Arthur Elessa Etuman

Isabelle Coll

Virna Rivera Salas

Arthur Elessa Etuman

OLYMPUS: An emission model to connect urban form, individual practices and atmospheric pollutant release

Keywords: IdF, Ile-de-France region, NOx, Nitrogen Oxides, NO2, Nitrogen Dioxide, PDL, Pays de la Loire, PM10, Particulate matter Emissions Agent-based modelling mobility Road transport

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Air quality modeling is an approach to simulate atmospheric concentrations of pollutants that relies essentially on the chemistry and physics of the atmosphere. Concentration fields are obtained by solving the mass conservation equation for each pollutant, which implies taking into account the anthropogenic and biogenic emission rates, the vertical and horizontal mixing of air masses, the physical and chemical reactivity of pollutants and finally, potential loss by deposition on continental surfaces. In usual air quality modeling approaches, emissions fluxes are derived from local inventories -most often developed for regulatory use -which rely on declarative data, reworked under the form of exhaustive hourly fields at the resolution of the simulation domain. In such context, the prospective simulations that aim to assess the impact of emissions control on air quality are commonly based on the application of abatement coefficients simulating the technological improvements.

Recently, this type of approach has become insufficient as current urban air quality issues are largely related to the densification of pollutant-emitting activities, with combustion processes related to fossil fuel mobility and building heating practices being at the forefront. It is therefore a political and societal problem that goes far beyond the question of possible technological developments on pollutant-emitting processes. From now on, the reduction of pollutant emissions in large cities will necessarily involve a more rational use of energy and the optimization of urban mobility. To meet air quality management expectations, the calculation of emissions should be based on the use of comprehensive tools capable of simulating all the activities resulting from the individual use of energy that may affect ambient air. These tools must therefore be able to apprehend the urban fabric, the shape of the city, its organization, its economy and its service offer in order to produce mobility fluxes for all city dwellers, as well as the spatio-temporal location of polluting activities, according to different public policy scenarios. Moreover, research on the urban environment is still based on very monothematic approaches. There is therefore a strong need for an innovating multidisciplinary research, which would take into account the societal and social drivers of pollutant emissions, and provide a cross-cutting view of urban issues. The approach we have developed aims to remove the technical barriers to interdisciplinarity, by developing of a modeling chain linking urban parameters simulated by a set of disciplinary models. It is structured around the OLYMPUS tool, which was built to integrate energy transition scenarios combining public policies, technological developments and individual practices, in order to produce data on household activity and calculate emissions related to combustion activities, for different urban forms. It provides input data for chemistry-transport models, for the simulation of air quality at the scale of an agglomeration. This article proposes to explore the functionalities of the new OLYMPUS tool and to discuss the linking of individual energy consumption practices with the structure of pollutant emissions within a metropolis, with the aim of showing how the model can broaden the scope of possibilities in the implementation of air quality emission scenarios. This paper refers to the very detailed technical description of OLYMPUS presented in [START_REF] Elessa Etuman | OLYMPUS v1.0: Development of an integrated air pollutant and GHG urban emissions modelmethodology and calibration over greater Paris[END_REF] and focuses more specifically on how the model uses and highlights the key parameters of pollutant emissions, providing us with a more systemic understanding of the determinants of the urban environment. More specifically, the sensitivity of the model to these determinants and the way OLYMPUS can help explore the main levers of air quality will be discussed, putting into perspective the potential of the model with regard to the needs of decision support.

The next paragraph is devoted to presenting the general approach underlying the development of the model. The structure of the code and the methodological and numerical choices are also presented and commented on, in view of the ambitions of the model. Then, in the last part, the data generated during the implementation of OLYMPUS in two French regions are presented as a basis for reflection on the key parameters of pollutant emissions in the model.

Addressing emissions, air quality and exposure issues with OLYMPUS

Model overview and aims

The OLYMPUS model main objective is to achieve a better understanding of the impact on the environment of individuals' decisions about energy use and mobility. To this end, the approach that has been implemented is a bottom-up and activity-based process, in which the description of the urban operation derives from the sum of the behaviors of all the individuals in the modelled population (Fig. 1). Here, "activity" is defined as a set of city dwellers' occupations that potentially induce energy consumption. In this set, we consider daily mobility, building heating (commercial or residential) as well as household energy consumption for domestic needs. These activities necessarily lead to diverse forms of use of urban tools or services, called practices. Practices include the use of technologies and tools as well as behaviors that are the adaptive responses of individuals to their environment, considering the ability to represent their environment, to learn and to anticipate. Thus, in our approach, the behavior of individuals is an element that must be taken into account when modelling practices, in the sense that it guides their choices and ways of carrying out their activities. In their review, De Witte et al., 2013 presented a wide range of socio-demographic characteristics that may play a role in the decision-making behavior of individuals, and notably in their choice of transport modes. In particular, they highlighted the interdependence of (1) socio-demographic indicators 3) indicators of daily mobility such as reasons for travel, average travel distances by purpose, travel cost, average travel time and departure time. In order to best characterize the impact of the individuals on greenhouse gas and air pollutant emissions at the metropolis scale, we have considered many of the above-mentioned indicators in our modeling approach. Fig. 1 shows a schematic representation of the 3 main modelling poles present in OLYMPUS: the populationshown as households and individuals in a given urban environment, the mobility -characterized by trips organized in space and time according to the activities and locations of individuals, and finally the emissions -resulting from residential and mobility practices, as inferred from the modelling of the behavior of individuals (choice of energy, modal choice, perception of the utility of urban services etc.). The main parameters that respectively characterize these 3 poles are (i) land use, and in particular the urban density, which defines the attractiveness of activity zones and the need for heating, (ii) urban trips, which length, origin and destination lead to modal choice and to the occupation of transport networks, and finally (iii) technologies for mobility and combustion, which determine the nature and amplitude of pollutant emissions.

In this approach, the structure of OLYMPUS makes it possible to integrate the population characteristics as drivers of their individual behavior, and further investigate the link between practices and energy consumption. In the current version of OLYMPUS, only demographic and residence-related parameters are taken into account in behavior modelling, but developments are underway to include socio-professional differentiation in the practices in order to investigate the issue of socio-differentiated energy consumption, and exposure to urban air pollution [START_REF] Coll | Analyse socio-spatiale de l'exposition des populations à la pollution atmosphérique en zone urbaine[END_REF]. In the end, as mentioned above, the OLYMPUS model has been developed to provide emissions to Chemistry-Transport Models (CTMs), both in hypothetical or prospective situations, along next-generation scenarios of economy, changes in practices and urban planning. Such ambitions have so far been inaccessible to CTMs due to the difficulty of designing and producing these scenarios. This is why the development of the OLYMPUS tool was based on its ability to integrate population scenarios from LUTI (Land Use Transport Interaction) models such as NEDUM-MODUS [START_REF] Yin | Appraising the environmental benefits of ride-sharing : The Paris region case study[END_REF]. This tool indeed considers the effect of public policies and regional master plans on household residential mobility, and can provide OLYMPUS with an initial scenario of housing and population distribution. In our specific modeling chain, OLYMPUS outputs are designed to be implemented in the CHIMERE chemistry-transport model [START_REF] Mailler | CHIMERE-2016: From urban to hemispheric chemistry-transport modeling[END_REF][START_REF] Menut | CHIMERE 2013: a model for regional atmospheric composition modelling[END_REF]. They include emissions of fine and coarse particles (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO) and a wide variety of volatile organic compounds (VOCs). The calculation of GHG emissions is also an important purpose of the model. Thus, we have included in our calculations emission factors that allow us to calculate the carbon dioxide emissions related to the simulated activities, and to inter-compare different urban scenarios on this basis. However, estimates of CO2 emissions at the scale of a city contain many uncertainty factors. To be validated, they require the joint implementation of several approaches (atmospheric flow measurements versus emission dispersion modelling for instance). This work remains to be done, and CO2 emissions are not described here. But pollutant emissions are only one facet of the data produced by OLYMPUS. Indeed, the model also produces a large number of intermediate diagnoses that help to understand the levers for emission control, but also data of specific interest such as daily mobility of individuals, which is critical for calculating a more realistic exposure to urban pollution [START_REF] Elessa Etuman | Addressing the issue of exposure to primary pollution in urban areas: Application to Greater Paris[END_REF].

In the literature, modeling pollutant emissions in urban areas can take a wide variety of forms, features and content. In addition to daily mobility simulation, the multi-agent model MOBISIM [START_REF] Antoni | Appropriation et développement par ThéMA Vers une modélisation multiscalaire (fractale) du développement urbain par système multi-agents 63[END_REF] has been configured to integrate Land Use Transport Interaction (LUTI) facilities that make it possible to simulate the residential mobility of households and thus to monitor the evolution of a city in the long term. Similarly, [START_REF] Chingcuanco | A microsimulation model of urban energy use: Modelling residential space heating demand in ILUTE[END_REF] implemented the calculation of residential heating demand in an urban energy model (ILUTE) capable of simulating the evolution of demography, land use and urban mobility over time. The coupling of economic, transport and air quality models is thus carried out in variable forms, giving more or less weight to the final air quality simulation, and allowing the production of environmental scenarios with very different degrees of refinement. OLYMPUS is part of this Fig. 2. OLYMPUS chart process and key concepts will to open up urban modelling towards more transdisciplinarity, by integrating the notion of energy consumption and mobility practices into a calculation chain that aims to explore individual exposure to air pollution. This is a highly innovative approach which makes it possible to propose new thinking on the levers of air quality and to accompany political reflection on changes in behaviors and the reduction of exposure to pollutants in metropolises.

