relative risk.

Variables that were signiicantly associated with subjective health status at the 20% level on univariate analysis were included in the multivariate analysis, after checking for collinearity between explanatory variables. Gender and age, considered as adjustment variables, were included in the multivariate analysis regardless of their signiicance level. Stepwise selection with backward elimination of subjective health status predictors was applied, with p > 0.05 for exclusion. Confounding factors were checked at each stage of model construction.

Statistical Analysis System software, version 9.1 for Windows (SAS Institute, Cary, NC, USA) was used for all analyses.

ReSUlTS

Comparison between study sample and reference group

Overall, the study sample did not differ signiicantly from the reference group concerning gender or severity of injury. However, the mean age of the study population was signiicantly lower than that of the reference group (37.2 years, standard deviation (SD) 17.0, vs 41.4 years, SD 20.6; p < 0.01) and the study sample included more car drivers but fewer pedestrians, inline-skate/scooter users and passengers than the reference group (p < 0.01) (Table I).

Description of the study sample

Three out of 4 victims (210/276; 76%) were male. The majority (76%) had M-AIS = injuries. The main injury sites were the lower limbs and head (68% and 55%, respectively).

For the question "How would you describe your health status today?", only 54 subjects (19.6%) responded "Fully recovered", the vast majority of respondents (222/276; 80.4%) reported being not fully recovered 1 year after trauma (139 subjects (50.4%) responded as "Improved but not recovered", 46 subjects (16.7%) as "Stabilized" and 37 subjects (13.4%) as "Deteriorated"). Bluecollar workers tended to have a higher probability of not fully recovered than white-collar workers (Table II). Overall injury severity was signiicantly linked to the percentage of victims re- porting not fully recovered at 1-year follow-up, which increased with degree of severity. The proportion of subjects reporting full recovery was lower in those with NISS ≥ 16 than with NISS between 9 and 15 (15% vs 27%; p < 0.05) (Table III).

The two global WHOQOL-BREF items assessing overall satisfaction with life and general sense of personal well-being showed that 51.1% (141 persons) of the population rated their quality of life as good or very good, and 42.8% (118 persons) were satisied or very satisied with their health status. A signiicant correlation was found between subjective health status and global health satisfaction on the WHOQOL-BREF (R = 0.49, p < 0.001). The group of victims reporting being fully recovered at 1-year follow-up had better scores in all quality of life domains than those reporting being not fully recovered. However, the difference is only signiicant for the physical and psychological domain (p < 0.001) (Table IV).

Predictive factors for poor subjective health at 1-year follow-up

After adjustment for the baseline characteristics, subjective health status was not found to be associated with age, gender, being in employment at the time of the crash, type of road user, or health status during the year before the crash. However, associations were found with socio-economic status, overall severity, and lower limb injury. Regarding socio-economic status, multivariate analysis showed that blue-collar workers had a 20% increased risk of poor subjective health compared with white-collar workers (p = 0.02). Similarly elevated risk was also observed for subjects with NISS ≥ 16 compared with NISS between 9 and 15 (p < 0.01); subjects with severe lower limb injury (M-AIS = 3-5) were more at risk of poor subjective health than those without such injury (Table V).

DISCUSSION

There have been only a few studies of health status after serious road trafic accidents in France. The present study examined the subjective health status of a population of road trafic accident victims with NISS ≥ 9 and identiied the main predictive factors for being not fully recovered 1 year after the crash. Regarding the main outcome measure, self-reported health status 1 year after the accident was "fully recovered" in only 20% of the study sample. Self-reported health status was not signiicantly associated with age, gender, being in employment, type of road user, or health status during the year preceding the accident, but rather with low socio-economic status, high injury severity, and presence of lower limb injury. Some features of the study population allow meaningful comparison with previous reports. The predominance of males (76% in the study sample, 74% in the reference group) is similar to that reported by Mayou & Bryant (7) in the Oxford study on adult victims followed up for 1 year. The mean age (37 years) was also similar to that of the major trauma population, aged ≥ 16 years, in Holtslag et al. and other reports (4,7,9,22), in which the mean age of adult victims ranged between 30 and 40 years. Lower limb injuries predominated in the present data, as they did in another study that reported 58% victims with lower limb injury (3).

