Delphine Dobler
email: delphine.dobler@gmail.com

Thierry Huck
email: thierry.huck@univ-brest.fr

Christophe Maes
email: christophe.maes@ird.fr

Nicolas Grima
email: nicolas.grima@univ-brest.fr

Bruno Blanke
email: bruno.blanke@univ-brest.fr

Elodie Martinez
email: elodie.martinez@ird.fr

Fabrice Ardhuin
email: fabrice.ardhuin@ifremer.fr

Large impact of Stokes drift on the fate of surface floating debris in the South Indian Basin

Keywords: Marine debris, Microplastics, Stokes drift, Indian Ocean, Lagrangian analysis, Ocean surface pathways

In the open ocean, floating surface debris such as plastics concentrate in five main accumulation zones centered around 30 • latitude, far from highly turbulent areas. Using Lagrangian advection of numerical particles by surface currents from ocean model reanalysis, previous studies have shown long-distance connection from the accumulation zones of the South Indian to the South Pacific oceans. An important physical process affecting surface particles but missing in such analyses is wave-induced Stokes drift. Taking into account surface Stokes drift from a wave model reanalysis radically changes the fate of South Indian particles. The convergence region moves from the east to the west of the basin, so particles leak to the South Atlantic rather than the South Pacific. Stokes drift changes the South Indian sensitive balance between Ekman convergence and turbulent diffusion processes, inducing either westward entrainment in the north of the accumulation zone, or eastward entrainment in the south.

Introduction

Plastics ending at sea are a major issue in our modern societies [START_REF] Law | Plastics in the marine environment[END_REF]. [START_REF] Cózar | Plastic debris in the open ocean[END_REF], van Sebille et al. (2015), [START_REF] Jambeck | Plastic waste inputs from land into the ocean[END_REF], among many others, have estimated that tens to hundreds of thousands tonnes of plastics end in the open ocean each year. Floating plastics travel long distances. They tend to accumulate around 30 • latitude in the five subtropical gyres [START_REF] Martinez | Floating marine debris surface drift: Convergence and accumulation toward the South Pacific subtropical gyre[END_REF][START_REF] Maximenko | Pathways of marine debris derived from trajectories of Lagrangian drifters[END_REF] and far from highly turbulent areas [START_REF] Van Sebille | Origin, dynamics and evolution of ocean garbage patches from observed surface drifters[END_REF]. The South Indian Ocean is of particular interest because of the high rate of plastic discharge from the coasts and rivers of the surrounding lands, and the specific dynamics that do not converge around the same longitudes along the basin [START_REF] Lebreton | Numerical modelling of floating debris in the world's oceans[END_REF][START_REF] Van Sebille | Origin, dynamics and evolution of ocean garbage patches from observed surface drifters[END_REF][START_REF] Maes | A Surface "Superconvergence" Pathway Connecting the South Indian Ocean to the Subtropical South Pacific Gyre[END_REF].

To simulate the trajectories and accumulation zones of floating debris, [START_REF] Maximenko | Pathways of marine debris derived from trajectories of Lagrangian drifters[END_REF] and [START_REF] Van Sebille | Origin, dynamics and evolution of ocean garbage patches from observed surface drifters[END_REF] used mean currents computed from a surface drifter database (using both those drogued at 15 m and those undrogued) and applied a transfer matrix method. They built the probability matrices of the particles in one grid cell to end up in another grid cell and integrated these matrices over time. Very recently, van der Mheen et al. (2019) used distinct transport matrices constructed from drogued drifter trajectories (representing currents at about 15 m depth), and undrogued drifters (representing surface currents), and found a particularly important influence on the southern Indian Ocean garbage patch. With the surface currents transport matrix, the Indian Ocean garbage patch is located to the west of the basin, and is highly dispersive. On the other hand, [START_REF] Maes | A Surface "Superconvergence" Pathway Connecting the South Indian Ocean to the Subtropical South Pacific Gyre[END_REF] and [START_REF] Lebreton | Numerical modelling of floating debris in the world's oceans[END_REF] used a method based on Lagrangian advection of numerical particles by surface current velocities from an ocean model reanalysis with data assimilation. They also found the five subtropical accumulation zones, but their South Indian accumulation zone is on the eastern side of the basin and leaks eastward towards the South Pacific.

Observations of floating debris are sparse in the Southern Hemisphere [START_REF] Cózar | Plastic debris in the open ocean[END_REF][START_REF] Isobe | Abundance of nonconservative microplastics in the upper ocean from 1957 to 2066[END_REF]. And although they are compatible with theoretical accumulation in subtropical gyres around 30 • latitude, they can neither confirm nor refute the zonal location of the South Indian accumulation zone nor the basin to which it most likely leaks. Although the community is building new tools to improve our ocean observation capacity [START_REF] Maximenko | Towards the integrated marine debris observing system[END_REF], it will take some time to have accurate datasets.

Here, we would like to understand which physical processes can explain such a difference in the location of the convergence zone in the South Indian Basin for the different types of simulations. Modelling studies based on the trajectories of Lagrangian particles and using different current products allow sensitivity experiments particularly adapted to investigate the processes responsible for the displacement of convergence zones.

Ekman convergence is generally believed to be the main physical process responsible for the accumulation of floating debris at 30 • latitude. The dispersion by mesoscale turbulence explains why, at 30 • latitude, accumulation zones tend to be located at the opposite of areas with high Eddy Kinetic Energy [START_REF] Van Sebille | Origin, dynamics and evolution of ocean garbage patches from observed surface drifters[END_REF]. This is particularly true when the basin is broad enough, mainly in the Pacific. Floating debris also feel two other dispersive forces: the windage, which is the direct wind force felt by the emerged part of a floating object, and the Stokes drift, which results from correlations between velocity and displacement in wind-generated waves. Both windage and Stokes drift are felt by the undrogued drifters used in [START_REF] Maximenko | Pathways of marine debris derived from trajectories of Lagrangian drifters[END_REF], but are not included in the reanalysis data used in [START_REF] Maes | A Surface "Superconvergence" Pathway Connecting the South Indian Ocean to the Subtropical South Pacific Gyre[END_REF]. They could explain the difference observed in the simulations for the South Indian Basin as proposed recently by [START_REF] Van Der Mheen | Role of indian ocean dynamics on accumulation of buoyant debris[END_REF]. We suppose that floating plastic debris are less prone to windage than to Stokes drift, as most of them have few or no emerged parts [START_REF] Beron-Vera | Inertia-induced accumulation of flotsam in the subtropical gyres[END_REF]. Thus, the impact of windage will not be assessed in this study to adequately separate the different processes. Stokes drift, on the other hand, is of the order of 1% of the wind speed [START_REF] Rascle | Drift and mixing under the ocean surface. a coherent one-dimensional description with application to unstratified conditions[END_REF][START_REF] Ardhuin | Observation and estimation of Lagrangian, Stokes, and Eulerian currents induced by wind and waves at the sea surface[END_REF][START_REF] Ardhuin | Measuring currents, ice drift, and waves from space: the sea surface kinematics multiscale monitoring (SKIM) concept[END_REF], with instantaneous values of up to 0.6 m/s in the Antarctic Circumpolar Current and annual averages of 0.2 m/s. [START_REF] Tamura | The Stokes drift and wave induced-mass flux in the North Pacific[END_REF] estimated that the mean annual surface Stokes drift is between 0.02 and 0.10 m/s, and that its horizontal divergence is comparable to Ekman pumping in the North Pacific. In this study, we assess the impact of the surface Stokes drift on the location of South Indian accumulation zone and leak pathways.

To properly account for this Stokes drift, a fully coupled ocean-wave model would ideally be required. However, no operational coupled model and corresponding reanalyses were available at the time of this study. Neglecting the small-scale fluctuation of Stokes drift introduced by the effect of currents on waves (e.g. [START_REF] Ardhuin | Small scale currents have large effects on wind wave heights[END_REF] we use a wave model reanalysis to estimate Stokes drift, following the method proposed by [START_REF] Isobe | Selective transport of microplastics and mesoplastics by drifting in coastal waters[END_REF]. The simple addition of these surface Stokes drift to surface velocities from an ocean model reanalysis will provide an a priori estimate of the impact of Stokes drift, in the same manner as [START_REF] Isobe | Selective transport of microplastics and mesoplastics by drifting in coastal waters[END_REF][START_REF] Iwasaki | Fate of microplastics and mesoplastics carried by surface currents and wind waves: A numerical model approach in the Sea of Japan[END_REF][START_REF] Fraser | Antarctica's ecological isolation will be broken by storm-driven dispersal and warming[END_REF], or recently [START_REF] Onink | The role of Ekman currents, geostrophy and Stokes drift in the accumulation of floating microplastics[END_REF]. To understand the changes observed in floating debris dynamics, we have also scaled the tracer equation to build a length over which the convergence of Ekman current and eddy dispersion processes are balanced.

This paper describes the impact of Stokes drift on the location of the South Indian accumulation zone and on long-distance leaking pathways, and sheds light on the surface dynamics of the South Indian through the length scale of the balance between convergence and dispersion processes. Section 2 presents the materials and methods, section 3 presents the results of the study in terms of Stokes drift impact and dynamical balance, and section 4 discusses the results and concludes.

Materials and methods

Particle advection by surface currents

As in [START_REF] Maes | A Surface "Superconvergence" Pathway Connecting the South Indian Ocean to the Subtropical South Pacific Gyre[END_REF], we used the Ariane Lagrangian analysis software [START_REF] Blanke | Kinematics of the Pacific Equatorial Undercurrent: an Eulerian and Lagrangian approach from GCM results[END_REF] to advect numerical particles by surface currents and simulate trajectories of floating debris at the surface. 910,000 particles were introduced at the initial time, homogeneously located with a particle in the center of each ocean surface grid cell of the bipolar C-grid of the ORCA 1/4 • ocean model. The Ariane software has been configured to prevent these particles from changing vertical levels by assuming that the buoyancy of the floating debris prevents them from sinking.