Model structure and operation

OLYMPUS is a numerical modeling platform whose code is entirely written in Python. OLYMPUS is a numerical modeling platform whose code is entirely written in Python and based on the simulation of the practices of a population of agents. To do this, it integrates meta-models -and notably activity-based models, gravity models and utility models that restitute the agents' decisions linked with mobility and energy consumption. In accordance with the aims and goals described above, OLYMPUS is structured around three main calculation blocks, as shown in Fig. 2 This figure presents the central concepts of the current implementation of the model (blue lozenges), the sequential organization of the calculations performed by mathematical models (green squares) and the main components (indicators and meta-parameters) produced during the simulation (pink squares). To illustrate how the OLYMPUS model works, the main results of the first two blocks relating to the same agent in the population have been grouped together in Fig. 3, which provides an illustration of the parameters produced and their properties. The results of the last block (pollutant emissions), following the implementation of OLYMPUS on 2 French regions, will be shown and commented in paragraph 3. All the elements described below are presented in advanced technical details in the reference technical paper for OLYMPUS [START_REF] Elessa Etuman | OLYMPUS v1.0: Development of an integrated air pollutant and GHG urban emissions modelmethodology and calibration over greater Paris[END_REF].

Simulation of population and incidence of land occupancy

The first of the 3 structural blocks allows the generation of a synthetic population based on an initial population distribution scenario derived either from local surveys or LUTI models. The synthetic population can be defined as a fictitious population of individuals defined by social characteristics constructed at the individual level from more aggregated data on the region to be simulated. OLYMPUS uses Bayesian statistics (conditional probabilities) to generate this synthetic population from subgroups of representative individuals, whose characteristics are derived from local census data (age, gender, main activity, income or level of education). According to these specificities, the agents are associated with a list of priority activities, including their own constraints such as commute trips, but also family constraints, and assigned a timetable (see graphs a and d of Fig. 3), again using Bayesian statistics. The objective of this whole step is to define population agents with a distribution of socio-demographic characteristics similar to that of the real population, in order to integrate these parameters in the further modelling of energy consumption and pollutant emissions in the territory. In parallel, the surveys also provide guides for the spatialization of individuals and households in the territory. It is a key input of the model that conditions the modelling of the urban form. In OLYMPUS, it imposes the values of urban density index (UDI), a scale of population density adapted to the region and which distinguishes between city centers, peri-urban and rural areas. UDI is used to assess the attractiveness of activity areas when creating travel matrices. This way, it will have repercussions on the determination of the preferred zones for shopping, leisure and employment. But UDI also affects the distribution of the type and size of housing (individual or collective) and the energy mix used in the buildings. It is therefore a key factor in the calculation of combustion emissions in the traffic and residential / commercial sectors targeted by OLYMPUS. Fig. 3 b) and c) show the housing density values and the share of individual and collective housing in an OLYMPUS simulation for the main areas of the Paris region, in 2009. It can be seen that the household properties are highly dependent on the local urban density.

Simulation of mobility practices

In the second block of Fig. 2, OLYMPUS i) builds for each agent of the population a loop of daily activities, ii) positions the activities on a map, and iii) proposes a modal choice calculation for carrying out these trips.

The generation of daily activities loops is based on the prioritization of activities via a probability function linking the characteristics of the individual, his constraints (family, employment) and the average number of trips per day. This function is scaled on aggregated household travel survey data. An example of a daily activity loop, associated with a probability of achievement, is schematized in Fig. 3(e) for an agent of the active population having children. This figure illustrates the priority of motives such as work or drop off/ pick up children's at /from school over leisure activities. The duration of the activities of population agents and the definition of their time use is carried out on the basis of the average duration of activities derived from census data. The priorities of population agents determine their timetable and the duration of their activities. As shown in Fig. 3(e), the modelled agent has to drop off his child at school at 9 a.m. and pick him up at 5 p.m., the duration of the other activities being conditioned by the first one. The location of agents' activities is constrained mainly by the density of activities of the urban area, which is qualified by the UDI. It is also constrained by the average distances observed by type of trip, which are indeed a territory-dependent parameter. Fig. 3(f) illustrates the average travel distances by motive (WORK, SCHOOL, OTHER) selected for the Île-de-France region. We can see that there are 4 subdimensions for education, as the main daily trip of a baby/child/teen/student population is quite different in term of distance and commuting modes. For example, the mobility of children is connected with the mobility of their parent if they are under 12yo, while the drop-off / pick-up also counts for young adults. Taking into account these constraints, the location of agents' activities is determined by a gravity model [START_REF] Huff | Defining and estimation a trading area[END_REF]. It is illustrated in Fig. 3(g) where the probability of working in a certain area depends on the density of activities (employment/population) and on a weighting by the average distances of attractiveness as illustrated in Fig. 3(h) for the Paris urban area. This allows the gravity model to assign a center of activity to each individual, taking into account a degree of variability in trips lengths. In IDF, mobility surveys show that despite the strong spatial hold of employment centers, their attractiveness is exerted at much greater distances than those of other travel motives. The IDU and the gravity approach make it possible to recreate these patterns in OLYMPUS.

The modal choice process is a decisive calculation step for pollutant emissions, as it determines the number of vehicles on the road network and the kilometers travelled. As mentioned above, the objective of OLYMPUS is to produce a realistic description of mobility practices in the region, based on the behavior of individuals in relation to transport demand and mobility supply. To this end, as explained in paragraph 2.1, an approach that takes into account the parameters determining individual choices has been implemented. The approach adopted is based on random utility theory and makes it possible to calculate the probability of choosing an alternative and thus to predict the mobility choices of city dwellers, based on certain assumptions about the distribution of the random component. This approach is based on the assumption that utility maximization influences the agent's decisions. Utility is represented in the form of a generalized travel cost that includes the transportation time as well as a material cost attributable to each mode. This includes deterministic parameters such as time, direct financial costs (tolls, fuel), but also comfort, wear and tear and vehicle maintenance. The diversity of responses then comes from the diversity of values of time for individuals and the generalized cost that each journey represents for them.

In the end, the modal choice is attributed via a logistic function (MNL) (see an illustration in Fig. 3 i). As mentioned above, more details on this numerical approach can be found in [START_REF] Elessa Etuman | OLYMPUS v1.0: Development of an integrated air pollutant and GHG urban emissions modelmethodology and calibration over greater Paris[END_REF]. This approach is considered robust and is commonly used in mobility modelling [START_REF] Malayath | Activity based travel demand models as a tool for evaluating sustainable transportation policies[END_REF]. In OLYMPUS, modal choice allows competition between soft modes, public transport and the use of private vehicles. Soft modes include walking and cycling, each of which has its own specificities and particularly in terms of general travel cost, but are then grouped together under the generic name "WALK".

Emissions

The pollutant and GHG emissions calculated by OLYMPUS result from the total energy demand of the buildings, and from the integration and spatialization of all agents' trips described above.

In OLYMPUS, the modelling of combustion emissions in buildings is based on combustion activities for residential, institutional and commercial heating, but also for hot water production and cooking. To determine household energy demand, the key data is the unit consumption of households, in kWh/m², and is derived from surveys. It is then cross-referenced with data on average housing areas by type of dwelling, type of household and housing area (based on the work carried out in Block 1). To model the institutional and commercial sector, the module is based on annual consumption data per employee and locates these emissions in the employment centers, according to their respective capacities. An additional adjustment is made by considering the severity of the winters of the year in question ("degree-day" parameter), and -for households -it is also possible to adjust energy efficiency depending on the heating equipment (modernity, quality, use). The model produces air pollutant emissions by type of fuel used. The energy mix data comes from local surveys. Based on this information, OLYMPUS calculates the emissions associated with fuel use in the residential and commercial sector, following the EEA guidebook for small combustion emissions (European Environment Agency, 2013).

To model traffic-related emissions, it is possible to use agent-based transport micro-simulation to predict the local specificities of traffic on an urban road network. This approach is based on real driving patterns such as instantaneous speed, takes into account emissions from idling and start/stop and is sensitive to the local effects of congestion. It is the approach developed in tools such as MATSim (Horni, A, Nagel, K and Axhausen, 2016), which makes it possible to capture a wide variety of driving situations [START_REF] Hülsmann | Integrated agent-based transport simulation and air pollution modelling in urban areas -the example of Munich[END_REF]. It can be forced by the outputs of a traffic allocation model [START_REF] Hao | Integrating an Activity-Based Travel Demand Model with Dynamic Traffic Assignment and Emission Models[END_REF]. The magnitude of the calculations involved and the enormous computing time required for traffic micro-simulations may be an obstacle to their implementation in large agglomerations, but the biggest limitation to performing such calculations over large areas is the need to provide highly accurate information on actual lane occupancy rates, driving behaviors and local specification of the vehicles in the fleet. Furthermore, the gains in accuracy achieved by this approach are only maintained if it is followed by an emissions calculation based on vehicle emission factors under the speed, acceleration and congestion conditions described by the traffic model. And these data are still incomplete and suffer from a lack of consistency, particularly with regard to congestion issues [START_REF] Zhang | Vehicle emissions in congestion: Comparison of work zone, rush hour and free-flow conditions[END_REF]. On the other hand, it is possible to implement road emission calculations from so-called "macroscopic" models, which are based on a static traffic allocation from global variables such as traffic flow, and use average speed emission functions (Bandeira et al., 2011, Hatzopoulou andMiller, 2010). These mesoscale approaches can also rely on 4-step models including (1) transport demand generation, (2) transport demand distribution, (3) modal choice and finally (4) transport demand allocation on the road network. One example is the MODUS tool of the Regional and Interdepartmental Direction of Equipment and Planning in Île-de-France. These models are most often coupled with emission calculation methodologies based on major road types, such as COPERT and HBEFA [START_REF] Keller | [END_REF][START_REF] Ntziachristos | COPERT: A European road transport emission inventory model[END_REF]. OLYMPUS uses this second approach to model road traffic emissions. It considers exhaust emissions from hot and cold vehicle exhausts, as well as emissions from tyre abrasion, car brakes and fuel evaporation. The approach used is that recommended by the EEA, which uses COPERT emission factors based on the average speed of a vehicle over a standard driving cycle. The car fleet in circulation, impacting emission factors, is derived from local surveys (see illustration in Fig. 3 j).