As described previously, severity and type of injury were found to be related to being not fully recovered. In several previous studies (2, 5-8), severe injury led to serious health impairment and was a predictive factor for long-term health status (5, 6, 8); for example, Mayou & Bryant (7) reported that victims with severe injuries had the most severe physical consequences. The present study found a signiicant difference in overall recovery between victims with NISS ≥ 16 and with NISS ranging between 9 and 15. Regarding type of injury, our data conirm the association between lower limb injury and being not fully recovered after major trauma (6,22,23). Other investigations reported associations between type of injury (e.g. spinal) and functional outcome (1,4); the present analysis failed to ind any signiicant association between health status at 1 year and spinal injury, but power was insuficient in this regard because few subjects had very severe lesions involving the spinal cord. Self-assessment of well-being is likely to underestimate the severity of the real outcome, as some victims, particularly those who sustained traumatic brain injury, sometimes experienced anosognosia, which may explain why in the present study, contrary to Holtslag et al.'s study (4), traumatic brain injury was not found to be a predictive factor for not fully recovering. The medical literature does not mention relationships between socio-economic status or type of road user and subjective health status 1 year after major road trauma. When adjusted for the global severity of the injury, blue-collar workers in the present study were 20% more likely to report not having fully recovered at 1 year than white-collar workers. Subjective poor health recovery is related to pain (24). Whereas blue collar workers do not have more limb injuries than other socioeconomic categories, it is possible that pain is more intolerable for people who have a physically demanding job and thus they are prevented from returning to work, which may explain why more blue-collar workers are not fully recovered. However, in a previous analysis (25), which investigated the predictors of return to work, the results show that there was no signiicant difference in the return to work between blue-collar workers and others when the severity was taken into account. On the other hand, Schreuder et al. (26) did not ind differences in job demands and health complaints in white-and blue-collar workers. Thus, we cannot establish whether blue-collar workers have an objective difference in their health status from others.

The present analysis showed no signiicant associations between the subjective health status at one year and gender, age, type of road user, being in employment at the time of the crash, or presence of comorbidity. Nevertheless, we decided to introduce the irst two factors as adjustment variables in the multivariate analysis, whatever their signiicance level. Gender, although correlating with health status in several studies (1, 2, 8), was not highlighted in the present study or other studies (5,6). The relationship between age and long-term health status after a crash has been studied previously (1,(4)(5)(6)8). Schluter & McClure (6) reported that age was a predictive factor for functional impairment at 12 months. A study of functional capacity after trauma in victims aged ≥ 15 years (1) showed that age was signiicantly related to functional impairment at both 12 and 24 months. These results oriented our hypothesis that age at the time of the accident might be a predictive factor for health status; however, this failed to emerge in the present logistic regression model after adjustment for the other factors.

Self-reported health has often been studied in major trauma populations, using general questions from the EUROQOL-5D, Short 5,7,9,22), Sickness Impact Proile (9, 23), or Quality of Well-Being Scale (2), assessing general and speciic dimensions of health status. In most of the above-cited articles, health status was deined as "good" when victims reported no problem in mental or physical health. The present study agrees with several previous publications (7,11) as to the choice of subjective assessment of outcome at 1 year. The correlation between objective and subjective health has been investigated and highlighted in the literature, particularly in populations with health problems (27)(28)(29)(30). Subjective health has been correlated with mortality. These results suggest that subjective health measurement validly relects signiicant implications for healthcare.

In our study, the majority of victims who reported a full recovery (44/54, 81.5%) rated a better quality of life than those who reported a not full recovery (97/222, 43.7%). The formulation used to describe subjective health gave more speciic information than the overall health item of the WHOQOL-BREF: with 4 modalities ("Fully recovered", "Improved but not recovered", "Stabilized" and "Deteriorated"), the present self-assessment relected the subjective health status of the victim related to the crash; in contrast, the WHOQOL-BREF subjective health item fails to specify whether any negative perception is related to the crash. The objective of the present study was not to analyse the victims' quality of life, but only their assessment of their health one year after the crash. We therefore preferred to use our own item, in order to have a good level of speciic information. Furthermore, the correlation between our own item and that of the WHOQOL-BREF was satisfactory.

By comparing our results about the WHOQOL-BREF with the results of a study in the general French population (31), we found that our severely injured patients had lower scores of quality of life than the general French population. However, the study in the general French population investigated only 3 of 4 domains of the WHOQOL-BREF and the question related to the WHOQOL-BREF was changed slightly in that general French study. It should be noted, however, that the French general population studied was older and had a greater proportion of women than our population study. Quality of life is very dependent on age and gender: women are more likely to have a less good quality of life than men; furthermore, quality of life decreases with age. Thus, the strong decrease in the quality of life of our population (which is composed of numerous young men) reveals the strong impact of the accident on quality of life, as reported by Baumann et al. (31). There were differences in the literature regarding the tools used to assess type and severity of injury. Although introduced in 1997 to improve outcome prediction in trauma victims, the NISS is, as yet, rarely used (5). Even so, certain studies of trauma victims have demonstrated that the NISS is better able to predict functional outcome (32), probability of survival (33) and need for intensive care (34) than the Injury Severity Score. We therefore predict that the NISS might be adopted as the new "gold standard" for severity scoring.