For the reference scenario, the Ariane software has been fed with the surface current velocities of the CMCC C-GLORS product in its v7 release [dataset] [START_REF] Storto | C-GLORSv5: an improved multipurpose global ocean eddy-permitting physical reanalysis[END_REF]. C-GLORS is a global reanalysis using the NEMO-OPA v3.6 ocean model in the ORCA 1/4 • configuration and assimilating data such as moorings and remote sensing data, including sea surface height based on altimetry and sea surface temperature. We used the currents from the first layer, which is centered at 0.5 m and is 1-meter thick. The dataset provided to us covers the period from 2005 to 2016 at daily frequency. We first checked that this new version gave the same results as in [START_REF] Maes | A Surface "Superconvergence" Pathway Connecting the South Indian Ocean to the Subtropical South Pacific Gyre[END_REF], which used a previous version.

For the scenario including Stokes drift, we added surface Stokes drift to the C-GLORS surface current velocities. The surface Stokes drift was provided by the IOWAGA dataset which is a reanalysis using the WAVEWATCH III wave model [dataset] [START_REF] Rascle | A global wave parameter database for geophysical applications. Part 2: Model validation with improved source term parameterization[END_REF]. This method has the advantage of simplicity with immediately available operational products, but may have two drawbacks: (i) it does not respect the 3D non-divergence criterion, and (ii) it assumes that C-GLORS does not include Stokes drift. (i) The ocean models we used enforce volume conservation leading to zero 3D divergence of the velocity field (taking into account the slight freshwater flux at the ocean surface due to evaporation and precipitation). Since we impose the particles to remain at the surface, horizontal surface currents are no longer non-divergent, mostly because of their wind-driven Ekman component (large-scale "geostrophic" ocean currents are mostly non-divergent, in terms of horizontal 2D divergence). Hence the addition of divergent surface Stokes drift does not appear to be a critical issue, as confirmed further by the comparison of the mean divergence of the surface current and of the Stokes drift (Figure 1). (ii) Because C-GLORS assimilates measurement data, and though Stokes drift is not explicitly modelled, some of its physics may be implicitly included: however it is very unlikely that the corrections to surface currents are related to the actual Stokes drift. Despite these minor drawbacks, this method will allow us to assess the potential impact of Stokes drift on surface dynamics and particle trajectories. In IOWAGA, the surface Stokes drift is computed from the wave spectrum (which ranges from 0.037 to 0.716 Hz) as follows:

U ss = σ(k) k(k, θ) cosh(2kD) sinh 2 (kD) F (k, θ) dk dθ, (1)
where k(k, θ) is the wavenumber, σ(k) the wave frequency, F (k, θ) the wave energy spectrum as a function of wavenumber and direction, and D the water depth. In the deep water approximation (kD 1), this simply reads:

U ss = 2 σ(k) k(k, θ) F (k, θ) dk dθ. (2)
The surface currents from C-GLORS and the Stokes drift from IOWAGA are not available on the same grid and at the same time frequency. C-GLORS is delivered on an ORCA 1/4 • bipolar C-grid with averages at daily frequency. The IOWAGA Stokes drift, computed with ECMWF operational analyses and forecasts wind forcing, is delivered on a 1/2 • regular A-grid at a 3-hour frequency. To add the two components together and conserve an eddy-permitting resolution, the Stokes drift was first averaged daily and then interpolated to the same grid as C-GLORS. This interpolation used the four nearest points weighted by the inverse square distance. At the time of this study, the IOWAGA dataset forced by ECMWF winds was available continuously for the period 2010 to 2017. Thus, the longest common and continuous period for IOWAGA and C-GLORS products was from 2010 to 2016. To compare the results with previous studies of 29 years of advection [START_REF] Maes | A Surface "Superconvergence" Pathway Connecting the South Indian Ocean to the Subtropical South Pacific Gyre[END_REF], we made five loops over this 7 year period and analysed the 29th year.

The annual-mean C-GLORS surface currents and IOWAGA surface Stokes drift, and their divergence, are shown in Figure 1. As expected, C-GLORS surface currents are convergent in the main accumulation zones (averaged values over the boxes shown in Figure 1 are between -2 and -3 10 -8 s -1 , which corresponds to convergence time scales of 1 to 1.5 years) and display the subtropical gyre circulation. In contrast, the IOWAGA surface Stokes drift is divergent in these regions, with averaged values between 0.8 and 1.7 10 -8 s -1 . This represents up to 50% of the CGLORS surface mean convergence and confirms the interest of studying the impact of Stokes drift on particle trajectories.

A characteristic length scale of balance

To understand the change in surface dynamics that occurs when Stokes drift is taken into account, we will first consider a scaling diagnosis based on the tracer equation:

∂C ∂t = -∇ h (u.C) + ∇ h (K d ∇ h C), (3)
where C = φ * ρ is the particle concentration in number of particles per volume of seawater, φ the number of particles per kilogram of seawater, ρ the density of seawater and K d the eddy diffusivity coefficient. The first term on the right-hand side of the equation, -∇ h (u.C), is scaled to the horizontal divergence of the annual-mean velocity field (either by using C-GLORS surface current velocities for the reference scenario, or by adding the C-GLORS surface current velocities with the IOWAGA surface Stokes drift for the Stokes drift scenario). The diffusivity coefficient K d is scaled as suggested by Ollitrault and Colin de Verdière ( 2002) by a characteristic eddy size multiplied by a velocity equivalent to the eddy kinetic energy:

K d ≈ R d1 * √ 2EKE
, with R d1 being the first baroclinic Rossby radius [dataset] [START_REF] Chelton | Geographical variability of the first baroclinic Rossby radius of deformation[END_REF] and EKE the eddy kinetic energy averaged over one year (as before, either by using C-GLORS surface current velocities for the reference scenario, or by adding the C-GLORS surface current velocities with the IOWAGA surface Stokes drift for the Stokes drift scenario). Scaling the characteristic eddy size by R d1 stems from [START_REF] Stammer | Global characteristics of ocean variability estimated from regional TOPEX/POSEIDON altimeter measurements[END_REF] analyses of the Topex-Poseidon sea surface heights, where he showed that characteristic eddy sizes were closer to the first baroclinic Rossby radius than to the Rhines scale, especially poleward of 20 • latitude. This implies that baroclinic instability is the main process of eddy generation on a global scale. The characteristic length L c over which the convergence balances the diffusion process then reads for ∇ h u < 0:

L c ≈ K d -∇ h u ≈ R d1 √ 2EKE -∇ h u . ( 4
)
L c is half the length scale of the "radius of the bell" of [START_REF] Maximenko | Pathways of marine debris derived from trajectories of Lagrangian drifters[END_REF] of 4k/|D|, where k is diffusivity and D is divergence. They estimated this length scale from the e-folding decay scale of particle concentration away from the center of the convergence regions and found values between 700 and 2500 km. But here we have chosen to keep also the information of divergent points D > 0. If in a region of radius L c , few divergent points are encountered, then the particle will tend to stay in this region. To complement this diagnosis, we therefore computed the probability that the particles will encounter divergent points on the length scale characteristic of the balance. For each grid point, we considered the disk of radius L c /2 and evaluated the percentage of grid cells where ∇ h u > 0. To understand the zonal entrainment pathways, we zonally averaged this percentage (leak probability) and added the information of the mean zonal current averaged both annually and zonally (leak direction). The zonal means were made between 55 • E and 110 • E in the South Indian Basin.

Results

Impact of the Stokes drift

Comparing the concentration of particles after 29 years of advection by ocean model surface currents including or not Stokes drift, we can see that the overall convergence around 30 • latitude is similar, but that the zonal distribution shows a large difference (Figure 2), as well as the time evolution of the number of particles in each accumulation zone (Figure 3). More specifically, the inclusion of the Stokes drift has a large impact in the South Indian Basin. First, the accumulation zone in the South Indian is sparse and now located on the western side of the Indian basin: the center of mass of the accumulation zone has shifted 32.5 • to the west. Secondly, 31% of the particles initially located in the South Indian Basin are now found in the accumulation zone of the South Atlantic (compared to 2% without Stokes drift). There are almost no more South Indian particles (0.5%) that end in the South Pacific accumulation zone (compared to 23% without Stokes drift). The time evolution of the number of particles in the South Indian Basin is peculiar compared with the other concentration zones, because it is not monotonic. First, there is an accumulation phase when the South Indian gains more particles than it loses them. Then there is a second phase when it only loses particles as there are no more particles to gain in the surroundings. The South Indian Basin is leaking, through a specific balance between convergence and dispersion processes that will be discussed more thoroughly in the section about dynamical balance. The time evolution of the particles shows that the South Indian Basin leaks much faster with the Stokes drift, there are almost no more particles in this area at the end of the simulation, when there were about 80,000 particles without Stokes drift. The South Atlantic accumulation zone still gains particles after 35 years of simulation with Stokes drift, whereas it had reached a maximum after only 7 years of advection without Stokes drift. Finally, the South Pacific accumulation zone no longer gains particles with Stokes drift after about 15 years of advection, whereas it still gains particles after 35 years of advection without Stokes drift. This could be partly due to the striking difference in the number of particles stuck to the coast, amounting to 67% after 29 yr of advection with Stokes drift compared to only 20% without Stokes drift. The addition of Stokes drift leads to a net inshore current along several coasts, causing the particles to get stuck in the same place along the coast (the way Ariane software handles "beaching"). The reason for this is twofold: the coastal dynamics of Stokes drift is not well taken into account by the global 1/2 • IOWAGA product, and the simple addition of Stokes drift implies a strong convergence towards the coast in several regions (as shown in red in Figure 1, bottom panel). Nevertheless, such a process implies difference in the extent of the convergence along the coasts but not in the main pathways and leakage relationships between basins.