Patterns and levers of emission modelling in OLYMPUS

This paragraph discusses the implementation of OLYMPUS on two French regions with radically different urban area distribution and territorial organization. Multiple-site studies indeed allow us to better assess the ability of OLYMPUS to understand the functioning of different urban structures and to explore the role of the parameters used in the model to prescribe practices and calculate pollutant emission fields. The first region to be studied is the Île-de-France (IDF), which includes the city of Paris and the 7 administrative departments surrounding it. This territory, which is home to about 12 million inhabitants, shows increased urbanization from the peripheral areas towards the dense city of Paris in the center, with the density of activities and networks following the same pattern. In particular, the public transport network is highly developed in and towards the center of Paris. The dense center of this territory constitutes the Greater Paris Metropolis, an administrative grouping that includes the city of Paris as well as a large number of municipalities bordering or close to Paris, and which holds the levers to act in a concerted manner in critical areas as urban planning, housing, economic development and the fight against climate change. This dense urban area encounters an acute air pollution problem, particularly in proximity to traffic where daily and annual regulatory thresholds for particulate matter and NO2 concentrations are regularly exceeded [START_REF] Airparif | Bilan de la qualité de l'air Année 2019 -Surveillance et information en Ile-de-France[END_REF]. This phenomenon is associated with the high density of domestic and commercial activities in the center of the metropolis as well as the very high demand for transport to Paris' inner suburbs, with heavy reliance on private vehicles and a high level of congestion on the roads. Our first analysis focuses on how OLYMPUS allows to recreate spatial patterns on different territories, using the input data described above, but also thanks to the selected numerical approaches and the use of key parameters such as UDI or attractiveness. The regional daily mobility calculated by OLYMPUS and presented in Fig. 4 (b), together with the traffic volumes Fig. 4 (c), show the importance of the distribution of UDI in the construction of mobility and the use of the road network, since the entire road network of the UDI 5 zone is heavily used, and that the main access road to the city of Paris (the peripheral ring, in yellow on the figure) is completely congested. At this point, the OLYMPUS results about road traffic are highly dependent on how individuals' modal choice is estimated (that is, strong acceptability or not of long travel time due to congestion). Consistently with the map of lane occupancy, it is in the center of the metropolis that OLYMPUS calculates the highest values of traffic-related emissions (shown in Fig. 4 (e), for nitrogen oxides -NOx). The 5 graphs in Fig. 5 are identical to those presented for the Paris region, except that the maxima on the scale are lower. The map in Fig. 5 (a) illustrates how the polycentric configuration of the PDL region is restored to OLYMPUS via the urban density index based on regional population surveys. Notably, in the south-west of the region, one can see the city of Nantes and its periphery, which are more densely inhabited than the rest of the territory. As mentioned above, it was necessary to reduce the size of the UDI bins compared to Paris in order to re-establish the gradient in typology of areas from the city center to the suburbs and to rural areas. Similarly, it was necessary to correct upstream the input information on the average distance associated with each trip motive. This use of survey data allows OLYMPUS to be highly adaptable to different territories. The central parameter here is the attractiveness of the activity zones, reconstructed via the UDI. Crossed with average travel times, it makes it possible to propose a distribution of areas likely to generate a trip during the simulation process and to create global mobility patterns that fit the reality of this territory. In the same way, OLYMPUS is able to associate to each agent a modal choice that is consistent with the local logic of mobility. These modifications being taken into account, it appears that the adaptability of OLYMPUS to a new territory is high. Indeed, the maps showing the origin-destination matrices resulting from commuting, as well as the road network occupancy rate, both demonstrate its capacity to restore multimodal mobility, without underestimating the attractiveness of secondary urban centers. Thus, the map in Fig. 5 (b) shows a more homogeneous distribution of destinations than in IDF, but above all a greater proximity between home and work for most commuting trips in PDL, despite the larger size of the region. The comparison of distances travelled by mode of transport between IDF and PDL is summarized in Fig. 6 (e), which confirms that urban multipolarity reduces overall distances travelled for home-work trips: OLYMPUS predicts a reduction by almost a factor of 2 in the average number of kilometers travelled by car in PDL (less than 4km) compared with IDF (more than 7.5km). However, the 90 th percentile of this distribution (more than 18km against 16km in the Paris region), shows the existence of a distribution tail pulled to higher values in the polycentric territory, which may be associated to inter-city trips by car and induces a high consumption of fossil energy. Similarly, the boxplots of the distances traveled on the public transport network show the inability of the PDL network to support long trips (i.e. over 3km), due to the limited size of the mainly urban networks. Using the functionalities of OLYMPUS, it would be of great interest to simulate the impact -on reducing road congestion on the major urban axes and on improving the durability of mobility -of a prolongation of the lines (tramway in particular) beyond the very center of the main agglomerations (Nantes mainly), in order to connect it with the neighboring municipalities. However, in the longer term, the development of transport to serve residential areas far from the city centers is likely to induce urban sprawl. Which is why such a scenario must be tested by taking first into account the residential mobility of the inhabitants of the region over the long term, using a LUTI model to provide the land use situation of the OLYMPUS simulation. Finally, in spite of the dispersion of destinations over the whole PDL territory, the attractiveness of the urban centers can still be seen in Fig. 5 (c) and Fig. 5 (e), through the high level of road occupation in and around the cities, and the elevated local road emissions. The distribution of the main zones of activity in the territory corresponds to the reality of these regions. However, similarly to the hypothetical transport network scenarios described above, it is possible to provide OLYMPUS with a different calculation of attractiveness, which would no longer be based on urban density but would be modelled on other land-use planning strategies. OLYMPUS thus would allow to study the impact of activity relocation policies on mobility and energy consumption on the territory.

In both territories, the energy consumption from building use in the territory is maximal in the urban centers (Fig. 4 (d) and Fig. 5 (d)) although it does not hint the distribution of emissions, since the energy mix is different between collective and individual habitats. Indeed, district heating networks are poorly developed in rural areas and the use of open fireplaces or biomass burning stoves is more massive in dispersed individual housing as can be found in the outer suburbs of Ile-de-France, and in the rural areas of Pays de la Loire. However, it is a common practice in provincial cities such as Nantes, and a frequent supplementary heating mode during cold weather events in metropolises such as Greater Paris. The link between UDI, type of housing and distribution of energy for heating (electricity, gas or wood) is thus a critical element in the OLYMPUS emissions modelling process, as in France 98% of particulate emissions in the residential and tertiary sectors are due to the use of heating appliances using biomass. The OLYMPUS emission maps for this sector is thus fully determined by the parameterization of the link between UDI / housing / energy mix per housing. In OLYMPUS, energy distribution is a parameter based on survey data, and therefore easily adaptable to the site under consideration, but the data does not always exist with the desired refinement. Moreover, the data are rapidly evolving (especially with the recent transition to closed fireplaces and more environmentally friendly stoves). In any case, it is an important air quality lever, the role of which in winter episodes of particulate pollution can and should be investigated. Quantitatively speaking, an interesting result is the ability of OLYMPUS to understand the utility of modal supply and to capture the modal share of private vehicle trips, using the random utility equations of the transport module. Thus, while OLYMPUS leads to a modal share of 40.4% in the Paris region for individual motorized trips (compared with 39.5% according to the Observatory of Mobility in IDF), it predicts a doubling of this share in the PDL region, with 78% of the total number of trips made in private vehicles (compared with 71% in the regional mobility surveys). Although the use of public transport in the PDL region seems to be slightly underestimated here (probably due to the lack of representation of intercity trains), these two results validate the relevance of the rational approach taken in OLYMPUS. The calculation of energy consumption in the residential/tertiary sector, as well as that of road NOx emissions, reproduces as before the distribution of urban land use and the effect of the densification of traffic flows around urban areas. In order to obtain a quantitative evaluation of the results, the emissions calculated by OLYMPUS on IDF and PDL were also compared to a reference inventory -here the inventories of the Airparif * and the Air Pays de la Loire † monitoring networks -for each of the modelled sectors of activity. An in-depth evaluation of the emissions produced by OLYMPUS in this configuration for the Paris region was carried out by Elessa Etuman and Coll, 2018. Some results are summarized here in Fig. 6, together with some results for Pays-de-la-Loire. As shown in Fig. 6 (a) (b) (c) and (d) for nitrogen oxides and fine particles, this rapid comparison exercise validates the model's ability to correctly size road activity as a whole over an area -i.e. taking into account trips, kilometers travelled and speciation of the fleet in circulation. Furthermore, the share of road emissions and residential combustion emissions for NOx and PM10 can be estimated from the bar plots of Fig. 6. Such a distribution -i.e. the predominance of road sources for NOx, while fine particles are mainly from domestic and commercial combustion -is in very good agreement with the literature [START_REF] Airparif | Bilan des émissions de polluants atmosphériques et de gaz à effet de serre en Ile-de-France pour l'année 2010 et historique[END_REF] for IDF region. Finally, it is important to remember that the issue of road traffic, at least in terms of total pollutant emitted, goes beyond the question of individual mobility, as commercial vehicles contribute significantly to emissions. However, this fraction of traffic is still poorly known because it involves a wide variety and a large number of actors and does not obey the simple laws of commuting mobility. The improvement of the representation of freight transport, via the simulation of origin-destination matrices, is one of the works in progress for the evolution of the OLYMPUS model.