The present report concerns a part of a cohort study with binary outcomes in which the outcome event was common (80% self-reported not fully recovered). Thus it was preferable to estimate relative risk (20, 21) than to calculate odds ratios, which are only an approximation of the relative risks.

The major importance of the present study is that it is one of the irst to report on long-term outcome in French victims of road trafic accidents. Other strong points are the prospective design and the complete baseline data provided by the Rhône Road Trauma Registry, which, with appropriate corrections, enable reliable extrapolation of the results to the total recorded casualty population and thus a non-negligible improvement in our knowledge of outcome of major road trauma in the Rhône Département.

There were limitations inherent in these analyses. Firstly, the conditions of collecting the information were not strictly the same for all victims. The slight difference in the result for the telephone interview is probably explained by the fact that more fully recovered people do not reply to the postal questionnaire or to the medical visit invitation than not fully recovered people. If there is a bias, it is more likely to be related to the non-responses than to the questions themselves. However, the non-response is quite low (15%) (Fig. 1) and the two groups are not, in fact, different except on age (which is taken into account in the analysis). Secondly, some studies in the literature show that claiming compensation is a factor associated with health status after trauma (35,36). A bias in self-report of recovery can be caused by the process of compensation in our population. The majority of the study population had not received compensation by the time of the 1 year follow-up (186/276, 67.4%). We made a comparison between the victims reported as fully recovered and those reported as not fully recovered concerning having received compensation or not. The difference was not signiicant (p = 0.899). Thus, it is unlikely that this introduced a bias in the self-report of recovery in our population. Thirdly, the metrological properties (accuracy, validity, reproducibility) of the whole ESPARR questionnaire are not controlled. However, the ESPARR questionnaire itself is built as often as possible using validated scales or indicators: the main variable (fully recovered/not fully recovered) is highly correlated with the second question of the WHOQOL-BREF (health satisfactory): the WHOQOL-BREF itself is a validated scale; SEC is an usual French socioeconomic scale; injuries were reported by medical staff and coded by the trained physician of the Rhone Registry (who were blind to the ESPARR status).

In conclusion, one year after a serious road trafic accident, the majority of victims still have problems with their health status. This study indicated the need to extend follow-up and even medical care beyond one year after injury in some subgroups. More speciically, it seems necessary to enhance and extend care in subjects at high risk of not fully recovering, such as manual workers, very seriously injured victims (NISS ≥16) and those with lower limb injuries. Moreover, according to its initial intent, the present follow-up study should be extended for several years in order to assess further the long-term consequences of road trafic accident injuries and o expand our knowledge of factors linked with better recovery.

Mazaux, Jean Louis Martin, Jacques Gaucher); all the hospital staff who accepted the interviewers' presence and referred victims; and the SAMU team who reported their daily emergency interventions.

Special thanks to Mr Jean-Louis Martin and Dr Muriel Rabilloud for data processing and statistical analysis advice, to M. Jean Iwaz for comments and manuscript editing, and to Iain McGill for English translation.

  PReDICTINg SelF-RePORTeD ReCOveRy ONe yeAR AFTeR MAJOR ROAD TRAFFIC ACCIDeNT TRAUMA Hoang-Thy Nhac-Vu, PharmD, MSc 1 , Martine Hours, MD, PhD 1 , Pierrette Charnay, MSc 1 , Laetitia Chossegros, MSc 1 , Dominique Boisson, MD, PhD 2 , Jacques Luauté, MD, PhD 2 , Etienne Javouhey, MD, PhD 1,3 , Amina Ndiaye, MD 1 and Bernard Laumon, MD, PhD 2 From the 1 Transport Work and Environmental Epidemiology Research and Surveillance Unit -UMRESTTE (UMR T9405) IFSTTAR, Université de Lyon, 2 Rehabilitation and physical medicine Unit, Hospital Henry Gabrielle, Hospices Civils de Lyon, Saint Genis-Laval and 3 Pediatric Intensive Care Unit, Hôpital Femme Mère Enfant, Hospices Civils de Lyon, Bron, France Predicting self-reported recovery after major trauma

  . As the Poisson regression Fig. 1. Process of patient inclusion. ESPARR: Etude et Suivi d'une Population d'Accidentés de la Route dans le Rhône.