In the simulation including Stokes drift, the pathway towards the South Pacific (very clear in Figure 2, top panel) was changed into a pathway towards the South Atlantic, which is more consistent with the results of [START_REF] Maximenko | Pathways of marine debris derived from trajectories of Lagrangian drifters[END_REF]. To illustrate these connections, the trajectories of two particles chosen from the 31% of particles that travel from the South Indian to the South Atlantic, and which are representative of the typical trajectories observed for this set, are shown in orange in Figure 4

Comparison with observed drifter trajectories

We would like to compare our results, at least qualitatively, with some of the actual drifter trajectories of the Global Drifter Program (GDP) database [dataset] (NOAA, 2018). This database gathers the locations of more than 23,022 drifters, which are usually drogued around 15 m [START_REF] Lumpkin | Advances in the application of surface drifters[END_REF]. We only considered the locations of drifters when the drogue was lost, which should better represent surface currents (van der [START_REF] Van Der Mheen | Role of indian ocean dynamics on accumulation of buoyant debris[END_REF]. Undrogued drifters trajectories are unfortunately also subject to windage, but these are the only observations we have with a global coverage, and the most closely related to surface debris. What are the trajectories of undrogued drifters that cross the South Indian Basin and that end in the South Atlantic or in the South Pacific? Using the basin boxes as defined in Figure 2, 1775 drifters cross the South Indian Basin at some point in their lifetimes: 25 of them end in the South Atlantic Basin and 9 in the South Pacific. Because there are so few of them, we have extended the South Atlantic and South Pacific boxes to cover the entire width of the basins (Figure 5). Then, 44 drifters end in the South Atlantic, and 49 in the South Pacific. Digging through the detailed trajectories of these drifters, out of the 44 drifters ending in the South Atlantic, the largest subset, totalling 32 drifters, reaches the Agulhas current from the northeast. Only 6 drifters originated from the eastern part of the Indian Basin and crossed the meridian 60 • E south of 20 • S, and in particular some of them followed the South Equatorial Current approximately eastward around 20 • S. 11 drifters originate from the Indian Ocean north of 20 • S, moving southward mostly along the east coast of Madagascar, and along the Tanzania coast. 10 drifters were already in the Mozambique Channel, and 5 in the western part of the basin between 45 • E and 60 • E. The other subset, totalling 12 drifters, enters the Indian Ocean from the southwest before joining the Agulhas current towards the Atlantic: 7 enters the Indian Ocean eastward through the Agulhas retroflection, before moving towards the Atlantic (4 of them were first in the South Atlantic Basin and 3 in the Cape Basin region); 5 come from the Antarctic Circumpolar Channel south of 45 • S and west of 0 • E, and move northeast into the Indian Basin. The 49 drifters ending in the South Pacific all followed very southern pathways: they crossed the South Indian box only through the southeast corner, and were launched south of 45 • S. The other drifters that cross the South Indian box end mainly in the South Indian box, or on the east coast of Africa or on the south coast of Australia (not shown).

In general, the trajectories of drifters are in good agreement with the trajectories of the Lagrangian model including Stokes drift, as shown in Fig- ure 4. It is clear that most of the selected drifters ending in the South Atlantic originate from the South Indian Basin north of 40 • S, whereas most of the selected drifters ending in the South Pacific originate from the Southern Ocean south of 45 • S (and have entered marginally the South Indian box in its southernmost region). These observations are more consistent with the simulation including Stokes drift, although undrogued drifters are also subject to windage, which is not taken into account in our numerical study, and although there are very few drifters relevant to support robust statistics. Most likely, the life of drifters is too short to see them moving into the South Atlantic or South Pacific (the lifetime is approximately 2 years for a drifter, and an entire loop in the South Indian Basin can take from a few months to a few years), but the South Indian Basin could also leak less rapidly than the simulation suggests.

Convergence and dispersive dynamical balance

To understand the dynamical mechanisms behind this trajectory change in our simulation, we scaled the tracer equation to estimate the length scale L c over which convergence and dispersive processes are balanced. This length scale was computed with one year of C-GLORS surface currents and the resulting map is shown in Figure 6. The white color represents the divergent areas, whereas the color scale shows the L c values computed for the convergent areas. The colorbar has been specially designed to highlight the variations of L c . The black dots represent the center of mass of the particles located in each accumulation zone after 29 years of advection with C-GLORS surface currents (like in Figure 2, top panel). The centers of mass in each accumulation zone are in good agreement with the regions containing the least divergent points and a relatively low value of L c with respect to a given latitude (because of a strong convergence of the mean currents and low values of Eddy Kinetic Energy). L c measures the typical horizontal displacement (in latitude or longitude) of particles subject to the convergence of mean currents and the dispersion by mesoscale turbulence. It could be said that for each location, the least divergent points are encountered in the surrounding region of diameter L c , the more likely particles will stay around that location.

To assess the impact of including Stokes drift, we compared the L c values computed in the reference case (C-GLORS surface currents) with the L c values computed in the case including Stokes drift. The left panel of Figure 7 shows the corresponding maps of L c for the South Indian Basin. When Stokes drift is included, the length scale of the balance is larger around 30 • S and there seems to be more divergent areas. To quantify these visual impressions, some zonal means have been computed and are shown on the right panel. The graph at the top right shows the corresponding L c length scale zonallyaveraged over the width of the South Indian Basin (between 50 and 110 • E) and expressed in degrees

(L c [deg] = L c [km]/Earth_Radius[km] * 180/π).
The figures confirm the overall tendency of higher L c values with Stokes drift. The graph in the middle right shows the probability of encountering a divergent point in an area of radius L c /2, zonally averaged over the width of the South Indian basin. This can be interpreted as a probability of leakage for South Indian particles. Again, the probability is generally higher with Stokes drift. Focussing on the latitude of the centers of mass 30 • S, this probability is slightly higher just north of 30 • S (up to 24 • S) than just south of it. Finally, the graph at the bottom right shows the zonal currents averaged annually and zonally over the width of the South Indian basin. This is interpreted as a direction of leakage. The current velocities are on average and in absolute value higher with Stokes drift. However, it is also noted that the C-GLORS surface current tends to be slightly eastward or almost zero from 40 • S to 20 • S and that this characteristic changes with Stokes drift: the current is now westward north of about 30 • S. These diagnoses are consistent with the particle advection simulation as shown by the particle trajectories (Figure 4). With C-GLORS currents only, South Indian particles follow the "southern pathway" toward the South Pacific via southern Australia. By adding Stokes drift, South Indian particles follow the "northern pathway" toward the South Atlantic via the Indian South Equatorial current and the Agulhas Current. These results highlight the sensitivity of the South Indian Basin surface dynamics. The values of L c , the probability of leakage and the direction of leakage are not very different when considering or not the Stokes drift, but these slight differences altogether cause ultimately a very different fate for floating particles.

Such sensitivity may be related to the specific dynamics of the Southern Indian Ocean. First, the signature of the eastward South Indian Ocean Countercurrent [START_REF] Palastanga | Flow structure and variability in the subtropical Indian Ocean: Instability of the South Indian Ocean countercurrent[END_REF] is not obvious in terms of surface velocities (Figure 1 and7c), and is mostly compensated by wind-driven Ekman velocities. Then, the latitude band extending roughly from 18 • S to 30 • S shows a high level of Eddy Kinetic Energy, associated with prominent longlived mesoscale structures propagating westward, the South Indian Ocean eddies [START_REF] Dilmahamod | SIDDIES corridor: A major east-west pathway of long-lived surface and subsurface eddies crossing the subtropical south indian ocean[END_REF], that could also contribute to the westward advection of particles.

Discussion and conclusion

Using drifter buoy data, [START_REF] Maximenko | Pathways of marine debris derived from trajectories of Lagrangian drifters[END_REF][START_REF] Van Sebille | Origin, dynamics and evolution of ocean garbage patches from observed surface drifters[END_REF] have shown that floating debris tend to accumulate mainly in the subtropics. Their prediction of the location of accumulation zones is fairly consistent with the few available observations [START_REF] Cózar | Plastic debris in the open ocean[END_REF]van Sebille et al., 2015). In particular, their South Indian accumulation zone lies on the western side of the basin and leaks towards the South Atlantic. Using surface currents from an ocean-model reanalysis, [START_REF] Maes | A Surface "Superconvergence" Pathway Connecting the South Indian Ocean to the Subtropical South Pacific Gyre[END_REF] have also found five accumulation zones in the subtropics but their dynamics of the accumulation zone in the South Indian is very different: the accumulation zone is located on the eastern side of the basin and particles leak towards the South Pacific. To understand which physical processes are responsible for this change of behaviour in the South Indian, we investigated the influ-ence of Stokes drift which impacts undrogued drifters and surface floating debris. The main idea is to reconcile the different models and datasets used in previous studies, according to the physical processes taken into account.