Discussion and perspectives

The OLYMPUS tool has been developed with the aim of providing innovative support for environmental decision support, through a reflection on energy consumption practices that have an impact on urban air quality. OLYMPUS is an emissions model that provides a cadastral inventory of pollutant -and potentially GHG -emissions related to the energy consumption of an entire urban population, for mobility as well as residential and tertiary combustions. It provides chemistry-transport models with an inventory of pollutant emissions capable of integrating the activity of a territory's inhabitants. At a time when many metropolises are rethinking their urban planning policy in order to face new economic, societal and environmental paradigms, modelling know-how must indeed enable the link between the individual and the city, in scenarios that seek to optimize the use of urban networks, or levers for energy-sustainable and responsible behaviors. In this article, we have presented the main operating principles of the OLYMPUS tool in order to demonstrate its potential contribution to the production of a new generation of emission scenarios for air quality. OLYMPUS relies on a large amount of survey data (household, mobility, census) to feed calculation algorithms that successively allow to recreate a synthetic population, to simulate activities and agendas at the individual scale, to define transport demand and energy consumption practices, and finally to spatialize the mobility of all simulated individuals taking into account the supply and saturation of the transport networks. In OLYMPUS, the activitybased transport demand construction approach allows to integrate an essential dimension in the classical transport modelling process: the consideration of constraints and household time-energy budget in the realization of the different trips. This is an asset that serves the objectives of the tool. It is indeed conceivable to vary -in innovative urban scenarios -some of these parameters such as the sequencing of journeys, but also to influence the mobility of different categories of agents and thus to highlight mobility levers.

The results obtained during the implementation of OLYMPUS on the Paris region (Île-de-France) and the Pays de la Loire were used to assess the quality of OLYMPUS outputs, and to understand the determinants of OLYMPUS in the code. They first allowed to conclude that the modeling approach developed in OLYMPUS is relevant for the construction of road traffic emission inventories, and that the model captures well the organization of daily trips, as well as the utility or disutility of the transport supply, even if some parts of the calculations -such as freight transport -still need to be improved. They also demonstrated its very good transposability, as OLYMPUS manages to recreate the structures of mobility in very different territories, relying on very global data -such as the average distances granted by trip purpose for example. Through these two examples, OLYMPUS finally made it possible to analyze the way in which urban organization (transport, density, centrality or not) forces energy consumption and fossil mobility. The comparative analysis of the results

for IDF and PDL shows that, regardless of the size of a region, the choice of a polycentric organization enables to limit the distances traveled, by creating better distributed activity centers (for commuting in particular). However, the lack of public transport supply outside the very city centers offsets this positive effect, and leads to heavy reliance on the car (more than 3 trips over 4 in PDL). This strong dependence on the fossil and individual mobility is an important penalty for the energy efficiency of the region. For all areas of this typepredominantly rural, organized around medium-sized towns with strong economic or demographic dynamics -it is essential to engage in a reflection on the possible evolutions of the territory. In particular, it will be necessary to identify urban development plans and public policies that will strengthen the economic, social and environmental value of the region, while ensuring the maintenance of the proximity effect existing throughout the territory, as well as the balance of urban centers. It is in this specific context that the OLYMPUS tool can help providing environmental diagnoses, through the set-up of scenarios and analyses of the main levers for mobility control, but also of the potential negative retroactions -such as indirect support to urban sprawl.

In particular, we have described the importance of an individual decision approach (random utility based on rational choice) in defining the modal shares of private cars, public transport and soft travel modes. The implementation of OLYMPUS in two regions showed that urban density and the attractiveness of different urban spaces spatially shape the mobility of the population. It therefore remains to be defined to what extent modal choice is sensitive to variations in the utility of transport modes in the same area, and whether this very favorable trend towards individual mobility in OLYMPUS is an effect of the model, or the representation of a reality where practices would be little affected by the variability of car utility. OLYMPUS makes it possible to envisage scenarios that offer a reflection on the value and variability of utility in different urban forms (spread out, dense, etc.) in order to identify actions and scenarios that facilitate the avoidance of car use (incentive measures, fuel taxes, restricted access to city hearts, increase in multimodal transport supply, etc.). This opens up interesting horizons in terms of the policies to be implemented to shake up the hierarchy of the utility of modes, change behaviors and decarbonise mobility practices.

In the end, the use of such a disaggregated approach to mobility produces matrices of individual trips, which constitutes a totally innovative set of data that can be critical when rethinking exposure to pollutants in urban areas. One of the major developments in our modelling chain indeed consists in crossing the agenda and location of individuals simulated by OLYMPUS with the pollutant concentration fields from CHIMERE -that have been reworked to account for proximity to traffic lanes [START_REF] Elessa Etuman | Addressing the issue of exposure to primary pollution in urban areas: Application to Greater Paris[END_REF]. This approach should make it possible to apprehend the outdoor exposure of individuals according to their neighborhoods of residence, their urban trips and their mobility practices, in a dynamic approach in resonance with the growing interest in geolocalised individual exposure.

Fig. 1 .

 1 Fig. 1. Schematic representation of the OLYMPUS modelling approach: main modeling poles and inter-relation of principal parameters

Fig. 3 .

 3 Fig. 3. Illustration of the main modelling steps of OLYMPUS (a) Setting-up of the relationships between individual characteristics, constraints and priorities for activity planning. (b) Definition of the type of urban environments through local household density, which sets the urban density index (UDI) (c) Distribution of the proportion of collective and individual households in specific urban environments (d) Age distribution of male agents in couples in the synthetic population, based on a probability mass function (PMF). (e) Representation of a daily schedule with priorities for the agent described in (a), including the probability of performing an activity, which reaches 1 for priority activities. (f) Distribution of average distances of attractiveness according to activities. (g) Diagram of the location of the activities of the agent described in (a). The probability that the agent's activities in a given zone depends on the size of the nodes of the graph. (h) Distribution of the attractivity functions of the scheme presented in (g). (i) Private car, public transport and soft mode options for the agent described in (a), and schematically spatialized. The agent's modal choice (thick line) is made according to the generalized cost of the transport mode (j) Distribution of the passenger car fleet technology (EURO) in 2009 in the Paris region

Fig. 4 .

 4 Fig. 4. OLYMPUS outputs on the Ile-de-France region for the year 2009. (a) Categorization of population distribution: the urban density index (UDI) of the territory. The darkest colors correspond to the highest density values. (b) Major Origin-Destination connections, destinations being represented as lines with the warmest colors corresponding to the most important mobility flows. (c) Daily passenger car fluxes on the regional road network. (d) Annual energy consumption in the residential sector. (e) Hourly emissions of nitrogen oxides from road transport.

  A simulation was carried out in 2009 with OLYMPUS, using household densities on the IDF from the French National Institute of Statistics and Economic Studies census data and socio-demographic attributes from the general census of the IDF population as forcing factors. The data on mobility are mainly derived from the IDF transport survey and the household travel survey, which shows the average number of journeys per day. Those main input data come mainly from local and national surveys. Other survey data are also used to define heating practices, vehicle fleets and combustion appliances. Fig.4(a) to (e) present the main outputs in cartographic format. The map of UDI indices Fig.4(a) shows this central organization around an urban core with dense housing and activities that corresponds fairly well to the area of highest UDI on this map. OLYMPUS was also implemented in the French region called "Pays de la Loire" (PDL), located in the west of France and including the medium-sized city of Nantes and several small urban centers such as Angers, La Roche-sur-Yon and Le Mans (Fig.5 (a)). A large part of the region is made up of rural areas with low density of dwelling and very few public transport connections. In addition, despite a total area 2.6 times larger than the Paris region, the PDL region has only 3.7 million inhabitants. It is therefore a fundamentally different territory from that of Greater Paris. In 2009, the average annual pollutant levels recorded in Pays de la Loire were below the regulatory limit values for NO2, with the exception of the main roads in Nantes, reflecting a high use of the road network to reach the city. As for Ile-de-France, the main input data for the model come from census data and mobility surveys (local and national), including the National Transportation and Travel Survey and survey data used to define average energy consumption per household.

Fig. 5 .

 5 Fig. 5. OLYMPUS outputs on the Pays-de-la-Loire region for the year 2009. (a) Categorization of population distribution: the urban density index (UDI) of the territory. The darkest colors correspond to the highest density values. (b) Major Origin-Destination connections, destinations being represented as lines with the warmest colors corresponding to the most important mobility flows. (c) Daily passenger car fluxes on the regional road network. (d) Annual energy consumption in the residential sector. (e) Hourly emissions of nitrogen oxides from road transport.

Fig. 6 .

 6 Fig. 6. Comparative analysis of the outputs of the OLYMPUS model. Figures (a) and (b) represent respectively the year-round nitrogen oxide emissions from the road transport sector and the residential and commercial combustion sector (a) for Ile-de-France and (b) for Pays de la Loire. Figures (c) and (d) represent the emissions of particulate matter with a diameter of less than 10 microns per year from the road transport sector and the residential and commercial combustion sector (a) for Ile-de-France and (b) for Pays de la Loire, respectively. The emissions modelled in (a), (b), (c) and (d) are compared with local emission inventories. Figure (e) shows the distribution of travel distances by different modes of transport (passenger car, public transit, walking).