	Major road trauma victims in the Rhône area
	(New Injury Severity Score 9, age 16 years)
	n=722
	Non-eSPARR cohort
	n=398
	• 317 late registrations
	• 77 refusals to participate
	• 4 psychiatric or language
	problems
	eSPARR cohort
	n=324
	Incomplete data
	n=48 (15%)
	• 35 non-respondents but alive
	• 12 lost to follow-up
	• 1 death at 6 months

Table I .

 I Description of the study population and comparison of characteristics with the reference group

	Characteristics	Study sample (n = 276) n (%)	Reference group (n = 446) n (%)	p-value
	Sex Male Female Type of road user Pedestrian, inline-skate/ scooter user, passenger Cyclist Motorized 2-wheel/ quad-bike driver Car driver Not known Maximum Abbreviated Injury Scale 3 4 5	210 (38.8) 66 (36.5) 71 (30.1) 33 (38.8) 104 (42.8) 67 (46.2) 1 (7.7) 214 (39.8) 46 (30.7) 16 (47.1)	331 (61.2) 115 (63.5) 165 (69.9) 52 (61.2) 139 (57.2) 78 (53.8) 12 (92.3) 324 (60.2) 104 (69.3) 18 (52.9)	0.57 < 0.01 0.07

Table II .

 II Estimations of the probabilities of not fully recovered vs fully recovered at 1-year follow-up after severe injury following a road trafic accident according to victims' characteristics. Results of univariate analyses

	Victims' characteristics	Fully recovered (n = 54) n (%)	Not fully recovered (n = 222) n (%)	Relative risk (95% conidence interval)
	Age at crash 16-24 years 25-44 years 45-64 years ≥ 65 years Sex Male Female Socio-economic status White-collar worker Intermediate-level employee Farmer, craftsman, shopkeeper Blue-collar worker Student, housewife Comorbidity No yes In employment at time of crash No yes Type of road user Pedestrian, inline-skate/scooter user, passenger Cyclist Motorized 2-wheel/quad-bike driver Car driver	26 (27.1) 15 (16.1) 10 (15.4) 3 (13.6) 43 (20.5) 11 (16.7) 20 (18.9) 12 (24.5) 4 (20.0) 4 (7.4) 14 (29.8) 39 (20.0) 15 (18.5) 8 (12.9) 46 (21.5) 8 (11.3) 6 (18.2) 26 (25.0) 14 (20.9)	70 (72.9) 77 (83.8) 55 (84.6) 19 (86.4) 167 (79.5) 55 (83.3) 86 (81.1) 37 (75.5) 16 (80.0) 50 (92.6) 33 (70.2) 156 (80.0) 66 (81.5) 54 (87.1) 168 (78.5) 63 (88.7) 27 (81.8) 78 (75.0) 53 (79.1)	1* 1.2 (1.0-1.3) 1.2 (1.0-1.4) 1.2 (1.0-1.5) 1 1.1 (0.9-1.2) 1** 0.9 (0.7-1.1) 1.0 (0.7-1.3) 1.1 (1.0-1.3) 0.9 (0.7-1.1) 1 1.0 (0.9-1.2) 1* 0.9 (0.8-1.0) 1* 0.9 (0.8-1.1) 0.8 (0.7-1.0) 0.9 (0.8-1.0)

*0.05 < p < 0.20;**p < 0.05 .

J Rehabil Med 43 Predicting self-reported recovery after major trauma

Table III .

 III Estimations of the risk of being not fully recovered vs fully recovered at 1-year follow-up after severe injury following a road trafic accident, according to injury severity and lesions. Results of univariate