The inclusion of Stokes drift has indeed a major effect on the fate of particles in the South Indian Ocean. The particles initially in the Indian Basin no longer travel eastward to the South Pacific but westward to the South Atlantic: they are entrained westward by the Indian South Equatorial Current at 20 • S, they reach and follow southward the east coast of Madagascar, follow the Agulhas Current, and end in the South Atlantic. We rationalized this change in dynamics by constructing a characteristic length scale L c over which the convergence and the diffusion processes are balanced. The maps of this length scale correspond fairly well to the center of mass of the accumulation zones. The account of Stokes drift has increased both L c and the probability of encountering divergent points within a radius of scale L c . It also favored a slightly higher probability of leakage north of 30 • S than south, associated with stronger westward average current in this area. These diagnoses are consistent with the results of particle advection simulations. Overall, it mainly shows that particles dynamics in the South Indian Basin is very sensitive and that particles from the South Indian could either stay in the South Indian Basin or take the southeast pathway to the South Pacific, or take the northwest pathway towards the South Atlantic.

In our simulation, the Stokes drift changed the balance in the South Indian Basin from an entrainment of floating particles to the east to an entrainment to the west. More generally, the inclusion of Stokes drift causes particles to change their trajectory or to be more entrained on the northwest pathway if they are already following it. However, Stokes drift influences only the upper few meters at the ocean surface [START_REF] Clarke | The relationship of near-surface flow, Stokes drift and the wind stress[END_REF], for instance), and the vertical distribution of microplastics over the global ocean is largely unknown [START_REF] Kukulka | The effect of wind mixing on the vertical distribution of buoyant plastic debris[END_REF][START_REF] Reisser | The vertical distribution of buoyant plastics at sea: an observational study in the North Atlantic Gyre[END_REF][START_REF] Song | Horizontal and Vertical Distribution of Microplastics in Korean Coastal Waters[END_REF]. Consequently a significant fraction of the microplastics in the subsurface layer are probably not influenced by Stokes drift and follow more generally the surface currents (the wind-driven Ekman component decreases with depth on a scale of about 10 m, much larger than Stokes drift). It is thus useful to keep in mind the different scenarios of particle advection by surface currents with and without Stokes drift.

Our results, which use only particle trajectories, are consistent with the recent and thorough analyses by [START_REF] Van Der Mheen | Role of indian ocean dynamics on accumulation of buoyant debris[END_REF], which used different transport matrices based on surface and 15 m deep dynamics to explain the particular sensitivity of the Southern Indian convergence region. Both studies confirm that more realistic surface currents (including Stokes drift) lead to a convergence region of the Southern Indian located west of the basin, and leaking towards the South Atlantic Basin. Our main addition to their results is that Stokes drift alone is sufficient to move the Southern Indian convergence region westward, whereas their study associates Stokes drift with a 1% wind speed windage. According to their study, changes in mean surface currents are sufficient to explain this displacement. In our simulations, the Indian convergence region does not appear as dispersive as in their results, and this is still obvious after 30 years of integration (maybe because the leakage to the Atlantic is weaker in our simulations) -in any case, this appears to be a second-order issue for microplastics, since a large and regular supply of debris from the Asian coasts of the Indian Basin continuously feeds this convergence region against dispersion and leakage to the South Atlantic. However, a comparison with their figures shows that the variability of currents (as opposed to their use of a single "mean" transport matrix) is important to support "super"-convergence routes to other ocean basins [START_REF] Maes | A Surface "Superconvergence" Pathway Connecting the South Indian Ocean to the Subtropical South Pacific Gyre[END_REF]. This variability is also important to allow the connection between the North Indian Basin (where large quantities of plastics enter the ocean) and the South Indian Basin, apparently a significant difference with their simulations (not shown here).

Although our study provides a good a priori knowledge of the impact of Stokes drift, it suffers from several approximations that should be addressed. The first approximation stems from the method chosen to include the Stokes drift itself. This additive method, based on the available reanalysis datasets, does not meet the 3D non-divergence criterion and assumes that no Stokes drift is present in the surface currents of the C-GLORS reanalysis, which cannot be fully verified because of the assimilation of surface data. The second approximation stems from the interpolation and daily average of Stokes drift on the C-GLORS space-time grid. The signal loss due to these smoothings should be quantified more precisely. As a first step, we ran an additional Ariane experiment where surface currents including Stokes drift were provided every 3-hours instead of every day: they were computed from the daily C-GLORS surface currents and 3-hour surface Stokes drift, as provided by the IOWAGA reanalysis, and simply interpolated to the C-GLORS spatial grid as described earlier. The results were surprisingly similar to those presented above for convergence regions and pathways. The only difference we have seen is a significant decrease in the number of particles that get stuck at the coast (57% after 29 yr of advection with the 3-hourly currents, instead of 67% with the daily currents), which could result from the stronger variability at high frequencies.

On the same topic, [START_REF] Maes | A Surface "Superconvergence" Pathway Connecting the South Indian Ocean to the Subtropical South Pacific Gyre[END_REF] pointed out that eddy mesoscale variability is essential for constructing exit routes from the South Indian Basin. The grid used for the C-GLORS reanalysis is at 1/4 • and is therefore only eddy permitting, not eddy resolving. The impact of current fields at a finer resolution, in such a sensitive region, is probably not negligible and should also be quantified (for instance Wang et al., 2015, about the Agulhas leakage and transport through the South Atlantic). In our simulation, we inseminated particles only at a given initial time and homogeneously over the whole ocean, but the sources of floating plastic debris are not homogeneously distributed. In order to make a consistent comparison with observation maps of floating debris, we should now adapt our particle input scenario to a more realistic scenario, both in space and time. The sensitivity of the trajectories of surface floating debris in the Indian Basin is particularly important to understand and quantify, as a large fraction of the world's plastic waste is released in this area, with significant input from Asian rivers [START_REF] Lebreton | Evidence that the Great Pacific Garbage Patch is rapidly accumulating plastic[END_REF]) and a very large population living on its coasts.

Figure 1 :

 1 Figure 1: Annual-mean C-GLORS surface currents (top panel) and IOWAGA surface Stokes drift (bottom panel), represented by arrows, and their divergence (shown by the color scale, convergence in red, divergence in blue). The average was made for the year 2014. The colored boxes are the boundaries of the areas used to compute the mean divergence in the main accumulation zones, as discussed in the text.

  . The corresponding reference particle trajectories (without Stokes drift) are displayed in light blue. Adding Stokes drift, Indian particles tend to flow northeast to 20/15 • S where they are entrained westward by the Indian South Equatorial Current to the east coast of Madagascar. They follow this east coast southward to the Cape Basin and into the South Atlantic. South of the Cape Basin, some particles are taken back northeastward by the South Indian Current or by the Antarctic Circumpolar Current and make one or two additional loops in the Indian Basin before ending up in the South Atlantic. Without Stokes drift, these particles tend to follow the Eastern Gyral Current to the west coast of Australia, then the Leeuwin Current along the west and south coasts of Australia. By adding Stokes drift in our simulation, the main changes are a northwesterly drift in the 35 • -20 • S latitude band and a westward entrainment by the Indian South Equatorial Current between 20 • -15 • S, that finally result in the export of the Indian particles to the South Atlantic Basin.

Figure 2 :

 2 Figure 2: Lagrangian advection of particles by C-GLORS surface current velocities (top panel) and by the IOWAGA Stokes drift added to C-GLORS surface current velocities (bottom panel). These maps show the particle concentration after 29 years of a simulation that cycled 5 times over the 2010-2016 period for input velocities. The color scale is the concentration of particles per grid cell with a log scale. The colored boxes are boundaries used to display the time evolution of the particles and to compute the percentage of particles from the South Indian Basin ending in the South Atlantic or the South Pacific Basin. The black dots represent the center of mass of the concentration of particles in each colored box.

Figure 3 :

 3 Figure 3: Time evolution of the number of particles in each convergence zone of the Southern Hemisphere when advected by C-GLORS surface currents only (dashed lines) or by the IOWAGA surface Stokes drift added to C-GLORS surface currents (plain lines). Beached particles (next to a land point) are excluded from the count. It should be noted that a much higher number of particles are blocked along the coast in regions with strong inshore currents when Stokes drift is added (67% totalling 610,000, compared to 20% totalling 182,000 without Stokes drift), hence reducing the total number of particles potentially trapped in convergence regions.

Figure 4 :

 4 Figure 4: Typical examples of particle trajectories with C-GLORS + surface Stokes drift velocities in orange, and with C-GLORS surface currents only in light blue. The green circles indicate the initial locations and the red squares the final locations after 29 years of advection. The particles were chosen from the 31% (23%) of particles initially located in the South Indian Basin and ending in the South Atlantic Basin (South Pacific) with (without) Stokes drift.

Figure 5 :

 5 Figure 5: Trajectories of undrogued drifters from the GDP database for drifters that have crossed the South Indian Basin as defined by the green box and that end in the South Atlantic (blue box) or South Pacific (red box). The green circle markers indicate the initial locations, and the red square markers the final locations. The jet colorscale is used to distinguish the trajectories of drifters: The warm (cold) side of the colorscale is used for drifters ending in the South Atlantic (South Pacific).

Figure 6 :

 6 Figure 6: Characteristic length L c (in km) over which the convergence and diffusion processes are balanced for the C-GLORS surface currents. The data were smoothed in space over distances of 1 • . The white color corresponds to positive divergence values. The black dots correspond to the centers of mass of the accumulation in each convergence zone for year 29 of the corresponding simulation of particle advection by the Ariane software (as defined in Figure 2).

Figure 7 :

 7 Figure 7: Probability and direction of leakage in the South Indian. Left panels: Characteristic length scale L c (in km) over which convergence and diffusion processes are balanced for the C-GLORS (top) and C-GLORS + Stokes drift (bottom) surface currents in the South Indian Basin. Right panels: zonal means over the South Indian Basin for a) the characteristic length L c of the balance between convergence and diffusion; b) the probability of encountering a divergent point on a disk with a radius L c /2; c) the zonal velocity averaged zonally and annually.