Acknowledgements

The work presented here received support from the French department Val-de-Marne, the French Ecological transition agency (ADEME), the PRIMEQUAL program (POLL-EPXO/Convention 1862C0012), the French National Agency for Research (VITE!/ANR-14-CE22-0013) and the Île-de-France region (DIM R2DS and DIM QI2). It was initiated in the frame of the LABEX Urban Futures and performed using HPC resources from GENCI-CCRT (grant no. 2017-t2015017232). We specially acknowledge AIRPARIF for providing data.


Mathieu Levaillant
email: mathieu.levaillant@gmail.com

MSc Lucie Levaillant

MSc Nicolas Lerolle

PhD Benoît Vallet

PhD Jean-François Hamel-Broza

Factors influencing medical students' choice of specialization: a gender based systematic review

Background

Students' choice of medical specialties has evolved throughout year, with a growing interest in quality of life and in technological specialties. We investigated the repartition of such choices in the world and its influencing factors with a focus on the gender's influence, for helping policy-makers to deal with medical shortage and territorial to specialty disconnect. Methods A systematic search was conducted on MEDLINE and Scopus from January 2010 to January 2020. Data extraction and analysis followed JBI and PRISMA recommendations. The selected articles had to focus on medical students, detail their choice of specialty, and look for factors influencing their choice. Articles were excluded if they only assessed the attractiveness of a specialty, or evaluated a public policy. This review was registered on PROSPERO, CRD 42020169227. Findings 751 studies were screened, and fifty-four were included. Surgery and internal medicine were the most wanted specialties, both in occidental and non-occidental countries. The main factors influencing the choice of specialty were lifestyle, work-life balance and discipline interest, with variation across different countries. Gender clearly affected this choice with 63.7% of men willing radiology and 14.7% of men in obstetrics and gynaecology. Interpretation Influential factors vary with specialty and are affected by the country of residence. Gender has a great impact in students' willingness to work in specific specialties. Policymakers should adapt their appealing strategies according to the country and the medical discipline concerned.

Introduction

Even if the number of physicians is higher than ever, the subspecialisations induced by new medical knowledge leads to a disconnect between specialists working in the world and population needs [START_REF] Zurn | Imbalance in the health workforce[END_REF]. Many students entering school with a career plan [START_REF] Budhathoki | Factors influencing medical students' motivation to practise in rural areas in low-income and middle-income countries: a systematic review[END_REF][START_REF] Svirko | Career choices of the United Kingdom medical graduates of 2005, 2008 and 2009: questionnaire surveys[END_REF][START_REF] Cleland | Associations between medical school and career preferences in Year 1 medical students in Scotland: Career preferences of Year 1 medical students[END_REF] often evolved throughout the course of their studies [START_REF] Cleland | Associations between medical school and career preferences in Year 1 medical students in Scotland: Career preferences of Year 1 medical students[END_REF].

Medical students' choices have evolved throughout time, with a growing interest in quality of life or in technological excellence [START_REF] Kaur | The medical schools outcomes database project: Australian medical student characteristics[END_REF]. Not only thoughtful individual factors such as interest for the discipline, work-life balance or role modelling can influence the specialization choice, but also sociobiological aspects. For example, gender can modify factors associated with the choice of several medical specialties: in the US, men-to-female ratio was 4.9:1 in obstetrics and gynaecology [START_REF] Klifto | Women Continue to Be Underrepresented in Surgery: A Study of AMA and ACGME Data from 2000 to 2016[END_REF], [START_REF] Mohamed | Factors considered by undergraduate medical students when selecting specialty of their future careers[END_REF].6% of last-year male medical students would chose internal medicine compared with 11.8% female in Rwanda [START_REF] Kansayisa | Gender-based analysis of factors affecting junior medical students' career selection: addressing the shortage of surgical workforce in Rwanda[END_REF], or in Korea, where 2.9% of male would be interested in paediatrics for 10.7% of woman [START_REF] Kim | Factors associated with the specialty choice of Korean medical students: a cross-sectional survey[END_REF]. Men are more interested by technical challenges, salary, career and prestige, women by time related aspects and societal orientation [START_REF] Evans | The Modern Medical School Graduate and General Surgical Training: Are They Compatible?[END_REF][START_REF] Bickel | Gender equity in undergraduate medical education: a status report[END_REF][START_REF] Lefevre | Career choices of medical students: a national survey of 1780 students: Motivation in medical student career choices[END_REF].

A shift in the gender ratio in medical students have occurred at the end of the 20 th century [START_REF] Drinkwater | The effect of gender on medical students' aspirations: a qualitative study[END_REF] from a male to a female over-representation, leading to difficulties for policymakers to adapt incentives, learning programs, schemes for managing physicians' flows and the specialization's issue. If many papers have investigated the gender imbalance among physicians [START_REF] Klifto | Women Continue to Be Underrepresented in Surgery: A Study of AMA and ACGME Data from 2000 to 2016[END_REF][START_REF] Kansayisa | Gender-based analysis of factors affecting junior medical students' career selection: addressing the shortage of surgical workforce in Rwanda[END_REF][START_REF] Kim | Factors associated with the specialty choice of Korean medical students: a cross-sectional survey[END_REF], only a few have looked for a gender-impact on students willingness before their specialty choice. This gender-ratio shift could lead to a change in the repartition of health workers and increase the disconnection between population's need and adequate care.

The purpose of this study was to investigate the factors influencing specialty' choice of medical students in the world, with emphasis on gender and differences between occidental and non-occidental countries.

Methods

A systematic review following PRISMA guidelines was undertaken to explore the factors influencing the choice of specialty among medical students and the influence of the gender. The review was registered on PROSPERO, n°42020169227.

Search strategy

We developed a search strategy according to Joanna Briggs Institute (JBI) reviewers manual (13) based on recommendations for systematic review of etiology and risk. Search was made on the 3 may of 2020, in MEDLINE and Scopus, from the 1 st of January 2010 to the 1 st of January 2020. Only the last decade was used to include articles, as the gender distribution could change over time [START_REF] Ariëns | Psychosocial risk factors for neck pain: a systematic review[END_REF]. MEDLINE and Scopus databases were screened using the keywords "speciality", "career choice", "motivation", "interest", "ambition", "influence", "factor" and "medical students" (search strategy in supplementary table 1).

Screening of literature

Screening and data extraction were done independently by two researchers (ML and JFH). In case of disagreement, a third researcher was involved (LL). The literature was firstly screened by title and abstract according to the inclusion and exclusion criteria. The remaining full-text articles were assessed for final inclusion.

Inclusion and exclusion criteria

For being included, the study had to focus on medical students after their selection year and before their specialization, detail the choice of specialty of each student, and look for factors influencing their choice.

Articles only referring to students before being admitted to medical studies, exploring students perceptions of specialties independently of their willingness, assessing practices' manner interest, evaluating a public policy such as incentives to choose a specialty or not being an English original study (i.e., protocol publication, letters, or comments) were excluded. No methodological criteria were applied for articles selection , Qualitative and quantitative paper could be collected.

If requested data was not available or further details were needed, the original study's authors were reached by email.

Data was extracted following an a priori defined grid (supplementary file, table 2). Extracted information included author, year of publication, research design and objectives, information about the reviewed study, and the specific information contributing to this review's central question. Extraction form was tested on five studies by each reviewer to ensure that all relevant results were extracted.

The quality of the included studies was assessed through Ariëns et al.'s score [START_REF] Ariëns | Psychosocial risk factors for neck pain: a systematic review[END_REF] modified for reviews focusing on influencing factors [START_REF] Soethout | Factors Associated with the Nature, Timing and Stability of the Specialty Career Choices of Recently Graduated Doctors in European Countries, a Literature Review[END_REF] (supplementary file, table 3). This score is suitable either for qualitative and quantitative studies.

Classification of specialities and factors influencing their choice

Fifteen different categories of specialties were a priori defined: anaesthesiology and intensive care, dermatology, ear, nose and throat (ENT), emergency room, general practice (GP), internal medicine, obstetrics and gynaecology (O&G), oncology, ophthalmology, paediatrics, pathology, psychiatry, radiology, social medicine & public health and surgery.

Seven pre-defined groups of influencing factors, based on a literature review of the topic, were explored along with gender: lifestyle and work-life balance (factors about doctors' schedule and the balance between happiness in a job and in every day's life), societal orientation (willingness to have an important work for the community and the population in needs), prestige and income (wage and place of a specialty in the community or among colleagues), place of practice (place where practicing, possibility to have a career in public or private facility), scope of practice (diversity of a specialty, possibility to perform a wide panel of acts), role model and university influence (students' academic background and influence of teachers on his choices) and interest toward the discipline (supplementary file, table 4).

A factor was considered as influencing a specialty choice either in a positive or a negative way.

Analysis procedure

Data analysis was planned to be stratified according to gender and origin country, dichotomized by occidental (OC) and non-occidental countries (NOC) according to the distinction made by S. Huntington [START_REF] Iqbal | Huntington The Clash of Civilizations and the Remaking of World Order[END_REF]. OC' category relate to North America, European Union, Australia and New-Zealand. NOC have been treated as a global category because of the foreseeable under-representation of these countries in our study [START_REF] Drinkwater | The effect of gender on medical students' aspirations: a qualitative study[END_REF]. For the gender analysis, we aggregated data from the papers in which full numbers of men and women interested in a particular discipline were provided for calculating the proportion of men as the number of men interested in a specialty divided by all the people interested in it. Finally, we performed a post-hoc analysis by country's income level according to World Bank ranking [START_REF]World Bank Country and Lending Groups -World Bank Data Help Desk[END_REF], either comparing low income with high income countries, and by stratifying OC and NOC by income level.