		Injury characteristics	Fully recovered (n = 54) n (%)	Not fully recovered (n = 222) n (%)	Relative risk (95% CI)
		Overall injury severity NISS 9-15 NISS ≥16 Head No lesion AIS 1-2 AIS 3-5 Neck No lesion AIS 1 or 2 Face No lesion lesion	27 (26.7) 27 (15.4) 27 (22.1) 15 (16.1) 12 (19.7) 53 (19.6) 1 (20.0) 39 (20.1) 15 (18.3)	74 (73.3) 1** 148 (84.6) 1.2 (1.0-1.3) 95 (77.9) 1 78 (83.9) 1.1 (0.9-1.2) 49 (80.3) 1.0 (0.9-1.2) 218 (80.4) 1 4 (80.0) 1.0 (0.6-1.5) 155 (79.9) 1 67 (81.7) 1.0 (0.9-1.2)
		Spine No lesion lesion Severe thoracic injury No yes	43 (20.7) 11 (16.8) 39 (20.2) 15 (18.1)	165 (79.3) 1 57 (83.2) 1.1 (0.9-1.2) 154 (79.8) 1 68 (81.9) 1.0 (0.9-1.2)
		Abdomen No lesion lesion		47 (20.7) 7 (14.3)	180 (79.3) 1 42 (85.7) 1.1 (0.9-1.2)
		Upper limbs No lesion AIS 1 or 2 AIS 3-5		28 (22.3) 13 (16.9) 13 (21.3)	110 (79.7) 64 (83.1) 1.0 (0.9-1.2) 1 48 (78.7) 1.0 (0.8-1.2)
		lower limbs No lesion AIS 1 or 2 AIS 3-5		21 (24.1) 13 (25.0) 20 (14.6)	66 (75.9) 1* 39 (75.0) 1.0 (0.8-1.2) 117 (85.4) 1.1 (1.0-1.3)
		*0.05 < p < 0.2; **p < 0.05.
		CI: conidence interval; NISS: New Injury Severity Score; AIS: Abbreviated Injury Scale.
	Table Iv. Global health-related quality of life and satisfaction, World Health Organization Quality of Life Questionnaire-Short Form (WHOQOL-
	BREF) scores with self-reported recovery subgroup
	WHOQOL-BREF	Fully recovered (n = 54) n (%)	Not fully recovered (n = 222) n (%)	Total (n = 276)
	How would you rate your quality of life? Very poor/poor Neither poor nor good Good/very good How satisied are you with your health? Very dissatisied/dissatisied Neither satisied nor dissatisied Satisied/very satisied WHOQOL-BREF scores, mean (SD) Physical area* Psychological area* Social relationships area Environmental area	3 (7.5) 7 (7.6) 44 (31.2) 2 (2.0) 5 (8.9) 47 (39.8) 83.3 (13.2) 71.3 (13.9) 77.8 (18.3) 74.5 (15.1)	37 (92.5) 85 (92.4) 97 (68.8) 97 (98.0) 51 (91.1) 71 (60.2) 59.7 (21.5) 60.2 (19.3) 67.9 (19.1) 63.0 (17.3)	40 92 141 99 56 118 64.3 (22.2) 62.4 (18.9) 69.8 (19.3) 65.3 (17.5)

*Test of difference between the fully recovered and not fully recovered group (p < 0.001). SD: standard deviation.

Table v .

 v Factors related to be not fully recovered 1 year after road trafic accident in ESPARR cohort subjects with major trauma (NISS ≥ 9). Resultsof the multivariate analysis(n = 275)

		Adjusted
	Factors	relative risk (95% CI)	p-value
	NISS ≥ 16 vs NISS 9, 15	1.2 (1.1-1.4) < 0.01
	lower limbs		0.03
	No lesion AIS 1 or 2 AIS 3-5 Socio-economic status White-collar worker Intermediate-level employee Farmer, craftsman, shopkeeper Blue-collar worker Student, housewife Age at crash 16-24 years 25-44 years 45-64 years ≥ 65 years Sex Female vs male	1 1.0 (0.8-1.2) 1.2 (1.0-1.3) 1 0.9 (0.8-1.1) 1.0 (0.8-1.3) 1.2 (1.0-1.3) 0.9 (0.7-1.2) 1 1.2 (1.0-1.3) 1.2 (1.0-1.3) 1.2 (0.9-1.4) 1.1 (0.9-1.3)	0.04 0.38 0.19
	NISS: New Injury Severity Score; AIS: Abbreviated Injury Scale; CI: conidence interval; ESPARR: Etude et Suivi d'une Population d'Accidentés de la Route dans le Rhône.

J Rehabil Med 43

ACKNOWLEDGEMENTS

The authors are grateful to the victims for their cooperation in data collection. The authors would like to thank all those who assisted in carrying out this study: Nadia Baguena, Jean Yves Bar, Amélie Boulanger, Elodie Paquelet, Stuart Nash, Véronique Sotton for collecting the data, Irène Vergnes for organizing the databases; Anne-Marie Bigot, Nathalie Demangel, Geneviève Boissier for subject database management; Blandine Gadegbeku and The Association for the Rhône Road Trauma Registry (ARVAC) for their help in collecting and providing medical data; the Scientiic Committee (Daniel Floret, François Chapuis, Jean Michel J Rehabil Med 43 We acknowledge funding from the French Ministry of Equipment, Transport, Housing, Tourism and Sea (Program Predit 3 "New Knowledge in the Field of Road Safety": N° SU0400066) and from the French Ministry of Health (Program PHRC 2003: PHRC-N03 and PHRC 2005: PHRC-N051).

The individual authors have no competing interests to declare.