Acknowledgements

This work was supported by the French National Research Agency (ANR) under the "Programme d'Investissements d'Avenir" ISblue (ANR-17-EURE-0015) and by LabexMER (ANR-10-LABX-19). We are grateful to Andrea Cipollone (CMCC) for providing the CGLORS surface current dataset, Mickael Accensi for his help with the IOWAGA dataset and Xavier Couvelard for interactions on coupled ocean-wave models.

A global inventory of small floating plastic debris. Environmental Research Letters 10 (12), 124006.

Wang, Y., Olascoaga, M. J., Beron-Vera, F. J., 2015. Coherent water transport across the south atlantic. Geophys. Res. Let. 42,[4072][4073][4074][4075][4076][4077][4078][4079]


Sabine Messayke
email: sabine.messaykeh@inserm.fr

Patricia Franco
email: patricia.franco@univ-lyon1.fr

Anne Forhan
email: anne.forhan@inserm.fr

Marie-Noëlle Dufourg

Marie-Aline Charles
email: marie-aline.charles@inserm.fr

Sabine Plancoulaine
email: sabine.plancoulaine@inserm.fr

Sleep habits and sleep characteristics at age 1 year in the ELFE birth cohort study

Keywords: Infant, sleep duration, night waking, sleep onset difficulty, birth-cohort, epidemiology Highlights

Introduction

Sleep is a vital physiological function and sleep needs vary depending on age. American recommendations for healthy sleep duration are from 14 to 17 h over a 24-h period for a newborn, 12 to 16 h including 2 to 3 naps from age 3 to 11 months, 11 to 14 h including 1 or 2 naps by age 1 to 2 years, and 10 to 13 h with or without a nap from age 3 to 5 years [START_REF] Paruthi | Recommended Amount of Sleep for Pediatric Populations: A Consensus Statement of the American Academy of Sleep Medicine[END_REF].

However, infants and toddlers (≤ 3 years old) are susceptible to sleep problems including sleep onset difficulties and night waking, with an estimated prevalence from 20% to 30% [START_REF] Tikotzky | Parenting and sleep in early childhood[END_REF][START_REF] Sette | Predictors of sleep disturbances in the first year of life: a longitudinal study[END_REF][START_REF] Byars | Prevalence, patterns, and persistence of sleep problems in the first 3 years of life[END_REF][START_REF] Blair | Childhood sleep duration and associated demographic characteristics in an English cohort[END_REF][START_REF] Reynaud | Night-waking and behavior in preschoolers: a developmental trajectory approach[END_REF][START_REF] Reynaud | Night-waking trajectories and associated factors in French preschoolers from the EDEN birth-cohort[END_REF][START_REF] Ottaviano | Sleep characteristics in healthy children from birth to 6 years of age in the urban area of Rome[END_REF]. In an English birth cohort of 11,500 children born in 1991-1992, night waking prevalence was 23% for infants at 6 months and 50% at 18 months and 49% for toddlers at 3 years [START_REF] Blair | Childhood sleep duration and associated demographic characteristics in an English cohort[END_REF].

In 1,346 children from the French EDEN birth cohort study, night waking prevalence was 22% for toddlers aged 2 years old [START_REF] Reynaud | Night-waking and behavior in preschoolers: a developmental trajectory approach[END_REF][START_REF] Reynaud | Night-waking trajectories and associated factors in French preschoolers from the EDEN birth-cohort[END_REF]. Ottaviano et al. also reported a night waking prevalence of 35% in toddlers < 2 years old, and a prevalence of sleep onset difficulties of 11% and 7.5% at age 12 and 24 months, respectively [START_REF] Ottaviano | Sleep characteristics in healthy children from birth to 6 years of age in the urban area of Rome[END_REF]. These sleep problems may lead to shorter sleep durations [START_REF] Hysing | Trajectories and predictors of nocturnal awakenings and sleep duration in infants[END_REF]. Sleep problems in early childhood are persistent over time into later childhood, adolescence and adulthood [START_REF] Mamun | Continuity and discontinuity of trouble sleeping behaviors from early childhood to young adulthood in a large Australian community-based-birth cohort study[END_REF][START_REF] Quach | Outcomes of child sleep problems over the school-transition period: Australian population longitudinal study[END_REF][START_REF] Sivertsen | Trajectories of sleep problems from childhood to adolescence: a population-based longitudinal study from Norway[END_REF]. There is much evidence that insufficient quantity and/or quality of sleep have a negative impact on children's physical and mental health development, cognitive function, behavior and academic success [START_REF] Reynaud | Sleep and its relation to cognition and behaviour in preschool-aged children of the general population: a systematic review[END_REF][START_REF] Chaput | Systematic review of the relationships between sleep duration and health indicators in the early years (0-4 years)[END_REF].

Low parental education and incomes, unfavorable neighborhood [START_REF] Singh | Rising Prevalence and Neighborhood, Social, and Behavioral Determinants of Sleep Problems in US Children and Adolescents, 2003-2012[END_REF], high maternal age, maternal depression [START_REF] Toffol | Maternal depressive symptoms during and after pregnancy are associated with poorer sleep quantity and quality and sleep disorders in 3.5-year-old offspring[END_REF][START_REF] Bat-Pitault | Altered sleep architecture during the first months of life in infants born to depressed mothers[END_REF], prematurity [START_REF] Blair | Childhood sleep duration and associated demographic characteristics in an English cohort[END_REF][START_REF] Pena | Racial/Ethnic and Socio-Contextual Correlates of Chronic Sleep Curtailment in Childhood[END_REF][START_REF] Mcdonald | Predictors of shorter sleep in early childhood[END_REF], low birth weight [START_REF] Blair | Childhood sleep duration and associated demographic characteristics in an English cohort[END_REF][START_REF] Mcdonald | Predictors of shorter sleep in early childhood[END_REF], breastfeeding [START_REF] Tikotzky | Parenting and sleep in early childhood[END_REF][START_REF] Hysing | Trajectories and predictors of nocturnal awakenings and sleep duration in infants[END_REF], and care outside the home [START_REF] Reynaud | Night-waking trajectories and associated factors in French preschoolers from the EDEN birth-cohort[END_REF][START_REF] Nevarez | Associations of early life risk factors with infant sleep duration[END_REF] have been found associated with infant and childhood sleep disturbances. Moreover, longitudinal studies within the first year of life suggest that parental sleep-related behaviors (e.g., needing the parent's presence to fall asleep, feeding before sleep and infant's sleep arrangement and location) play a critical role in the consolidation of the infant's sleep [START_REF] Tikotzky | Parenting and sleep in early childhood[END_REF][START_REF] Hysing | Trajectories and predictors of nocturnal awakenings and sleep duration in infants[END_REF][START_REF] Paul | Mother-Infant Room-Sharing and Sleep Outcomes in the INSIGHT Study[END_REF][START_REF] Mindell | Sleep location and parent-perceived sleep outcomes in older infants[END_REF]. However, few studies were performed of infants in large representative cohorts [START_REF] Blair | Childhood sleep duration and associated demographic characteristics in an English cohort[END_REF][START_REF] Hysing | Trajectories and predictors of nocturnal awakenings and sleep duration in infants[END_REF][START_REF] Huang | An Linc and Liu Xi-chengb. The epidemiology of sleep and its disorder in Chinese children aged 0-5 years[END_REF][START_REF] Mindell | Developmental aspects of sleep hygiene: findings from the 2004 National Sleep Foundation Sleep in America Poll[END_REF][START_REF] Connor | Prenatal mood disturbance predicts sleep problems in infancy and toddlerhood[END_REF], and none in France. Hence, we aimed to describe infant sleep characteristics (sleep duration, night waking, and sleep onset difficulties) and to identify their relationships with sleep habits in a French representative sample of infants aged 1 year old.

Materials and Methods

Study design

This analysis was based on data from the "Étude Longitudinale Française depuis l'Enfance" (ELFE) study, a prospective multidisciplinary nationally representative birthcohort study. It included 18,329 newborn infants in a random sample of 349 maternity units in 2011 [START_REF] Vandentorren | Rationales, design and recruitment for the Elfe longitudinal study[END_REF] with the aim of examining many aspects of the lives of these children from the perspectives of health, social sciences and environmental health up to adulthood. Inclusion took place during 25 selected recruitment days over four waves comprising 4 to 8 days each, from April 2011 and covering all four seasons. Inclusion criteria were infants born after 33 weeks' gestation to mothers ≥ 18 years older and who were not planning to move outside of Metropolitan France in the following 3 years and were able to read French, Arabic, Turkish, or English. Participating mothers signed consent for themselves and their infant. Fathers signed consent for the infant's participation when present on inclusion days or were informed about their rights to oppose. The participation rate was 51%.

Data Collection

Data were collected by trained interviewers via standardized questionnaires, all previously tested in a pilot study [START_REF] Vandentorren | Rationales, design and recruitment for the Elfe longitudinal study[END_REF]. Each mother was interviewed face to face at the maternity unit for medical information about their pregnancy, their newborn and their general characteristics. Information was completed with records from obstetric and pediatric medical files. Telephone interviews with mothers and fathers were conducted at 2 and 12 months post-partum. Data were taken from the maternal questionnaires; missing maternal answers were completed by the fathers when available.

Participants

Infants whose parents withdrew consent within the first year (n = 128) or for whom it was not possible to verify the eligibility criteria due to missing data (n = 407) were excluded from the study, thereby resulting in 17,794 eligible infants among the 18,329 infants initially included. We excluded twins (n = 548), preterm infants (< 37 weeks' amenorrhea) (n = 756), families who did not reply at the 12-month follow-up (n = 3,705), infants without any data on sleep characteristics at 12 months (i.e., sleep onset difficulties, night waking and sleep duration [n = 247]) and those without any data on sleep habits (parental presence when falling asleep, feeding to fall asleep, sleeping with a blankie, use of pacifier or finger sucking before sleep, infant sleep arrangement, and sleeping in parent's room (n = 64). The final sample was 12,474 infants. The complete-case sample, excluding individuals with missing data for co-variables (n = 691), was 11,783 infants.