Role of the funding source

This study did not benefit from any funding.

Results

577 articles were identified through the Scopus database, and 390 through the MEDLINE database. 751 articles remained after removing the duplicates. 406 articles were excluded based on their title, 253 based on their abstract and 38 based on the full-text (Figure 1).

Figure 1: flow chart

The 54 articles included in the systematic review were all cross-sectional and observational: 50 were quantitative studies, three were qualitative and one was both (Table 1). 23 studies were performed in OC (42.6%) and 31 in NOC (57.4%), for a total of 29 different countries involved. Most of studies (92.6%) were based on a survey addressed toward medical students in different countries. The mean response rate (RR) of all surveys included in this review was 69.1% (61.0% in OC and 73.7% in NOC), with a total sample size of 26 270 students surveyed. The mean age of the respondents was 23.9 years (25.6 in OC, 23.0 in NOC). The gender percentage was 48.5% men (42.8% in OC, 52.7% in NOC).

Seven studies (3 OC and 4 NOC) were rated of very good quality with a methodological score of 16 to 17 points. 21 studies scored between 14 and 15 and ranked good quality. 22 studies scored between 11 and 13 points and ranked average quality and 4 had a poor assessment. 2). Each percentage is referring to the number of the article quoting the studied factor as influencing the students' choice, either in occidental or non-occidental countries.

Table 1: Included studies assessing the factors influencing the choice of specialty among medical students around the world

Surgery was the most attractive specialty according to medical students followed by Internal Medicine, either in OC and NOC. Attractiveness of some specialties varied depending on origin country: GP attended the third place in OC (10.5%) and the sixth in NOC (5.3%). Dermatology and Social Medicine were more attractive in NOC (4.0% VS 0.4% and 1.4% VS 0.2% respectively), and anaesthesiology in OC (6.3% VS 3.8%).

The influence of work-life balance and interest for the discipline differed greatly depending on the country. Work-life balance had a bigger impact on the choice of surgery, general practice, psychiatry, anaesthesiology, emergency room and social medicine mostly in OC, and on pathology mostly in NOC. The interest for the discipline influenced the choice of internal medicine, psychiatrists, anaesthesiology only in OC.

Gender had an impact on the choice of surgery, O&G and GP mostly in OC, and on O&G and paediatrics in NOC (supplementary table 5).

As the country income level could be an important confounder in our analyses, we stratified them depending on the country income level, according to World Bank ranking [START_REF]World Bank Country and Lending Groups -World Bank Data Help Desk[END_REF]. 23 OC and 23 NOC were high-income. No changes were observed for the three first influencing factors either for OC or NOC, and the first three specialties chosen were also the same. The percentage of men did not show any major difference. The main differences were for the second and the third most wanted specialty. According to NOC, the top three were Surgery (64.1%), Emergency Room (62.9%) and Radiology (62.5%), whereas for high-income NOC, they were Surgery (57.5%), ENT (55.6%) and Anesthesiology (48.8%). No additional analyses were performed in the specific low-income level countries, as the low-income level countries were systematically NOC.

Fourteen of the included studies reported data concerning the association between the interest in a specialty and the gender, 5 from OC and 9 from NOC. Specialties attracting a higher proportion of men were radiology (men percentage(MP)=63.7%), surgery (MP=60.9%), emergency medicine (MP=57.6%) and ENT (MP=57.3%), whereas those interesting mostly women were O&G (MP=14.7%), GP (MP=26.9%), (paediatrics MP=27.3%) and dermatology (MP=29.6%) (Figure 3). The highest gaps between NOC and OC MP among countries were found for radiology, anaesthesiology, ophthalmology and paediatrics. Radiology and anaesthesiology had higher MP in OC (71% and 51.3%) than in NOC (62.5% and 46.1%) whereas MP were higher in NOC for paediatrics (37.1% in OC and 17.4% in NOC) and ophthalmology (50.3% in NOC and 32.6% in OC). Above the abscissa axis are represented the men percentage and proportion of medical students' interests among occidental countries. Below the axis, the same data are provided for non-occidental countries. Abscissa axis represents the percentage of men among each specialty. The size of each bubble reflects the relative proportion of medical students' interest in a specialty. The bubbles have been vertically distributed to allow a better readability, without another meaning of the ordinate axis. For example, radiology interested 0.7% of medical students', and mostly men (above70% men percentage) in occidental countries, and 2.5% in non-occidental countries, with less men interested (between 60 and 65% men percentage). ER: Emergency Room, ICU: Intensive Care Unit, O&G: Obstetrics and Gynaecology.

Men were mainly interested in surgery and internal medicine, both in OC and NOC, with a higher preference for surgery in NOC (45.8% VS 25.3%).

Both in OC and in NOC, women were more attracted by O&G (respectively 11.5% VS 2.8% and 8.2% VS 2.9%). In OC, women were much more interested in paediatrics than men (13.6% vs 4.8%). Such a difference was not highlighted in NOC (10.3% vs 10.4%). Finally, GP was found more appealing in OC than in NOC, both for men and women (respectively 8.6% and 13.3% in OC and 2.2% and 4.3% in NOC) (Figure 4). Percentages lower than 1% are not reported on the graph. ICU: Intensive Care Unit, O&G: Obstetrics and Gynaecology, ENT: Ear, Nose, Throat.

Discussion

In this review including 23 occidental and 31 non-occidental studies, lifestyle and work-life balance such as interest for the discipline happened to be frequently quoted as affecting the selection of a specialty. Gender was also a key factor with a high percentage of women in O&G (MP=14.7%) and a high percentage of men in surgery (MP=61.0%).

Influencing factors were difficult to isolate from career choice. Career decision-making is an evolving process. Querido et al. showed that first-year students were more personally oriented (geography, self-confidence, positive attitude toward patient population) compared to final-year students interests (lifestyle, workload, personal experiences) [START_REF] Soethout | Factors Associated with the Nature, Timing and Stability of the Specialty Career Choices of Recently Graduated Doctors in European Countries, a Literature Review[END_REF].

Other studies have shown a raise of lifestyle factors for students' specialty choice [START_REF] Matteson | Selection of medical specialties: preferences versus choices[END_REF]. This growing of lifestyle importance must be taken into account by policymakers because the less attractive a specialty is, the more important the shortage will be, and so the workload. The frequent appearance of interest as an influencing factor, more than prestige or income argues against a possible benefit from only raising physician's salary to deal with the challenge of access to care.

Gender influence -an important aim of our review -has rarely been investigated in previous research [START_REF] Drinkwater | The effect of gender on medical students' aspirations: a qualitative study[END_REF]. We showed that women ratio was higher in specialties whose choices are affected by interest for the discipline and societal orientation, while the men ratio was higher in technical disciplines, mostly linked to lifestyle and income, confirming the results of studies showing that career decision-making process is influenced by gender [START_REF] Cleland | Associations between medical school and career preferences in Year 1 medical students in Scotland: Career preferences of Year 1 medical students[END_REF][START_REF] Bickel | Gender equity in undergraduate medical education: a status report[END_REF][START_REF] Lefevre | Career choices of medical students: a national survey of 1780 students: Motivation in medical student career choices[END_REF][START_REF] Hamid | Speciality preference with respect to gender among medical students of Pakistan[END_REF][START_REF] Dorsey | Influence of Controllable Lifestyle on Recent Trends in Specialty Choice by US Medical Students[END_REF]. Although women seem to prefer primary care careers over medical specialty, linked with better work-life balance and wider practice's types, this is no longer only a women's issue, the family argument becoming just as important for men [START_REF] Diderichsen | Few gender differences in specialty preferences and motivational factors: a cross-sectional Swedish study on last-year medical students[END_REF][START_REF] Gibis | The Career Expectations of Medical Students[END_REF].

Gender ratio might be interpreted with caution, as for country whose population gender ratio might not be 1:1, having such a medical sex ratio around 1:1 may illustrate an overrepresentation of a specific gender. For example, the proportion of men in Kuwait medical students could be estimated at 42.9% [START_REF]World Bank Country and Lending Groups -World Bank Data Help Desk[END_REF], while the proportion of men in the whole Kuwait population was 58.1% (74).

In our review, OC and NOC countries representation was fairly balanced. Societal orientation along with prestige and income appeared mainly in NOC studies whereas place of practice and role modeling or academic status were pointed out principally in OC studies. Men were more interested in radiology and anaesthesiology in OC than in NOC, and more interested in ophthalmology and paediatrics in NOC than in OC.

Many confounders may exist when analyzing the impact of OC and NOC countries, especially since our categorization gather in a same group China, Japan, the India subcontinent and Arabia. However, taking into account the income-level of the countries (one of the most important confounders) did not change the estimated impact of the type of country. Even though, these results should not be extrapolated to each different NOC, as political or cultural factors that may be essential in the specialty choice process have not been investigated. Even if socio-demographic factors other than gender may also affect specialty choice [START_REF] Santana | Getting the right balance? A mixed logit analysis of the relationship between UK training doctors' characteristics and their specialties using the 2013 National Training Survey[END_REF], such factors like ethnicity need to be contextualised: a specific ethnicity can be predominant in a country and stand for a minority in another, and so were not included in this systematic review analysis.

One strength of our overview is not only presenting factors with a known association with medical career decision-making in general but also with a specialty-specific career preference. These results may be taken into account by public authorities to adapt their access to care policies: GP may not be attracted by higher wages nor academic career, whereas surgeons might be. Longitudinal study should be realised for assessing the evolution of factors influencing students until the definitive choice.

This study should be considered with limitations. First, specialties and influencing factors categories can suffer from lack of precision. Another categorization could have provided slightly different results. Specialties categorisation chosen may bias gender's estimation as they aggregate subspecialties with different gender repartition. For example, surgery stands either for orthopaedics (1 men for 5.9 women in 2016 in the US) and general surgery (1 men for 1.7 women in 2016 in the US), with very different gender percentage [START_REF] Klifto | Women Continue to Be Underrepresented in Surgery: A Study of AMA and ACGME Data from 2000 to 2016[END_REF]. The studied influential factors may not be exhaustive. Relevant information can be missing, as the willingness of train or practice abroad . Specialty choice may be driven by the possibility for students to practice abroad, or by the needs of the countries they want to work in.

Secondly, students considered in our review were at different stages of their training. Perception may change during training, young students having more interest for income and prestige than young doctors [START_REF] Are | The influence of medical school on career choice: A longitudinal study of students' attitudes toward a career in general surgery[END_REF]. Analysing by stages of medical education or running a prospective follow-up study could help in avoiding this bias, but was not possible in this study.

Thirdly, our work did not consider the specific medical demographic condition of each country, specialty may be more attractive for students' according to the number of already existing practitioners. Students' willingness may also be driven by the global health context, guessing that after the COVID-19 outbreak, specialists in infectious disease and intensivists will be more attractive.

Moreover, our study does not separate public and private medical schools, neither the length of training for each subspecialty, especially students' debt could impact their choice for shorter-trained subspecialties.

Comparing data between specialties and countries might have been limited because each country may not offer the same type of subspecialties neither the same status with it. For example, wages can be 2 to 10 times higher from a country to another, and could change influencing factors [START_REF]OECD iLibrary | Remuneration of doctors (general practitioners and specialists[END_REF]. Also, a GP or a surgeon might not have the same work organisation in every country.

For all studies included in the data synthesis, the levels of methodological quality was assessed by Soethout et al. checklist [START_REF] Soethout | Factors Associated with the Nature, Timing and Stability of the Specialty Career Choices of Recently Graduated Doctors in European Countries, a Literature Review[END_REF]. These criteria however slightly favor quantitative studies over qualitative ones. Qualitative studies can only obtain a maximum score of 15

where quantitative studies can obtain a maximum of 17 points. The results from studies with a low methodical quality are properly expected more uncertain and should be interpreted carefully. Even though, when excluding all studies with a methodological score under 13, results were slightly the same and ranks of factors influencing medical students' choice did not vary.

To conclude, influencing factors change either from a specialty or a country to another. Factors that influence a medical students' choice of specialty are not the same in occidental countries than in others. Gender have an important impact in students' choice of specialty.

Policymakers need to adapt their attracting strategies according to the specificity of the willingness of students in their country and the medical discipline concerned. Further investigations looking on the imbalance between future needs and specialists repartition could also be helpful.

Declaration of Interest

We declare no competing interest

Figure 2 :

 2 Figure 2: Proportion of articles in which each influence factor was founded significantly associated to the choice of a specialty, sorted by occidental and non-occidental countries. Each percentage is referring to the number of the article quoting the studied factor as influencing the students' choice, either in occidental or non-occidental countries.

Figure 3 :

 3 Figure 3: Graphic representation of the specialty of interest and the men percentage among all countries.Above the abscissa axis are represented the men percentage and proportion of medical students' interests among occidental countries. Below the axis, the same data are provided for non-occidental countries. Abscissa axis represents the percentage of men among each specialty. The size of each bubble reflects the relative proportion of medical students' interest in a specialty. The bubbles have been vertically distributed to allow a better readability, without another meaning of the ordinate axis. For example, radiology interested 0.7% of medical students', and mostly men (above70% men percentage) in occidental countries, and 2.5% in non-occidental countries, with less men interested (between 60 and 65% men percentage). ER: Emergency Room, ICU: Intensive Care Unit, O&G: Obstetrics and Gynaecology.

Figure 4 :

 4 Figure 4: Graphic representation of the specialties wanted by medical students', according to their gender and the country they completed their studies. Percentages lower than 1% are not reported on the graph. ICU: Intensive Care Unit, O&G: Obstetrics and Gynaecology, ENT: Ear, Nose, Throat.

  Factors influencing students' choices were studied depending on each discipline. The most frequent factor was lifestyle and work-life balance, quoted by 33 studies (60.0%) as an important factor. Interest in the discipline and gender were quoted respectively by 25 (45.5%) and 21 (38.2%) studies. Other factors appeared in less than a third of the studies.

	Fehlmann A. 2019(41) Ster MP. 2017(65) Influencing factors Switzerland (O) Slovenia (O)	QT QT	1749 343	Final year Final year	56.0% NA	37.0% 38.5%	NA 24.9		Good Very good
	First author and year Grasreiner D. 2018(42) Subba S.H. 2012(66)	Country Germany (O) India (NO)	Design QT QT	Sample 720 373	Period of study First to sixth year Fourth, sixth and eight	Response 13.1% NA	Men 24.9% 50.4%	Mean age 24 20.2 (1.6)	Methodology 9	Quality Average Poor
	Abdulrahman M. 2016(19) Gutierrez-Cirlos C. Sutton PA. 2014(67) 2019(43) Akhund S. 2012(20) Hamid S. 2019(44) Wang K.-I. 2007(68)	United Arab Emirates Mexico (NO) UK (O) (NO) Pakistan (NO) Pakistan (NO) Taiwan (NO)	QT QT QT QT QT QT	size 956 697 482 148 314 185	All undergraduate students Final year semester Final year Semester 1, 3, 7, 9 Fourth and final year Fifth to seventh year	rate 46.0% 81.0% NA 43.7% 54.5% 92.5%	percentage 44.6% 35.0% 41.0% 54.0% NA NA	NA 24 (1) NA 20.5 (2.2) 22.63 (1.473) NA	assessment	Good Good Good Average Average Good
	Al-Fouzan R. 2012(21) Ibrahim M. 2014(45) Wu S.M. 2014(69) Ie K. 2018(46) Zarkovic A. 2006(70) Societal orientation among with prestige and income was more important in NOC studies Kuwait (NO) QT 387 All except first year 91.7% UK (O) QT 641 Final year and graduate 12.0% Hong-Kong (NO) QT 247 Fifth year 93.9% Japan (NO) QT 1408 Final year students' 74.0% New Zealand (O) QT 256 Final year -One to Fourth year 64.0%	42.9% 44.0% 54.5% 66.3% 51.4%	21.45 (1.72) NA 23 (1.49) 24 NA		Average Very good Very good Good Average
	Al-Mendalawi MD. Kawamoto R. 2016(47) (respectively 43.8% VS 17.4% and 37.5% VS 21.7%) whereas place of practice and role Iraq (NO) QT 108 Final year 91.5% Japan (NO) QT 368 First to fifth year 88.2% postgraduate	64.8% 61.7%	NA 21.4 (3.6)		Good Good
	2010(22) Alahwal H.M.S. 2010(23) Alawad AA. 2015(24) Kazzi AA. 2001(48) modeling or academic status were pointed out principally in OC studies (respectively 21.7% Saudi Arabia (NO) QT 151 Interns 60.0% Sudan (NO) QT 647 First to fifth year 73.0% USA (O) QT 393 NA 76.0% Khader Y. 2008 Jordan (NO) QT 440 Second fourth and sixth year 77.7% VS 6.3% and 39.1% VS 21.9%) (Figure	66.0% 38.6% NA 64.0%	NA NA 28 (3) 21.1 (2.0)		Average Average Average Good
	Alenezi M. 2019(25) 2005(49) Khater-Menassa B.	Saudi Arabia (NO) Lebanon (NO)	QT QT	75 127	Interns Final year	NA 97.0%	52.0% 74.0%	24.49 (2.15) 25		Average Good
	Alkhaneen H. 2018(26) Kiolbassa K. 2011(50)	Saudi Arabia (NO) Germany (O)	QT QT	436 1299	Second and third phases All years	53.4% 11.0%	57.0% 40.0%	NA 24.1 (3.1)		Average Average
	AlKhilaiwi RM. 2018(27) Kumar R. 2011(51)	Saudi Arabia (NO) India (NO)	QT QT	236 282	Fifth and final year All years	78.7% 74.4%	64.8% 89.0%	NA 20.89		Average Good
	Alshahrani M. 2014(28) Kuzman M.R. 2014(52)	Saudi Arabia (NO) Croatia (O)	QT QT	379 122	Fourth, fifth and sixth year Final year	58.0% 61.0%	33.3% 36.0%	NA 24.38 (0.819)		Average Good
	Alsubaie N. 2016(29) Lam CY. 2016(53)	Saudi Arabia (NO) Hong-Kong (NO)	QT QT	252 233	Second and third year All medical graduates	81.8% 73.7%	50.4% 47.6%	NA 23		Good Very good
	Anand R. 2019(30) Lefevre JH. 2010(11)	India (NO) France (O)	QT QT	364 1780	Bachelor Sixth year	79.1% 68.8%	37.9% 38.0%	NA 23.8 (1.4)		Good Very good
	Anna Muscatello MR. Lefèvre JH. 2010(54)	USA (O) France (O)	QT QT	335 1742	Forth year Sixth year	93.0% 67.0%	52.9% 38.0%	24.3 (2.1) 23.8 (1.4)		Average Very good
	2017(4-7)(31) Azizzadeh A. 2003(32) Lydon S. 2015(55) Barber S. 2018(33) Mehmood SI. 2013(56) Bien A. 2019(34) Newton DA. 2005(57) Bilal M. 2018(35) Onyemaechi N. 2017(58) Chen YC. 2014(36) Osborn HA. 2007(59) Correia Lima de Souza L. 2015(37) Pianosi K. 2016(60) Querido S. 2018(61)	USA (O) Ireland (O) UK (O) Saudi Arabia (NO) Germany (O) USA (O) Pakistan (NO) Nigeria (NO) Taiwan (NO) Canada (O) Brazil (NO) Canada (O) Netherlands (O)	QT QT QT & QL QT QT QT QT QT QT QT QT QL QL	111 334 280 590 361 1334 1400 152 405 323 1225 70 24	Forth year Medical students and doctors Final and penultimate years First to fifth year Fourth, fifth or final year Fourth year Final year Final year Interns Fourth year Medical students and doctors NA Final year	69.0% NA 89.0% 92.5% 70.9% 73.0% 100% 98.0% 91.4% 59.0% 79.2% NA NA	60.8% 50.7% 51.0% 57.0% 33.4% 51.0% 33.0% 72.4% 59.4% 46.7% 37.5% NA 16.7%	NA NA NA 21.5 (2.5) NA 28.1 (3.2) 24 25.8 (2.5) 26.81 (3.44) 26 24 NA NA	9	Average Good Good Good Average Average Good Good Average Very good Poor Good Poor
	Diderichsen S. 2013(38) Rouhani M. 2017(62)	Sweden (O) UK (O)	QT QT	372 137	Final year All years	89.0% NA	42.0% 38.0%	27.5 NA		Good Average
	Du J. 2009(39) Saigal P. 2007(63)	New Zealand (O) Japan (NO)	QT QL	87 NA	First to fifth year NA	0.25% NA	37.0% NA	NA NA	9	Average Poor
	Enoch L. 2013(40) Scott AJ. 2017(64)	USA (O) South Africa (NO)	QT QT	145 245	Forth year First to sixth year	88.0% 24.4%	54.0% 44.0%	26.2 (1.6) 21.4		Average Average