Infant sleep characteristics at age 1

Sleep onset difficulties were assessed by the question "When you put your infant to bed, does he/she have trouble falling asleep; for example, he/she calls or cries for a long time?" Possible answers were "never, "sometimes", "often". Night waking was assessed by the question "On this week, how many nights did your baby wake up (if the infant was sick this week, answer for a week without illness)?" Possible answers were "never", "1 or 2 nights", "3 to 6 nights", and "every night"). Night waking was classified into 3 groups: never, 1-2 nights/week, and > 2 nights/week, and the last category was considered frequent. Total sleep duration / 24 h was calculated based on the answers (in hours and minutes) to the following questions: "On average, what is his/her total sleep time during night?" and "On average, what is the total duration of his/her day sleep. Add up all his/her naps in one day".

Total sleep duration was then classified into 3 groups: ≤12h/24h, 13-14h/24h, and >14h/24h.

The threshold of 12h/24h was used to define short sleep duration because of sample size issues (7% of the infants slept <11h/24h at age 1 in the cohort).

Infant sleep habits at age 1

Information on sleep environment and habits were collected at 1 year. Pacifier or finger sucking before sleep (coded as none, pacifier only, thumb/finger only, both pacifier and thumb/finger), feeding to fall asleep (yes/no), sleeping with a blankie (yes/no), parental presence when falling asleep (yes/no), sleep arrangement (i.e., where the infant falls asleep and ends sleep: falls asleep and ends sleep in own bed/crib, falls asleep in own bed/crib but ends it in parent's bed, falls asleep and ends sleep elsewhere than in own bed/crib), and sleeping in parent's room (yes/no).

Parental and infant general characteristics

Data were collected at the maternity ward, at 2 months and at age 1 year. Information collected during the maternity stay was used only in case of missing values at 2 months. These data concerned maternal birth place (born in France or abroad), maternal age at delivery (<25, 25-29, 30-34 and >34 years), maternal psychological difficulties during pregnancy (yes/no), first child (yes/no), birth weight and child's sex. At 1 year, the consumption unit (CU) was calculated as the number of family cohabitants weighted according to the age of each co-habitant (<14 or ≥14 years old). The familial income by CU at age 1 year was then classified as ≤850, 851-1200, 1201-1700, 1701-2200, 2201-3000, >3000 €/month/CU. Additional information was collected on maternal education attainment (lower secondary, upper secondary, intermediate, higher), infant age at the interview and infant exposure to tobacco smoke, based on the question "Currently, is your child exposed to tobacco smoke?". Predominant breastfeeding duration (i.e., the only milk received was breast milk) was considered never, <3 months, 3 to 6 months, >6 months [START_REF] De Lauzon-Guillain | Use of infant formula in the ELFE study: The association with social and health-related factors[END_REF]. Main childcare arrangement at age 1 year was coded as community child-care (i.e., preschool, nursery school, or child-care center), nursery assistant (external to the infant's home) and home child-minding (parents, family member, neighbors, or nanny).

Statistical methods

To provide representative descriptive statistics for birth in 2011 in France, descriptive data (percentages for discrete variables and means for continuous variables) were weighted to take into account the inclusion procedure and biases related to non-consent. Weighting also included calibration on margins from the state register's statistical data and the 2010 French National Perinatal study [START_REF] Blondel | Trends in perinatal health in France from 1995 to 2010. Results from the French National Perinatal Surveys[END_REF] on the following variables: age, region, marital status, migration status, level of education, and parity. Potential confounding factors included in the models were identified from the literature and were selected with the Directed Acyclic Graphs method (www.dagity.net). Associations between sleep habits and each sleep characteristic (total sleep duration, night waking and sleep onset difficulties) were tested by multinomial logistic regression analyses adjusted for infant and parental characteristics and recruitment specificities (mother's region of residence, size of maternity unit and wave of recruitment).

The significance level was set at p ≤ 0.05. All analyses were performed with SAS 9.3 (SAS, Cary, NC, on an AIX 7.1 platform).

Results

Preliminary analyses

Among 18,329 infants enrolled at birth, 11,783 (64%) met the inclusion criteria and had complete information for covariates. As compared with non-included infants (n = 6,546), included infants were more frequently born to a mother with a higher educational level (41% vs 26%, p <10 -4 ), age 30 to 34 years old (39% vs 31%, p <10 -4 ) and fewer psychological difficulties during pregnancy (12% vs 14%, p = 0.0002). In addition, included infants more frequently lived in a family with higher income/CU (47% vs 35% with income/CU>1700 €, p <10 -4 ).

Population description

General characteristics of the studied population are in Table 1. Overall, 49% of infants were girls, 43% were first born. At age 1 year, about half of the infants were homeminded and 4% were exposed to tobacco smoke.

Sleep characteristics at 1 year

The total mean sleep duration at 1 year was 13h36min, with mean nocturnal and daytime sleep duration 10h42min and 2h54min, respectively (Table 2). In total, 20% of the infants slept ≤12h/24h, 22% presented frequent night waking (>2 nights/week) and 16% presented frequent sleep onset difficulties. Also, 29% and 10% of the infants required the parental presence and feeding to fall asleep, respectively and 82% used a blankie; 49% sucked only a pacifier, 19% sucked only their thumb/finger and 23% sucked both a pacifier and thumb/finger. About 9% of the infants did not fall asleep nor end their sleep in their bed/crib and about 15% slept in the parent's room.

Associations between sleep characteristics and sleep habits at age 1 year

Table 3 presents the adjusted results.

Factors associated with infant total sleep duration per 24h at age 1 year

Both parental presence when falling asleep and ending the night elsewhere than in their own bed/crib were more likely with short sleep duration (≤12h/24h) and less likely with long sleep duration (>14h/24h) as compared with 13-14h/24h sleep. With reference to the same group, feeding to fall asleep was more likely with short sleep duration; non-nutritive sucking (pacifier, thumb/finger or both) was more likely with long sleep duration, and sleeping with a blankie was less likely with short sleep duration. In addition, probabilities of night waking and sleep onset difficulties were increased with short sleep duration, with a dose effect. Sleeping in the parents' room was not associated with infant sleep duration.

Factors associated with infant night waking at age 1 year

Parental presence, being fed to fall asleep and sucking a pacifier with or without the thumb/finger were more likely with night waking as compared with never night waking, whereas the need for a blankie was more likely with only a few nights waking (1-2 nights/week). Sleeping elsewhere than in the infant's bed/crib was associated with risk of night waking; falling asleep in the bed/crib and ending it elsewhere was more frequent with both few (1-2 nights/week) and frequent (>2 nights/week) night waking; and falling asleep and ending it elsewhere was associated with only frequent night waking as compared with not waking. Sleeping in parents' room was more likely with frequent night waking. Short sleep duration and sleep onset difficulties were both positively associated with night waking frequencies, and long sleep duration (>14h/24h) decreased the risk of night waking.

Factors associated with infant sleep onset difficulties at age 1 year

Parental presence was associated with sleep onset difficulties. Feeding to fall asleep was also associated with frequent sleep onset difficulties as compared with never having sleep onset difficulties. In reference to the same group, sucking both the thumb/finger and the pacifier was associated with risk of few sleep onset difficulties, whereas sucking the thumb/finger only was associated with lower risk of sleep onset difficulties but not significantly. Falling asleep in the infant's own bed/crib but ending it in the parents' bed was associated with increased risk of sleep onset difficulties whatever the frequency, and falling asleep and ending sleep elsewhere than in the infant's own bed/crib was associated with reduced risk of few sleep onset difficulties but increased risk of frequent sleep onset difficulties. Sleeping in the parents' room was not associated with infant sleep onset difficulties.

Discussion

Total sleep duration

The analysis of the ELFE cohort data showed mean sleep durations of 13h36min/24h and 10h42min/night in 1-year-old French infants. This latter result is similar to the mean night-sleep duration observed in 1,255 children 2 to 5 years old in the French EDEN motherchild cohort recruited in 2003-2006 [START_REF] Plancoulaine | Night sleep duration trajectories and associated factors among preschool children from the EDEN cohort[END_REF]. Although these results agree with the American Academy of Sleep Medicine recommendations for the pediatric population to promote optimal health [START_REF] Paruthi | Recommended Amount of Sleep for Pediatric Populations: A Consensus Statement of the American Academy of Sleep Medicine[END_REF], the mean sleep durations in the current study were in the higher limits of their recommendations and were higher than those in other studies [START_REF] Blair | Childhood sleep duration and associated demographic characteristics in an English cohort[END_REF][START_REF] Galland | Normal sleep patterns in infants and children: a systematic review of observational studies[END_REF][START_REF] Hense | Factors that influence weekday sleep duration in European children[END_REF][START_REF] Williams | Norms and trends of sleep time among US children and adolescents[END_REF]. These latter studies from England [START_REF] Blair | Childhood sleep duration and associated demographic characteristics in an English cohort[END_REF], European countries (excluding France) [START_REF] Hense | Factors that influence weekday sleep duration in European children[END_REF] and the United States [START_REF] Williams | Norms and trends of sleep time among US children and adolescents[END_REF] as well as a systematic review [START_REF] Galland | Normal sleep patterns in infants and children: a systematic review of observational studies[END_REF] reported total sleep durations between 12h40min and 13h/24h at about age 1 year, so French parents report that their infants sleep 30-60min/24h more than their counterparts in developed countries. In a recent literature review of sleep in the first 12 months of life, sleep duration per 24 h tended to be longer in European countries than in the United States, New Zealand, Israel and China [START_REF] Dias | Reference values and changes in infant sleepwake behaviour during the first 12 months of life: a systematic review[END_REF]. In the current study, this difference seems to be mainly due to longer nap duration in French children but similar nightsleep duration as their peers.