O: Occidental country, NO: Non-occidental country, USA: United States of America, UK: United Kingdom, NA: Not Available All studies were cross-sectional and observational study. QT: Quantitative, QL: qualitative When available, ages are expressed in years, with mean and, when provided, standard deviation into brackets. Quality grade are assessed through the Ariens et al.'s score, and range from 0 (worst methodological quality) to 17 (best one).

The authors have no support or funding to report.

Author contributions

Mathieu Levaillant, Jean-François Hamel-Broza and Lucie Levaillant contributed to the literature search, figures, study design and data collection. All the authors contributed to data analysis, interpretation and writing.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Data sharing statement

The authors declare that the data collected was gathered from publicly available studies and is available upon reasonable request.


M Kern

I Douma

P Denis

Fracture de Xen ® Broken Xen ®

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Déclaration de liens d'intérêts

Les auteurs déclarent ne pas avoir de liens d'intérêts.

Figure 1 .

 1 Figure 1. Xen ® moulé sous la conjonctive et cassé au niveau de sa portion distale. Cliché en lumière bleue après instillation de fluorescéine. Cliché noir et blanc. Cliché couleur.


Saturday night retinopathy: a French case of post-compressive CRAO Nous rapportons le cas d'un patient de 39 ans qui s'est présenté aux urgences générales un soir pour oeil gauche rouge et indolore avec baisse de l'acuité visuelle survenue depuis le matin au réveil (>12h). Il présentait également des douleurs et paresthésies au bras gauche. Il s'était endormi à table la veille au soir, la tête posée sur son bras comprimant son oeil après une importante

L'explication est multifactorielle :

-Mécanique par élévation de la pression intra-oculaire due à la compression de l'oeil et diminution du retour veineux avec stase en amont du à la compression globale orbitaire.

-Circulatoire avec une probable baisse tensionnelle artérielle et diminution de la pression de perfusion orbitaire en raison de l'état de stupeur ajouté à la possible prise de drogues. cas risquent de se multiplier vu l'essor de l'alcoolisation aiguë massive et de la prise de drogues récréatives au sein de la population générale dans les pays développés [START_REF] Malihi | Is Saturday Night Retinopathy Being Reported More Frequently?[END_REF]. Seules l'information et la prévention permettraient d'éviter ces handicaps fonctionnels lourds.

Abstract:

We report herein the first French case of Saturday Night Retinopathy. A 39-year-old man presented to the emergency room with unilateral vision loss in the left eye with redness but no pain.

Visual acuity OS was "light perception" and OD 20/20. The left eye was hyperemic with a fixed, dilated pupil; fundus examination revealed a macular cherry-red spot within a pale, ischemic retina.

The patient was admitted to a stroke center. The neurological work-up and head CT were normal.

He also underwent evaluation for possible carotid or cardiac etiologies, all of which were negative.

Current smoking was the only cardiovascular risk factor found.

The patient reportedly fell asleep face down at his kitchen table after consuming a large amount of alcohol, with his left eye pressed into his arm throughout the night. Six similar cases have been

  consommation d'alcool. L'examen général et neurologique étaient sans anomalie. Le patient avait comme antécédents une dépression sévère sous neuroleptique et benzodiazépine et une addiction au tabac. Il rapportait la consommation occasionnelle de cannabis et de cocaïne. Il n'avait pas d'autre pathologie et notamment pas d'hypertension artérielle ni de diabète ni de dyslipidémie. L'examen retrouvait une acuité visuelle effondrée à gauche (perception lumineuse) et normale à droite (0 logMar). L'examen (figure 1) montrait à gauche un oedème érythémateux palpébral supérieur et inférieur, une hyperhémie conjonctivale, une mydriase aréactive avec iris déformé. La cornée et le cristallin étaient claires, la chambre antérieure calme. La pression intra oculaire était de 14mmHg. Il n'y avait ni ptosis ni limitation oculomotrice. Le fond d'oeil (FO) à gauche (figure 2) révélait une pâleur de la rétine avec une tache rouge cerise de la fovéa. Le calibre des vaisseaux était réduit. La papille était colorée avec bords nets. L'examen de l'oeil droit était sans particularité (figure 3). L'OCT maculaire réalisé en urgence (figure 4) montrait une hyperréflectivité des couches internes de la rétine. Le diagnostic d'occlusion d'artère centrale de rétine (OACR) était alors retenu. Au contrôle sept jours plus tard, la vision restait limitée à la perception lumineuse, l'hyperréflectivité des couches interne de la rétine à l'OCT avait régressé et laissé place à une atrophie maculaire (figure 5), l'angiographie à la fluorescéine retrouvait des vaisseaux normalement perfusés et l'absence de vascularite. Le scanner cérébral et des troncs supra-aortiques avec injection ne retrouvait aucune anomalie, notamment aucune compression ni anévrisme vasculaire local, ni stigmate d'accident vasculaire cérébral (AVC) silencieux sur le parenchyme cérébral. Les carotides étaient saines. Le scope-ECG s'inscrivait en rythme sinusal sans arythmie cardiaque. Le bilan biologique était sans particularité, notamment CRP négative, bilan hépatique normal mais toxiques urinaires positifs au cannabis et opiacés. Pour expliquer cette OACR, les causes habituelles athéromateuse et cardioemboligène n'ont pas été retenues. L'hyperhémie conjonctivale avec cercle péri-kératique, la déformation pupillaire et l'oedème palpébral inflammatoire retrouvés le premier jour peuvent témoigner d'une ischémie oculaire et orbitaire à minima. L'histoire du patient et les éléments cliniques et paracliniques sont compatibles avec une compression locale déjà décrite sous le nom de « Saturday Night Retinopathy ». Des OACR unilatérales survenues lors d'anesthésie générale avec compression orbitaire prolongée par les masques d'anesthésie ont été décrites en 1948 et 1953. [1] Des essais chez des singes ont montré qu'une compression orbitaire d'au moins 60 minutes dans un contexte d'hypotension artérielle pouvait causer une OACR. [2] C'est en faisant l'analogie avec ces cas que Jayam décrit en 1974 une OACR survenue chez un patient alcoolisé après s'être endormi en écrasant son oeil. Il appelle cette entité le « Saturday Night Retinopathy ». [2] Cinq autres cas ont été décrits depuis 2015.[3] [4] [5] Une compression orbitaire est rapportée systématiquement. Une ophtalmoplégie (complète ou incomplète), un ptosis, un oeil rouge, un oedème de cornée, une hypertonie intra oculaire sont parfois retrouvés. [3] [4] L'acuité visuelle est effondrée à « perception lumineuse » ou « voit bouger la main ». Le fond d'oeil met en évidence une OACR.

Figure 1 .

 1 Figure 1. Photographie de l'oeil gauche le lendemain de l'OACR post-compressive.

Figure 2 .

 2 Figure 2. Photographie du fond d'oeil (OCT Topcon DRI OCT Triton) de l'oeil gauche

Figure 3 .

 3 Figure 3. Photographie du fond d'oeil (OCT Topcon DRI OCT Triton) de l'oeil droit sans

Figure 4 .

 4 Figure 4. L'OCT maculaire de l'oeil gauche (OCT Topcon DRI OCT Triton) réalisé en

Figure 5 .

 5 Figure 5. OCT maculaire de l'oeil gauche (OCT Topcon DRI OCT Triton) une semaine après :

  reported in the literature since 1973. The prognosis for vision is dismal. Only public awareness and prevention might avoid this serious functional disability. Déclaration d'intérêts : Les auteurs déclarent n'avoir aucun lien d'intérêts en relation avec cet article.