Sleep duration is the most-studied sleep characteristic. A couple of sleep habits observed associated with infant's shorter sleep duration in the current study have been reported in infants and toddlers, namely, parental presence when falling asleep [START_REF] Ottaviano | Sleep characteristics in healthy children from birth to 6 years of age in the urban area of Rome[END_REF][START_REF] Plancoulaine | Night sleep duration trajectories and associated factors among preschool children from the EDEN cohort[END_REF][START_REF] Yu | Parental behaviors and sleep/wake patterns of infants and toddlers in Hong Kong, China[END_REF], infant sleep arrangements outside his/her own bed during the night [START_REF] Paul | Mother-Infant Room-Sharing and Sleep Outcomes in the INSIGHT Study[END_REF][START_REF] Sadeh | Sleep and sleep ecology in the first 3 years: a web-based study[END_REF], feeding to fall asleep [START_REF] Plancoulaine | Night sleep duration trajectories and associated factors among preschool children from the EDEN cohort[END_REF][START_REF] Yu | Parental behaviors and sleep/wake patterns of infants and toddlers in Hong Kong, China[END_REF], and frequent night waking and sleep onset difficulties [START_REF] Plancoulaine | Night sleep duration trajectories and associated factors among preschool children from the EDEN cohort[END_REF]. Sleeping in the parents' room was not associated with sleep duration, which suggests that even if parents who share the room with their infant might be more aware of disrupted sleep, they do not report different sleep durations as compared with parents whose infant sleeps in another room, as was observed at this age by others [START_REF] Paul | Mother-Infant Room-Sharing and Sleep Outcomes in the INSIGHT Study[END_REF][START_REF] Yu | Parental behaviors and sleep/wake patterns of infants and toddlers in Hong Kong, China[END_REF]. In this cross-sectional analysis, the observed relations between child's sleep and parental sleep strategies may be bi-directional [START_REF] Sadeh | Parenting and infant sleep[END_REF]. However, interventions modifying the previously cited sleep habits have been found to improve sleep duration in young children [START_REF] Mindell | Developmental aspects of sleep hygiene: findings from the 2004 National Sleep Foundation Sleep in America Poll[END_REF][START_REF] Kahn | Behavioral interventions for pediatric insomnia: One treatment may not fit all[END_REF]. Nevertheless, to our best knowledge, we report for the first time the association of several self-soothing and comforting behaviors with longer sleep duration such as non-nutritive sucking (thumb/pacifier) or the use of a blankie [START_REF] Butler | Pacifier Use, Finger Sucking, and Infant Sleep[END_REF].

Night waking

The prevalence of frequent night waking (>2 nights/week) as declared by parents concerned 22% of 1-year old French infants. This finding is globally similar to previous findings, especially those obtained in the EDEN French cohort for 2-year-old children (22%) [START_REF] Reynaud | Night-waking trajectories and associated factors in French preschoolers from the EDEN birth-cohort[END_REF], the Richman et al. study (20% for toddlers between age 1 and 2 years) [START_REF] Richman | Sleep problems in young children[END_REF], and the Bruni et al. study (17.5% for Italian infants aged 12 months with >2 wakings/night) [START_REF] Bruni | Longitudinal study of sleep behavior in normal infants during the first year of life[END_REF]. However, our results were lower than those reported in the United States for about 1,200 children from 0 to 36 months old (31% at 15 months and 28% at 24 months) [START_REF] Weinraub | Patterns of developmental change in infants' nighttime sleep awakenings from 6 through 36 months of age[END_REF] and those reported in Italy for 563 infants (34% for infants aged 13 to 24 months) [START_REF] Ottaviano | Sleep characteristics in healthy children from birth to 6 years of age in the urban area of Rome[END_REF].

As for shorter sleep duration, parental presence [START_REF] Reynaud | Night-waking trajectories and associated factors in French preschoolers from the EDEN birth-cohort[END_REF][START_REF] Ottaviano | Sleep characteristics in healthy children from birth to 6 years of age in the urban area of Rome[END_REF][START_REF] Yu | Parental behaviors and sleep/wake patterns of infants and toddlers in Hong Kong, China[END_REF], feeding to fall asleep [START_REF] Reynaud | Night-waking trajectories and associated factors in French preschoolers from the EDEN birth-cohort[END_REF][START_REF] Yu | Parental behaviors and sleep/wake patterns of infants and toddlers in Hong Kong, China[END_REF], and ending the night elsewhere than in the infant's own bed/crib [START_REF] Hysing | Trajectories and predictors of nocturnal awakenings and sleep duration in infants[END_REF] were associated with frequent night waking. Results on parents' room sharing suggest that room-sharing parents reported more night waking because they were more aware of them. Also, parents of infants with sleep troubles may prefer to have infants closer to them at night. However, then we would expect that room sharing would also be associated with other sleep troubles such as shorter sleep duration or sleep onset difficulties, which were not observed. Infants sucking a pacifier (with or without finger/thumb) were at increased risk of night waking per week whatever the frequency as compared with non-suckers or finger/thumb only suckers at age 1 year. Only a few studies focused on pacifier use or finger/thumb sucking and sleep at around age 1 year old [START_REF] Butler | Pacifier Use, Finger Sucking, and Infant Sleep[END_REF][START_REF] Balaban | Infant sleep and the influence of a pacifier[END_REF]. They all focused on number of wakings per night. Butler et al. observed an increased number of wakings per night for infants using pacifiers as compared with finger/thumb suckers or non-suckers in a sample of 104 infants aged 0 to 11 months in the United States [START_REF] Butler | Pacifier Use, Finger Sucking, and Infant Sleep[END_REF]. When focusing on older infants (≥ 7 months, n = 57), the authors observed an increased number of wakings per night in pacifier users as compared with thumb/finger users but not as compared with non-suckers. They explained the difference by the ability for pacifier users to find it by themselves in the bed [START_REF] Butler | Pacifier Use, Finger Sucking, and Infant Sleep[END_REF]. Along the same lines, Balaban et al. did not observe any difference in number of wakings per night in pacifier users versus non-suckers in 100 infants aged 5 to 13 months (mean 10.58 months). Discrepancies between our results and these latter studies may result from the night waking scale used (per night vs per week), the statistical analyses performed (distribution comparisons, covariance analyses vs multinomial logistic regressions), the sample size and the confounders taken into account.

Sleep onset difficulties

The prevalence of sleep onset difficulties at age 1 year was 53%, with 16% of infants presenting frequent difficulties. Few studies reported the prevalence of sleep onset difficulties in infants. Jiang [START_REF] Bruni | Longitudinal study of sleep behavior in normal infants during the first year of life[END_REF]. Most of the studies used a clinical definition of problematic sleep latency, but our results are based on a qualitative question, less precise and more susceptible to subjective answers. However, mean sleep latency reported at age 1 year is quite short (14.7 min by Bruni et al. [START_REF] Bruni | Longitudinal study of sleep behavior in normal infants during the first year of life[END_REF] and 9.3 min by Dias et al. [START_REF] Dias | Reference values and changes in infant sleepwake behaviour during the first 12 months of life: a systematic review[END_REF]), and reporting troubles falling asleep with cries and calls for a long time, as we did, might correspond to infants with increased sleep latency that meet the clinical definition.

Several sleep habits were associated with sleep onset difficulties, such as parental presence, and feeding to fall asleep. These elements are known to be associated with sleep problems in young children [START_REF] Tikotzky | Parenting and sleep in early childhood[END_REF][START_REF] Reynaud | Sleep and its relation to cognition and behaviour in preschool-aged children of the general population: a systematic review[END_REF] and infants [START_REF] Mindell | Sleep location and parent-perceived sleep outcomes in older infants[END_REF][START_REF] Bruni | Longitudinal study of sleep behavior in normal infants during the first year of life[END_REF]. Sucking both a pacifier and thumb/finger was associated with more troubles falling asleep as compared with not sucking, whereas sucking a thumb/finger only tended to be associated with fewer sleep onset difficulties, which suggests that finger/thumb sucking may facilitate more sleep than a pacifier. Bulter et al. [START_REF] Butler | Pacifier Use, Finger Sucking, and Infant Sleep[END_REF] also suggested this hypothesis, whereas in the Mauch et al. study in Australia, focusing on the reason and the predictors of pacifier introduction, pacifiers were mainly introduced by first-time mothers to soothe their infant (78.3%) and to help them fall asleep (57.4%) [START_REF] Mauch | Predictors of and reasons for pacifier use in first-time mothers: an observational study[END_REF]. Confirmation of the relation between non-nutritive sucking, especially pacifier use, and sleep onset difficulties is needed.

Strengths and limitations

This study was performed in a large nationwide sample of infants from metropolitan France, and allowed for analysis of current sleep habits within the French general population with good statistical power. However, some limitations need to be recognized. Most of the data and especially sleep data were collected by questionnaires that may possibly lead to misestimations of sleep characteristics. Sleep duration, calculated by using bedtime and wake-up time reflects infant's time in bed; night wakings were those noticed by parents (i.e., signaled by the infants) and reflected infants with difficulties falling back asleep by themselves. These situations may lead to overestimation of sleep duration and underestimation of night waking. Objective sleep measured by actigraphy would have provided better estimations of sleep characteristics but had not been considered in this nationwide study including 13,000 infants because of cost and logistic issues. However, the use of questionnaires for sleep characteristic estimation is a classical approach used in large epidemiological studies [START_REF] Blair | Childhood sleep duration and associated demographic characteristics in an English cohort[END_REF][START_REF] Galland | Normal sleep patterns in infants and children: a systematic review of observational studies[END_REF][START_REF] Williams | Norms and trends of sleep time among US children and adolescents[END_REF][START_REF] Dias | Reference values and changes in infant sleepwake behaviour during the first 12 months of life: a systematic review[END_REF]. Information on co-sleeping (bed-sharing) was not directly available but was approximated by the sleep arrangement. Finally, this was a crosssectional analysis and causality cannot be inferred.

Conclusion

We report data on the infant sleep quality and quantity as well as associated sleep habits from a large national cohort study that can serve as a reference for infants living in France.

The study confirmed known sleep habit factors such as parental presence, feeding to fall asleep and infant sleep arrangement (particularly, ending sleep elsewhere than in their own bed/crib), associated with shorter sleep duration and frequent night waking. It also specifically highlighted sleep habits associated with sleep onset difficulties but also with longer sleep durations (non-nutritive sucking and using a blankie). Notably, most of the sleep habits associated with poor sleep characteristics may be open to change with intervention programs [START_REF] Mindell | Implementation of a nightly bedtime routine: How quickly do things improve? Infant behavior & development[END_REF][START_REF] Honaker | Real-World Implementation of Infant Behavioral Sleep Interventions: Results of a Parental Survey[END_REF]. Longitudinal studies will be needed, when data on follow-ups will be available, to analyze the persistence of the observed relations between early sleep habits, especially self-soothing, and later sleep characteristics or patterns of sleep over time.

However, this work provides additional information to consider when making recommendations related to sleep behaviors. national de la jeunesse et de l'éducation populaire (INJEP). Via the RECONAI platform, it receives a government grant managed by the National Research Agency under the "Investissements d'avenir" programme (ANR-11-EQPX-0038). aOR, adjusted odds ratio; 95% CI, confidence interval. Significant association (p <.05) were highlighted in bold The multiple multinomial logistic regressions (for total sleep duration, night waking and sleep onset difficulties) were adjusted on the recruitment factors (mother's region of residence, wave of recruitment, size of maternity unit), familial characteristics (maternal birth place, maternal age at delivery, maternal psychological difficulties during pregnancy, maternal education attainment, predominant breastfeeding duration, and familial income/consumption unit), and infant characteristics (infant's age during the interview, sex, birth weight, first child, child exposed to tobacco smoke at 1 year, and main childcare arrangement during daytime at age 1 year).

  The study received approvals from the Consultative Committee for the Treatment of Information for Health Research (Comité Consultatif sur le Traitement des Informations pour la Recherche en Santé), the national data protection authority (Commission Nationale Informatique et Libertés), and the National Statistics Council.

  et al. reported a prevalence of sleep latency >30 min (30.6%) in 1,129 Chinese infants aged 1 to 23 months [43] and Ottaviano et al. reported a prevalence of 11% and 7.6% for sleep latency >30 min in Italian children aged 6-12 months and 12-23 months [8]. More recently, Bruni et al. reported bedtime difficulties in 13.8% of Italian infants aged 12 months

Table 1 .

 1 Weighted*general characteristics of the studied ELFE population (N=11,783)

	% or mean §

Table 2 .

 2 Weighted* sleep characteristics and sleep habits for studied ELFE children at age 1 year (N=11,783)

	% or mean §

Table 3 .

 3 Multinomial logistic regression analysis of sleep habits and sleep characteristics at age 1 year in the studied ELFE population (N=11,783)

		Total sleep duration		Night waking		Sleep onset difficulties
		(Reference=13-14h/24h)		(Reference= Never)		(Reference= Never)
		≤ 12 h	> 14 h		1-2 nights/week	>2 nights/week		Sometimes	Often
		N=1,977	N=3,910		N=3,724	N=2,445		N=4,377	N=1,627
		aOR [95% CI]	aOR [95% CI]	P	aOR [95% CI]	aOR [95% CI]	P	aOR [95% CI]	aOR [95% CI]	P
				value			value			value
	Sleep habits
	Parental presence when falling	1.55 [1.35-1.79]	0.63 [0.55-0.72]	<10 -3	1.36 [1.19-1.56]	1.73 [1.49-2.01]	<10 -3	2.10 [1.84-2.39]	12.02 [10.17-14.21]	<10 -3
	asleep (yes)
	Feeding to fall asleep (yes)	1.52 [1.27-1.81]	0.94 [0.77-1.14]	<10 -3	1.50 [1.24-1.81]	2.04 [1.67-2.49]	<10 -3	1.12 [0.93-1.34]	1.27 [1.01-1.59]	0.16
	Sleeping with a blankie (yes)	0.73 [0.62-0.86]	1.08 [0.94-1.24]	<10 -3	1.20 [1.04-1.38]	1.09 [0.92-1.29]	0.05	1.14 [0.99-1.30]	0.90 [0.73-1.10]	0.03
	Use of pacifier or finger sucking			10 -3			<10 -3			<10 -3
	before sleep
	None	Reference	Reference		Reference	Reference		Reference	Reference
	Thumb/finger only	0.97 [0.78-1.19]	1.51 [1.25-1.81]		0.94 [0.78-1.13]	0.86 [0.69-1.07]		0.94 [0.79-1.12]	0.79 [0.61-1.02]
	Pacifier only	1.00 [0.82-1.23]	1.31 [1.09-1.58]		1.32 [1.10-1.59]	1.39 [1.12-1.71]		0.92 [0.77-1.10]	0.84 [0.65-1.08]
	Pacifier and thumb/finger	1.04 [0.83-1.30]	1.38 [1.13-1.68]		1.48 [1.22-1.80]	1.46 [1.16-1.83]		1.22 [1.01-1.47]	1.08 [0.82-1.42]
	Infant sleep arrangement			<10 -3			<10 -3			<10 -3
	Falls asleep and ends sleep in own	Reference	Reference		Reference	Reference		Reference	Reference
	bed/crib
	Falls asleep in own bed/crib but	1.33 [1.14-1.54]	0.78 [0.68-0.91]		2.02 [1.74-2.35]	3.41 [2.92-3.99]		1.66 [1.44-1.91]	2.16 [1.79-2.60]
	ends sleep in parents' bed
	Falls asleep and ends sleep	1.64 [1.35-1.99]	0.68 [0.52-0.88]		1.14 [0.90-1.43]	2.22 [1.78-2.77]		0.50 [0.39-0.64]	1.56 [1.24-1.97]
	elsewhere than in own bed/crib
	Sleeping in parents' room (yes)	1.11 [0.95-1.31]	0.88 [0.75-1.03]	0.06	1.04 [0.89-1.22]	1.33 [1.13-1.58]	0.01	0.90 [0.78-1.05]	0.82 [0.67-1.00]	0.10
	Sleep characteristics
	Total sleep duration in 24h						<10 -3			<10 -3
	≤12 h				1

.20 [1.05-1.37] 1.71 [1.48-1.97] 1.35 [1.18-1.53] 1.91 [1.62-2.25]

	13-14 h	Reference	Reference	Reference	Reference
	> 14 h	0.

85 [0.77-0.93] 0.74 [0.65-0.84] 0.76 [0.7-0.83] 0.52 [0.44-0.62]

	Night waking		<10 -3			<10 -3
	Never	Reference	Reference		Reference	Reference
	1-2 nights/week	1.22 [1.07-1.39]	0.85 [0.77-0.93]		1.62 [1.48-1.78]	1.72 [1.46-2.03]
	> 2 nights/week	1.73 [1.50 -2.00]	0.74 [0.65-0.84]		2.05 [1.82-2.31]	3.52 [2.95-4.19]
	Sleep onset difficulties		<10 -3		<10 -3
	Never	Reference	Reference	Reference	Reference
	Sometimes	1.38 [1.21-1.57]	0.76 [0.70-0.84]	1.63 [1.48-1.78]	2.06 [1.82-2.32]
	Often	1.96 [1.66-2.31]	0.53 [0.44-0.64]	1.70 [1.44-2.01]	3.44 [2.88-4.11]

Acknowledgements

We thank the scientific coordinators (M. A. Charles, B. Geay, H. Léridon, C. Bois, M.-N. Dufourg, J. L. Lanoé, X. Thierry, and C. Zaros), IT and data managers, statisticians (A. Rakotonirina, R. Kugel, R. Borges-Panhino, M. Cheminat, and H. Juillard), administrative and family communication staff, study technicians (C. Guevel, M. Zoubiri, L. Gravier, I. Milan, and R. Popa) of the ELFE coordination team, and the families who gave their time for the study. The ELFE survey is a joint project between the French Institute for Demographic Studies (INED) and the National Institute of Health and Medical Research (INSERM), in partnership with the French blood transfusion service (Etablissement français du sang, EFS), Santé publique France, the National Institute for Statistics and Economic Studies (INSEE), the Direction générale de la santé (DGS, part of the Ministry of Health and Social Affairs), the Direction générale de la prévention des risques (DGPR, Ministry for the Environment), the Direction de la recherche, des études, de l'évaluation et des statistiques (DREES, Ministry of Health and Social Affairs), the Département des études, de la prospective et des statistiques (DEPS, Ministry of Culture), and the Caisse nationale des allocations familiales (CNAF), with the support of the Ministry of Higher Education and Research and the Institut

Funding Sources

S. Messayke was funded by the Azm and Saade association (PhD grant).

Conflict of interest

The authors declare that they have no conflicts of interest.